Consumer vulnerability across key markets in the European Union : final report.
Date: 2016-09-27
Institutions: Consumers, Health, Agriculture and Food Executive Agency
Subjects: EU Member State, Iceland, Internet, Norway, advertising, advertising malpractice, comparative advertising, consumer behaviour, consumer policy, consumer protection, consumer survey, electronic commerce, energy industry, financial services, impact of advertising

Consumer vulnerability across 
key markets in the European 
Union  

Final report 

Contract n° 2013 86 05 EAHC 2013/CP/08 

Written by: London Economics, VVA Consulting and Ipsos Mori consortium 

Date: January 2016 

Justice and 
Consumers 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
EUROPEAN COMMISSION 

Produced by Consumers, Health, Agriculture and Food Executive Agency (Chafea) on behalf of 

Directorate-General for Justice and Consumers 
Directorate E  —  Consumers Unit E.1 (Consumer Markets) 

Contact: Marthe H. Austgulen 

E-mail: JUST-CONSULT-E1@ec.europa.eu 

European Commission 
B-1049 Brussels 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Consumer vulnerability across 
key markets in the European 
Union  

Final Report 

Abstract 

The  study  examines  the  incidence  of  vulnerability  across  the  EU28  and  Iceland  and 
Norway, and the factors explaining any observed vulnerability. The study uses a range of 
information  sources  (literature  review,  stakeholder  interviews,  consumer  survey  and 
consumer  experiments  in  five  countries)  and  focuses  on  three  key  sectors  in  the 
European  Union.  The  key  finding  is  that  incidence  of  vulnerability  is  the  highest  when 
consumers  face  complex  advertising  or  when  consumers  do  not  compare  deals  at  all  or 
have  problems  comparing  deals  because  of  market-related  factors  or  personal  factors. 
The  analysis  of  potential  drivers  of  vulnerability  shows  that  market-related  drivers  are 
particularly important as these are consistently linked with many vulnerability indicators 
in the statistical analysis. Among the three sectors of particular interest, the incidence of 
vulnerability  is  markedly  higher  in  the  energy  and  finance  sectors  than  in  the  online 
sector.  The  study  concludes  with  a  number  of  recommendations  focusing  on  policies  to 
address  consumer  vulnerability  and  on  the  methodologies  to  employ  in  future  major 
studies of consumer vulnerability. 

 
 
 
 
 
 
 
 
 
 
 
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Luxembourg: Publications Office of the European Union, 2016 

PDF 

ISBN 978-92-9200-703-4 

doi: 10.2818/056024 

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Table of Contents 

TABLE OF CONTENTS 

GLOSSARY .................................................................................................................XIV 

EXECUTIVE SUMMARY .................................................................................................XVI 

1. 

BACKGROUND, INTRODUCTION AND RESEARCH OBJECTIVES OF THE STUDY ............. 22 

1.1.  Policy context ............................................................................................. 22 
1.2.  Scope of the study ...................................................................................... 23 
1.3.  Need for the present study ........................................................................... 23 
1.4.  Research objectives of the study ................................................................... 24 
1.5.  Research activities ...................................................................................... 25 
1.6.  Structure of the report ................................................................................. 25 

2. 

RESEARCH METHODS USED IN THE STUDY ............................................................. 27 

2.1.  Literature review......................................................................................... 27 
2.2.  Stakeholder consultation .............................................................................. 28 
2.3.  Consumer survey ........................................................................................ 29 
2.4.  Behavioural experiments .............................................................................. 30 
2.5.  Biographical consumer interviews ................................................................. 37 
2.6.  Discussion guide ......................................................................................... 37 
Input from the expert group ......................................................................... 37 
2.7. 

3. 

CONCEPTUALISATION 
OPERATIONALISATION OF VULNERABILITY ............................................................. 39 

THEORETICAL 

FRAMEWORK 

AND 

OF 

A 

3.1.  Vulnerability definitions ............................................................................... 41 
3.2.  Conceptualising and typologising vulnerability ................................................ 44 
3.3.  Dimensions of vulnerability used in the present study ...................................... 46 
3.4.  Potential causes of vulnerability .................................................................... 49 
3.5.  Expected effects of vulnerability.................................................................... 52 
3.6.  Operationalisation of the concept of consumer vulnerability .............................. 59 
3.7.  Operationalisation of potential drivers of vulnerability ...................................... 73 

4. 

INCIDENCE OF CONSUMER VULNERABILITY ............................................................ 82 

4.1. 

Incidence  of  various  vulnerability  dimensions  in  the  EU28  and  Norway  and 
Iceland ...................................................................................................... 85 
4.2.  Extent that consumers are vulnerable across a number of dimensions ............. 131 

5. 

CONCEPT OF THE ‘AVERAGE CONSUMER’ .............................................................. 138 

Insights from the literature review and stakeholder consultation ..................... 139 

5.1. 
5.2.  Vulnerability characteristics of  the  “average”  consumer based on the survey 

data ........................................................................................................ 147 

6.  DRIVERS OF AND FACTORS LINKED TO CONSUMER VULNERABILITY ........................ 168 

6.1.  Overview of the survey and experiment data analysis supporting this chapter .. 172 
6.2.  Personal and demographic characteristics .................................................... 175 
6.3.  Behavioural drivers of vulnerability ............................................................. 196 
6.4.  Market-related drivers of vulnerability and consumers’ experience in markets .. 217 
6.5.  Access drivers of vulnerability..................................................................... 241 
6.6.  Situational drivers of vulnerability ............................................................... 255 

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Table of Contents 

6.7.  Country and country group level analysis of drivers of vulnerability ................. 273 

7. 

POPULATION OF THE THEORETICAL FRAMEWORK OF VULNERABILITY ...................... 282 

7.1.  Criteria for selecting drivers to be included in the framework .......................... 283 
7.2.  Drivers and relationships to be included in the framework .............................. 286 
7.3.  Population of the theoretical framework ....................................................... 303 
7.4.  Permanent versus transitory nature of consumer vulnerability ........................ 306 

8. 

ROLE OF MARKETING PRACTICES ........................................................................ 308 

8.1.  Problematic  marketing  practices  identified  via  the  literature  review  and 

stakeholder consultation ............................................................................ 309 
8.2.  Evidence from the behavioural experiments ................................................. 314 

9. 

COMPLEXITIES OF CONSUMER VULNERABILITY ACROSS DIFFERENT SECTORS ......... 334 

9.1.  Consumer vulnerability in the financial sector ............................................... 335 
9.2.  Consumer vulnerability in the energy sector ................................................. 347 
9.3.  Consumer  vulnerability 

the  online  environment  and  electronic 

in 

communications sector .............................................................................. 357 
9.4.  Comparison of vulnerability across the three sectors ..................................... 368 

10.  BEST PRACTICE POLICY MEASURES IN MEMBER STATES AND AT EU LEVEL .............. 372 

10.1.  Measures addressing consumer vulnerability ................................................ 372 
10.2.  Institutional structure and role of civil society ............................................... 374 
10.3.  Typology of Member States ........................................................................ 375 

11.  CONCLUSIONS, RECOMMENDATIONS AND POLICY OPTIONS ................................... 381 

11.1.  Conclusions .............................................................................................. 381 
11.2.  Recommendations for the Consumer Scoreboards and market studies ............. 384 
11.3.  Recommendations and conclusions concerning the UCPD Guidance ................. 389 
11.4.  Policy options for mitigating consumer vulnerability ...................................... 391 

REFERENCES AND ANNEXES ....................................................................................... 396 

REFERENCES ............................................................................................................ 397 

ANNEX 1  COUNTRY FICHES ..................................................................................... 408 

ANNEX 2  RESEARCH METHODS USED IN THE STUDY .................................................. 565 

ANNEX 3 

INDICATORS OF VULNERABILITY CONSTRUCTED FROM THE SURVEY DATA ..... 593 

ANNEX 4 

INCIDENCE RATE OF VULNERABILITY BY SUB-GROUP ................................... 607 

ANNEX 5  SECTOR-LEVEL  INFORMATION  ON  WHERE  CONSUMERS  GET  INFORMATION 

WHEN COMPARING DEALS ......................................................................... 679 

ANNEX 6  DETAILED ANALYSIS OF VULNERABILITY AND NON-VULNERABILITY ACROSS 

A NUMBER OF DIMENSIONS ....................................................................... 684 

ANNEX 7  CLUSTER ANALYSIS OF INDICATORS OF VULNERABILITY .............................. 705 

ANNEX 8    DATA ANALYSIS UNDERTAKEN TO EXAMINE THE DRIVERS OF AND FACTORS  

LINKED TO CONSUMER VULNERABILITY ...................................................... 715 

ANNEX 9 

FACTOR  ANALYSIS  UNDERTAKEN 
OPERATIONALISATION OF VULNERABILITY .................................................. 734 

IN  THE  VALIDATION  OF  THE 

ANNEX 10  FINDINGS FROM THE IN-DEPTH CONSUMER INTERVIEWS ............................. 741 

ANNEX 11  INTERVIEW LIST ....................................................................................... 754 

ii 

 
 
 
 
Table of Contents 

ANNEX 12  TASK A2 INTERVIEW GUIDE ....................................................................... 757 

ANNEX 13  CONSUMER QUESTIONNAIRE AND BEHAVIOURAL EXPERIMENT ...................... 760 

iii 

 
 
 
 
Tables, Figures and Boxes 

Figures 

Figure 1: Example deals presented under the remedy in the energy sector experiment  .......... 33 

Figure 2: Example deals presented under the remedy in the online sector experimen .............. 34 

Figure 3: Example deals presented under the remedy in the finance sector experiment ........... 36 

Figure 4: Example deals presented under the remedy in the cross-cutting experiment ............ 37 

Figure 5: Theoretical framework structure ......................................................................... 41 

Figure 6: Approach to Operationalising consumer vulnerability ............................................. 59 

Figure 7:  Incidence of various vulnerability indicators – EU28 ............................................. 88 

Figure 8:  Variation in the incidence of the vulnerability indicators across EU28 Member States 97 

Figure 9:  Frequency of being a “high outlier” or a “low outlier” ............................................ 99 

Figure 10: Frequency of being a “high outlier” or a “low outlier” in Dimension 1 ................... 100 

Figure 11: Frequency of being a “high outlier” or a “low outlier” in Dimension 2 ................... 101 

Figure 12: Frequency of being a “high outlier” or a “low outlier” in Dimension 3 ................... 102 

Figure 13: Frequency of being a “high outlier” or a “low outlier” in Dimension 4 ................... 103 

Figure 14: Frequency of being a “high outlier” or a “low outlier” in Dimension 5 ................... 104 

Figure 15: Share of consumers who took action when they encountered problems – country level 
results  from  the  Consumer  Conditions  Scoreboard  and  from  survey  undertaken  for 
the present study ......................................................................................... 107 

Figure 16: Standard deviation of vulnerability perception factor (rated on a scale of 1   to 4) by 
personal characteristics – EU28 ...................................................................... 110 

Figure 17: Mean combined rating of extent to which consumers feel vulnerable  - EU28 Member 
States and IS and NO ................................................................................... 111 

Figure  18:  Country  level  share  of  consumers  who  feel  vulnerable  "to  a  great  extent"  for  any 
reason  and  country  level  combined  mean  rating  of  the  extent  to  which  that 
consumers feel vulnerable ............................................................................. 112 

Figure  19:  Mean  rating  and  share  of  consumers  who  feel  vulnerable  due  to  financial  and 
employment reasons - EU28 Member States and IS and NO .............................. 113 

Figure  20:  Mean  rating  and  share  of  consumers  who  feel  vulnerable  due  to  health  and  age 
reasons - EU28 Member States and IS and NO ................................................. 114 

Figure 21: Mean rating and share of consumers who feel vulnerable due to being a minority and 
personal reasons - EU28 Member States and IS and NO .................................... 115 

Figure 22: Indicators of ‘feeling uninformed about prices etc’ - EU28 Member States and IS and 
 .................................................................................................... 118 

NO 

Figure 23: Average rating of feeling informed (1 = very well informed, 4 -= not informed at all) - 
EU28 Member States and IS and NO ............................................................... 118 

Figure 24: EU28-wide incidence of Dimension 4 vulnerability indicators ............................... 122 

Figure  25:  Mean  rating  of  complexity  of  offers  (  1=  not  complex,  4=very  complex)  in  EU28 
Member States and IS and NO ....................................................................... 131 

Figure 26: Share of survey respondents reporting particular dimension of vulnerability (allocation 
is not mutually exclusive) – EU28 ................................................................... 134 

Figure 27: Matrix of personal and demographic characteristics linked to survey-based indicators 
of vulnerability ............................................................................................. 177 

iv 

 
 
 
Tables, Figures and Boxes 

Figure 28:  Matrix of behavioural characteristics linked to survey-based indicators of vulnerability 
 .................................................................................................... 200 

Figure  29:  Matrix  of  market-related  characteristics  linked  to  survey-based  indicators  of 
vulnerability................................................................................................. 221 

Figure  30:  Matrix  of  access-related  characteristics  linked  to  survey-based  indicators  of 
vulnerability................................................................................................. 244 

Figure 31:  Matrix of situational characteristics linked to survey-based indicators of vulnerability .
 .................................................................................................... 258 

Figure 32:  Framework of causes and effects of consumer vulnerability ............................... 305 

Figure  33:  Share  of  respondents  who  chose  the  correct  offer  among  those  who  based  their 
choices on each feature, first round of the electricity sector experiment ....................
 .................................................................................................... 320 

Figure  34:  Share  of  respondents  who  chose  the  correct  offer  among  those  who  based  their 
choices on each feature, second round of the electricity sector experiment .......... 322 

Figure  35:  Share  of  respondents  who  chose  the  correct  offer  among  those  who  based  their 
choices on each feature, first round of the online sector experiment ................... 324 

Figure  36:  Share  of  respondents  who  chose  the  correct  offer  among  those  who  based  their 
choices on each feature, second round of the online sector experiment .....................
 .................................................................................................... 325 

Figure  37:  Share  of  respondents  who  chose  the  correct  offer  among  those  who  based  their 
choices on each feature, first round of the finance sector experiment ........................
 .................................................................................................... 326 

Figure  38:  Share  of  respondents  who  chose  the  correct  offer  among  those  who  based  their 
choices on each feature, second round of the finance sector experiment ...................
 .................................................................................................... 327 

Figure  39:  Level  of  vulnerability  on  average  across  all  dimensions,  by  country  group  from  the 
approach-based typology .............................................................................. 380 

Figure 40: Mode effects - how difficult is making ends meet ............................................... 582 

Figure 41: Mode effects – financial difficulties friends and relatives ..................................... 583 

Figure 42: Mode effects – small print .............................................................................. 584 

Figure 43: Mode effects – know contract conditions bank................................................... 584 

Figure 44: Mode effects – read recent communication bank ............................................... 585 

Figure 45: Mode effects – best offer ................................................................................ 586 

Figure 46: Mode effects – best interest rate ..................................................................... 587 

Figure 47: Question on best interest rate, survey for the present study and Eurobarometer 342, 
by mode   .................................................................................................... 588 

Figure  48:  Question  on  best  offer,  survey  for  the  present  study  and  Eurobarometer  342,  by 
 .................................................................................................... 589 

mode 

Figure  49:  Consumer  grouping  based  on  perception-based  measure  (“feeling  vulnerable”)  and 
experience-based measure (“experience of problems”) – EU28 ................................
 .................................................................................................... 686 

Figure  50:  Consumer  grouping  based  on  perception-based  measure  (“feeling  vulnerable”)  and 
experience-based measure (“experience of problems”) – EU28 + NO and IS ........ 687 

Figure  51:  Consumer  grouping  based  on  perception-based  measure  (“feeling  uninformed”)  and 
experience-based measure (“experience of problems”) – EU28 ................................
 .................................................................................................... 688 

Figure  52:  Consumer  grouping  based  on  perception-based  measure  (“feeling  uninformed”)  and 
experience-based measure (“experience of problems) – EU28 + NO and IS ......... 689 

v 

 
 
 
 
 
 
 
 
 
Tables, Figures and Boxes 

Figure  53:  Energy  sector:  consumer  grouping  based  on  perception-based  measure  (“feeling 
vulnerable”) and experience-based measure (“comparing deals”) .............................
 .................................................................................................... 690 

Figure  54:  On-line  sector:  consumer  grouping  based  on  perception-based  measure  (“feeling 
vulnerable”) and experience-based measure (“comparing deals”) .............................
 .................................................................................................... 690 

Figure  55:  Financial  sector:  consumer  grouping  based  on  perception-based  measure  (“feeling 
vulnerable”) and experience-based measure (“comparing deals”) .............................
 .................................................................................................... 690 

Figure  56:  Energy  sector:  consumer  grouping  based  on  perception-based  measure  (“feeling 
vulnerable”) and experience-based meaure (“comparing deals”) – EU28 + NO and IS .
 .................................................................................................... 692 

Figure  57:  On-line  sector:  consumer  grouping  based  on  perception-based  measure  (“feeling 
vulnerable”)  and  experience-based  measure  (“comparing  deals”)  –  EU28  +  NO  and 
 .................................................................................................... 693 
IS 

Figure  58:  Financial  sector:  consumer  grouping  based  on  perception-based  measure  (“feeling 
vulnerable”)  and  experience-based  measure  (“comparing  deals”)  –  EU28  +  NO  and 
 .................................................................................................... 694 
IS 

Figure  59:  Energy  sector:  consumer  grouping  based  on  perception-based  measure  (“feeling 
uninformed”) and experience-based measure (“comparing deals”) – EU28 ................
 .................................................................................................... 695 

Figure  60:  On-line  sector:  consumer  grouping  based  on  perception-based  measure  (“feeling 
uninformed”) and experience-based measure (“comparing deals”) – EU28 ................
 .................................................................................................... 695 

Figure  61:  Financial  sector  consumer  grouping  based  on  perception-based  measure  (“feeling 
uninformed”) and experience-based measure (“comparing deals”) – EU28 ................
 .................................................................................................... 696 

Figure  62:  Energy  sector:  consumer  grouping  based  on  perception-based  measure  (“feeling 
uninformed”) and experience-based measure (“comparing deals”)  – EU28 + NO and 
 .................................................................................................... 696 
IS 

Figure  63:  On-line  sector:  consumer  grouping  based  on  perception-based  measure  (“feeling 
uninformed”) and experience-based measure (“comparing deals”)  – EU28 + NO and 
 .................................................................................................... 697 
IS 

Figure  64:  Financial  sector:  consumer  grouping  based  on  perception-based  measure  (“feeling 
uninformed”) and experience-based measure (“comparing deals”)  – EU28 + NO and 
 .................................................................................................... 698 
IS 

Figure  65:  Energy  sector:  consumer  grouping  based  on  perception-based  measure  (“feeling 
vulnerable”)  and experience-based measure (“not choosing optimally in experiment) 
– Experiment countries ................................................................................. 699 

Figure  66:  On-line  sector:  consumer  grouping  based  on  perception-based  measure  (“feeling 
vulnerable”)  and experience-based measure (“not choosing optimally in experiment) 
– Experiment countries ................................................................................. 699 

Figure  67:  Financial  sector:  consumer  grouping  based  on  perception-based  measure  (“feeling 
vulnerable”)  and experience-based measure (“not choosing optimally in experiment) 
– Experiment countries ................................................................................. 700 

Figure  68:  Cross-cutting  sector  :  consumer  grouping  based  on  perception-based  measure 
(“feeling  vulnerable”)    and  experience-based  measure  (“not  choosing  optimally  in 
experiment) – Experiment countries ............................................................... 700 

Figure  69:  Energy  sector:  consumer  grouping  based  on  perception-based  measure  (“feeling 
vulnerable”)  and experience-based measure (“not choosing optimally in experiment) 
– Experiment countries ................................................................................. 701 

vi 

 
 
 
 
 
 
 
 
 
Tables, Figures and Boxes 

Figure  70:  On-line  sector:  consumer  grouping  based  on  perception-based  measure  (“feeling 
vulnerable”)  and experience-based measure (“not choosing optimally in experiment) 
– Experiment countries ................................................................................. 702 

Figure  71:  Financial  sector:  consumer  grouping  based  on  perception-based  measure  (“feeling 
vulnerable”)  and experience-based measure (“not choosing optimally in experiment) 
– Experiment countries ................................................................................. 703 

Figure  72:  Cross-cutting  sector  :  consumer  grouping  based  on  perception-based  measure 
(“feeling  vulnerable”)    and  experience-based  measure  (“not  choosing  optimally  in 
experiment) – Experiment countries ............................................................... 704 

Figure 73:  Cluster dendrogram and map ........................................................................ 707 

Figure 74:  Group vulnerability level ............................................................................... 709 

Figure 75:  Vulnerability characteristics clusters ............................................................... 711 

vii 

 
 
Tables, Figures and Boxes 

Tables 

Table 1: Dimensions of vulnerabilty within the OFT’s ‘Access, Assess, Act’ framework ............. 49 

Table 2: Expected effects of each vulnerability dimension .................................................... 57 

Table 3: Mapping concepts of vulnerability from the literature to survey-based indicators ........ 61 

Table 4: Mapping concepts of vulnerability from the literature to experiment-based indicators . 62 

Table 5: Indicators of vulnerability based on the survey ...................................................... 63 

Table 6: Potential drivers of vulnerability ........................................................................... 74 

Table 7: Explanatory variables included in regressions ........................................................ 76 

Table  8:  Distribution  of  incidence  of  various  indicators  of  vulnerability  across  EU28  Member 
States and IS and NO ..................................................................................... 89 

Table 9: Dimension 1 vulnerability indicators in EU28 Member States and IS and NO ............ 106 

Table 10: Dimension 2 vulnerability indicators in EU28 Member States and IS and NO .......... 108 

Table 11: Mean rating of extent to which consumers feel vulnerable on a scale of 1 "not at all" to 
4 "to a great extent" - EU28 Member States and IS and NO ............................... 110 

Table 12: Dimension 3 vulnerability indicators in EU28 Member States and IS and NO .......... 116 

Table  13:  Dimension  4  vulnerability  indicators  in  EU28  Member  States  and  IS  and  NO    – 
Problems comparing deals, part 1 ................................................................... 123 

Table 14: Dimension 4 vulnerability indicators in EU28 Member States and IS and NO – Problems 
comparing deals, part 2 ................................................................................ 124 

Table 15: Dimension 4 vulnerability indicators in EU28 Member States and IS and NO – Problems 
switching providers ....................................................................................... 126 

Table 16: Dimension 4 vulnerability indicators in EU28 Member States and IS and NO - Exclusion 
 .................................................................................................... 127 

Table 17: Dimension 5 vulnerability indicator in EU28 Member States and IS and NO ............ 128 

Table 18: Distribution of survey respondents by number of vulnerability dimensions ............. 135 

Table 19: Vulnerability by dimension across EU28 Member States and IS and NO ................. 136 

Table 20: Case law overview .......................................................................................... 142 

Table 21: Mapping median responses from the survey to the dimensions and indicators identified 
as the main drivers of vulnerability ................................................................. 149 

Table 22: Mapping median responses from the survey to the dimensions and indicators identified 
as the main drivers of vulnerability  by country ................................................ 154 

Table 23: Mapping median responses from the survey to the dimensions and indicators identified 
as  the  main  drivers  of  vulnerability    -  number  of  deviations  from  the  median  by 
country   .................................................................................................... 159 

Table 24: Mapping median responses from the survey to the dimensions and indicators identified 
as part of the UCPD definition of the average consumer .................................... 160 

Table 25: Mapping median responses from the survey to the dimensions and indicators identified 
as part of the UCPD definition of the average consumer, by country ................... 163 

Table 26: Impact of personal and demographic characteristics on indicators in Dimension 1 and 
Dimension 2 ................................................................................................ 180 

viii 

 
 
 
 
Tables, Figures and Boxes 

Table  27:  Impact  of  personal  and  demographic  characteristics  on  indicators  in  Dimension  3; 
Having difficulty obtaining and assimilating information ..................................... 185 

Table 28: Impact of personal and demographic characteristics on experiment performance ... 188 

Table  29:  Impact  of  personal  and  demographic  characteristics  on  Indicator  8  in  Dimension  4; 
Has  problems  comparing  deals  due  to  personal  factors,  market  related  factors,  and 
access-related factors ................................................................................... 190 

Table  30:  Impact  of  personal  and  demographic  characteristics  on  Indicator  9  in  Dimension  4; 
Has  not  switched  due  to  personal  factors,  market-related  factors,  access-related 
factors, termination costs, and bundling .......................................................... 191 

Table  31:  Impact  of  personal  and  demographic  characteristics  on  Indicators  10  and  11  in 
Dimension 4; Excluded from e-commerce and declined a loan ............................ 192 

Table  32:  Impact of  personal  and  demographic  characteristics  on  Indicator  12  in  Dimension  5; 
Feeling of vulnerability due to complexity of offers ........................................... 196 

Table 33: Impact of behavioural characteristics on indicators in Dimension 1 and Dimension 2 .....
 .................................................................................................... 202 

Table  34:  Impact  of  behavioural  characteristics  on  indicators  in  Dimension  3;  Having  difficulty 
obtaining and assimilating information ............................................................ 206 

Table 35: Impact of behavioural characteristics on experiment performance ........................ 209 

Table  36:  Impact  of  behavioural  characteristics  on  Indicator  8  in  Dimension  4;  Has  problems 
comparing deals due to personal  factors, market related factors, and access-related 
 .................................................................................................... 212 
factors 

Table 37: Impact of behavioural characteristics on Indicator 9 in Dimension 4; Has not switched 
due  to  personal  factors,  market  related  factors,  access-  related  factors,  termination 
costs, and bundling ...................................................................................... 213 

Table  38:  Impact  of  behavioural  characteristics  on  Indicators  10  and  11  in  Dimension  4; 
Excluded from e-commerce and declined a loan ............................................... 215 

Table  39:  Impact  of  behavioural  characteristics  on  Indicator  12  in  Dimension  5;  Feeling  of 
vulnerability due to complexity of offers .......................................................... 217 

Table 40: Impact of market-related characteristics on indicators in Dimension 1 and Dimension 2 
 .................................................................................................... 223 

Table 41: Impact of market-related characteristics on indicators in Dimension 3; Having difficulty 
obtaining and assimilating information ............................................................ 227 

Table  42:  Impact  of  market-related  characteristics  and  consumers’  experience  in  markets  on 
experiment performance ............................................................................... 231 

Table 43: Impact of market-related characteristics on Indicator 8 in Dimension 4; Has problems 
comparing deals due to personal factors, market related factors, and access- related 
 .................................................................................................... 234 
factors 

Table  44:  Impact  of  market-related  characteristics  on  Indicator  9  in  Dimension  4;  Has  not 
switched  due  to  personal  factors,  market  related  factors,  access-  related  factors, 
termination costs, and bundling ..................................................................... 236 

Table  45:  Impact  of  market-related  dcharacteristics  on  Indicators  10  and  11  in  Dimension  4; 
Excluded from e-commerce and declined a loan ............................................... 238 

Table  46:  Impact  of  market-related  characteristics  on  Indicator  12  in  Dimension  5;  Feeling  of 
vulnerability due to complexity of offers .......................................................... 239 

Table 47: Impact of access-related characteristics on indicators in Dimension 1 and Dimension 2 .
 .................................................................................................... 245 

Table 48: Impact of access-related characteristics on indicators in Dimension 3; Having difficulty 
obtaining and assimilating information ............................................................ 248 

Table 49: Impact of access-related characteristics on experiment performance .................... 250 

ix 

 
 
 
 
Tables, Figures and Boxes 

Table 50: Impact of access-related characteristics on Indicator 8 in Dimension 4; Has problems 
comparing deals due to personal factors, market related factors, and access- related 
 .................................................................................................... 252 
factors 

Table  51:  Impact  of  access-related  characteristics  on  Indicator  9  in  Dimension  4;  Has  not 
switched  due  to  personal  factors,  market  related  factors,  access-  related  factors, 
termination costs, and bundling ..................................................................... 253 

Table  52:  Impact  of  access-related  characteristics  on  Indicators  10  and  11  in  Dimension  4; 
Excluded from e-commerce and declined a loan ............................................... 254 

Table  53:  Impact  of  access-related  characteristics  on  Indicator  12  in  Dimension  5;  Feeling  of 
vulnerability due to complexity of offers .......................................................... 254 

Table 54: Impact of situational drivers on indicators in Dimension 1 and Dimension 2 .................
 .................................................................................................... 260 

Table 55: Impact of situational drivers on indicators in Dimension 3;  Having difficulty obtaining 
and assimilating information .......................................................................... 263 

Table 56: Impact of situational characteristics on experiment performance .......................... 266 

Table  57:  Impact  of  situational  characteristics  on  Indicator  8  in  Dimension  4;  Has  problems 
comparing deals due to personal factors, market-related factors, and access- related 
 .................................................................................................... 269 
factors 

Table 58: Impact of situational characteristics on Indicator 9 in Dimension 4;  Has not switched 
due  to  personal  factors,  market  related  factors,  access-related  factors,  termination 
costs, and bundling ...................................................................................... 270 

Table 59: Impact of situational characteristics on Indicators 10 and 11 in Dimension 4; Excluded 
from e-commerce and declined a loan ............................................................. 271 

Table 60: Impact of situational characteristics on Indicator 12 in Dimension 5 ..................... 272 

Table 61: Criteria for selecting drivers to be included in the theoretical framework ............... 285 

Table  62:  Impacts  of  selected  drivers  and  characteristics  on  the  likelihood  of  vulnerability  in 
Dimension 1 ................................................................................................ 287 

Table  63:  Impacts  of  selected  drivers  and  characteristics  on  the  likelihood  of  vulnerability  in 
Dimension 2 ................................................................................................ 289 

Table  64:  Impacts  of  selected  drivers  and  characteristics  on  the  likelihood  of  vulnerability  in 
Dimension 3 ................................................................................................ 291 

Table  65:  Impacts  of  selected  drivers  and  characteristics  on  the  likelihood  of  vulnerability  in 
Dimension 4 – Problems comparing ................................................................ 293 

Table  66:  Impacts  of  selected  drivers  and  characteristics  on  the  likelihood  of  vulnerability  in 
Dimension 4 – Problems switching .................................................................. 296 

Table  67:  Impacts  of  selected  drivers  and  characteristics  on  the  likelihood  of  vulnerability  in 
Dimension 4 – Exclusion ................................................................................ 298 

Table  68:  Impacts  of  selected  drivers  and  characteristics  on  the  likelihood  of  vulnerability  in 
Dimension 4 – Experiment ............................................................................. 300 

Table  69:  Impacts  of  selected  drivers  and  characteristics  on  the  likelihood  of  vulnerability  in 
Dimension 5 ................................................................................................ 302 

Table 70: Share of respondents choosing the optimal offer ................................................ 316 

Table 71: Treatment effects by country (percentage points) .............................................. 317 

Table 72: Treatment effect regression coefficients and marginal effects ............................... 318 

Table 73: Energy sector experiment follow-up question results........................................... 319 

Table 74: Online sector experiment follow-up question results ........................................... 323 

Table 75: Finance sector experiment follow-up question results .......................................... 326 

x 

 
 
 
Tables, Figures and Boxes 

Table 76: Cross-cutting experiment follow-up question results ........................................... 328 

Table  77:  Treatment  effects  (difference-in-proportions)  and  difference-in-difference  effects  by 
behavioural and socio-demographic subgroups................................................. 331 

Table 78: Incidence of vulnerability in the finance sector based on sector specific indicators .. 337 

Table  79:  Share  of  respondents  who  gave  each  response  to  questions      on  experience  and 
engagement with markets ............................................................................. 344 

Table 80: Incidence of vulnerability in the energy sector based on sector specific indicators .. 349 

Table 81: Incidence of vulnerability in the online sector based on sector specific indicators .... 359 

Table  82:  Incidence  of  vulnerability  in  three  sectors  based  on  sector    specific  indicators  –  All 
EU28 countries for which data are  available .................................................... 368 

Table 83:  Country grouping based on the main elements of the national approach to consumer 
vulnerability................................................................................................. 377 

Table  84:  Comparison  of  country  typologies  based  on  approach  to  consumer  vulnerability  and 
incidence of vulnerability ............................................................................... 378 

Table 85: Country and language ..................................................................................... 566 

Table  86:  Number  of  interviews  conducted  online,  by  telephone  (CATI)  and  via  in-home  self-
completion (CAPI) ........................................................................................ 567 

Table 87: Country, details offline component and partner .................................................. 568 

Table 88: Fieldwork dates .............................................................................................. 570 

Table 89: Average survey time ....................................................................................... 571 

Table 90: Final sample - age groups, by country (excl. CATI only countries) ........................ 573 

Table 91: Final sample – inactive population, by country (excl. CATI only countries) ............. 575 

Table 92: Sample outcomes – low education, by country (excl. CATI only countries)............. 576 

Table 93: Percentage of respondents with a mother tongue different from the official language(s) 
spoken in their country ................................................................................. 578 

Table 94: Overlapping sample online/CATI....................................................................... 580 

Table 95: Number of in-depth interviews ......................................................................... 591 

Table 96: Indicator of vulnerability based on the survey .................................................... 593 

Table  97:  Above  average  vulnerability  by  country  and  characteristic,  number  of  vulnerability 
indicators .................................................................................................... 607 

Table 98: Above average vulnerability in countries due to personal and demographic (age) ... 614 

Table  99:  Above  average  vulnerability  in  countries  due  to  personal  and  demographic  drivers 
(household size, population density) ............................................................... 616 

Table 100:  Above average  vulnerability in countries due to  personal and demographic (gender, 
education, mother tongue) ............................................................................ 619 

Table 101: Above average vulnerability in countries due to situational drivers (occupation) ... 622 

Table 102: Above average vulnerability in countries due to situational drivers (financial situation, 
personal situation) ................................................................................................ 626 

Table  103:  Above  average  vulnerability  in  countries  due  to  situational  drivers  (dependent 
children, single parent, friends) ...................................................................... 632 

Table 104: Above average vulnerability in countries due to behavioural drivers (trust 1, 2) ... 635 

Table 105: Above average vulnerability in countries due to behavioural drivers (credulity, risk) ...
 .................................................................................................... 638 

Table 106: Above average vulnerability in countries due to behavioural drivers (impulsivity 1, 2) .
 .................................................................................................... 641 

xi 

 
 
 
Tables, Figures and Boxes 

Table 107: Above average vulnerability in countries due to behavioural drivers (impulsivity 3, 4) .
 .................................................................................................... 643 

Table 108: Above average vulnerability in countries due to behavioural drivers (knowledge tests .
 .................................................................................................... 646 

Table  109:  Above  average  vulnerability  in  countries  due  to  market-related  and  experience 
drivers (T&Cs, knowledge, compares deals - energy) ........................................ 648 

Table  110:  Above  average  vulnerability  in  countries  due  to  market-related  and  experience 
drivers (read last bill - energy) ....................................................................... 652 

Table  111:  Above  average  vulnerability  in  countries  due  to  market-related  and  experience 
drivers (knows contract, compares deals - online) ............................................ 656 

Table  112:  Above  average  vulnerability  in  countries  due  to  market-related  and  experience 
drivers(read last communication - online) ........................................................ 660 

Table  113:  Above  average  vulnerability  in  countries  due  to  market-related  and  experience 
drivers (knows contract, compares deals - finance) .......................................... 665 

Table  114:  Above  average  vulnerability  in  countries  due  to  market-related  and  experience 
drivers (read last communication - finance) ..................................................... 668 

Table 115: Above average vulnerability in countries due to access drivers (type) ................. 673 

Table 116: Above average vulnerability in countries due to access drivers (frequency) .......... 676 

Table 117: Indicators of ‘getting information from few sources’: energy sector ..................... 680 

Table 118: Indicators of ‘getting information from few sources’: On-line sector .................... 681 

Table 119: Indicators of ‘getting information from few sources’: financial sector ................... 682 

Table 120: Categories of variables to be used to operationalise consumer vulnerability ......... 685 

Table  121: Factor analysis of dimensions ........................................................................ 710 

Table 122: Sample sizes for each consumer group for each experiment............................... 713 

Table 123: Explanatory variables included in regressions ................................................... 719 

Table 124: Market-related and experience variables ......................................................... 731 

Table 125: Results of factor analysis on the survey indicators of vulnerability ...................... 738 

Table 126:  Country fiche - Belgium ................................................................................ 410 

Table 127:  Country fiche - Bulgaria ................................................................................ 416 

Table 128:  Country fiche – Czech Republic...................................................................... 421 

Table 129:  Country fiche - Denmark .............................................................................. 426 

Table 130:  Country fiche - Germany .............................................................................. 431 

Table 131:  Country fiche - Estonia ................................................................................. 437 

Table 132:  Country fiche - Ireland ................................................................................. 442 

Table 133:  Country fiche - Greece ................................................................................. 448 

Table 134: Country fiche - Spain .................................................................................... 453 

Table 135:  Country fiche - France .................................................................................. 458 

Table 136:  Country fiche - Croatia ................................................................................. 464 

Table 137:  Country fiche - Italy ..................................................................................... 469 

Table 138:  Country fiche - Cyprus ................................................................................. 475 

Table 139:  Country fiche - Latvia ................................................................................... 480 

Table 140:  Country fiche - Lithuania .............................................................................. 484 

xii 

 
 
 
 
Tables, Figures and Boxes 

Table 141:  Country fiche - Luxembourg .......................................................................... 489 

Table 142:  Country fiche - Hungary ............................................................................... 493 

Table 143:  Country fiche - Malta ................................................................................... 498 

Table 144:  Country fiche - Netherlands .......................................................................... 503 

Table 145:  Country fiche - Austria ................................................................................. 508 

Table 146:  Country fiche - Poland .................................................................................. 513 

Table 147:  Country fiche - Portugal ............................................................................... 518 

Table 148:  Country fiche – Romania .............................................................................. 523 

Table 149: Country fiche - Slovenia ................................................................................ 528 

Table 150:  Country fiche - Slovakia ............................................................................... 533 

Table 151:  Country fiche - Finland ................................................................................. 538 

Table 152: Country fiche - Sweden ................................................................................. 544 

Table 153:  Country fiche – United Kingdom .................................................................... 549 

Table 154:  Country fiche - Iceland ................................................................................. 556 

Table 155:  Country fiche - Norway ................................................................................ 560 

Table 156:  Interview list ............................................................................................... 754 

xiii 

 
 
 
Glossary 

Glossary 

Terminology abbreviations 

ACER 

APR 

APRC 

ATM 

BEUC 

BIAC 
CADS 

CAPI 

CATI 

CAWI 

CCD 

CCS 

CRD 

CEER 

CMS 

EC 

ECCG 

EEA 

EPV 

EU 

FCA 

G-S 

ICT 

IT 

kWh 

Mbps 

MMS 

MS 
OECD 

OFT 

PIN  

pp 

Agency for the Cooperation of Energy Regulators 

Annual Percentage Rate 

Annual Percentage Rate of Charge 

Automated Transaction Machine 

Bureau Européen des Unions de Consommateurs 

Broadband Internet Access Charges 

Consumer Attention Deficit Syndrome  

Computer Assisted Personal Interview 

Computer Assisted Telephone Interview 

Computer Assisted Web Interview 

Consumer Credit Directive  

Consumer Conditions Scoreboard 

Consumer Rights Directive 

The Council of European Energy Regulators 

Consumer Market Scoreboard 

European Commission 

European Consumer Consultative Group  

European Economic Area 

Events per variable (regression method) 

European Union 

Financial Conduct Authority (United Kingdom) 

General-to-Specific econometric approach 

Information Communication Technology 

Information Technology 

kilowatt-hour 

Megabits per second 

Market Monitoring Survey 

Member State 

Organisation for Economic Co-operation and Development 

UK Office of Fair Trading (closed in 2014) 

Personal Identification Number 

Percentage Points 

T&Cs/Ts&Cs  Terms and Conditions 

TCR 

UCPD 

WHO 

Tariff Comparison Rate 

Unfair Commercial Practices Directive 

World Health Organisation 

xiv 

 
 
 
 
 
 
Glossary 

Member State codes 
Belgium 
BE 

BG 

CZ 

DK 

DE 

EE 

IE 

EL 

ES 

FR 

HR 

IT 

CY 

LV 

Bulgaria 

Czech Republic 

Denmark 

Germany 

Estonia 

Ireland 

Greece 

Spain 

France 

Croatia 

Italy 

Cyprus 

Latvia 

LT 

LU 

HU 

MT 

NL 

AT 

PL 

PT 

RO 

SI 

SK 

FI 

SE 

UK 

Lithuania 

Luxembourg 

Hungary 

Malta 

Netherlands 

Austria 

Poland 

Portugal 

Romania 

Slovenia 

Slovakia 

Finland 

Sweden 

United Kingdom 

xv 

 
 
 
 
 
Executive summary 

Executive summary 

Policy context 
The president of the European Commission, Jean-Claude Juncker, committed to deliver 
change  and  to  focus  on  the  big  political  challenges  in  Europe.  To  achieve  this,  he 
created  a  new  streamlined  Commission  including  a  “strong  consumer  portfolio”1.  This 
portfolio  is  called  upon  to  play  a  vital  role  in  delivering  on  the  priorities  of  the  2014 
Commission  Political  Guidelines2,  in  particular  the  Digital  Single  Market,  the  Energy 
Union,  the  Internal  Market,  the  Capital  Markets  Union  and  Jobs,  Growth  and 
Investment.  

Built on the Consumer Programme 2014-2020, the EU consumer policy focuses on four 
key areas:  

1)  a  single  market  of  safe  products  for  the  benefit  of  citizens  and  as  a  component  of 
competitive  businesses  and  traders;  2)  a  single  market  where  citizens  are  well 
represented  by  professional  consumer  organisations  whose  capacity  is  built  to  meet 
the challenges of today's economic environment; 3) a market where citizens are aware 
and  exercise  their  rights  as  consumers  so  that  they  contribute  to  the  growth  of 
competitive  markets,  citizens  must  enjoy  access  to  redress  mechanisms  in  case  of 
problems  without  needing  to  resort  to  court  procedures  which  are  lengthy  and  costly 
for  them  and  the  governments;  4)  a  concrete  and  effective  collaboration  between 
national bodies to support the enforcement of consumer rights, support the consumers 
with advice. 

The  broad  concept  of  consumer  vulnerability  is  relevant  to  each  of  these  objectives, 
and  consumer  vulnerability  is  identified  as  a  key  challenge  to  be  tackled  within  this 
context. 

Consumer  vulnerability  was  also  identified  as  a  key  concern  in  the  Commission  Staff 
Working Document3 on knowledge-enhancing aspects of consumer empowerment, and 
the European Economic and Social Committee4 and consumer organisations5 have also 
focused attention on the issue of vulnerable consumers.  
Furthermore, a 2012 resolution of the European Parliament6 called on the Commission 
to  consider  reinforcing  the  rights  of  vulnerable  consumers  as  a  key  priority,  and  to 
include  the  dimension  of  consumer  vulnerability  in  the  work  of  the  Consumer 
Scoreboard to have a clearer overview of the needs of vulnerable consumers. 

While there is a substantial amount of EU consumer law and the consumer dimension 
is  a  key  element  of  many  EU  policies,  in  2012  the  Commission  acknowledged  that  a 
comprehensive framework is needed to address challenges such as those linked to the 
digitalisation of daily life and the specific needs of vulnerable consumers.7 The second 
EC  report  on  consumer  policy8  highlights  a  number  of  activities  with  a  focus  on 
consumer vulnerability, such as the establishment of the Vulnerable Consumer Working 

1 http://europa.eu/rapid/press-release_IP-14-984_en.htm  
2 http://ec.europa.eu/priorities/docs/pg_en.pdf  
3 http://ec.europa.eu/consumers/archive/strategy/docs/swd_document_2012_en.pdf  
4 http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:C:2013:011:0054:0058:EN:PDF  
5http://ec.europa.eu/consumers/archive/empowerment/docs/eccg_opinion_consumers_vulnerability_022013

_en.pdf   

6 http://www.europarl.europa.eu/sides/getDoc.do?type=TA&reference=P7-TA-2012-0209&language=EN& 

ring=A7-2012-0155    

7 http://europa.eu/rapid/press-release_IP-12-491_en.htm  
8 http://ec.europa.eu/consumers/strategy-programme/policy-

strategy/documents/consumer_policy_report_2014_en.pdf  

xvi 

 
 
                                                 
Executive summary 

Group, which brings together a range of stakeholders from many Member States in the 
context of the Citizens Energy Forum. 

The  Unfair  Commercial  Practices  Directive  (UCPD)  provides  for  specific  protection  of 
consumers  who  are  particularly  vulnerable  due  to  their  mental  or  physical  infirmity, 
age or credulity. In its 2013 assessment of the UCPD9, the Commission found it should 
itself  take  a  more  prominent  role  in  monitoring  and  coordinating  the  enforcement  of 
the UCPD by Member States, and that further efforts should be made to strengthen the 
enforcement  of  the  UCPD  in  relation  to  vulnerable  consumers.  At  present  the 
Commission  is  revising  the  Guidance  document  on  the  application  of  the  UCPD, 
including, potentially, the part on vulnerable consumers. 

The importance of the present study 

This study is important because it fills a knowledge gap by adding substantially to the 
evidence base in the area of consumer vulnerability. In particular, the study: 

  Demonstrates how consumer vulnerability can be operationalised and explored, 

conceptually and empirically through data collection. 

  Provides insights to assist consumer policy making, such as insights into the key 
factors linked to consumer vulnerability, which policy measures or types of 
measures are most effective in mitigating consumer vulnerability, and the role 
of problematic commercial practices. 

  Provides insights useful to help the development of the EU’s consumer evidence 
base, including the Consumer Scoreboards, Market Studies, and Behavioural 
Studies. 

  Provides insights relevant to the UCPD and updating of the UCPD Guidance 

document. 

Methodology of the study  

Five  different  research  activities  were  undertaken  during  the  course  of  the  study, 
namely a literature review, stakeholder consultations (national  consumer associations, 
country  experts  and  EU-level  stakeholder  organisations)  a  consumer  survey  in  all  EU 
Member  States,  Iceland  and  Norway,  behavioural  experiments,  and  biographical 
consumer interviews in five EU countries. The behavioural experiments were specific to 
the  three  key  sectors  of  interest  to  the  study,  namely  the  energy,  finance  and  online 
environment sectors, and the fourth was a cross-cutting experiment.  

Main findings 

Vulnerability definitions 

In  the  literature  there  exists  no  single,  commonly  adopted  definition  of  consumer 
vulnerability  and  most  often  vulnerability  refers  to  an  ex-ante  assessment  of  the 
likelihood  of  a  potential  negative  outcome  in  terms  of  consumer  well-being.  It  is  an 
assessment of risk, rather than a reflection of a negative outcome that has or is certain 
to materialise. Two broad vulnerability categories emerge from the literature, namely, 
vulnerability  which  relates  to  personal  characteristics  of  the  consumer  and  a  broader 
concept which takes into account the transactional situations in which consumers find 
themselves.  Most  recent  definitions  also  recognise  that  vulnerability  is  not  a  static 
condition. Consumers may move in and out of states of vulnerability and they may be 
vulnerable  in  respect  of  some  categories  of  transaction  but  not  others.  In  addition, 

9 http://ec.europa.eu/justice/consumer-marketing/files/ucpd_report_en.pdf  

xvii 

 
 
 
                                                 
Executive summary 

vulnerability  is  best  viewed  as  a  spectrum  rather  than  a  binary  state.  Yet,  in  some 
policy  contexts,  it  may  be  important  to  recognise  that  some  personal  characteristics 
can imply that vulnerability remains an enduring characteristic for particular groups of 
consumers.   

Vulnerability dimensions 

Based on the findings from the literature review, five core vulnerability dimensions are 
used in the study:  

1.  Heightened risk of negative outcomes or impacts on well-being - interpreting 

vulnerability as an ex-ante assessment of the likelihood of a negative outcome, 
as a loss of welfare due to choices in markets, and as higher susceptibility to 
harm and diminished well-being. 

2.  Having characteristics that limit ability to maximise well-being - interpreting 
vulnerability as characteristics that limit consumers’ ability to maximise their 
utility and well-being, as consumers' diminished capacity to understand 
advertising and product effects, and as consumers’ certain abiding 
characteristics. 

3.  Having difficulty in obtaining or assimilating information - interpreting 

vulnerability as limitations obtaining or assimilating consumer information and 
‘informational vulnerability’ which refers to scenarios where suppliers may have 
better information than consumers.  

4.  Inability or failure to buy, choose or access suitable products - interpreting 

vulnerability as consumer inability to choose or access products and services 
which are suitable for their needs or do so without disproportionate effort or as 
not having access to beneficial products or services, including a distinction 
between buying unsuitable goods or services and failing to buy suitable goods 
and services.  

5.  Higher susceptibility to marketing practices, creating imbalances in market 

interactions - interpreting vulnerability as the effect of marketing practices and 
consumers’ special susceptibility. 

Vulnerability incidence rates 

To  operationalise  the  five  dimensions,  the  study  develops  a  number  of  general  and 
sector-specific  (energy,  finance  and  online)  indicators  which  can  be  populated  with 
information from the survey and the experiments.  

  The highest vulnerability incidence rates are observed EU-wide for the 

indicators of dimension 5 of vulnerability, namely “Higher susceptibility to 
marketing practices”, reflecting the inability of consumers of choosing the 
optimal deal in the experiments.  

  The second highest incidence rates are observed in the case of dimension 4 
vulnerability. In particular, vulnerability arises because consumers do not 
compare deals or have problems comparing deals because of market-related 
factors or personal factors. Overall, almost 75% of EU-28 consumers exhibit at 
least one such dimension 4 vulnerability.  

  Dimension 2 indicators, namely individual personal characteristics, do not have 
particularly high vulnerability incidence rates. However, overall, about 23% of 
consumers are vulnerable on the basis of one or several personal 
characteristics. Among the personal characteristics, financial and employment 
circumstances show the highest incidence rate.  

  The incidence of dimension 1 and 3 vulnerability indicators is relatively low, 

generally 10% or less and never exceeds 15%. Overall, both the analysis of the 
incidence of vulnerability across the EU as whole and of the variation of 
vulnerability across EU Member States highlights that marketing practices, lack 

xviii 

 
 
Executive summary 

of use of information or imperfect information and access issues are the most 
frequent causes of vulnerability.  

In terms of the  variation of vulnerability incidence  rates across the  EU, Germany, the 
Netherlands  and  Norway  are  the  countries  where  the  vulnerability incidence  rates  are 
in  general  markedly  lower  than  the  EU-28  rates  while  the  opposite  is  true  in  Cyprus, 
and to a somewhat lesser extent in Croatia and Romania. 

Among  the  three  sectors  of  particular  interest  for  the  study  (finance,  energy  and 
online),  the  incidence  of  vulnerability  tends  to  be  higher  in  the  energy  and  financial 
sectors than in the online sector for the majority of the vulnerability indicators. 

Each  of  the  sectors  however  has  important  specificities.  In  particular,  in  the  financial 
and  energy  sectors,  complexity  presents  a  challenge  for  a  very  wide  range  of 
consumers as they are likely to find it difficult to understand and compare offers.   

The average consumer 

The  Unfair  Commercial  Practices  Directive  distinguishes  the  notion  of  the  average 
consumer  from  the  vulnerable  consumer.  The  average  consumer  is  defined  as  a 
consumer  “who 
is  reasonably  well-informed  and  reasonably  observant  and 
circumspect,  taking  into  account  social,  cultural  and  linguistic  factors”.  Given  the  two 
different  consumer  concepts  in  the  UCPD  the  study  also  reviews  the  extent  to  which 
the  “average”  consumer  is  vulnerable  under  one  or  several  of  the  vulnerability 
dimensions.  Overall,  for  each  of  the  vulnerability  indicators,  the  “average  consumer,” 
as represented by the median consumer, exhibits few signs of vulnerability.  

Vulnerability drivers 

Among  the  set  of  drivers  of  vulnerability  (personal  and  demographic  characteristics, 
behavioural  drivers  /  characteristics,  market-related  drivers,  access  drivers  and 
situational  drivers)  considered  by  the  study,  market-related  drivers  are  found  to  be 
particularly important, since these are often consistently linked with many vulnerability 
indicators  across  multiple  dimensions,  and  often  have  the  strongest  effects  on 
individual indicators. 

However,  other  types  of  drivers  are  also  important,  especially  with  respect  to  certain 
dimensions and vulnerability indicators. For example: 

  Among the behavioural drivers, being trusting has particularly strong effects on 
feeling uniformed about prices, quality, etc. and feeling vulnerable due to the 
complexity of offers;  

  Among the access drivers, using the internet to search for information has a 

strong impact on being able to choose between offers; and, 

  Among the situational drivers, finding it hard to make ends meet is linked with 

vulnerability across many dimensions. 

Using  the  results  from  the  analysis  of  the  incidence  and  drivers  of  vulnerability  the 
study populates a theoretical framework of vulnerability developed on the basis of the 
literature  review.  Such  an  approach  provides  a  concise  overall  picture  of  the  most 
important drivers and effects of vulnerability. The key points to note are that: 

  Market-related drivers are especially important, in particular being unable to 

read contract terms and conditions and being disengaged from markets (e.g. in 
terms of not knowing contract conditions, or not reading communications). This 
suggests that measures that ensure information is presented in a salient and 
accessible way, methods to raise awareness of tools that can aide comparison 
in markets, and measures that increase consumer awareness of market 
conditions, may be effective at mitigating vulnerability.    

xix 

 
 
 
Executive summary 

  Behavioural drivers also have important effects, in particular more impulsive 

consumers are, on the whole, more likely to be vulnerable on several 
dimensions while consumers who are willing to take risks are generally less 
vulnerable. These results suggest that behavioural biases such as risk aversion 
and cognitive limitations are relevant to vulnerability, and that measures that 
target these factors, for example interventions to improve the  presentation of 
information, may be effective in reducing vulnerability.  

  Among the situational drivers, finding it difficult to make ends meet and having 
friends who cannot make ends meet are found to be especially important. This 
suggests that measures to reduce financial pressure on consumers (e.g. social 
tariffs) may be effective in reducing vulnerability. 

The  study  also  examines  the  interaction  between  marketing  practices  and  consumer 
vulnerability.  Four  behavioural  experiments  were  undertaken  to  assess  the  effect  of 
various  common  marketing  practices  on  consumer  decision  making,  namely  the  level 
of  prominence  given  to  important  information  (in  the  financial  sector),  the  impact  of 
dripping prices and information (in the on-line sector), the complexity of tariffs (in the 
energy  sector)  and  the  impact  of  the  use  of  a  teaser  rate  (across  the  three  sectors). 
On  average  across  the  four  experiments,  62.5%  of  the  consumers  managed  to  select 
the best offer under the marketing practice. However, when consumers were provided 
with clearer information 69.2 % of the consumers selected the best offer, a statistically 
significant increase of almost 7 percentage points. 

Revised definition of vulnerability 

This  study  explores  the  definitions  of  vulnerability  used  in  literature,  highlighting  the 
shift from definitions focusing on personal characteristics of the consumer to definitions 
taking into account the overall situation in which the consumers  find themselves.  The 
consequent investigation of vulnerability drivers and marketing practices confirms that 
the  broader  market  environment  is  an  important  element  of  vulnerability,  but  that 
temporary or permanent characteristics of the consumer also play an important role.  

Based  on  the  identified  literature,  the  study  aimed  to  operationalise  consumer 
vulnerability in terms of a set of five dimensions. This operationalisation can in turn be 
used to update and enhance existing vulnerability definitions. A “vulnerable consumer” 
could therefore be defined as: 

“A  consumer,  who,  as  a  result  of  socio-demographic  characteristics,  behavioural 
characteristics, personal situation, or market environment: 

Is at higher risk of experiencing negative outcomes in the market; 

 
  Has limited ability to maximise their well-being;  
  Has difficulty in obtaining or assimilating information; 
 
 

Is less able to buy, choose or access suitable products; or  
Is more susceptible to certain marketing practices”  

Measures to address vulnerability 

Measures addressing consumer vulnerability in place at the EU and Member State level 
include: 

  Support measures, including both financial and non-financial support; 
  Protection measures targeting economic actors and aiming to eliminate 

problematic practices; and 

  Awareness-raising measures, such as information campaigns or provision of 

specific advice.  

Examining  national  approaches,  measures  addressing  consumer  vulnerability  are 
relatively common across the EU. At the same time, a considerably smaller number of 

xx 

 
 
  
Executive summary 

Member  States  appear  to  have  developed  a  broader  strategic  approach  to  consumer 
vulnerability.  

However,  no  clear  pattern  emerges  from  a  comparison  of  a  classification  of  Member 
States  based  on  identified  measures  and  strategic  approaches  with  the  observed 
incidences  of  vulnerability.  This  finding  suggests  that  there  is  no  clear  link  between 
incidence  of  vulnerability  and  the  national  approach  to  vulnerability.  However,  it  is 
important  to  note  that  the  analysis  cannot  identify  the  direction  of  potential  causality 
(i.e.  whether  measures  addressing  vulnerability  result  in  lower  incidence,  or  whether 
high incidence of vulnerability spurs the introduction of relevant measures at national 
level). 

Policy implications 

Given  the  complexity  of  consumer  vulnerability,  what  constitutes  a  good  practice 
depends  on  the  specific  driver  or  marketing  practice  to  be  addressed,  as  well  as  the 
broader  market  environment.  Nevertheless,  the  study  identifies  a  set  of  broad 
measures likely to have a positive impact. These include:  

  Well targeted support  measures in the energy-sector that address  fuel poverty 

among vulnerable consumers; 

  Measures  addressing  marketing  practices  that  exploit  consumers’  vulnerable 

situations; and 

  Measures improving the quality and transparency of information.  

More  specifically  a  range  of  potential  policy  options  are  proposed  for  mitigating  the 
various effects of the different types of vulnerability drivers:  

  Personal and demographic drivers: ensure that information is accessible to non-
native speakers as not being a native speaker is among the various personal 
and demographic drivers and a major source of vulnerability; measures to help 
young people overcome the feeling of being unassertive or problems with 
comparing deals and to help older people overcome problems of choosing and 
accessing deals; 

  Behavioural drivers: measures to address issues with complex, misleading or 
difficult to understand information and provision of simple, clear, transparent 
and comprehensive information; 

  Market-related drivers: provision of simple, clear, transparent and 

comprehensive information, awareness-raising campaigns about comparison 
tools and existing market conditions; 

  Access drivers: measures to improve access to, knowledge of and confidence in 
the online environment, improved physical accessibility of public and private 
commercial buildings and potentially redefining discriminatory treatment; 
  Situational drivers: measures to alleviate or mitigate financial pressures on 
individuals and households (including well targeted support measures in the 
energy sector to address fuel poverty) and measures assisting consumers who 
are totally or largely confined to their home. 

xxi 

 
 
 
 
1 │ Background, introduction and research objectives of the study 

1.  BACKGROUND, INTRODUCTION AND RESEARCH OBJECTIVES OF THE 
STUDY 

This chapter describes the policy context in which the study is set, sets out the scope 
of  the  study,  explains  the  need  for  the  study,  presents  the  research  objectives  of  the 
study, outlines the research activities, and finally lays out the structure of the report. 

1.1. Policy context 

The  president  of  the  European  Commission,    Jean-Claude  Juncker,  committed  to 
deliver change and to focus on the big political challenges in Europe.  To achieve this, 
he  created  a  new  streamlined  Commission  including  a  “strong  consumer  portfolio”10. 
This  portfolio  is  called  upon  to  play  a  vital  role  in  delivering  on  the  priorities  of  the 
2014  Commission  Political  Guidelines11,  in  particular  the  Digital  Single  Market,  the 
Energy  Union,  the  Internal  Market,  the  Capital  Markets  Union  and  Jobs,  Growth  and 
Investment.  

Built on the Consumer Programme 2014-2020, the EU consumer policy focuses on four 
key areas:  

1)  a  single  market  of  safe  products  for  the  benefit  of  citizens  and  as  a  component  of 
competitive  businesses  and  traders;  2)  a  single  market  where  citizens  are  well 
represented  by  professional  consumer  organisations  whose  capacity  is  built  to  meet 
the challenges of today's economic environment; 3) a market where citizens are aware 
and  exercise  their  rights  as  consumers  so  that  they  contribute  to  the  growth  of 
competitive  markets,  citizens  must  enjoy  access  to  redress  mechanisms  in  case  of 
problems  without  needing  to  resort  to  court  procedures  which  are  lengthy  and  costly 
for  them  and  the  governments;  4)  a  concrete  and  effective  collaboration  between 
national bodies to support the enforcement of consumer rights, support the consumers 
with advice. 

The  broad  concept  of  consumer  vulnerability  is  relevant  to  each  of  these  objectives, 
and  consumer  vulnerability  is  identified  as  a  key  challenge  to  be  tackled  within  this 
context. 

Consumer  vulnerability  was  also  identified  as  a  key  concern  in  the  Commission  Staff 
Working  Document12  on  knowledge-enhancing  aspects  of  consumer  empowerment,, 
and  the  European  Economic  and  Social  Committee13  and  consumer  organisations14 
have also focussed attention on the issue of vulnerable consumers.  

Furthermore, a 2012 resolution of the European Parliament15 called on the Commission 
to consider reinforcing of the  rights of vulnerable consumers as a key priority, and to 
include  the  dimension  of  consumer  vulnerability  in  the  work  of  the  Consumer 
Scoreboard to have a clearer overview of the needs of vulnerable consumers. 

10 http://europa.eu/rapid/press-release_IP-14-984_en.htm  
11 http://ec.europa.eu/priorities/docs/pg_en.pdf  
12 http://ec.europa.eu/consumers/archive/strategy/docs/swd_document_2012_en.pdf  
13 http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:C:2013:011:0054:0058:EN:PDF  
14 http://ec.europa.eu/consumers/archive/empowerment/docs/eccg_opinion_consumers_vulnerability_022013_en.pdf  
15 http://www.europarl.europa.eu/sides/getDoc.do?type=TA&reference=P7-TA-2012-0209&language=EN&ring=A7-

2012-0155  

22 

 
 
                                                 
1 │ Background, introduction and research objectives of the study 

While there is a substantial amount of EU consumer law and the consumer dimension 
is  a  key  element  of  many  EU  policies,  in  2012  the  Commission  acknowledged  that  a 
comprehensive framework is needed to address challenges such as those linked to the 
digitalisation of daily life and the specific needs of vulnerable consumers.16 The second 
EC  report  on  consumer  policy17  highlights  a  number  of  activities  with  a  focus  on 
consumer vulnerability, such as the establishment of the Vulnerable Consumer Working 
Group,  which  brought  together  a  range  of  stakeholders  from  many  Member  States  in 
the context of the Citizens Energy Forum. 

The  Unfair  Commercial  Practices  Directive  (UCPD)  provides  for  specific  protection  of 
consumers  who  are  particularly  vulnerable  due  to  their  mental  or  physical  infirmity, 
age or credulity. In its 2013 assessment of the UCPD18, the Commission found it should 
itself  take  a  more  prominent  role  in  monitoring  and  coordinating  the  enforcement  of 
the UCPD by Member States, and that further efforts should be made to strengthen the 
enforcement  of  the  UCPD  in  relation  to  vulnerable  consumers.  At  present  the 
Commission  is  revising  the  Guidance  document  on  the  application  of  the  UCPD, 
including, potentially, the part on vulnerable consumers. 

1.2. Scope of the study 

The  Consumer  Agenda  identifies  five  sectors  as  being  particularly  problematic  for 
consumers,  namely  digital  services,  financial  services,  transport,  food  and  energy, 
whereas  European  Parliament’s  reports  identify  digital  services,  financial  services, 
transport,  electronic  communications  and  energy  as  primary  sectors  where 
vulnerability occurs.  

The  present  study  focuses  on  consumer  vulnerability  in  three  of  these  sectors, 
specifically  the  energy  sector,  the  financial  sector,  and  the  online  environment 
including electronic communications. 

The  study  covers  all  28  EU  Member  States,  Norway  and  Iceland,  and,  through  the 
various  research  activities  outlined  below,  examines  the 
issue  of  consumer 
vulnerability  in  a  broad  sense,  taking  into  account  the  diverse  range  of  concepts  and 
definitions of vulnerability that exist in the academic and policy literature. 

1.3. Need for the present study 

There  is  a  need  to  investigate  how  markets  that  are  identified  as  problematic  for 
consumers  operate  for  consumers  who  might  be  considered  to  be  vulnerable.  Hence, 
there  is  a  need  for  the  present  study  in  order  to  fill  a  knowledge  gap  by  adding 
substantially to the evidence base in the area of consumer vulnerability. In particular, 
there is a need for the study to: 

  Demonstrate how consumer vulnerability can be operationalised and explored, 

both conceptually and empirically through data collection. 

  Provide  insights  regarding  the  drivers  of  and  key  factors  linked  to  consumer 

vulnerability. 

16 http://europa.eu/rapid/press-release_IP-12-491_en.htm  
17 http://ec.europa.eu/consumers/strategy-programme/policy-

strategy/documents/consumer_policy_report_2014_en.pdf  

18 http://ec.europa.eu/justice/consumer-marketing/files/ucpd_report_en.pdf  

23 

 
 
 
                                                 
1 │ Background, introduction and research objectives of the study 

  Provide  insights  to  assist  consumer  policy-making,  such  as  which  policy 
measures  or  types  of  measures  are  most  effective  in  mitigating  vulnerability, 
and the role of problematic commercial practices. 

  Provide insights useful to help the development of the EU’s consumer evidence 
base,  including  the  Consumer  Scoreboards,  market  studies,  and  behavioural 
studies. 

  Provides insights relevant to the UCPD and updating of the UCPD guidance. 

1.4. Research objectives of the study 

The Terms of Reference set out 15 research questions (for reference later in the report 
we label these RQ1 to RQ15): 

RQ1:  Is  it  possible  to  map  and  classify  different  types  of  consumer  vulnerabilities? 
How  can  this  be  done  in  a  manner  that  allows  establishing  operational 
definitions  of  what  is  an  average  consumer,  and  what  consumer  skills  are 
required to avoid vulnerability for each of the sectors investigated? 

RQ2:  Is  it  possible  to  discern  specific  socio-demographic  characteristics  which  point 
toward  a  higher  risk  of  consumer  vulnerability  (e.g.  age,  income,  education, 
employment status, migrant, living with disabilities, living in rural/urban areas, 
lack  of  access  to  internet  at  home,  etc.)?  If  so,  what  are  the  implications  of 
embodying multiple vulnerability characteristics? 

RQ3:  In  specific  sectors,  to  what  extent  is  consumer  vulnerability  linked  to 
problematic  marketing  practices  or  to  a  particular  socio-economic  status?  To 
what  extent  is  alleviation  of  vulnerability  linked  to  market  developments  (e.g. 
more  simplification  or  transparency  of  offers)  or  to  changes  in  individual 
behaviour (e.g. consumer education, specific training etc.)? 

RQ4:  Is  consumer  vulnerability  to  be  regarded  more  as  a  structural  condition,  or  a 
transitory  one?  What  are  the  coping  strategies/responses  employed  by 
consumers in a situation of vulnerability? What are the chances for a person to 
improve  or  exit  his/her  condition  of  consumer  vulnerability?  What  are  the 
factors that can contribute to this possible exit/improvement? 

RQ5:  How  to  explore  the  complexities  of  consumer  vulnerability  across  different 

sectors? 

RQ6:  Are  socio-demographic  characteristics  found  to  indicate  vulnerability  in  one 

sector, likely to indicate vulnerability also in other sectors? 

RQ7:  Is it possible to identify drivers of vulnerability per sector, based on an in-depth 
analysis  of  specific  market  mechanisms  and  subsequently  to  assess  which 
drivers of vulnerability are likely to operate across sectors? 

RQ8:  How  to  best  test  (through  behavioural  experiments)  what  behaviour  and  skills 
are  assumed  to  be  those  of  an  average  consumer  per  sector  specific 
problematic  marketing  practice?  Are 
there  certain  socio-demographic 
characteristics  which  make  persons  more  susceptible  to  problematic  marketing 
practices in specific purchase situations? 

RQ9:  How to map and classify the best practice (policy) measures in place in Member 
States  and  at  EU  level  to  alleviate  consumer  vulnerability  in  an  operational 
manner  (legislation,  guidance  documents,  self-regulatory  tools,  inspections, 

24 

 
 
1 │ Background, introduction and research objectives of the study 

complaint mechanisms, labelling schemes, help-lines, education and information 
provision, work of relevant NGOs etc.)? 

RQ10: Is it possible to test and assess a selection of best practice (policy) responses in 
place  in  Member  States  (and  at  EU  level)  as  to  whether  they  are  able  to 
counteract vulnerability drivers mapped per sector? 

RQ11: What  are  the  barriers  to  implementing  effective  measures  for  alleviating 
consumer  vulnerability  per  sector,  including  cultural  and  socio-economic 
aspects? 

RQ12: How to identify the most effective intervention tools and prepare the ground for 
proposing  effective  and  evidence-based  responses  from  the  viewpoint  of 
consumer  policy?  How  to  identify  whether  a  policy  initiative  would  be  effective 
to mitigate consumer vulnerability within and/or across sectors? 

RQ13: Based  on  the  evidence  collected  in  the  study,  which  sector  specific  and/  or 
cross-cutting  recommendations  can  be  proposed  to  support  the  Commission's 
work to alleviate consumer vulnerability? 

RQ14: How  can  the  investigation  methods  of  the  future  Consumer  Scoreboards  and 
market studies conducted by the Commission be refined, in order to ensure that 
data relevant for consumer vulnerability is collected and analysed? 

RQ15: How  can  the  insights  from  the  study  provide  advice  on  whether  and  how  the 
chapter  on  'Vulnerable  Consumers'  of  the  UCPD  Guidance  could  be  reviewed/ 
expanded? 

1.5. Research activities 

Five different research activities have been undertaken during the course of the study: 

  Literature review 

  Stakeholder consultation 

  Consumer survey 

  Behavioural experiments 

  Biographical consumer interviews 

1.6. Structure of the report 

The report comprises of twelve chapters: 

1.  Background, introduction and research objectives of the study 
2.  Research methods used in the study 
3.  Conceptualisation of a theoretical framework and operationalisation of 

vulnerability 

4.  Incidence of consumer vulnerability 
5.  Drivers of and factors linked to consumer vulnerability 
6.  Population of the theoretical framework of vulnerability 
7.  Role of marketing practices 
8.  Complexities of consumer vulnerability across different sectors 
9.  Concept of the ‘average consumer’ 
10.  Best practice policy measures in member states and at EU level 

25 

 
 
 
1 │ Background, introduction and research objectives of the study 

11.  Conclusions, recommendations and policy options  

26 

 
 
 
 
 
2 │ Research methods used in the study 

2.  RESEARCH METHODS USED IN THE STUDY 

The  five  different  research  activities  undertaken  during  the  course  of  the  study  (the 
literature review, stakeholder consultation, consumer survey, behavioural experiments, 
and biographical consumer interviews) are discussed in turn in the following sections. 
The  final  section  in  this  chapter  outlines  the  input  received  during  the  study  from  a 
number  of  experts  drawn  from  across  the  three  sectors  of  focus  for  the  study.  More 
technical details of the research methods used in the study can be found in Annex 2. 

2.1. Literature review 

The objectives of the literature review were to: 

  Provide an overview  of  the literature  regarding the definitions of vulnerability, 

 

drivers of vulnerability and vulnerability indicators. 
Identify  and  group  marketing  practices  that  are  problematic  for  vulnerable 
consumers. 

  Provide  a  more  detailed  overview  of  drivers,  indicators,  and  practices  for  the 
financial,  energy,  and  online  environment  and  electronic  communication 
sectors. 

The outputs from the review were: 

  Classification of the different types of consumer vulnerability 
  Typology of vulnerability drivers and indicators 
  Groupings of problematic marketing practices 
  Country fiches on best practice measures 

The selection of literature for the literature review used two separate approaches: 

  Systematic database searches using keyword terms; 
  Consultation  with  study  experts  and  DG  JUST,  including  drawing  on  literature 
identified by DG JUST, as well as by the study team during the proposal stage. 

Since  initial  database  searches  using  broad  terms  “consumer  AND  vulnerability”, 
“consumer* AND vulnerable”, “average consumer*” and “consumer protection” allowed 
us to identify a relatively limited number of relevant studies, consultation with experts 
played  an  important  role  in  identifying  relevant  literature.  Relevant  studies  were 
reviewed  and  synthesised  using  a  common  format,  which  identified  vulnerability 
drivers,  definitions  and  typologies,  vulnerability  indicators  and  problematic  practices. 
This  synthesis  was  in  turn  the  basis  for  the  findings  contained  within  the  following 
report sections.  

The team has identified and screened 172 documents. 80 were reviewed in detail, out 
of  which  66  were  synthesised  and  relevant  information  on  vulnerability  drivers, 
indicators,  and  problematic  practices  was  extracted.  81  remaining  studies  were 
deemed not directly relevant. For the remaining 11 documents, the full text could not 
be obtained. Additional detail concerning the literature review methodology is outlined 
in  Section  3.2.  The  literature  list  is  presented  in  the  references  section  (immediately 
before Annex 1). 

27 

 
 
 
2 │ Research methods used in the study 

2.2. Stakeholder consultation 

Stakeholder  consultation  aimed  to  supplement  the  literature  review,  with  a  particular 
focus on identifying best practice measures to address consumer vulnerability in the 28 
Member  States  plus  Iceland  and  Norway  and  in  turn  contributing  to  country  fiches 
focusing on national approaches to consumer vulnerability. 

Stakeholder consultation carried out as part of the preparatory review tasks consisted 
of: 

  Stakeholder  interviews:  The  study  team  has  conducted  three  types  of 
interviews: interviews with national consumer organisations (or other national-
level  stakeholders)  in  order  to  complete  the  country  fiches;  interviews  with 
sector-experts  at  national  level  to  feed  into  the  sector-specific  overview  of 
consumer  vulnerability;  as  well  as  interviews  with  other  EU-level  stakeholder 
organisations  relevant  to  the  topic  of  consumer  vulnerability  in  order  to 
supplement  the  literature  review.  The  full  list  of  interviewees  is  presented  in 
the Annex.  

  Focus  groups:  Five  mini  focus  groups  were  conducted  in  Milan,  Italy.  The 
objective  was  to  provide  further  information  for  the  design  of  the  consumer 
survey  and  behavioural  experiments,  and  to  provide  additional  context  in  the 
interpretation  of  future  findings  across  the  study.  The  groups  were  elderly, 
unemployed, low  education, immigrants and those with a disability. The focus 
group  report  has  been  provided  as  a  separate  deliverable  to  the  Commission 
along with the audio recordings. 

Stakeholder  interviews  with  consumer  organisations  and  sector-specific  stakeholders 
(sector-focused  consumer  organisations  or  regulators)  were  carried  out  by  telephone 
and  used  a  semi-structured  interview  guide  (see  Annex  12).  Given  the  challenges  in 
securing  interviews  with  consumer  organisations,  the  study  team  made  a  significant 
effort  to  secure  a  sufficient  number  of  interviews.  In  some  cases  interviews  with 
national  authorities  or  sector-specific  interviews  were  used  to  gain  information  about 
general approaches to  consumer  vulnerability, where national consumer  organisations 
were unable or unwilling to participate. In some cases stakeholders provided answers 
to the interview questions in written form.  

It is important to note that the input from consumer organisations consisted primarily 
of the organisation representatives highlighting issues encountered in their daily work. 
These may include specific practices encountered, investigated, or acted upon, as well 
as  specific  case  law.  Generally,  however,  this  input  was  not  based  on  systematic 
research into vulnerability and practices targeting vulnerable consumers conducted by 
consumer organisations. Hence, while the  consumer organisation input has been very 
valuable in identifying drivers, indicators, and practices, it is important to note that it 
does not allow us to draw definitive conclusions concerning the frequency or impact of 
specific practices. 

The detailed focus group composition was as follows: 

  Elderly group: 1 female (separated), 1 male (married) aged 65 to 75. 
  Unemployed  group:  1  male,  1  female,  both  of  them  with  family,  both 

unemployed for 2-5 years and currently looking for a new job. 

  Low  education  group:  1  female,  1  male  aged  25  to  45,  either  discontinued 

education or achieved low education standards. 

28 

 
 
2 │ Research methods used in the study 

 

Immigrant  group:  1  female  from  the  Philippines,  1  male  from  Romania, 
under  40  years  old,  both  first-generation  immigrants  who  moved  to  Italy  2-5 
years ago, average education standards, average Italian fluency (speaking and 
writing).  

  Disability group: 1 male, 1 female, aged 25 to 55, both affected with physical 
disabilities (1 of them suffering from a chronic condition), unable to lead a fully 
autonomous life or in need of part-time care. 

It is important to note that in the case of the members of the immigrant group, native 
language  plays  an  important  role.  For  instance,  immigrants  whose  native  language  is 
very  different  to  language(s)  used  in  the  host  country  (in  this  case  Italian)  may  face 
different  challenges  than  those  whose  native  language  is  closer  to  the  host  country’s 
(e.g.  Romanian  and  Italian).  Therefore  it  is  difficult  to  fully  isolate  the  immigrant 
experience  from  language  factors.  Another  challenge  faced  during  the  focus  groups 
was  the  diversity  of  the  disability  group  and,  as  a  consequence,  the  very  diverse 
experiences of the participants. 

2.3. Consumer survey 

A  consumer  survey  was  conducted  in  all  EU  Member  States,  Norway  and  Iceland. 
Behavioural  experiments  were  run  in  conjunction  with  the  survey  in  five  countries 
(details of the experiments are given in section 2.4). The countries where behavioural 
experiments were included in the survey were Denmark, Lithuania, Portugal, Romania 
and the UK. These countries were selected for the experiments in order to maintain a 
good geographic balance. 

In  order  to  reach  the  potentially  vulnerable  consumers  the  consumer  survey  was 
conducted via a mixed-mode design: online (CAWI) and telephone interviews (CATI) in 
countries  without  behavioural  experiments  and  online  and  in-home  self-completion 
(CAPI)  in  the  countries  with  behavioural  experiments.19  In  two  countries  (Cyprus  and 
Malta)  online  panels  weren’t  available  and  the  entire  survey  was  conducted  through 
telephone interviews. 

The overarching objective of the consumer survey was to map vulnerability patterns in 
three  investigated  sectors:  the  energy  sector,  the  financial  sector  and  the  online 
sector.  The  survey  questions  particularly  focussed  on  investigating  how  consumers 
understand  and  choose  their  energy  suppliers,  their  financial  services  providers  and 
their telecom providers. 

2.3.1.  Pilot 

In  order  to  collect  preliminary  information  from  the  general  public  which  would  serve 
as  support  for  finalising  the  questionnaire  for  the  main  survey,  a  pilot  study  was 
implemented  in  the  UK.  The  questionnaire  was  piloted  in  order  to  test  for  length  of 
questionnaire  and  any  difficulties  respondents  may  have  with  understanding  the 
behavioural  experiments.  The  pilot  allowed  necessary  changes  to  the  final  version  of 
the survey to be implemented before the full fieldwork was undertaken. 

19 Bulgaria, Germany and Poland did not conduct an experiment but used CAPI instead of CATI 

for feasibility reasons. 

29 

 
 
 
                                                 
2 │ Research methods used in the study 

2.3.2.  Sample 

The  target  audience  of  the  consumer  survey  and  experiment  was  the  general 
population. The survey targeted the general population aged 16 to 75 in each country, 
but respondents older than 75 are also included in the sample (the oldest respondent 
in  the  dataset  is  99  years  old).  In  each  of  the  big  or  middle-sized  countries  at  least 
1,000  respondents  were  interviewed.  For  the  seven  smallest  countries  at  least  500 
consumers  were  interviewed.  For  each  country  the  best  interviewing  mode  or  a 
combination  of  interviewing  modes  was  determined,  online,  telephone,  and  in-home-
self-completion. 

2.3.3.  Online panels 

The online part of the main stage fieldwork was conducted using Ipsos’ online panels. 
In some countries, Ipsos’ panels were supplemented with partner panels, either due to 
Ipsos  not  currently  having  a  panel  in  that  country  or  where  the  Ipsos  panel  was  too 
small to achieve the target number of interviews. All the work conducted was managed 
centrally, with one scripting, data collection and data delivery process.  

2.3.4.  Fieldwork dates 

Fieldwork was completed between 20 February and 17 April 2015. 

2.3.5.  Interview length 

During the main stage fieldwork, the average online survey length with the experiment 
ranged  from  20  minutes  in  the  UK  to  26  minutes  in  Lithuania.  In  the  experiment 
countries,  the  offline  (CAPI/CATI)  interview  length  ranged  from  18  minutes  in  the  UK 
to 45 minutes in Denmark. 

In  countries  where  the  experiments  were  not  conducted,  the  length  of  the  interview 
was  shorter.  The  average  online  survey  length  for  these  countries  ranged  from  11 
minutes  in  Belgium,  the  Netherlands  and  Italy,  to  15  minutes  in  Estonia,  Iceland, 
Latvia  and  Slovakia.  In  the  non-experiment  countries,  the  offline  interviews  took 
between 11 minutes in Greece and 23 minutes in Slovakia. 

2.3.6.  Weighting and outputs 

Two sets of weights have been applied to the data:  

1)  Within each country, the data has been weighted by demographic variables 
to correct for any biases in the achieved sample profile compared to known 
population statistics (gender, age, urbanisation, region, working status and 
education). 

2)  Across countries, the data has been weighted to ensure that each country is 

represented according to its population size in the EU-wide results. 

2.4. Behavioural experiments 

Four behavioural experiments were conducted as part of the study. Three experiments 
were specific to the three key sectors of interest to the study – the energy, finance and 
online environment sectors – and the fourth was a cross-cutting experiment.  

30 

 
 
2 │ Research methods used in the study 

Each  experiment  tested  the  impact  on  consumer  decision  making  of  a  marketing 
practice  treatment  and  a  remedy  treatment.  Both  the  marketing  practices  and  the 
remedies  were  developed  based  on  practices  observed  in  the  relevant  sectors  in 
Member States. 

All  four  experiments  involved  respondents  being  presented  with  and  asked  to  choose 
between  alternative  offers/deals  (e.g.  energy  deals,  consumer  loans,  and  packaged 
broadband, telephones and TV packages) over two ‘rounds’. In the first round of each 
experiment, respondents chose between two offers. In the second round, the offer that 
they  selected  in  the  first  round  became  ‘their  current  deal’,  and  they  chose  between 
that deal and two ‘new’ offers. 

Before  respondents  could  see  details  of  the  new  offers  in  the  second  round,  they 
needed to click a button and incur a short time delay, which was intended to simulate 
search  costs.  However,  respondents  were  free  to  select  any  of  the  deals  that  they 
could  see  at  any  time  (i.e.  in  the  second  round  they  could  decide  to  stay  with  their 
current deal without viewing either of the new deals). 

The  deals  were  constructed  so  that  in  each  round  of  each  experiment  there  was  an 
optimal  choice  (i.e.  a  deal  that  was  objectively  better  in  terms  of  price  and/or  other 
characteristics).  This  means  we  have  clear  performance  measures  from  the 
experiment,  which  allow  us  to  assess  the  relative  impacts  of  the  marketing  practice 
and remedy treatments. 

Each  respondent  was  randomly  allocated  to  two  of  the  four  experiments.  Each 
respondent  performed  one  of  the  two  experiments  they  were  allocated  to  under  the 
remedy treatment, and the other under the marketing practice treatment. Hence  each 
respondent made four experiment choices in total. 

The  experiments  were  run  in  five  countries  in  conjunction  with  the  consumer  survey, 
as a separate module within the survey. The five experiment countries were Denmark, 
Lithuania,  Portugal,  Romania  and  the  United  Kingdom.  These  countries  were  selected 
to ensure balanced geographic coverage of the EU. 

In  the  following  sections  we  describe  each  of  the  four  experiments  in  more  detail,  in 
particular  the  marketing  practice  and  remedy  treatments  in  each  experiment.  Further 
details  (e.g.  the  wording  of  questions  and  mock-ups  of  the  deals  shown  to 
respondents)  can  be  seen  in  Annex  13  which presents  the  full  script of  the  consumer 
questionnaire and behavioural experiment. 

2.4.1.  Energy sector experiment 

The  energy  sector  experiment  focussed  on  complex  tariff  pricing  (the  marketing 
practice)  and  new  information  tools  to  provide  consumers  with  useful  information  to 
help them compare  the costs of different tariffs (the remedy). The motivation for this 
experiment  design  came  from  a  number  of  previous  studies  and  references,  for 
example: 

  Tariffs  can  be  presented  in  a  number  of  different  ways  which  leads  to 
difficulties in comparison for consumers, and can impact choice of tariff due to 
price presentation. A key recommendation from the 2010 SANCO study on the 
functioning  of  retail  electricity  markets  for  consumers  in  the  EU  was 
“Encourage  suppliers  to  present  their  tariff  offers  in  a  way  that  is  clear  and 

31 

 
 
 
2 │ Research methods used in the study 

transparent  for  consumers  to  interpret  and  compare  across  suppliers”  (ECME 
Consortium 2010).  

  The  European  Commission  (2013)  Vulnerable  Consumer  Working  Group 
Guidance  Document  on  Vulnerable  Consumers  recommended  that  Member 
States  should  ensure  that  information  on  bills  is  clear  and  that  it  is  easy  for 
consumers to compare tariffs charged by energy companies.  

  ACER  and  CEER  (2013)20  report  that  consumer  choices  can  be  facilitated  by 
publishing  easily  comparable  unit  prices  in  terms  of  standing  charges  and 
variable rates for standard consumption profiles.  

  The  Energy  Efficiency  Directive  (2012/27/EU)  requires  that  information  is 
provided in an easily comparable format allowing consumers to compare deals 
on a like-for-like basis (Article 10(3.e)). However, evidence from the UK found 
that  consumer  struggle  to  identify  the  cheapest  energy  offer  due  to  complex 
tariffs (Ofgem 2013; Which? 2012 and 2014). 

Under  the  marketing  practice  treatment,  alternative  electricity  deals  with  complex 
pricing were presented to respondents. These deals comprised of a standing charge per 
day,  a  unit  rate  for  the  first  block  of  electricity  consumed  (e.g.  750kWh  per  year), 
another unit rate for additional electricity, and in some cases a discount. Such complex 
pricing of tariffs is a marketing practice that has been present in some Member States, 
for example in the UK prior to the regulator’s 2013 Retail Market Review, which sought 
to address such practices. 

Under  the  remedy  treatment,  the  same  electricity  deals  were  presented  with  a  Tariff 
Comparison  Rate  (TCR)  and  a  Personal  Projection,  which  are  information  tools 
introduced by the UK regulator following its Retail Market Review in 2013. These tools 
establish  a  common  means  of  calculating  estimated  annual  energy  costs  that  are 
personal to the consumer: 

  The TCR is the cost of a tariff per kWh (i.e. €cent per kWh) calculated based on 
‘medium’  energy  use,  which  is  defined  as  3,200  kWh  per  year  (see  Ofgem 
2013a para. 3.37). 

  The Personal Projection is the cost of a tariff over one year calculated based on 
the consumer’s personal (actual or estimated) level of consumption. Where the 
consumer’s  actual  annual  consumption is  not  available  suppliers  are  “required 
to  use  their  best  estimate  of  consumption  for  a  12-month  period,  taking  into 
account all relevant factors” (see Ofgem 2013a para. 3.46). 

Two  example  deals  presented  under  the  remedy  treatment  are  shown  in  the  figure 
below. 

In the experiment, each respondent’s Personal Projection was calculated based on their 
consumption  profile,  which  was  told  to  them  at  the  start  of  the  experiment.  Their 
consumption  profile  was  in  turn  calculated  based  on  respondents’  answers  to  several 
preliminary questions (e.g. the size of their home and whether they use electricity on 

20 Annual Report on the Results of Monitoring the Internal Electricity and Gas Markets in 2012: 
http://www.acer.europa.eu/Electricity/Market%20monitoring/Pages/Reports.aspx    Since  the 
experiment  was  designed,  more  recent  versions  of  this  report  have  been  published: 
http://www.europarl.europa.eu/meetdocs/2014_2019/documents/itre/dv/acer_market_moni
toring_report_2014_/acer_market_monitoring_report_2014_en.pdf  

32 

 
 
                                                 
2 │ Research methods used in the study 

evenings and weekends or all day), an approach that is similar to that used by actual 
energy suppliers. 

Figure 1: 

Example deals presented under the remedy in the energy sector 

experiment 

2.4.2.  Online sector experiment 

The online sector experiment simulated the consumer choice of a bundled broadband, 
telephone and TV package deal. The use of this type of product to represent the online 
environment  sector  was  due  to  the  prevalence  of  such  packages  in  the  EU.  The 
Consumer  market  study  on  the  functioning  of  the  market  for  internet  access  and 
provision  (DG  SANCO  2012)  found  that  across  the  EU,  86%  of  consumers  have  a 
bundled  package  while  14%  have  stand-alone  internet  access  (EC  DG  SANCO  2012). 
The  most  common  form  of  bundle  reported  by  consumers  in  the  study  was  internet 
plus  fixed  telephony  (36%),  followed  by  internet  plus  fixed  telephony  and  TV  (19%), 
with 11% of consumers in the EU reporting that they held internet plus TV packages. 
According  to  a  DG  Connect  (2013)  study  on  Broadband  Internet  Access  Charges 
(BIAC), 45% of offers available in the EU were internet only, while 24% were internet 
plus phone, 20% included internet plus telephone and TV, while 11% of offers included 
internet and TV. 

The marketing practice examined in the online experiment was ‘dripping’ of prices and 
information relating to deals during the purchasing process, i.e. where different pieces 
of information are revealed to consumers at different stages in the process.  

Dripping of information and prices across the purchasing process can lead to difficulties 
for  consumers  to  identify  and  choose  the  best  deal  given  their  requirements.  For 
example,  two  behavioural  studies  for  the  Office  of  Fair  Trading  in  the  UK  (2011  and 
2013)  found  that  the  price  dripping  can  cause  up  to  a  25%  loss  in  consumer  welfare 
compared to straight per unit pricing. Recent research for Freeview and the Post Office 
in  the  UK  (Opinium  and  University  of  Reading  2013)  found  that  suppliers  drip 
information and the actual price can increase by more than 200% over 24 months once 
additional products and services are added. 

Price  and  information  dripping  also  adds  to  complexity  for  consumers.  A  study  by 
Communications  Chambers  (2013)  found  that  complex  product  information  is  a  key 
search  cost  for  consumers  and  this  is  magnified  when  products  have  multiple 
components, while a behavioural study in the UK for Ofcom (2011) into presentation of 
broadband  package  information  found  that  the  majority  of  respondents  could  not 
identify the best offer. 

33 

Deal NDeal PPersonal Projection: £369 per yearPersonal Projection: £388 per yearTariff Comparison Rate: 15.25p/kWhTariff Comparison Rate: 15.97p/kWhStanding charge: 4.3p/dayStanding charge: 11.9p/dayFirst 750kWh per year: 22.8p/kWhFirst 800kWh per year: 21.8p/kWhAdditional kWh: 11.9p/kWhAdditional kWh: 13.1p/kWhRates include direct debit discountDirect debit discount: 6%Choose this dealChoose this deal 
 
 
 
2 │ Research methods used in the study 

The  remedy  tested  in  the  experiment  was  the  provision  of  the  total  price  for  each 
package  at  the  first  stage  of  the  purchasing  process  (i.e.  up-front).  This  remedy  is 
based  on  a  policy  from  Denmark,  which  requires  broadband  suppliers  to  tell  the 
consumer upfront on the first page they view the total minimum price they will pay for 
a given package. This minimum price includes the price for the broadband service and 
the price for all other package components.21 

In  the  experiment,  under  both  treatments  (marketing  practice  and  remedy) 
information  relating  to  alternative  offers  and  the  prices  of  these  offers  was  dripped 
through  the  purchasing  process.  The  purchasing  process 
in  the  experiment 
environment  comprised  of  three  steps,  over  the  course  of  which  characteristics  of 
alternative  deals  (including  download  speed,  download  volume,  monthly  subscription 
price,  a  discount  on  monthly  subscription,  free  daytime  minutes,  free  night 
time/weekend calls, TV channels, and the set-up fee) were dripped to respondents. 

Under the remedy treatment (but not the marketing practice treatment) the total cost 
of each offer over 12 months was presented up-front at the first stage of the process. 

Two example packages presented under the remedy treatment are shown in the figure 
below. 

Figure 2: 

Example deals presented under the remedy in the online sector 

experiment 

21  “Best  Practise  in  the  Telecommunications  Industry:  Guidelines  from  the  Danish  Consumer 

Ombudsman”. 
http://www.consumerombudsman.dk/~/media/Consumerombudsman/dco/goodmarketingpr
actis%2000%20pdf.pdf 

Recommends prices be shown in the following manner: Marketing relating to telecom equipment 
and  services  should  quote  the  total  minimum  price  to  be  paid  by  the  subscriber  as  a  result  of 
contractual  obligations.  This  amount  should  be  quoted  as  one,  total  price  (total  minimum 
charge). 

34 

Provider NProvider PBroadband+Telephone+TVBroadband+Telephone+TV12 month contract with renewal option12 month contract with renewal optionNo set-up fee!5% Discount!Up to 30Mb per secondUp to 30Mb per secondUnlimited downloadsUnlimited downloadsUsed to be £45 per monthUsed to be £40 per monthYou get a discount of 2% on your monthly subscriptionYou get a discount of 5% on your monthly subscriptionNow £44 per month with discountNow £38  per month with discountTotal cost for 12 months £529Total cost for 12 months £656Click to see moreClick to see more 
 
 
                                                 
2 │ Research methods used in the study 

2.4.3.  Finance sector experiment 

The  finance  sector  experiment  tested  the  effect  of  the  prominence  of  information 
required  to  be  included in  advertising  for  consumer  credit  under  the Consumer  Credit 
Directive (87/102/EEC), Article 4(2) (a)-(f). These elements, which are to be illustrated 
on a representative example, are: 

a)  Borrowing rate 
b)  Total amount of credit 
c)  APRC and, of a representative example 
d)  Credit agreement duration 
e)  In  the  case  of  deferred  payments,  the  cash  price  and  the  amount  of  any 

advance payment 

f)  Total  amount  payable  and  amount  of  the  instalments  to  be  paid  by  the 

consumer 

The  experiment  simulated  the  choice  of  an  unsecured  personal  loan  (sometimes  also 
called  a  consumer  loan  by  lenders),  which  could  be  used  to  go  on  holiday  or  buy 
consumer  goods  such  as  TVs.  This  type  of  credit  product  was  used  in  the  experiment 
since  it  is  comparable  across  countries,  i.e.  these  types  of  loans  are  available  across 
the  EU,  and  it  is  a  relatively  easy  concept  for  respondents  to  understand.  In  the 
experiment,  respondents  were  told  they  are  seeking  a  loan  to  buy  a  good  such  as  a 
holiday, a television or other product for their home.  

In the presentation of personal loans to consumers, elements a) to d) and f) above are 
required  to  be  shown  in  a  clear,  concise  and  prominent  way,  by  means  of  a 
representative example in the advertising. Element (e) is required only in the case of a 
credit in the form of deferred payment of a specific good or service. 

The  loan  type  was  unsecured  credit  not  linked  to  the  purchase  of  a  good  or  service, 
and  with  a  contractually  determined  credit  amount  and  repayment  period.  That  is, 
credit  provided  on  the  basis  of  a  credit  agreement  in  which  the  total  amount  of  the 
credit  is  specified  and  the  repayment  method  (monthly  repayments  in  this  case)  is 
specified. The loans were fixed rate loans for a period of 12 months.  

The  experiment  treatments  tested  the  effect  of  the  prominence  of  information.  The 
marketing practice treatment is based on a websweep of advertisements in Denmark, 
Lithuania,  Poland,  Portugal  and  the  United  Kingdom,  and  an  example provided  by  DG 
Justice and Consumers from Poland. These marketing practices provide all information 
required within the Consumer Credit Directive (CCD) for unsecured personal loans, but 
this information is not always shown in a prominent way. The remedy is also based on 
a  websweep  of  advertisements,  and  in  this  instance  the  information  required  by  the 
CCD  is  shown  in  at  least  a  prominent  way  as  the  headline  figures.  For  example,  the 
interest rate is often shown as the most prominent feature of an advertisement.  

Under  the  remedy  treatment,  APR  of  each  loan  offered  to  respondents  was  shown  in 
large  font  at  the  top  of  the  offer,  alongside  the  loan  amount,  duration  and  interest 
rate. 

Under  the  marketing  practice  treatment,  the  APR  was  shown  in  smaller  text  at  the 
bottom of the offer, alongside the loan amount, interest rate, fee, monthly repayment, 
and total amount repayable. 

35 

 
 
 
2 │ Research methods used in the study 

Two  example  loans  presented  under  the  remedy  treatment  are  shown  in  the  figure 
below. 

Figure 3: 

Example deals presented under the remedy in the finance sector 

experiment 

2.4.4.  Cross-cutting experiment 

The  cross-cutting  experiment  examined  the  marketing  practice  of  “teaser  rates”,  i.e. 
where  an  initial,  more  favourable  price  or  rate  is  attached  to  a  product  for  a  certain 
period of time, after which it changes to another price/rate which is less favourable for 
the consumer. Consumer issues that may arise due to the use of teaser rates are that 
(see Erta et al. 2013, for further discussion): 

  Consumers  may  miscalculate  how  much  they  will  use/pay  for  products  with 
teaser  rates  (e.g.  consumers  may  underestimate  their  future  credit  card 
borrowing  or  how  long  they  will  continue  to  pay  for  a  subscription).  In 
particular  this  problem  may  arise  if  consumers  are  overconfident  about  their 
self-control or suffer from present-bias. Hence consumers may choose products 
with teaser rates when it is not in their best interests. 

  Consumers  suffer  from  forgetfulness  and  inertia,  meaning  they  neglect  to 
undertake  tasks  that  are  in  their  interests.  As  a  result  consumers  may  fail  to 
switch away from products with teaser rates when these rates expire. 

The cross-cutting experiment was ‘framed’ using three products, one from each of the 
three key study sectors; electricity deals (energy sector), broadband packages (online 
sector) and savings accounts (finance sector). 

Under  both  the  marketing  practice  treatment  and  the  remedy  treatment,  some  deals 
offered  to  respondents  had  teaser  rates  and  other  deals  had  flat  rates,  i.e.  this 
dimension was the same across treatments. 

However, under the remedy treatment (but not the marketing practice treatment) the 
total cost or interest paid (in the case of savings accounts) of each deal over 6, 12 and 
24 months was shown upfront to respondents. 

For example, two deals shown to respondents under the broadband packages framing 
and remedy treatment are shown in the figure below. 

36 

Loan NLoan amount: £93012 month personal loan3.7% fixedAPR 3.8%Representative example: Assumed borrowing of £930 over 12 months at a fixed rate of 3.70% per annum, with no arrangement and credit fee would result in an APR of 3.8%, monthly repayments of £79.06, equal repayments each months, total amount repayable £948.72.Loan PLoan amount: £93012 month personal loan2.7% fixedAPR 17.0%Representative example: Assumed borrowing of £930 over 12 months at a fixed rate of 2.70% per annum, with £67.10 arrangement and credit fee would result in an APR of 17.0%, monthly repayments of £84.31, equal repayments each months, total amount repayable £1,011.72.Choose this loanChoose this loan 
 
 
 
2 │ Research methods used in the study 

Figure 4: 

Example deals presented under the remedy in the cross-cutting 

experiment 

2.5. Biographical consumer interviews 

The  qualitative  element  of  the  research  consisted  of  conducting  in-depth  face-to-face 
interviews in five Member States: Denmark, Lithuania, Portugal, Romania and the UK. 
A total of nine participants were interviewed for each country. 

2.6.  Discussion guide 

The  discussion  guide  used  for  conducting  the  in-depth  interviews  was  designed  by 
Ipsos, with input from the European Commission and London Economics. 

The  document  was  structured  around  the  three  main  topics,  aiming  to  collect 
information about difficulties consumers may encounter when dealing with the energy 
sector, finance sector and in the internet sector.   

The  main  objective  was  to  identify  possible  areas  where  consumers  may  experience 
certain difficulties or may feel disadvantaged when dealing with these companies.  We 
aimed  to  explore  respondents’  personal  experiences  when  dealing  with  different 
retailers, possible causes of feeling unconfident or disadvantaged, as well as attitudes 
and coping strategies when faced with these types of difficulties. 

Each interview began with an introduction to the study, followed by a brief discussion 
about  respondents’  background,  with  the  aim  to  identify  possible  factors  which  may 
contribute to vulnerability, distrust or feeling disadvantaged.  

2.7. Input from the expert group 

As part  of the  study the consortium drew upon the expertise of a number of experts. 
The experts were drawn from across the three sectors of focus for the study (energy, 
finance and online) and we included a legal expert to assist with the case law review.  

The experts were specifically drawn upon to assist in the preparatory phases to ensure 
that the framework adopted was robust and appropriate for the study. Specifically, the 
consortium  shared  the  initial  list  of  identified  literature  with  the  expert  panel  and  the 
experts  were  asked  to  review  the  literature  selection  and  identify  additional  relevant 

37 

Provider QProvider MBroadband+Telephone+TVBroadband+Telephone+TVTotal cost over:Total cost over:    - 6 months: £141    - 6 months: £254    - 12 months: £465    - 12 months: £509    - 24 months: £1113    - 24 months: £1018£8.25 per month for the first 4 monthsFlat rate: £42.4 per month£54 per month thereafterChoose this offerChoose this offer 
 
 
 
 
2 │ Research methods used in the study 

sources to be consulted. A particular focus was on ensuring the coverage of the three 
sectors,  with  experts  asked  to  identify  studies  focusing  on  consumer  vulnerability  in 
their respective sectors of expertise.  

Another  important  key  input  from  the  expert  panel  was  during the  review  of  relevant 
case law. The consortium consulted the legal expert, Prof. Peter Rott in order to assist 
the team in identifying relevant cases for review.  

Input  from  the  panel  was  reduced  as  the  project  progressed  with  the  design  of  the 
fieldwork  materials  drawing  upon  the  expertise  of  our  consortium  members,  the 
European Commission, the EC Joint Research Centre Behavioural Economic Specialists, 
and  the  sector  specialists  drawn  from  across  the  Commission  and  present  on  the  EC 
project Steering Committee. Furthermore, comment and review of the final report was 
provided  by  specialists  drawn  from  the  EC  Steering  Committee  for  the  project,  and 
members  of  the  EC  Vulnerable  Consumer  Working  Group  and  the  Consumer  Markets 
Expert Group to whom the draft findings from the study were presented. 

38 

 
 
  
 
3 │ Conceptualisation of a theoretical framework and operationalisation of vulnerability 

3.  CONCEPTUALISATION OF A THEORETICAL FRAMEWORK AND 
OPERATIONALISATION OF VULNERABILITY 

Box 1:  Summary of Chapter 3 

This  chapter  begins  with  an  overview  of  how  the  concept  of  consumer  vulnerability 
has  been  addressed  in  the  relevant  literature.  This  includes  how  the  concept  is 
defined,  the  different  dimensions  which  it  takes,  and  a  diverse  array  of  respective 
drivers  as  well  as  effects.  The  analysis  sections  then  distil  these  ideas  into  an 
operationalised set of vulnerability dimensions, where each is further associated with 
a set of quantifiable indicators.   

As is widely recognised, there is no single, commonly adopted definition of consumer 
vulnerability.  The  chapter  discusses  the  different  ways  in  which  the  concept  can  be 
interpreted. Vulnerability most often refers to an ex-ante assessment of the likelihood 
of a potential negative outcome in terms of consumer well-being. It is an assessment 
of  risk,  rather  than  a  reflection  of  a  negative  outcome  that  has  or  is  certain  to 
materialise.  

Two  broad  vulnerability  categories  emerge,  namely,  vulnerability  which  relates  to 
personal  characteristics  of  the  consumer  and  a  broader  concept  which  takes  into 
account the transactional situations in which consumers find themselves. 

Most  recent  definitions  recognise  that  vulnerability  is  not  a  static  condition. 
Consumers  may  move  in  and  out  of  states  of  vulnerability  and  they  may  be 
vulnerable  in  respect  of  some  categories  of  transaction  but  not  others.  In  addition, 
vulnerability is best viewed as a spectrum rather than a binary state.  

Yet,  in  some  policy  contexts,  it  may  be  important  to  recognise  that  some  personal 
characteristics  can  imply  that  vulnerability  remains  an  enduring  characteristic  for 
particular groups of consumers.   

The analysis of the literature is synthesised into five core dimensions of vulnerability, 
which  are  taken  forward  throughout  the  study  as  the  organising  structure  according 
to  which  prevalence  of  vulnerability  is  empirically  assessed.  These  five  core 
dimensions are:  

1) 
Heightened  risk  of  negative  outcomes  or  impacts  on  well-being  - interpreting 
vulnerability as an ex-ante assessment of the likelihood of a negative outcome, as a 
loss  of  welfare  due  to  choices  in  markets,  and  as  higher  susceptibility  to  harm  and 
diminished well-being. 

2) 
Having  characteristics  that  limit  ability  to  maximise  well-being  -  interpreting 
vulnerability  as  characteristics  that  limit  consumers’  ability  to  maximise  their  utility 
and  well-being,  as  consumers  of  diminished  capacity  to  understand  advertising  and 
product effects, and as consumers’ certain abiding characteristics. 

Having  difficulty  in  obtaining  or  assimilating  information  -  interpreting 
3) 
vulnerability  as  limitations  obtaining  or  assimilating  consumer  information  and 
‘informational  vulnerability’  which  refers  to  scenarios  where  suppliers  may  have 
better information than consumers.  

4) 

Inability  or  failure  to  buy,  choose  or  access  suitable  products  -  interpreting 

39 

 
 
3 │ Conceptualisation of a theoretical framework and operationalisation of vulnerability 

vulnerability  as  consumer  inability  to  choose  or  access  products  and  services  which 
are suitable for their needs or do so without disproportionate effort or as not having 
access  to  beneficial  products  or  services,  including  a  distinction  between  buying 
unsuitable goods or services and failing to buy suitable goods and services.  

5) 
Higher  susceptibility  to  marketing  practices,  creating  imbalances  in  market 
interactions  -  interpreting  vulnerability  as  the  effect  of  marketing  practices  and 
consumers’ special susceptibility, a combination of consumers’ characteristics and the 
techniques used by marketers to render them particularly vulnerable, powerlessness 
due  to  imbalanced  market  interactions  and  consumption  of  marketing  messages, 
consumers at greater risk of mis-selling, and with diminished capacity to understand 
the role of advertising. 

In terms of effects, the first dimension differs from the remaining as it relates directly 
to outcomes  of market participation, rather than actions or decisions involved in the 
transactional  process.  The  effects  more  directly  relevant  to  the  second  dimension 
include instances  where  consumers  face  detriment  or  negative  outcomes  as  a  result 
of their personal characteristics. Effects attributable to the third dimension are similar 
to  the  above  but  stem  from  consumers  not  obtaining  and  interpreting  relevant 
information,  regardless  of  their  personal  characteristics.  The  fourth  dimension’s 
relevant potential effects include most of the above (except those relating directly to 
information)  as  well  as  effects  specific  to  limitations  in  terms  of  access  (including 
access to redress). The relevant potential effects for Dimension 5 largely mirror those 
relevant to Dimension 2, with the distinction that they stem  from vendors’ practices 
rather than personal characteristics. 

The chapter concludes with a proposed list of indicators which is to be taken forward 
to assess each of the five vulnerability dimensions.  

1.  Indicators  for  Dimension  1:  Heightened  risk  of  negative  outcomes  or  impacts  on 
well-being 

1. Unassertive when experienced a problem buying or using goods or services 
2. Overpaid for services 

2.  Indicators  for  Dimension  2:  Having  characteristics  that  limit  ability  to  maximise 
well-being 

3. Perception of own vulnerability due to personal characteristics 

3.  Indicators  for  Dimension  3:  Having  difficulty  in  obtaining  or  assimilating 
information 

4. Does not feel informed 
5. Gets information from few sources  
6. Does not compare deals due to information-related factors 
7.  Has  not  recently  switched  due  to  being  unsure  about  where  to  get 
information  

4.  Indicators  for  Dimension  4:  Inability  or  failure  to  buy,  choose  or  access  suitable 
products 

8.  Does  not  compare  deals  due  to  a)  personal  factors,  b)  market-related 
factors and c) access-related factors 
9.  Has  not  recently  switched  due  to  a)  personal  factors,  b)  market-related 
factors,  c)  access-related  factors,  d)  termination  costs  and  e)  bundling  of 
offers 
10. Excluded from e-commerce 
11. Declined a loan 

40 

 
 
 
 
 
 
 
 
 
 
 
3 │ Conceptualisation of a theoretical framework and operationalisation of vulnerability 

5. Indicators for Dimension 5: Higher susceptibility to marketing practices 

12. Perception of own vulnerability due to marketing practices 

13. Performance in the behavioural experiments 

This chapter conceptualises a theoretical framework of consumer vulnerability which is 
examined and populated via the analysis presented in the following chapters. Drawing 
on key insights from the literature, the framework establishes the primary concepts of 
consumer  vulnerability  and  considers  the  potential  causes  and  likely  main  effects  of 
vulnerability.  By  mapping  and  classify  different  types  of  vulnerability,  this  chapter 
provides part of the answer to research question RQ1 (set out in section 1.4). 

In  addition,  this  chapter  also  sets  out  how  the  concept  of  consumer  vulnerability  is 
operationalised,  and  how  the  potential  drivers  of  vulnerability  are  operationalised,  for 
the purposes of the analysis presented later in this report. 

In  the  theoretical  framework  the  different  causes  of  vulnerability,  including  personal 
characteristics  and  the  vulnerability  drivers  discussed  below,  influence  the  risk  of 
vulnerability  in  a  given  dimension  (the  dimensions  are  set  out  in  section  3.3  below), 
which in  turn  results  in  the  effects  of  vulnerability  associated  with  that  dimension,  as 
shown in Figure 5. 

Figure 5: 

Theoretical framework structure  

3.1. Vulnerability definitions 

The key initial step in understanding consumer vulnerability is to provide an overview 
of the different definitions of consumer vulnerability. Hence, this section examines the 
definitions of vulnerability recognised in the literature. 

The term “vulnerability” is used in a broad range of contexts and is therefore present 
in a wide body of academic and grey literature. It can generally be seen as referring to 
an  ex-ante  assessment  of  the  likelihood  of  a  potential  negative  outcome  (see  for 
instance  Povel  2009).  Applying  this  concept  to  consumer  policy,  vulnerability  would 
therefore  refer  to  a  potential  negative  impact  on  consumer  well-being.  The  UK 

41 

Personal characteristicsBehavioural driversMarket-related driversAccess driversSituational driversVulnerability Dimension XEffects of Dimension X:•.........•.........•.........•.........CausesEffects 
 
 
 
 
3 │ Conceptualisation of a theoretical framework and operationalisation of vulnerability 

Financial  Services  Consumer  Panel  (2012)22,  for  instance,  sees  vulnerability  as 
meaning “there is a higher risk of consumer detriment but does not mean that the risk 
actually has, or will, crystallise”. 

There are a number of definitions of consumer vulnerability used in academic and grey 
literature. They can be divided into two broad categories, namely:  

  Definitions focusing on personal characteristics of the consumer; and 

  Broader  definitions  taking  into  account  the  overall  situation  in  which  the 

consumers find themselves. 

The former category includes the following definitions: 

  Ringold  (1995)  defines  vulnerable  consumers  as  individuals  who  have 
“diminished  capacity  to  understand  the  role  of  advertising,  product  effects,  or 
both” (Ringold 1995 in Baker et al. 2005). 

  Smith  and  Cooper-Martin  (1997)  use  a  similar,  but  somewhat  broader 
definition,  seeing  vulnerable  consumers  as  “more  susceptible  to  economic, 
physical,  or  psychological  harm  in,  or  as  a  result  of  economic  transactions 
because  of  characteristics  that  limit  their  ability  to  maximise  their  utility  and 
well-being” (Smith and Cooper-Martin 1997 in Baker et al. 2005). 

  Burden (1998) sees vulnerability as a difficulty in obtaining or assimilating the 
information needed to make decisions about goods and services and as loss of 
welfare  as  a  result  of  buying  inappropriate  goods  or  services,  or  of  failing  to 
buy appropriate goods and services. 

  Similarly,  Overall  (2004)  suggests  that  some  consumers  may  be  vulnerable 
because  they  have  a  greater  difficulty  than  others  in  obtaining  or  assimilating 
consumer  information  into  their  decision  making  (Overall  2004  in  Brennan, 
Zevallos and Binney 2011). 

The  second  category  of  definitions  takes  a  broader  view  of  vulnerability  with  an 
increased focus on the situation in which consumers find themselves: 

  Andreasen and Manning (1990) define vulnerable consumers as those who “are 
at  a  disadvantage  in  exchange  relationships  where  that  disadvantage  is 
attributable  to  characteristics  that  are  largely  not  controllable  by  them” 
(Andreasen and Manning 1990 in Clifton et al. 2013). 

  Brenkert (1998) notes that vulnerability refers to a combination of “consumers’ 
special  characteristics  and  the  means  or  techniques  which  marketers  use  that 
render them especially vulnerable” (Brenkert 1998 in Wolburg 2005).  

  Consumer  Futures  (2001)  defines  vulnerable  consumers  as  “those  whose 
circumstances make them vulnerable to suffering consumer disadvantage”. 

  Baker, Gentry and Rittenburg (2005) define consumer vulnerability as a “state 
of powerlessness that arises from an imbalance in marketplace interactions or 
from the consumption of marketing messages and products”. According to this 
definition  it  “occurs  when  control  is  not  in  an  individual’s  hands,  creating  a 

22 https://www.fs-cp.org.uk/  

42 

 
 
 
                                                 
3 │ Conceptualisation of a theoretical framework and operationalisation of vulnerability 

dependence  on  external  factors  (e.g.,  marketers)  to  create  fairness  in  the 
marketplace”.  

  Hill  and  Kozup  (2007)  in  turn  interpret  the  Baker,  Gentry  and  Rittenburg 
definition  as  “existing  in  a  state  of  powerlessness  that  occurs  when  control  is 
abused by transaction partners and leads to an unhealthy dependence”. 

  Finally,  Stearn  (2012),  while  defining  vulnerable  consumers  as  “people  who 
cannot choose or access essential products and services which are suitable for 
their needs or cannot do so without disproportionate effort/cost/time”, provides 
a broader definition of vulnerability as “the condition in which a consumer is at 
greater risk of mis-selling, exploitation or being put at a disadvantage in terms 
of accessing or using a service, or in seeking redress”. 

The two types of definitions mirror the  key development in the academic approach to 
the  concept  of  vulnerability,  namely  the  transition  from  the  “disadvantaged 
consumer” approach to a “vulnerable consumer” approach (see for instance Baker, 
Gentry and Rittenburg 2005; and, Garret and Toumanoff 2010). The former approach 
associates  vulnerability  with  specific  groups  which  are  disadvantaged  in  the 
marketplace  (in  US  research  the  four  key  indicators  are  income,  age,  education,  and 
race  or  ethnicity),  while  the  latter  approach  sees  vulnerability  as  the  result  of  an 
interaction  between  internal  and  external  factors  and  therefore  not  limited  to  specific 
socioeconomic groups.    

Stakeholder  interviews  have  also  shown  that  when  consumer  organisations  approach 
consumer  vulnerability,  they  tend  to  favour  the  broader  approach  to  the  concept, 
recognising  that  depending  on  the  situation  all  consumers  can  be  vulnerable,  with 
complexity  of  financial  services  (discussed  in  more  detail  in  the  following  sections) 
being an often-cited example.  

Two common elements of vulnerability definitions are power and control. Mourali and 
Nagpal  (2013)  in  their  study  on  the  role  of  power  in  consumer  decisions  note  that 
“power  is  commonly  defined  as  a  person's  relative  capacity  to  control  valuable 
resources or administer punishments”. In broad terms, vulnerability could therefore be 
seen  as  lack  of  power  to  control  marketplace  interactions.  All  consumers  could 
therefore find themselves in such a state depending on the nature of the marketplace, 
their  personal  characteristics,  and  the  situation  they  find  themselves  in  at  the  given 
moment.  

While  research  on  consumer  vulnerability  moves  away  from  focusing  on  specific 
socioeconomic  groups,  there  are  still  arguments  for  retaining  elements  of  the  initial 
approach.  Commuri  and  Ekici  (2008)  recognise  that  vulnerability  is  dependent  on 
external  and  situational  factors,  but  note  that  from  a  policy-making  perspective  it  is 
important to be able to target specific well-defined consumer groups. They propose to 
view  consumer  vulnerability  as  a  “sum  of  two  components:  a  systemic  class-based 
component and a transient state-based component” (Commuri and Ekici 2008). As a 
result, they conceptualise vulnerability as the sum of: 

1.  The  vulnerability  they  are  likely  to  experience  by  virtue  of  certain  abiding 
characteristics that are either demographic in nature or socioculturally enforced 
[; and] 

2.  The  vulnerability  specific  only  to  the  current  episode  of  consumption  (and 

therefore not accounted for by 1. above (Commuri and Ekici 2008) 

43 

 
 
 
3 │ Conceptualisation of a theoretical framework and operationalisation of vulnerability 

Another  key  element  of  the  more  recent  definitions  of  consumer  vulnerability  is  the 
fact  that  it  is  a  dynamic  concept.  Griffiths  and  Harmon-Kizer  (2011)  recognise  that 
“consumers may move in and out of situations where they experience vulnerability or 
are  at  risk  for  a  defined  period  of  time”.  At  the  same  time,  in  some  cases  personal 
characteristics (or the class-based component of vulnerability, using the Commuri and 
Ekici  conceptualisation)  can  imply  that,  for  some  consumers,  vulnerability  is  an 
enduring characteristic.   

In addition to viewing vulnerability as dynamic, Harrison and Chalmers (2013) suggest 
that  it  could  also  be  viewed  as  a  spectrum  and  not  a  binary  state,  which  would  help 
show that vulnerability does not have to be enduring and can arise from a combination 
of factors.  

Having  examined  current  definitions  of  vulnerability  and  the  overall  developments  in 
the  approach  to  the  concept,  it  is  worthwhile  to  examine  the  current  EU  definition  of 
vulnerability from this perspective. The main vulnerability definition at EU level has its 
source  in  Directive  2005/29/EC  of  11  May  2005  concerning  unfair  business-to-
consumer commercial practices in the internal market (‘Unfair Commercial Practices 
Directive’ or UCPD), which notes that vulnerability “may arise out of the consumers’ 
mental  or  physical  infirmity,  age  or  credulity”.  Although  this  definition  puts  emphasis 
on  personal  characteristics,  it  recognises  that  these  can  contribute  to  vulnerability, 
rather than be the sole source or manifestation of consumer vulnerability (i.e. they are 
the  class-based  component),  which  can  be  compatible  with  the  more  recent 
approaches to consumer vulnerability.  

A  final  important  discussion  concerns  the  term  “vulnerability”  itself.  Although  the 
term  is  widely  used  in  literature,  some  stakeholder  groups  view  it  as  problematic.  In 
particular, the term can be seen as having patronising connotations.  

As  can  be  seen  in  the  discussion  outlined  in  this  section,  the  concept  of  consumer 
vulnerability  is  a  diffuse  one,  with  existing  academic  and  grey  literature  approaching 
consumer  vulnerability in  a  number  of  ways.  In  addition,  from  a  policy  perspective,  a 
general agreement that vulnerability is a dynamic concept extends the potential target 
group for any intervention to the entire population of consumers.  

Prior  to  exploring  potential  causes  and  effects  of  vulnerability,  it  is  important  to  first 
examine ways of typologising vulnerability. This is done in the following section.  

3.2. Conceptualising and typologising vulnerability 

There  are  a  number  of  ways  to  typologise  vulnerability  once  the  concept  has  been 
defined.  Baker,  Gentry  and  Rittenburg  (2005)  highlight  a  range  of  typologies  used  in 
the  literature,  including  actual  and  perceived  vulnerability,  where  the  former  is 
experienced by an individual and the latter occurs where others believe an individual to 
be  vulnerable,  but  the  individual  may  not  necessarily  agree  or  may  not  actually  be 
vulnerable  (Smith  and  Cooper-Martin  1997  in  Baker,  Gentry  and  Rittenburg  2005). 
Another  way  of  conceptualising  vulnerability,  reflecting  the  more  recent  definitions  of 
vulnerability, is the distinction between  vulnerability stemming from  internal  factors 
(individual  characteristics  and  individual  states)  and  external  conditions  (Baker, 
Gentry  and  Rittenburg  2005).  A  third  categorisation  is  that  of  endogenous  and 
exogenous  vulnerability,  as  well  as  permanent  and  temporary  vulnerability 
(European  Parliament  Committee  on  the  Internal  Market  and  Consumer  Protection 
2012 in Waddington 2013). 

44 

 
 
3 │ Conceptualisation of a theoretical framework and operationalisation of vulnerability 

Shultz  and  Holbrook  (2009)  conceptualise  vulnerability  as  a  two  dimensional  matrix, 
with  one  dimension  referring  to  consumers  knowing  what  is  beneficial  to  them 
(“Knowledge of Beneficial Means–End Relationships”) and the other dimension referring 
to  consumers  having  access  to  beneficial  products  or  services  (“Access  to  Beneficial 
Means”).  People  who  do  not  know  “what  is  good  for  them”  and  cannot  access  it  are 
seen  as  doubly  vulnerable,  those  with  knowledge  and  no  access  are  economically 
vulnerable, while those with access but lack of knowledge are denoted as  culturally 
vulnerable.    

Morgan,  Schuler  and  Stotlman  (1995)  have  developed  a  more  extensive  typology  of 
personal characteristics. In their typology, designed with a particular focus on product 
safety and pharmaceuticals, they distinguish four consumer groups based on their: 

  Physical  sensitivity,  referring  to,  in  the  context  of  product  safety,  consumers 

being particularly sensitive to specific substances; 

  Physical competency, referring to consumer’s physical capabilities; 

  mental competency, referring to consumer’s mental capabilities; and 

  Sophistication  level,  referring  to  consumers’  educational  or  socioeconomic 

background. 

While the above typologies focus on sources of vulnerability, an alternative is to look at 
how  vulnerabilities  can  be  exploited  in  practice.  Cartwright  (2011)  has  developed  the 
following taxonomy of vulnerabilities:  

 

Informational  vulnerability:  Informational  vulnerability  refers  to  situations 
where  service  providers  have  superior  information  to  some  groups  of 
consumers and use this to their advantage. For example, a company offering a 
complex  financial  service  may  have  superior  information  about  the  product 
over a particular consumer group and either charge them an unfair price or sell 
a product to the consumer group which is not what they were expecting.    

  Supply  vulnerability:  Supply  vulnerability  refers  to  situations  where  a 
particular  consumer  group  cannot  participate  in  the  market  even  though  they 
wish to, or they have less choice in the market due to their characteristics. This 
may  be  because  they  have  been  priced  out  of  the  market  (and  it  is  not 
profitable  for  suppliers  to  satisfy  them  in  the  market),  or  there  are  fewer 
goods/services available which satisfy their budget.  

  Redress  vulnerability:  Redress  vulnerability  refers  to  situations  where  a 
particular consumer group faces particular difficulties in obtaining redress. For 
example,  a  consumer  may  not  take  action  after  experiencing  a  problem 
because  the  complaints  process  is  too  burdensome,  time  consuming  or 
expensive. This can exacerbate other vulnerability drivers.23 

  Pressure  vulnerability:  Pressure  vulnerability  refers  to  consumer  groups 
being subject to pressure from the provider of the good/service, such that the 
consumption decision is not fully voluntary (‘pressure selling’).  

 

Impact  vulnerability:  Impact  vulnerability  refers  to  situations  where  certain 
consumer groups suffer a greater loss as a result of their decision compared to 
other  consumers.  Impact  vulnerability  is  usually  a  consequence  of  another 

23 Invitation to Tender, pp21, EAHC-2013-CP-08 (http://ec.europa.eu/chafea/)  

45 

 
 
                                                 
3 │ Conceptualisation of a theoretical framework and operationalisation of vulnerability 

source  of  vulnerability  such  as  informational  vulnerability.  For  example,  for  a 
low-income  consumer  who  makes  a  large  loss  on  a  financial  product  due  to 
lack of information, the fact that the consumer has low income exacerbates the 
impact of the informational vulnerability because the loss is a larger proportion 
of their income.  

These  types  of  vulnerabilities  can  in  turn  be  linked  to  the  vulnerability  drivers, 
identified in the following sections. Cartwright (2015) also links these vulnerabilities to 
specific responses, such as: 

  Different  means  of  improving  education  and  information  (including  disclosure 

and controlling misleading information); 

 

Introducing cooling off periods; 

  Product bans and other product regulations;  

  Responsibilisation (subjecting firms to a duty to treat customers fairly without 

specifying specific ways that should be done);  

 

Improving redress;  

  Providing financial compensation; and 

  Addressing  supply 

through  competition-based  response,  public  service 

obligations, or public provision.  

In  addition  to  helping  identify  specific  responses,  the  discussion  of  vulnerability 
typologies  can  also  help  to  develop  a  broader  framework  for  identifying  how  to  best 
address vulnerability. In particular, the concept of being  doubly vulnerable is a useful 
one, since it suggests that particular attention should be paid to consumers who are in 
a  position  of  double  vulnerability.  If  the  two  dimensions  of  vulnerability  were  to  be 
taken  as  exogenous/external  and  endogenous/internal  (i.e.  as  used  by  Baker,  Gentry 
and Rittenburg 2005), the focus should be on consumers who are not only affected by 
external  factors,  but  who  also  have  endogenous/internal  characteristics  which 
potentially make them even more vulnerable.   

Vulnerability  typologies  are  also  valuable  for  analysing  the  existing  definitions  of 
vulnerability  present  in  EU  law.  As  noted  previously,  the  current  UCPD  definition 
focuses  on  “Commercial  practices,  which  are  likely  to  materially  distort  the  economic 
behaviour  only  of  a  clearly  identifiable  group  of  consumers  who  are  particularly 
vulnerable to the practice or the underlying product because of their mental or physical 
infirmity, age or credulity”. The definition is currently based on internal or endogenous 
factors, although it does recognise that particular practices (i.e. external factors) play a 
role  in  vulnerability.  At  the  same  time,  the  current  use  of  the  concept  of  “average 
consumer”  does  not  appear  to  effectively  take  into  account  external  or  exogenous 
vulnerability, nor account for potentially temporary vulnerability. 

3.3. Dimensions of vulnerability used in the present study  

For  the  purpose  of  the  present  study,  the  discussion  of  vulnerability  concepts  in  the 
literature (described in sections 3.1 and 3.2) is synthesised into five core dimensions of 

46 

 
 
3 │ Conceptualisation of a theoretical framework and operationalisation of vulnerability 

vulnerability,  whose  prevalence  can  then  be  assessed  empirically  via  the  survey  and 
experiment data. These five core dimensions are:24 

1)  Heightened  risk  of  negative  outcomes  or  impacts  on  well-being:  This 
dimension reflects the general finding in the literature (as noted in section  3.1 
above)  that  consumer  vulnerability  can  be  seen  as  referring  to  an  ex-ante 
assessment of the likelihood of a negative outcome (e.g. see Povel 2009) and 
hence  would  refer  to  a  potential  negative  impact  on  consumer  well-being. 
Relevant  studies  cited  above  also 
include  Burden  (1998),  who  sees 
vulnerability  as  a  loss  of  welfare  due  to  choices  in  markets,  and  Smith  and 
Cooper-Martin  (1997)  whose  definition  of  vulnerability  refers  to  higher 
susceptibility to harm and diminished well-being (see section 3.1). 

2)  Having  characteristics  that  limit  ability  to  maximise  well-being:  This 
dimension  is  based  on  Smith  and  Cooper-Martin’s  (1997)  definition  of 
vulnerability,  which  refers  to  “characteristics  that  limit  [consumers’]  ability  to 
maximise their utility and well-being” (Smith and Cooper-Martin 1997 in Baker 
et al. 2005), as well as other studies. Other studies cited above which relate to 
this  dimension  include  Ringold  (1995)  (in  Baker  et  al  2005),  who  defines 
vulnerable  consumers  in  terms  of  their  “diminished  capacity”  to  understand 
advertising  and  product  effects,  and  Commuri  and  Ekici  (2008),  whose 
conceptualisation  of  vulnerability  includes  vulnerability  that  consumers  are 
likely to experience due to certain abiding characteristics (see section 3.1). 

3)  Having difficulty in obtaining or assimilating information: This dimension 
is  derived  primarily  from  two  studies  cited  above,  Overall  (2004)  and  Burden 
(1998),  both  of  which  propose  that  vulnerability  relates  to  obtaining  or 
assimilating consumer information (see section 3.1). Furthermore, Cartwright’s 
(2011)  taxonomy  of  vulnerabilities  includes  ‘informational  vulnerability’,  which 
refers to  scenarios where  suppliers have better information than some groups 
of consumers and use this to their advantage (see section 3.2). 

4)  Inability  or  failure  to  buy,  choose  or  access  suitable  products:  This 
dimension  reflects  the  findings  of  several  studies  cited  above,  which  equate 
vulnerability  to  not  choosing  or  accessing  suitable  goods  and  services.  In 
particular,  Stearn  (2012)  defines  vulnerable  consumers  as  those  who  cannot 
choose  or  access  products  and  services  which  are  suitable  for  their  needs  or 
cannot do so without disproportionate effort (see section  3.1), and Shultz and 
Holbrook’s  (2009)  concept  of  vulnerability  includes  a  dimension  referring  to 
consumers  not  having  access  to  beneficial  products  or  services  (see  section 
3.2).  In  addition,  Burden’s  (1998)  view  of  vulnerability  relates  to  a  loss  of 
welfare due to buying unsuitable goods or services, or of failing to buy suitable 
goods and services (see section 3.1).  

5)  Higher  susceptibility  to  marketing  practices,  creating  imbalances  in 
market interactions: This dimension is based on several studies cited above, 
which  associate  consumer  vulnerability  with  the  effect  of  marketing  practices 

24  It  should  be  noted  that  two  of  these  dimensions  (heightened  risk  of  negative  outcomes  or 
impacts  on  well-being  and  higher  susceptibility  to  marketing  practices)  are  defined  relative 
to the average consumer. 

47 

 
 
                                                 
3 │ Conceptualisation of a theoretical framework and operationalisation of vulnerability 

and  consumers’  special  susceptibility  to  marketing  practices.  In  particular, 
Brenkert (1998) notes that vulnerability refers to a combination of consumers’ 
characteristics  and  the  techniques  used  by  marketers  to  render  them 
particularly  vulnerable;  Baker  et  al.  (2005)  refer  to  powerlessness  due  to 
imbalanced  market  interactions  and  consumption  of  marketing  messages25; 
Stearn’s  (2012)  definition  of  vulnerability  refers  to  the  consumer  being  at 
greater  risk  of  mis-selling;  and  Ringold  (1995)  refers  to  consumers  having 
diminished capacity to understand the role of advertising (see sections 3.1 and 
3.2). 

In  addition,  it is  useful to  consider  how  these  core  dimensions  of  vulnerability  can  be 
broken  down  into  more  specific  consumer  actions  or  stages  in  the  consumer  decision 
making process that are related to or affected by each dimension of vulnerability: 

  The  first  dimension  relates  to  outcomes  for  consumers  as  a  result  of  their 
participation  in  markets  (rather  than  actions  or  decisions  involved  in  the 
process of participating in markets). As such, this dimension can be related to 
all consumer actions and decisions if these impact negatively on outcomes. 

  Within  the  second  dimension,  different  characteristics  may  affect  consumers’ 
access  to  information  and  products,  their  ability  to  make  assessments  of 
available offers, and their ability to act appropriately on these assessments. 

  Within  the  third  dimension,  consumers  firstly  obtain  and  then  interpret  that 
information, and then use this information in their decision-making and choices 
in markets. 

  Within  the  fourth  dimension,  consumers  compare  offers  (assessing  the 
advantages  and  disadvantages  of  each  offer),  purchase  goods  and  services, 
and  switch  between  deals  and  suppliers,  to  access  the  best  products  and 
services in the market. 

  Within  the  fifth  dimension,  marketing  practices  can  make  it  more  difficult  for 
consumers to access information, assess alternatives offers, and act to get the 
best deals. 

The  five  core  dimensions  of  vulnerability  can  be  placed  into  a  common  framework  by 
drawing on the OFT’s useful ‘Access, Assess, Act’ framework of consumer behaviour. In 
this framework, consumers’ interactions with suppliers in well-functioning markets are 
broken down into three stages:26 

  Accessing information on the various offers available in the market;  

  Assessing these offers in a well-reasoned way; and 

  Acting on this information and analysis by purchasing the good or service that 

offers the best value to the consumer. 

25 In turn  Hill and Kozup (2007)  interpret this definition as existing in a state of powerlessness 

which occurs when transaction partners abuse their control. 

26  Office  of  Fair  Trading  (2010),  ‘What  does  Behavioural  Economics  Mean  for  Competition 
Policy?’.  The  OFT’s  framework  has  subsequently  been  used  in  various  studies,  such  as 
‘Behavioural  economics  and  its  impact  on  competition  policy’,  a  report  for  the  Netherlands 
Authority  for  Consumers  and  Markets,  and  ‘What  can  behavioural  economics  say  about  GB 
energy consumers?’, a report by the UK energy regulator. 

48 

 
 
                                                 
3 │ Conceptualisation of a theoretical framework and operationalisation of vulnerability 

Table  1  below  shows  how  the  five  vulnerability  dimensions  fit  within  the  OFT’s 
framework  of  consumer  behaviour,  including  how  the  dimensions  overlap  across  the 
different stages of the framework. 

We  can  also  see  from  this  framework  how  the  dimensions  might  be  expected  to 
interact  with  one-another;  dimensions  further  to  the  left  in  Table  1  may  impact  on 
those  further  to  the  right,  since  those  further  to  the  right  are  further  along  the 
consumer  decision  making  process,  and  dimensions  that  overlap  vertically  in  Table  1 
(i.e.  within  a  given  stage  of  the  consumer  behaviour  framework)  may  be  expected  to 
interact. 

Table 1:  Dimensions of vulnerabilty within the OFT’s ‘Access, Assess, Act’ 

framework 

Consumer behaviour framework stages 

Access → 

Assess → 

Act → 

Outcomes 

y
t
i
l
i

b
a
r
e
n
u
v

l

f
o

s
n
o

i

i
s
n
e
m
d
t
n
a
v
e
l
e
R

D2: Having characteristics that limit ability to maximise 
well-being; characteristics linked to… 

Access 
constraints 

Ability to make 
assessments 

Ability to take 
actions 

D3: Having difficulty in obtaining or 
assimilating information 

Obtaining 
information 

Interpreting 
information 

D4: Inability or failure to buy, choose 
or access suitable products 

Comparing offers 

Switching and 
purchasing 

D5: Higher susceptibility to marketing 
practices; practices that affect… 

Consumers’ 
assessments of 
offers 

Actions taken by 
consumers in 
markets 

D1: Heightened 
risk of negative 
outcomes or 
impacts on well-
being 

3.4. Potential causes of vulnerability 

Drawing  on  the  theoretical  discussion  of  vulnerability  presented  in  the  previous 
chapter, this section outlines three types of potential causes of vulnerability: 

  Vulnerability  drivers,  which  are  mechanisms  through  which  individual 
consumers  can  become  vulnerable  and  through  which  their  vulnerabilities  can 
be exploited. 

  Personal  characteristics  (both  permanent  and  temporary)  that  are 
associated  with  vulnerability,  recognising  the  fact  that  it  is  possible  that  no 
specific group of consumers is always vulnerable or always not vulnerable, and 
that vulnerability is a dynamic concept. 

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3 │ Conceptualisation of a theoretical framework and operationalisation of vulnerability 

  Problematic  practices,  which  are  examples  of  practices  which,  either 
intentionally  or  unintentionally,  exploit  vulnerability  to  the  detriment  of 
consumers.  

These  three  concepts  are  outlined  in  the  following  subsections.  Chapter  6  presents 
detailed analysis of these potential causes of vulnerability based on both the literature 
and results from the consumer survey and experiment data. 

3.4.1.  Vulnerability drivers 

As  noted  above,  vulnerability  drivers  refer  to  mechanisms  through  which  individual 
consumers  can  become  vulnerable  and  through  which  their  vulnerability  can  be 
exploited  in  the  market.  Drawing  on  the  discussion  of  the  concept  of  vulnerability  in 
previous  sections,  vulnerability  drivers  can  relate  to  the  individual,  as  well  as  to  the 
broader  market  environment.  The  Vulnerable  Consumer  Working  Group  (2013),  in  its 
“Guidance  Document  on  Vulnerable  Consumers”,27  examined  a  range  of  vulnerability 
drivers  in  the  energy  sector,  classifying  the  drivers  into  market  conditions,  individual 
circumstances,  living  conditions,  and  social/natural  environment.  The  current  study 
uses this as a basis and develops a broader typology of drivers, while the literature and 
stakeholder  interviews  were  in  turn  used  to  populate  the  typology.  The  broad 
categories  of  drivers,  which  are  discussed  in  more  detail  later  in  the  report,  are  as 
follows:  

  Behavioural drivers relate to the individual and include biases and heuristics, 
as  well  as  broader  cognitive  limitations.  For  example,  behavioural  drivers 
included  biases  in  decision  making  such  as  status  quo  bias,  loss  aversion  and 
time discounting. Lunn and Lyons (2010) provide a comprehensive overview of 
a range of relevant biases and heuristics, which are explored in more detail in 
section  6.3.  Behavioural  drivers  also  include  cognitive  limitations,  such  as  the 
“consumer attention deficit syndrome” (CADS) (Berg and Gornitzka 2012). 

  Market-related  drivers  refer  to  the  functioning  of  the  market  and  ways  in 
which  the  functioning  of  the  market  can  contribute  to  consumer  vulnerability. 
These  include  information  problems  (i.e.  consumers  not  having  enough 
information  to  make  informed  decisions),  which  can  be  linked  to  Cartwright’s 
concept of “information vulnerability” (Cartwright 2011). Other market-related 
drivers  include  competition  problems,  where  the  nature  of  competition  can 
result  in  consumers  being  vulnerable,  for  instance  due  to  existence  of 
“imperfect  markets”  (Consumer  Futures  2001).  Market-related  drivers  are 
further examined in section 6.4. 

  Access drivers refer to a range of mechanisms through which consumers can 
have restricted access to markets, goods and services, and are therefore linked 
to what was typologised by Cartwright (2011) as “supply vulnerability”. These 
include,  for  example,  physical  access  restrictions  (e.g.  due  to  mobility  and 
location  –  see  Clifton  2013,  Stearn  2012,  Blocker  et  al.  2013),  online  access 
restrictions  (i.e.  being  unable  to  access  online  services),  restrictions  due  to 
being  unable  to  use  certain  payment  methods,  and  restricted  access  to 
information. Access drivers are investigated in more detail in section 6.5. 

27

 http://ec.europa.eu/energy/sites/ener/files/documents/20140106_vulnerable_consumer_report_0.pdf  

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3 │ Conceptualisation of a theoretical framework and operationalisation of vulnerability 

  Situational  drivers  refer  to  mechanisms  through  which  consumers’  current 
(temporary  or  permanent)  situation  results  in  vulnerability.  This  includes 
situations  that  make  consumers  more  susceptible  to  marketing  practices  and 
situations such as financial difficulties and life changes (e.g. becoming a single 
parent). Situational drivers are examined in more detail in section 6.6. 

For  more  in-depth  discussion  and  analysis  of  these  vulnerability  drivers,  including 
empirical analysis of the impacts of these drivers on a range of survey-based indicators 
of vulnerability, see Chapter 6. 

3.4.2.  Personal characteristics associated with vulnerability 

Although  consumer  vulnerability  is  a  dynamic  concept  which  should  not  simply  be 
associated  with  specific  consumer  groups,  according  to  the  literature  some  personal 
and demographic characteristics are linked with vulnerability. Furthermore, as noted by 
Commuri  and  Ekici  (2008),  identifying  particular  groups  can  assist  policymakers  in 
designing interventions. 

In  particular,  the  literature  identifies  a  range  of  characteristics  which  may  be 
associated  with  a  higher  risk  of  vulnerability,  including  age,  gender,  low  education, 
disability,  cultural  background  and  ethnicity,  and  location.  The  links  between  these 
characteristics and the risk of vulnerability, including empirical analysis of the effects of 
these  characteristics  on  a  range  of  survey-based  indicators  of  vulnerability,  are 
explored in more detail in section 6.2. 

3.4.3.  Problematic practices 

A  range  of  problematic  practices  were  identified  through  the  literature  review  and 
stakeholder consultation. These include: 

  Practices  which  exploit  behavioural  drivers,  such  as  biases  and  cognitive 
limitations. For example, these include practices which exploit the framing bias 
(i.e. where  consumer  make different choices  depending on how information is 
presented  to  them)  such  as  drip  pricing  and  reference  pricing.  Other  type  of 
practice  in  this  category  include,  for  example,  time  limited  offers,  dynamic 
pricing  and  bating.  E-commerce  is  an  area  where  such  practices  can  often  be 
observed (European Parliament 2011), although they can also be encountered 
offline. 

  Practices which exploit situational drivers, such as those exploiting consumers’ 
lack of digital sophistication, or practices which exploit new life situations (e.g. 
motherhood, as highlighted by The VOICE Group 2010). 

  So called “double disadvantages”, namely practices which result in consumers 
who  are  already  in  a  disadvantaged  position  incurring  additional  costs  or  not 
being able to benefit from savings that are available to other consumers. Such 
practices  include,  for  example,  situations  where  some  consumers  are  steered 
towards  more  expensive  deals  due  to  being  unable  to  use  certain  payment 
methods.  An  example  is  where  the  best  energy  deals  are  available  to 
consumers who pay via direct debit, a form of  payment which is not available 
to those without a transactional bank account (Stearn 2012). 

  Practices which cause a lack of access and accessibility problems. This refers to 
practices  which  deny  certain  vulnerable  groups,  such  as  consumers  with 
disabilities  or  older  consumers,  access  to  goods,  services  and  markets. 
Examples  of  such  practices  include  call  centres  being  unprepared  to  service 

51 

 
 
3 │ Conceptualisation of a theoretical framework and operationalisation of vulnerability 

customers  with  disabilities,  and  information  such  as  contract  terms  and 
conditions  being  provided  in  formats  that  are  not  accessible  to  some 
consumers,  for  instance  through  inaccessible  websites  or  displays  (BEUC 
2013b). 

These  practices  are  discussed  in  more  detail  in  Chapter  7,  where  evidence  on  the 
impact of marketing practices based on the behavioural experiments is presented. 

3.5. Expected effects of vulnerability 

In addition to examining the drivers of vulnerability, it is also important to consider the 
likely effects of vulnerability. The following sub-sections outline the expected effects of 
vulnerability based on the literature review and the stakeholder and expert interviews, 
linking them to each of the five vulnerability dimensions. 

3.5.1.  Dimension 1: Heightened risk of negative outcomes or impacts on well-
being 

The first dimension differs from the  remaining dimensions in that it relates directly to 
outcomes  for  consumers  as  a  result  of  their  participation  in  markets,  rather  than  to 
actions or decisions involved in the process. This in turn means that all potential effects 
that  constitute  a  tangible  direct  financial  or  non-financial impact  on  consumers  are  of 
relevance  to  this  dimension.  Relevant  potential  effects  identified  in  the  literature  and 
through stakeholder interviews therefore include: 

  Experiencing  problems  when  buying  or  using  goods  or  services  (e.g.  Europe 

Economics 2011, BEUC 2013b). 

  Failing to solve problems that occur when buying or using goods or services. 

 

Inability  to  obtain  redress  due  to  cost  or  time  required  by  the  complaint 
procedure (Nardo et al. 2011). 

  Being  faced  with  high  cost  of  obtaining  support  (e.g.  calling  help-lines  from 

prepaid mobile phones) (e.g. Stearn 2012). 

  Higher relative impact of losses on financial products on low-income consumers 

(e.g. as discussed by Cartwright 2011). 

  Financial impact of high APR rates on consumers unable to access mainstream 
financial products (payday loans and their high APR rates have been identified 
as  problematic  by  stakeholders  in  Estonia,  UK,  Finland,  Iceland,  Portugal, 
Sweden, and Slovakia). 

  Over-indebtedness  resulting  from  suboptimal  financial  decisions  (e.g.  Stearn 

2012). 

  Higher energy use and costs as a result of unemployment (e.g. EC Vulnerable 

Consumer Working Group 2013). 

  Disconnection  in  the  energy  sector  (e.g.  Centre  for  Consumers  and  Essential 

Services 2014). 

  Being a victim of a fraud or scam (e.g. Lee and Geistfeld 1999). 
  Paying more than necessary for goods or services (e.g. Stearn 2012). 
  Health  impact  due  to  lack  of  access  to  affordable  energy  tariffs  (e.g.  Brophy 

Haney et al. 2009). 

All of the above effects involve negative outcomes for consumers. These can either be 
financial impacts (e.g. higher costs), negative experiences which may or may not have 
financial impacts (e.g. problems during purchases, problems obtaining redress, being a 
victim  of  a  fraud  or  scam),  and  non-financial  impacts  (e.g.  energy  disconnection  or 
health impacts). In addition, a tangible feeling of vulnerability and lack of information 

52 

 
 
3 │ Conceptualisation of a theoretical framework and operationalisation of vulnerability 

can also be relevant to this dimension as these impacts are directly experienced by the 
consumer. Hence, the following potential effects are also of relevance: 

  Feeling vulnerable due to marketing practices (e.g. Ofgem 2013). 
  Feeling  vulnerable  due  to  personal  characteristics  (e.g.  Davis  and  Pechmann 

2013). 

  Feeling uninformed in markets (e.g. regarding price and quality, etc). 

Conversely, effects that are not directly experienced by consumers are not categorised 
as relevant to this dimension.  

3.5.2.  Dimension 2: Having characteristics that limit ability to maximise well-
being 

The second dimension refers to the way in which different personal characteristics may 
negatively affect consumers’ well-being (e.g. because these characteristics affect their 
access  to  information  and  products,  their  ability  to  make  assessments  of  available 
offers,  and  their  ability  to  act  appropriately  on  these  assessments).  The  potential 
effects relevant to the second dimension therefore refer to instances where consumers 
face detriment or negative outcomes as a result of their personal characteristics. Such 
effects could include: 

  Being a victim of a fraud or scam (e.g. Lee and Geistfeld 1999) 
  Feeling  vulnerable  due  to  personal  characteristics  (e.g.  Davis  and  Pechmann 

2013) 

  Feeling uninformed in markets (e.g. regarding price and quality, etc.) 
  Basing consumer decisions on limited information (e.g. Which? 2013) 
  Signing  contracts  without  fully  understanding  the  implications  (e.g.  Wilson, 

Howell, and Sheehan 2009). 

  Being misled by or misunderstanding information or advertising (e.g. Synovate 

2011). 

  Having problems comparing goods and services (e.g. Lunn and Lyons 2010). 
  Not comparing goods and services (e.g. due to “consumer attention deficit” as 

outlined in Berg and Gornitzka 2012). 

  Not  switching  deals  when  it  is  in  the  consumer's  interest  (e.g.  Financial 

Conduct Authority 2008). 

  Being offered unsuitable or more costly goods or services (e.g. Lumpkin 2010). 
  Purchasing suboptimal goods or services (e.g. Lunn and Lyons 2010) 
  Making a purchase that is not in the consumer's interest (e.g. Lunn and Lyons 

2010). 

  Not making a purchase that is in the consumer's interest (e.g. Lunn and Lyons 

2010). 

  Paying more than necessary for goods or services (e.g. Stearn 2012). 
  Being denied access to goods or services (e.g. D'Rozario and Williams 2005). 

3.5.3.  Dimension 3: Having difficulty in obtaining or assimilating information 

The  third  dimension  relates  to  the  way  consumers  obtain  and  interpret  information. 
The relevant potential effects are therefore similar to those outlined above, but in this 
case  they  stem  from  consumers  not  obtaining  and  interpreting  relevant  information, 
regardless of their personal characteristics. Such effects could therefore include: 

  Feeling uninformed in markets (e.g. regarding price and quality, etc) 
  Basing consumer decisions on limited information (e.g. Which? 2013). 

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3 │ Conceptualisation of a theoretical framework and operationalisation of vulnerability 

  Signing  contracts  without  fully  understanding  the  implications  (e.g.  Wilson, 

Howell, and Sheehan 2009). 

  Being misled by or misunderstanding information or advertising (e.g. Synovate 

2011). 

  Having  problems  comparing  goods  and  services  (e.g.  European  Parliament 

2011). 

  Not comparing goods and services (e.g. due to “consumer attention deficit” as 

outlined in Berg and Gornitzka 2012). 

  Not  switching  deals  when  it  is  in  the  consumer's  interest  (e.g.  Financial 

Conduct Authority 2008). 

  Purchasing suboptimal goods or services (e.g. Lunn and Lyons 2010). 
  Making a purchase that is not in the consumer's interest (e.g. Lunn and Lyons 

2010). 

  Not making a purchase that is in the consumer's interest (e.g. Lunn and Lyons 

2010). 

  Paying more than necessary for goods or services (e.g. Stearn 2012). 

3.5.4.  Dimension 4: Inability or failure to buy, choose or access suitable 
products 

The fourth dimension relates to the way in which consumers compare offers (assessing 
the advantages and disadvantages of each offer), purchase/access goods and services, 
and  switch  between  deals  and  suppliers.  The  relevant  potential  effects  therefore 
include all the effects outlined above (except for those relating directly to information), 
along with those referring specifically to access (including access to redress): 

  Having problems comparing goods and services (e.g. Lunn and Lyons 2010). 
  Not comparing goods and services (e.g. due to “consumer attention deficit” as 

outlined in Berg and Gornitzka 2012). 

  Not  switching  deals  when  it  is  in  the  consumer's  interest  (e.g.  Financial 

Conduct Authority 2008). 

  Purchasing suboptimal goods or services (e.g. Lunn and Lyons 2010). 
  Making a purchase that is not in the consumer's interest (e.g. Lunn and Lyons 

2010). 

  Not making a purchase that is in the consumer's interest (e.g. Lunn and Lyons 

2010). 

  Paying more than necessary for goods or services (e.g. Stearn 2012). 
  Being denied access to goods or services (e.g. D'Rozario and Williams 2005). 
  Social exclusion as a result of exclusion from  specific markets (e.g. University 

of Amsterdam 2010). 

  Health  impact  due  to  lack  of  access  to  affordable  energy  tariffs  (e.g.  Brophy 

Haney et al. 2009). 

The  effects  relating  to information  (i.e.  the  first  four  potential  effects  listed  in  section 
1.1.3)  are  excluded  here,  since  they  refer  to  earlier  stages  of  the  purchasing  process 
(i.e.  when  consumers  obtain  and  interpret  information)  and  are  covered  under 
Dimension 3.  

3.5.5.  Dimension 5: Higher susceptibility to marketing practices 

The final dimension refers to consumers being susceptible to practices that hinder the 
process  of  accessing  information  and  offers.  The  relevant  potential  effects  therefore 
largely  mirror  those  relevant  to  Dimension  2,  although  in  this  case  they  stem  from 
vendors’ practices rather than personal characteristics:  

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3 │ Conceptualisation of a theoretical framework and operationalisation of vulnerability 

  Being a victim of a fraud or scam (e.g. Lee and Geistfeld 1999). 
  Feeling vulnerable due to marketing practices (e.g. Ofgem 2013). 
  Signing  contracts  without  fully  understanding  the  implications  (e.g.  Wilson, 

Howell, and Sheehan 2009). 

  Being misled by or misunderstanding information or advertising (e.g. Synovate 

2011). 

  Having  problems  comparing  goods  and  services  (e.g.  European  Parliament 

2011). 

  Not comparing goods and services (e.g. due to “consumer attention deficit” as 

outlined in Berg and Gornitzka 2012). 

  Not  switching  deals  when  it  is  in  the  consumer's  interest  (e.g.  Financial 

Conduct Authority 2008). 

  Being offered unsuitable or more costly goods or services (e.g. Lumpkin 2010). 
  Purchasing suboptimal goods or services (e.g. Lunn and Lyons 2010) 
  Making a purchase that is not in the consumer's interest (e.g. Lunn and Lyons 

2010). 

  Not making a purchase that is in the consumer's interest (e.g. Lunn and Lyons 

2010). 

  Paying more than necessary for goods or services (e.g. Stearn 2012). 
  Loss of control over personal data due to online services' lack of transparency 

(e.g. Rustad et al. 2012). 

3.5.6.  Sector specific effects 

Most  of  the  potential  effects  of  vulnerability  identified  above  are  not  sector  specific. 
However, several effects can be grouped by sector, in particular: 

  Effects specific to the energy sector include higher energy use and costs as a 

result of unemployment, disconnection in the energy sector, and health 
impacts due to lack of access to affordable energy tariffs. 

  Effects specific to the financial sector include higher relative impact of losses 
on financial products on low-income consumers, financial impact of high APR 
rates on consumers unable to access mainstream financial products, and over-
indebtedness resulting from suboptimal financial decisions. 

  The only effect specific to the online sector is loss of control over personal 

data due to online services’ lack of transparency. 

All other effects set out above could apply across multiple sectors. 

3.5.7.  Interactions between expected effects 

When considering the effects discussed above, it is important to note that these effects 
are interactive, in that an effect in one dimension can contribute to an effect in another 
dimension.  For  example,  consumers  finding  it  difficult  to  make  ends  meet  given  the 
energy  prices  they  pay  due  to  signing  an  overly  costly  energy  contract  may  turn  to 
payday loans with can contribute to over-indebtedness.  

The  attribution  of  the  potential  effects  to  the  various  vulnerability  dimensions  is 
summarised in Table 2 below. As can be seen from the table, the likely effects of the 
various  dimensions  of  vulnerability  are  wide-ranging  and  some  effects  are  effects  of 
more  than  one  dimension.  For  example,  paying  more  than  necessary  for  goods  or 
services (see for instance Hill and Kozup 2007) is an effect of the first dimension since 
it is a negative outcome for consumers, and it is also an effect of the second dimension 

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3 │ Conceptualisation of a theoretical framework and operationalisation of vulnerability 

since consumers with particular characteristics (e.g. no internet access, low IT skills, or 
without access to certain payment methods) are more likely to face higher prices due 
to being unable to access the most advantageous offers. 

A key effect of several dimensions of vulnerability is that consumers obtain suboptimal 
goods  or  services  (e.g.  the  2011  European  Parliament  study  on  consumers  in  the 
digital environment highlights the challenges with regard to consumers’ ability to judge 
the quality of the product). This is a potential effect of several vulnerability dimensions 
since it may be linked to personal characteristics (dimension 2) or may arise due to the 
information available to consumers (dimension 3), consumers’ inability to make proper 
choices  between  options  (dimension  4),  or  consumers  being  put  under  pressure  or 
their lack of awareness of market conditions being exploited (dimension 5). 

Similarly,  other  potential  effects  which  span  several  vulnerability  dimensions  include 
consumers paying a higher price for a good or service than they would otherwise, and 
consumers  obtaining  a  good  or  service  that  is  not  in  their  interest  since  it  does  not 
correspond  to  their  needs  or  is  not  a  good  or  service  that  they  need  or  planned  to 
purchase  (e.g.  Cartwright  (2011),  highlights  the  challenge  of  consumers  being 
encouraged  to  access  inappropriate  financial  products).  In  both  of  these  cases, 
consumers  are  faced  with  either  a  financial  loss  or  reduced  well-being  as  a  result  of 
obtaining a good or service that is not suited to their requirements. 

Another example of a potential effect is being denied access to goods or services. This 
could  be  a  result  of  limited  physical  access  (e.g.  due  to  disability  or  living  in  remote 
areas),  being  excluded  from  certain  online  offers  (e.g.  due  to  lack  of  access  or  IT 
skills),  or  being  excluded  from  services  (or  being  offered  unsuitable  services)  due  to 
age  or  financial  situation.  Stearn  (2012)  provides  a  comprehensive  summary  of 
different  access  challenges  faced  by  vulnerable  consumers,  including  the  example  of 
UK consumers without the possibility to use Direct Debit being unable to access most 
cost-effective offers.   

Furthermore,  consumers  may  be  offered  unsuitable  or  more  costly  goods  or  services 
due to firms possessing more information than consumers (dimension 3) or firms using 
practices  to  exploit  consumers’  particular  circumstances  (such  as  their  financial 
situation  or  facing  significant  life  changes)  (dimension  5).  Examples  of  the  former 
include  information  asymmetry  in  the  case  of  “pay  with  data”  contracts  in  the  online 
environment (as detailed by Warner and Sloan 2012). The latter situation is effectively 
captured  by  the  study  of  marketing  to  expectant  or  new  mothers  (The  VOICE  Group 
2010). 

Finally, for lower income consumers, any impact on consumer finances can carry with 
it considerable consequences in terms of health and well-being, as it can influence the 
ability to afford necessities such as shelter, food, clothing or health care. 

56 

 
 
 
 
3 │ Conceptualisation of a theoretical framework and operationalisation of vulnerability 

Table 2: Expected effects of each vulnerability dimension 

Dimension 1  
Heightened risk of 
negative 
outcomes or 
impacts on well-
being 
 

 

 

 

 

 

 

 

 

 

 

 

 

Effects 
Experiencing problems when buying or using goods 
or services 
Failing to solve problems that occur when buying or 
using goods or services 
Inability to obtain redress due to cost or time 
required by the complaint procedure 

Being faced with high cost of obtaining support (e.g. 
calling help-lines from prepaid mobile phones) 
Higher relative impact of losses on financial products 
on low-income consumers 

Financial impact of high APR rates on consumers 
unable to access mainstream financial products 
Over-indebtedness resulting from suboptimal 
financial decisions 
Higher energy use and costs as a result of 
unemployment 

Disconnection in the energy sector 

Being a victim of a fraud or scam 

Feeling vulnerable due to marketing practices 

Feeling vulnerable due to personal characteristics 

Feeling uninformed in markets (e.g. regarding price 
and quality etc.) 
Basing consumer decisions on limited information 

57 

Vulnerability dimension  
Dimension 3  
Having difficulty in 
obtaining or 
assimilating 
information 

Dimension 4  
Inability or failure to 
buy, choose or 
access suitable 
products 

Dimension 2 
Having 
characteristics that 
limit ability to 
maximise well-
being 

Dimension 5  
Higher 
susceptibility to 
marketing 
practices  

 

 

 

 

 

 

 

 

 
 
 
  
  
  
  
  
  
  
  
 
  
 
  
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Vulnerability dimension  
Dimension 3  
Having difficulty in 
obtaining or 
assimilating 
information 

Dimension 4  
Inability or failure to 
buy, choose or 
access suitable 
products 

Dimension 2 
Having 
characteristics that 
limit ability to 
maximise well-
being 
 

3 │ Conceptualisation of a theoretical framework and operationalisation of vulnerability 

Table 2: Expected effects of each vulnerability dimension 

Effects 
Signing contracts without fully understanding the 
implications 
Being misled by or misunderstanding information or 
advertising 
Having problems comparing goods and services 

Not comparing goods and services 
Not switching deals when it is in the consumer's 
interest 

Being offered unsuitable or more costly goods or 
services 

Purchasing suboptimal goods or services 

Making a purchase that is not in the consumer's 
interest 

Not making a purchase that is in the consumer's 
interest 
Paying more than necessary for goods or services 

Being denied access to goods or services 

Social exclusion as a result of exclusion from specific 
markets 
Health impact due to lack of access to affordable 
energy tariffs 

Loss of control over personal data due to online 
services' lack of transparency 

Dimension 1  
Heightened risk of 
negative 
outcomes or 
impacts on well-
being 

 

  

 

 

 
 

 

 

 

 

 

 

 

 

 

 
 

 

 

 

 

 

 
 

 

 

 

 

 

 

 

Dimension 5  
Higher 
susceptibility to 
marketing 
practices  

 

 

 

 
 

 

 

 

 

 

 

58 

 
 
 
  
  
  
  
  
  
  
  
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
3 │ Conceptualisation of a theoretical framework and operationalisation of vulnerability 

3.6. Operationalisation of the concept of consumer vulnerability 

This  section  discusses  how  to  operationalise  the  concept  of  consumer  vulnerability 
using  the  main  insights  from  the  previous  sections  in  this  chapter  and  data  from  the 
consumer survey and experiments. The objective is to define consumer vulnerability in 
such a way that it can be practically measured and investigated. 

The operationalisation is based on the following four steps: 

1)  Examination of the concepts of vulnerability in the literature 
2)  Identification of  the various dimensions of these concepts 
3)  Translation of these dimensions into measurable indicators 
4)  Populating the indicators with actual data using the responses to the survey 

questions. 

The first two steps are addressed in the sections 3.1, 3.2 and 3.3 above. The third and 
fourth steps are addressed below. 

Figure 6: 

Approach to Operationalising consumer vulnerability 

The five dimensions of consumer vulnerability set out in section  3.3 can be translated 
into  indicators,  which  in  turn  can  be  mapped  to  questions  and  variables  from  the 
consumer survey, as shown in Table 3 below.  

For  each  vulnerability  dimension  Table  3  lists  a  number  of  indicators  and  for  each  of 
the  indicator,  it  identifies  the  survey  questions  which  can  be  used  to  populate  the 
indicators.  The  question  numbers  shown  in  the  table  refer  to  the  questions  in  the 
survey questionnaire which can be found in Annex 13.28  

In  addition  to  the  survey-based  indicators  set  out  in  Table  11,  further  indicators 
relating to dimensions 4 and 5 are available from the behavioural experiment dataset. 
These indicators are set out in Table 4. 

The indicators in each dimension are as follows: 

  Heightened  risk  of  negative  outcomes  or  impacts  on  well-being:  Two 
indicators are allocated to this dimension. The first signifies that a consumer is 

28  Note  that  some  indicators  could  fall  under  more  than  one  dimension;  e.g.  “Has  not  recently 
switched  due  to  personal or  market-related  factors”  could  fall  under  dimension  1  (negative 
outcomes) or dimension 4 (failure to choose suitable products). Question Q21.2 (“You were 
unable to read your energy, telecommunications or banking contract’s terms and conditions 
due to overly small print”) is not included among the variables to be used to operationalise 
consumer vulnerability since it does not match any dimension or indicator in the framework 
(this  question  does  not  relate  to  a  market  interaction).  However  Q21.2  is  included  among 
the explanatory variables as a market-related driver. 

59 

1. ConceptsHow is vulnerability conceptualised in the literature2. DimensionsWhat are the dimensions of these concepts3. IndicatorsTranslate these dimensions into measurable indicators4. QuestionsPopulate the indicators via the survey questions 
 
 
                                                 
3 │ Conceptualisation of a theoretical framework and operationalisation of vulnerability 

unassertive  in  terms  of  taking  action  when  they  experience  a  problem  buying 
or using goods or services. It is allocated to this dimension since failure to act 
to resolve a problem is likely to result in a negative outcome for the consumer. 
The  second,  paying  more  for  services  due  to  being  unable  to  use  certain 
payment methods, is allocated to this dimension since overpaying for a good or 
services  is  unambiguously  a  negative  outcome  for  the  consumer  as  it  implies 
that the consumer is financially worse off as a result.  

  Having  characteristics  that  limit  ability  to  maximise  well-being:  Only 
one main indicator is included in this dimension. This indicator is based on the 
only survey question which explicitly links consumers’ perceptions of their own 
vulnerability  to  particular  personal  or  demographic  characteristics.  However, 
within  this  main  indicator  a  number  of  sub-indicators  relating  to  specific 
characteristics  (age,  employment,  health,  etc.)  are  also  examined  (see  Table 
5). 

  Having  difficulty  in  obtaining  or  assimilating  information:  The  four 
indicators  in  this  dimension  all  focus  on  issues  relating  to  information.  These 
include a subjective indicator signifying the extent to which consumers do not 
feel  informed,  as  well  as  more  objective  indicators  concerning  where 
consumers  get  information  from  when  comparing  deals,  and  the  impact  of 
information-related  factors  on  comparing  and  switching  deals.  It  should  be 
noted  that  some  indicators  in  this  dimension  are  investigated  separately  for 
each of the three key sectors of the study (see Table 5). 

 

Inability  or  failure  to  buy,  choose  or  access  suitable  products:  This 
dimension  is  represented  by  the  largest  number  of  indicators  (eight  in  total 
including  sub-indicators),  mainly  because  it  is  itself  relatively  wide-ranging. 
The  first  two  indicators  signify,  respectively,  that  a  consumer  has  problems 
comparing  deals  or  does  not  switch  deals  due  to  various  factors,  including 
personal,  market-related  and  access-related  factors.  These  indicators  are 
included  in  this  dimension  since  having  problems  comparing  deals  and  being 
prevented  from  switching  would  result  in  failure  to  obtain  goods  or  services 
that  best  meet  the  consumer’s  needs.  Two  further  indicators,  being  excluded 
from  e-commerce  and  declined  for  a  loan,  are  included  in  this  dimension 
because  these  relate  to  the  accessibility  of  goods  or  services.  In  addition, 
respondents’ overall performance in the experiments is included as an indicator 
in  this  dimension  since  this  indicator  reflects  consumers’  ability  to  choose  the 
best offer, irrespective of the marketing practice they are faced with. It should 
also be noted that several indicators in this dimension are examined separately 
for each of the three key sectors (see Table 5). 

  Higher  susceptibility  to  marketing  practices:  The  indicators  in  this 
dimension all related to the impact of marketing practices. In particular, these 
include  an  indicator  signifying  the  extent  to  which  consumers  do  not  feel 
vulnerable due to offers, terms or conditions being too complex, and indicators 
demonstrating the effect of marketing practices on consumers’ ability to select 
the best offer based on the behavioural experiment data. 

The  number  of  indicators  varies  across  the  dimensions  for  two  main  reasons.  Firstly, 
some dimensions represent broader concepts and so encompass more indicators29; and 

29  For  example,  the  fourth  dimension  is  a  broader  concept  compared  to,  say,  to  the  third 

dimension which is focussed specifically on information. 

60 

 
 
                                                 
3 │ Conceptualisation of a theoretical framework and operationalisation of vulnerability 

secondly,  the  design  of  the  survey  allows  us  to  construct  more  indicators  for  some 
dimensions than others. 

Table 3:  

Mapping concepts of vulnerability from the literature to survey-

based indicators 

Dimension 

Indicators 

Questions/variables 

1. Heightened risk of 
negative outcomes or 
impacts on well-being 

1. Unassertive when 
experienced a problem 
buying or using goods or 
services 

2. Overpaid for services 

2. Having characteristics 
that limit ability to 
maximise well-being 

3. Perception of own 
vulnerability due to personal 
characteristics 

3. Having difficulty in 
obtaining or assimilating 
information 

4. Does not feel informed 

Did not take action when 
experienced a problem when buying 
or using goods or services in last 12 
months (Q22) 

Paid more for services in last 12 
months due to being unable to use 
a certain payment method (Q21.1) 

Feels vulnerable because of health 
problems (Q16.1), financial 
circumstances (Q16.2), 
employment situation (Q16.3), age 
(Q16.5), belonging to a minority 
group (Q16.6), personal issues 
(Q16.7), other reasons (Q16.8) 

How informed feels about prices 
etc. when buying goods and 
services (Q17.1) 

5. Gets information from 
few sources  

Where gets information to compare 
deals  (A7, B6, C4)  

6. Does not compare deals 
due to information-related 
factors 

7. Has not recently switched 
due to being unsure about 
where to get information  

4. Inability or failure to 
buy, choose or access 
suitable products 

8. Does not compare deals 
due to a) personal, b) 
market-related and c) 
access-related factors 

9. Has not recently switched 
due to a) personal factors, 
b) market-related factors, 
c) access-related factors, d) 
termination costs and e) 
bundling of offers 

Whether compares deals (A6, B5, 
C3) 
How difficult finds it to compare 
deals (A8, B7, C5) 
Why finds it difficult to compare 
deals (A9a, B8a, C6a) 
Why never compares deals (A9b, 
B8b, C6b)  

Whether has switched in last 5 
years (A21, B13, C11) 
Why has never switched (A23, B15, 
C13) 

Whether compares deals (A6, B5, 
C3) 
How difficult finds it to compare 
deals (A8, B7, C5)  
Why finds it difficult to compare 
deals (A9a, B8a, C6a) 
Why never compares deals (A9b, 
B8b, C6b) 

Whether has switched in last 5 
years (A21, B13, C11) 
Why has never switched (A23, B15, 
C13) 
Has not switched in last 12 months 
due to termination costs or 
bundling (Q21.3, Q21.6)  

61 

 
 
 
 
 
 
 
3 │ Conceptualisation of a theoretical framework and operationalisation of vulnerability 

Table 3:  

Mapping concepts of vulnerability from the literature to survey-

based indicators 

Dimension 

Indicators 

Questions/variables 

10. Excluded from e-
commerce 

11. Declined a loan 

Did not make a purchase online in 
last 12 months due to difficulty of 
process or not having payment  
card (Q21.4, Q21.5) 

Whether has tried but failed to 
obtain a loan in the last 5 years 
(Q13b)  

5. Higher susceptibility to 
marketing practices 

12. Perception of own 
vulnerability due to 
marketing practices 

Feels vulnerable because offers, 
terms or conditions are too complex 
(Q16.1) 

As well as the survey-based indicators in the table above, experiment-based indicators 
relating  to  dimensions  4  and  5  are  constructed  from  the  experiment  data.  These 
indicators are set out in Table 4. 

Table 4:  

Mapping concepts of vulnerability from the literature to 

experiment-based indicators 

Dimension 

Indicators 

Questions/variables 

4. Inability or failure to 
buy, choose or access 
suitable products 

Overall performance in the 
experiments (irrespective of 
treatment) 

Experiment choices under both the 
marketing practice and remedy 
treatments 

5. Higher susceptibility to 
marketing practices 

Negative effect of marketing 
practices on decision 
making  

Experiment choices under the 
marketing practice treatments 
Treatment effects (i.e. differences 
between choices under the 
marketing practice and remedy 
treatments in the experiments) 

The  indicators  in  the  operationalisation  of  vulnerability  set  out in  Table  3  and  Table  4 
are  fully  defined  for  the  purposes  of  the  data  analysis  in  Table  5  below.  Table  5 
presents  57  indicators  in  total,  since  for  some  indicators  in  the  operationalisation  in 
Table 3 a number of different measures can be constructed from the data.30 As can be 
seen  from  Table  5,  while  some  indicators  are  binary  (i.e.  “Yes”  or  “No)  others  are 
based on scales (e.g. “very easy” to “very difficult”, or “not at all” to “a great extent”).  

30  For  example,  the  third  indicator in  Table  11,  Perception  of  own  vulnerability due  to  personal 
characteristics,  is  coded  as  a  set  of  seven  binary  variables,  one  for  each  characteristic 
mentioned in the question, as well as a scale variable signifying the maximum extent that a 
consumer feels vulnerable due to any characteristic mentioned in the question. 

62 

 
 
 
 
                                                 
3 │ Conceptualisation of a theoretical framework and operationalisation of vulnerability 

Table 5:  Indicators of vulnerability based on the survey 

#  Name 

Code 
(Unique code 
matching 
output files) 

Sector 

Type 

Relevant 
consumers 

Data 
availability 

Meaning 

Questions 
(Survey 
question 
numbers) 

Dimension 1: Heightened risk of negative outcomes or impacts on well-being 
1 

1 

non-
specific 

Binary: 1 = "Yes", 0 = 
"No" 

Unassertive (took no 
action) when experienced 
a problem 

Those who have 
experienced a 
problem 

All countries 

Q22 

Did not do anything when experienced a problem when 
buying or using goods or services in past 12 months 

2 

Overpaid for services due 
to being unable to use 
certain payment methods 

2 

non-
specific 

Binary: 1 = "Yes", 0 = 
"No" 

All 

All countries 

Q21.1 

Dimension 2: Having characteristics that limit ability to maximise well-being 
3 

3 

All 

Perception of own 
vulnerability due to 
personal characteristics 

non-
specific 

Scale: 1 = "Not 
vulnerable" - 4 = "Very 
vulnerable" 

4 

5 

6 

7 

8 

9 

10 

11 

‘Feels vulnerable to 'a 
great extent' due to age’ 

3_b2_age 

non-
specific 

Binary: 1 = "Yes", 0 = 
"No" 

‘Feels vulnerable to 'a 
great extent' due to 
employment situation’ 
 ‘Feels vulnerable to 'a 
great extent' due to 
financial circumstances’ 
‘Feels vulnerable to 'a 
great extent' due to health 
problems’ 
‘Feels vulnerable to 'a 
great extent' due to 
belonging to a minority 
group’ 
‘Feels vulnerable to 'a 
great extent' due to 
personal issues’ 
‘Feels vulnerable to 'a 
great extent' due to other 
reasons’ 
‘Feels vulnerable to 'a 
great extent' due to any 
personal characteristic’ 

3_b2_emp 

3_b2_fin 

3_b2_hea 

3_b2_min 

3_b2_per 

3_b2_oth 

3_b2_any 

non-
specific 

non-
specific 

non-
specific 

non-
specific 

non-
specific 

non-
specific 

non-
specific 

Binary: 1 = "Yes", 0 = 
"No" 

Binary: 1 = "Yes", 0 = 
"No" 

Binary: 1 = "Yes", 0 = 
"No" 

Binary: 1 = "Yes", 0 = 
"No" 

Binary: 1 = "Yes", 0 = 
"No" 

Binary: 1 = "Yes", 0 = 
"No" 

Binary: 1 = "Yes", 0 = 
"No" 

All 

All 

All 

All 

All 

All 

All 

All 

Dimension 3: Having difficulty in obtaining or assimilating information 

63 

All countries 

Q16.1, Q16.2, 
Q16.3, Q16.5, 
Q16.6, Q16.7, 
Q16.8 

All countries 

Q16.5 

All countries 

Q16.3 

All countries 

Q16.2 

All countries 

Q16.1 

All countries 

Q16.6 

All countries 

Q16.7 

All countries 

Q16.8 

All countries 

Q16.1, Q16.2, 
Q16.3, Q16.5, 
Q16.6, Q16.7, 
Q16.8 

Overpaid for energy, telecommunication or banking services 
due to being unable to use certain payment methods in past 
12 months 

Extent feels vulnerable or disadvantaged when choosing and 
buying goods or services due to personal characteristics; 
maximum score across the following characteristics: health 
problems, financial circumstances, employment situation, 
age, belonging to a minority group, personal issues, or other 
reasons 
Feels vulnerable or disadvantaged when choosing and 
buying goods or services to 'some extent' or 'a great extent' 
due to age 
Note: Used in the analysis in sections 4.1 and 4.2. 
Feels vulnerable or disadvantaged when choosing and 
buying goods or services to 'a great extent' due to 
employment situation 
Feels vulnerable or disadvantaged when choosing and 
buying goods or services to 'a great extent' due to financial 
circumstances 
Feels vulnerable or disadvantaged when choosing and 
buying goods or services to 'a great extent' due to health 
problems 
Feels vulnerable or disadvantaged when choosing and 
buying goods or services to 'a great extent' due to belonging 
to a minority group 

Feels vulnerable or disadvantaged when choosing and 
buying goods or services to 'a great extent' due to personal 
issues. 
Feels vulnerable or disadvantaged when choosing and 
buying goods or services to 'a great extent' due to other 
reasons 
Feels vulnerable or disadvantaged when choosing and 
buying goods or services to 'a great extent' due to any of 
the following personal characteristics: health problems, 
financial circumstances, employment situation, age, 
belonging to a minority group, personal issues, or other 
reasons 

 
 
 
 
3 │ Conceptualisation of a theoretical framework and operationalisation of vulnerability 

Table 5:  Indicators of vulnerability based on the survey 

#  Name 

Code 
(Unique code 
matching 
output files) 

Sector 

Type 

Relevant 
consumers 

Data 
availability 

Meaning 

Questions 
(Survey 
question 
numbers) 

Does not feel informed 
about prices etc. 

4 

4_b2 

non-
specific 

non-
specific 

Scale: 1 = "Very 
informed" - 4 = "Not at all 
informed" 
Binary: 1 = "Yes", 0 = 
"No" 

All 

All 

All countries 

Q17.1 

All countries 

Q17.1 

Does not feel informed about product about price, quality, 
conditions, etc. when choosing and buying goods and 
services 
Feels 'not at all' informed about product about price, quality, 
conditions, etc. when choosing and buying goods and 
services 

12 

13 

14 

15 

16 

‘Feels 'not at all' informed 
about product price, 
quality, conditions, etc. 
when choosing and buying 
goods and services’ 
Gets information from 
adverts only/Compares 
product deals by using 
information from 
advertisements only - 
energy sector * 

Gets information from 
adverts only/Compares 
product deals by using 
information from 
advertisements only - 
online sector * 
Gets information from 
adverts only/Compares 
product deals by using 
information from 
advertisements only - 
finance sector * 

5_adv_ene 

energy 

Binary: 1 = "Yes", 0 = 
"No" 

Those who have 
compared deals 

5_adv_onl 

online 

Binary: 1 = "Yes", 0 = 
"No" 

Those who have 
compared deals 

5_adv_fin 

finance 

Binary: 1 = "Yes", 0 = 
"No" 

Those who have 
compared deals  

17 

Has problems comparing 
deals due to information-
related factors- energy 
sector* 

18 

Has problems comparing 
deals due to information-
related factors – on-line 
sector* 

6_ene 

energy 

Binary: 1 = "Yes", 0 = 
"No" 

All 

6_onl 

online 

Binary: 1 = "Yes", 0 = 
"No" 

All 

A7 

B6 

C4 

A9a, A9b 

B8a, B8b 

All in UK, RO, LT, PT. 
Online respondents in 
DK, SE, LU, NL, FI, 
DE, FR, BE, SK, AT, 
EE, CZ, IE, IS, NO. 
No data for LV, SI, 
ES, HU, MT, HR, CY, 
PL, IT, EL, BG. 
Online respondents in 
all countries. 
CATI respondents 
from CY and MT who 
have an Internet 
connection at home. 
All in UK, RO, LT, PT. 
Online respondents in 
DK, SE, LU, NL, FI, 
DE, FR, BE, SK, AT, 
EE, CZ, IE, IS, NO.  
No data for LV, SI, 
ES, HU, MT, HR, CY, 
PL, IT, EL, BG. 
All in UK, RO, LT, PT. 
Online respondents in 
DK, SE, LU, NL, FI, 
DE, FR, BE, SK, AT, 
EE, CZ, IE, IS, NO.  
No data for LV, SI, 
ES, HU, MT, HR, CY, 
PL, IT, EL, BG. 
Online respondents in 
all countries. 
CATI respondents 
from CY and MT who 
have an Internet 
connection at home. 

Gets information from advertisements ONLY when 
comparing deals offered  different gas and/or electricity 
providers 

Gets information from advertisements ONLY when 
comparing deals offered by different internet service 
providers 

Gets information from advertisements ONLY when 
comparing deals offered by different banks 

Finds it difficult to compare deals in the energy sector due 
to one or more of the following reasons: I do not know 
where I can find this information; Suppliers do not provide 
enough information.  OR Never compares deals in the 
energy sector due to one or more of the following reasons: I 
do not know where I can find this information; Suppliers do 
not provide enough information 

Finds it difficult to compare deals in the finance sector due 
to one or more of the following reasons: I do not know 
where I can find this information; Suppliers do not provide 
enough information.  OR Never compares deals in the 
finance sector due to one or more of the following reasons: I 
do not know where I can find this information; Suppliers do 
not provide enough information 

64 

 
 
 
 
 
 
 
 
3 │ Conceptualisation of a theoretical framework and operationalisation of vulnerability 

Table 5:  Indicators of vulnerability based on the survey 

#  Name 

Code 
(Unique code 
matching 
output files) 

Sector 

Type 

Relevant 
consumers 

Data 
availability 

19 

Has problems comparing 
deals due to information-
related factors – financial 
sector* 

6_fin 

finance 

Binary: 1 = "Yes", 0 = 
"No" 

All 

20 

Has not switched due to 
information-related factors 
– energy sector** 

7_ene 

energy 

Binary: 1 = "Yes", 0 = 
"No" 

Those who have an 
energy supplier 

21 

Has not switched due to 
information-related factors 
– on-line sector** 

7_onl 

online 

Binary: 1 = "Yes", 0 = 
"No" 

Those who have an 
internet connection 

22 

Has not switched due to 
information-related factors 
– financial sector** 

7_fin 

finance 

Binary: 1 = "Yes", 0 = 
"No" 

Those who have a 
bank account 

Dimension 4: Inability or failure to buy, choose or access suitable products 
23 

8_1_ene 

energy 

Does not compare product 
deals - energy sector 
Finds it very difficult to 
compare product deals - 
energy sector 
Does not compare product 
deals - online sector 

8_2_ene 

energy 

8_1_onl 

online 

Binary: 1 = "Yes", 0 = 
"No" 

Binary: 1 = "Yes", 0 = 
"No" 
Binary: 1 = "Yes", 0 = 
"No" 

All 

All 

All 

24 

25 

65 

All in UK, RO, LT, PT. 
Online respondents in 
DK, SE, LU, NL, FI, 
DE, FR, BE, SK, AT, 
EE, CZ, IE, IS, NO  
No data for LV, SI, 
ES, HU, MT, HR, CY, 
PL, IT, EL, BG. 
All in UK, RO, LT, DK, 
PT. 
No data for SE, LU, 
NL, FI, DE, FR, BE, 
SK, AT, EE, CZ, IE, 
IS, NO, LV, SI, ES, 
HU, MT, HR, CY, PL, 
IT, EL, BG. 
All in UK, RO, LT, DK, 
PT. 
No data for SE, LU, 
NL, FI, DE, FR, BE, 
SK, AT, EE, CZ, IE, 
IS, NO, LV, SI, ES, 
HU, MT, HR, CY, PL, 
IT, EL, BG. 
All in UK, RO, LT, DK, 
PT. 
No data for SE, LU, 
NL, FI, DE, FR, BE, 
SK, AT, EE, CZ, IE, 
IS, NO, LV, SI, ES, 
HU, MT, HR, CY, PL, 
IT, EL, BG. 

All counries 

All countries 

Online respondents in 
all countries. 
CATI respondents 
from CY and MT who 
have an Internet 
connection at home. 

Questions 
(Survey 
question 
numbers) 

C6, C6b 

A23 

B15 

C13 

A6 

A8 

B5 

Meaning 

Finds it difficult to compare deals in the online sector due to 
one or more of the following reasons: I do not know where I 
can find this information; Suppliers do not provide enough 
information.  OR Never compares deals in the online sector 
due to one or more of the following reasons: I do not know 
where I can find this information; Suppliers do not provide 
enough information 

Never switched electricity supplier in the last 5 years for one 
of the following reasons: I am unsure about where to get 
information to help me make a good choice 

Never switched home Internet provider or Internet tariff 
scheme in the last 5 years for one of the following reasons: 
I am unsure about where to get information to help me 
make a good choice 

Never switched main bank account in the last 5 years for 
one of the following reasons: I am unsure about where to 
get information to help me make a good choice 

Never compares deals in the energy sector 

Finds it very difficult to compare deals in the energy sector 

Never compares deals in the online sector 

 
 
 
 
 
 
3 │ Conceptualisation of a theoretical framework and operationalisation of vulnerability 

Table 5:  Indicators of vulnerability based on the survey 

#  Name 

Code 
(Unique code 
matching 
output files) 

Sector 

Type 

Relevant 
consumers 

Data 
availability 

Meaning 

Questions 
(Survey 
question 
numbers) 

B7 

Finds it very difficult to compare deals in the online sector 

26 

Finds it very difficult to 
compare product deals - 
online sector 

8_2_onl 

online 

Binary: 1 = "Yes", 0 = 
"No" 

All 

27 

28 

29 

Does not compare product 
deals - finance sector 
Finds it very difficult to 
compare product deals - 
finance sector 
Has problems comparing 
deals due to personal 
factors – energy sector* 

8_1_fin 

finance 

8_2_fin 

finance 

8a_ene 

energy 

Binary: 1 = "Yes", 0 = 
"No" 
Binary: 1 = "Yes", 0 = 
"No" 

Binary: 1 = "Yes", 0 = 
"No" 

All 

All 

All 

30 

Has problems comparing 
deals due to personal 
factors – on-line sector 

8a_onl 

online 

Binary: 1 = "Yes", 0 = 
"No" 

All 

Online respondents in 
all countries. 
CATI respondents 
from CY and MT who 
have an Internet 
connection at home. 
All countries 

All countries 

All in UK, RO, LT, PT. 
Online respondents in 
DK, SE, LU, NL, FI, 
DE, FR, BE, SK, AT, 
EE, CZ, IE, IS, NO.  
No data for LV, SI, 
ES, HU, MT, HR, CY, 
PL, IT, EL, BG. 

Online respondents in 
all countries. 
CATI respondents 
from CY and MT who 
have an Internet 
connection at home. 

C3 

C5 

A9a, A9b 

B8a, B8b 

31 

Has problems comparing 
deals due to personal 
factors – finance sector* 

8a_fin 

finance 

Binary: 1 = "Yes", 0 = 
"No" 

All 

C6, C6b 

Online respondents in 
SE, LU, NL, FI, DE, 
FR, BE, SK, AT, EE, 
CZ, IE, IS, NO. 
CATI respondents 
from CY and MT who 
have an Internet 
connection at home. 

Never compares deals in the finance sector 

Finds it very difficult to compare deals in the finance  sector 

Finds it difficult to compare deals in the energy sector due 
to one or more of the following reasons: I do not have 
enough time; I do not know how to compare; I am not 
interested; I am not financially minded; I am not technically 
minded (good at technical things).  OR Never compares 
deals in the energy sector due to one or more of the 
following reasons: I do not have enough time; I do not 
know how to compare; I am not interested; I am not 
financially minded; I am not technically minded (good at 
technical things) 
Finds it difficult to compare deals in the on-line sector due 
to one or more of the following reasons: I do not have 
enough time; I do not know how to compare; I am not 
interested; I am not financially minded; I am not technically 
minded (good at technical things). OR Never compares 
deals in the online sector due to one or more of the 
following reasons: I do not have enough time; I do not 
know how to compare; I am not interested; I am not 
financially minded; I am not technically minded (good at 
technical things) 
Finds it difficult to compare deals in the finance sector due 
to one or more of the following reasons: I do not have 
enough time; I do not know how to compare; I am not 
interested; I am not financially minded; I am not technically 
minded (good at technical things). OR Never compares 
deals in the finance sector due to one or more of the 
following reasons: I do not have enough time; I do not 
know how to compare; I'm not interested; I am not 
financially minded; I am not technically minded (good at 
technical things) 

66 

 
 
3 │ Conceptualisation of a theoretical framework and operationalisation of vulnerability 

Table 5:  Indicators of vulnerability based on the survey 

#  Name 

32 

Has problems comparing 
deals due to market-
related factors – energy 
sector* 

Code 
(Unique code 
matching 
output files) 

Sector 

Type 

Relevant 
consumers 

Data 
availability 

8b_ene 

energy 

Binary: 1 = "Yes", 0 = 
"No" 

All 

All in UK, RO, LT, PT. 
Online respondents in 
DK, SE, LU, NL, FI, 
DE, FR, BE, SK, AT, 
EE, CZ, IE, IS, NO.  
No data for LV, SI, 
ES, HU, MT, HR, CY, 
PL, IT, EL, BG. 

Questions 
(Survey 
question 
numbers) 

A9a, A9b 

33 

Has problems comparing 
deals due to market-
related factors – on-line 
sector 

8b_onl 

On-line 

Binary: 1 = "Yes", 0 = 
"No" 

All 

34 

Has problems comparing 
deals due to market-
related factors – finance 
sector* 

8b_fin 

finance 

Binary: 1 = "Yes", 0 = 
"No" 

All 

B8a, B8b 

Online respondents in 
all countries. 
CATI respondents 
from CY and MT who 
have an Internet 
connection at home. 

C6, C6b 

All in UK, RO, LT, PT. 
Online respondents in 
DK, SE, LU, NL, FI, 
DE, FR, BE, SK, AT, 
EE, CZ, IE, IS, NO. 
No data for LV, SI, 
ES, HU, MT, HR, CY, 
PL, IT, EL, BG. 

Meaning 

Finds it difficult to compare deals in the energy sector due 
to one or more of the following reasons: There are too many 
offers to choose from; Hard to compare like for like, prices 
are provided in different formats, deals include different 
offers (bundles/ packages of services); The wording used by 
suppliers is difficult to understand; Offers are time limited/ a 
better offer may come out next month. OR Never compares 
deals in the energy sector due to one or more of the 
following reasons: There are too many offers to choose 
from; Hard to compare like for like, prices are provided in 
different formats, deals include different offers (bundles/ 
packages of services); The wording used by suppliers is 
difficult to understand; Offers are time limited/ a better 
offer may come out next month; Switching is a hassle 
Finds it difficult to compare deals in the on-line sector due 
to one or more of the following reasons: There are too many 
offers to choose from; Hard to compare like for like, prices 
are provided in different formats, deals include different 
offers (bundles/ packages of services); The wording used by 
suppliers is difficult to understand; Offers are time limited/ a 
better offer may come out next month. OR Never compares 
deals in the online sector due to one or more of the 
following reasons: There are too many offers to choose 
from; Hard to compare like for like, prices are provided in 
different formats, deals include different offers (bundles/ 
packages of services); The wording used by suppliers is 
difficult to understand; Offers are time limited/ a better 
offer may come out next month; Switching is a hassle 
Finds it difficult to compare deals in the finance sector due 
to one or more of the following reasons: There are too many 
offers to choose from; Hard to compare like for like, prices 
are provided in different formats, deals include different 
offers (bundles/ packages of services); The wording used by 
banks is difficult to understand; Offers are time limited/ a 
better offer may come out next month. OR Never compares 
deals in the finance sector due to one or more of the 
following reasons: There are too many offers to choose 
from; Hard to compare like for like, prices are provided in 
different formats, deals include different offers (bundles/ 
packages of services); The wording used by banks is difficult 
to understand; Offers are time limited/ a better offer may 
come out next month; Switching is a hassle 

67 

 
 
3 │ Conceptualisation of a theoretical framework and operationalisation of vulnerability 

Table 5:  Indicators of vulnerability based on the survey 

#  Name 

35 

Has problems comparing 
deals due to access-
related factors – energy 
sector* 

Code 
(Unique code 
matching 
output files) 

Sector 

Type 

Relevant 
consumers 

Data 
availability 

8c_ene 

energy 

Binary: 1 = "Yes", 0 = 
"No" 

All 

All in UK, RO, LT, PT. 
Online respondents in 
DK, SE, LU, NL, FI, 
DE, FR, BE, SK, AT, 
EE, CZ, IE, IS, NO. 
No data for LV, SI, 
ES, HU, MT, HR, CY, 
PL, IT, EL, BG. 

Questions 
(Survey 
question 
numbers) 

A9a, A9b 

36 

Has problems comparing 
deals due to access-
related factors – on-line 
sector 

8c_onl 

On-line 

Binary: 1 = "Yes", 0 = 
"No" 

All 

37 

Has problems comparing 
deals due to access-
related factors – financial 
sector* 

8c_fin 

finance 

Binary: 1 = "Yes", 0 = 
"No" 

All 

38 

Has not switched due to 
personal factors- energy 
sector** 

9a_ene 

energy 

Binary: 1 = "Yes", 0 = 
"No" 

Those who have an 
energy supplier 

39 

Has not switched due to 
personal factors – on –line 
sector** 

9a_onl 

online 

Binary: 1 = "Yes", 0 = 
"No" 

Those who have an 
internet connection 

B8a, B8b 

Online respondents in 
all countries. 
CATI respondents 
from CY and MT who 
have an Internet 
connection at home. 

C6, C6b 

A23 

B15 

All in UK, RO, LT, PT. 
Online respondents in 
DK, SE, LU, NL, FI, 
DE, FR, BE, SK, AT, 
EE, CZ, IE, IS, NO.  
No data for LV, SI, 
ES, HU, MT, HR, CY, 
PL, IT, EL, BG. 
All in UK, RO, LT, DK, 
PT. 
No data for SE, LU, 
NL, FI, DE, FR, BE, 
SK, AT, EE, CZ, IE, 
IS, NO, LV, SI, ES, 
HU, MT, HR, CY, PL, 
IT, EL, BG. 
All in UK, RO, LT, DK, 
PT. 
No data for SE, LU, 
NL, FI, DE, FR, BE, 
SK, AT, EE, CZ, IE, 
IS, NO, LV, SI, ES, 
HU, MT, HR, CY, PL, 
IT, EL, BG. 

Meaning 

Finds it difficult to compare deals in the energy sector due 
to one or more of the following reasons: There is only one 
provider in my area; I live in a housing cooperative where 
such services are collectively negotiated; I live in rented 
accommodation and my landlord does not allow me to 
switch. OR Never compares deals in the energy sector due 
to one or more of the following reasons: There is only one 
provider in my area; I live in a housing cooperative where 
such services are collectively negotiated; I live in rented 
accommodation and my landlord does not allow me to 
switch 
Finds it difficult to compare deals in the online sector due to 
one or more of the following reasons: There is only one 
provider in my area; I live in a housing cooperative where 
such services are collectively negotiated; I live in rented 
accommodation and my landlord does not allow me to 
switch.  OR Never compares deals in the online sector due 
to one or more of the following reasons: There is only one 
provider in my area; I live in a housing cooperative where 
such services are collectively negotiated; I live in rented 
accommodation and my landlord does not allow me to 
switch 
Finds it difficult to compare deals in the finance sector due 
to one or more of the following reasons: There is only one 
provider in my area.  OR Never compares deals in the 
finance sector due to one or more of the following reasons: 
There is only one provider in my area; I am currently in a 
poor financial situation (bank account overdraft) 

Never switched electricity supplier in the last 5 years for one 
of the following reasons: I wouldn't know how to switch 
even if I wanted to; I did not know it was possible to switch; 
I never thought about the issue 

Never switched home Internet provider or Internet tariff 
scheme in the last 5 years for one of the following reasons: 
I wouldn't know how to switch even if I wanted to; I did not 
know it was possible to switch; I never thought about the 
issue 

68 

 
 
 
 
3 │ Conceptualisation of a theoretical framework and operationalisation of vulnerability 

Table 5:  Indicators of vulnerability based on the survey 

#  Name 

Code 
(Unique code 
matching 
output files) 

Sector 

Type 

Relevant 
consumers 

Data 
availability 

40 

Has not switched due to 
personal factors – financial 
sector** 

9a_fin 

finance 

Binary: 1 = "Yes", 0 = 
"No" 

Those who have a 
bank account 

41 

Has not switched due to 
market-related factors – 
energy sector** 

9b_ene 

energy 

Binary: 1 = "Yes", 0 = 
"No" 

Those who have an 
energy supplier 

42 

Has not switched due to 
market-related factors – 
on-line sector** 

9b_onl 

online 

Binary: 1 = "Yes", 0 = 
"No" 

Those who have an 
internet connection 

43 

 Has not switched due to 
market-related factors – 
financial sector 

9b_fin 

finance 

Binary: 1 = "Yes", 0 = 
"No" 

Those who have a 
bank account 

44 

Has not switched due to 
access-related factors – 
energy sector** 

9c_ene 

energy 

Binary: 1 = "Yes", 0 = 
"No" 

Those who have an 
energy supplier 

45 

Has not switched due to 
access-related factors – 
on-line sector** 

9c_onl 

online 

Binary: 1 = "Yes", 0 = 
"No" 

Those who have an 
internet connection 

All in UK, RO, LT, DK, 
PT. 
No data for SE, LU, 
NL, FI, DE, FR, BE, 
SK, AT, EE, CZ, IE, 
IS, NO, LV, SI, ES, 
HU, MT, HR, CY, PL, 
IT, EL, BG. 
All in UK, RO, LT, DK, 
PT. 
No data for SE, LU, 
NL, FI, DE, FR, BE, 
SK, AT, EE, CZ, IE, 
IS, NO, LV, SI, ES, 
HU, MT, HR, CY, PL, 
IT, EL, BG. 
All in UK, RO, LT, DK, 
PT. 
No data for SE, LU, 
NL, FI, DE, FR, BE, 
SK, AT, EE, CZ, IE, 
IS, NO, LV, SI, ES, 
HU, MT, HR, CY, PL, 
IT, EL, BG. 
All in UK, RO, LT, DK, 
PT. 
No data for SE, LU, 
NL, FI, DE, FR, BE, 
SK, AT, EE, CZ, IE, 
IS, NO, LV, SI, ES, 
HU, MT, HR, CY, PL, 
IT, EL, BG. 
All in UK, RO, LT, DK, 
PT. 
No data for SE, LU, 
NL, FI, DE, FR, BE, 
SK, AT, EE, CZ, IE, 
IS, NO, LV, SI, ES, 
HU, MT, HR, CY, PL, 
IT, EL, BG. 
All in UK, RO, LT, DK, 
PT. 
No data for SE, LU, 
NL, FI, DE, FR, BE, 
SK, AT, EE, CZ, IE, 
IS, NO, LV, SI, ES, 
HU, MT, HR, CY, PL, 
IT, EL, BG. 

Questions 
(Survey 
question 
numbers) 

C13 

A23 

B15 

C13 

A23 

B15 

Meaning 

Never switched main bank account in the last 5 years for 
one of the following reasons: I wouldn't know how to switch 
even if I wanted to; I did not know it was possible to switch; 
I never thought about the issue 

Never switched electricity supplier in the last 5 years for one 
of the following reasons: I don't think there is any difference 
between the suppliers to make switching worthwhile; 
Switching is a hassle; It is difficult to compare the offers of 
different electricity providers 

Never switched home Internet provider or Internet tariff 
scheme in the last 5 years for one of the following reasons: 
I don't think there is any difference between the providers 
to make switching worthwhile; Switching is a hassle ; It is 
difficult to compare the offers of different Internet service 
providers 

Never switched main bank account in the last 5 years for 
one of the following reasons: I don't think there is any 
difference between the providers to make switching 
worthwhile; Switching is a hassle ; It is difficult to compare 
the offers of different banks 

Never switched electricity supplier in the last 5 years for one 
of the following reasons: I am in debt with my current 
supplier/s so don't think I can switch; I live in rented 
accommodation and don't think my landlord will allow me to 
switch; I live in a housing cooperative where such services 
are collectively negotiated; No other supplier is available in 
the area where I live 

Never switched home Internet provider or Internet tariff 
scheme in the last 5 years for one of the following reasons: 
I am in debt with my current provider/s so don't think I can 
switch; I live in rented accommodation and don't think my 
landlord will allow me to switch; I live in a housing 
cooperative where such services are collectively negotiated; 
No other provider is available in the area where I live 

69 

 
 
 
 
 
 
 
 
3 │ Conceptualisation of a theoretical framework and operationalisation of vulnerability 

Table 5:  Indicators of vulnerability based on the survey 

#  Name 

Code 
(Unique code 
matching 
output files) 

Sector 

Type 

Relevant 
consumers 

Data 
availability 

46 

Has not switched due to 
access-related factors – 
financial sector** 

9c_fin 

finance 

Binary: 1 = "Yes", 0 = 
"No" 

Those who have a 
bank account 

47 

48 

49 

Has not switched due to 
termination costs 

9d_ter 

Has not switched due to 
bundling 
Excluded from e-
commerce due to difficulty 
of the process 

9e_bun 

10a 

non-
specific 

non-
specific 
online 

Binary: 1 = "Yes", 0 = 
"No" 

Binary: 1 = "Yes", 0 = 
"No" 
Binary: 1 = "Yes", 0 = 
"No" 

All 

All 

All 

50 

Excluded from e-
commerce due to not 
having a payment card 

10b 

online 

Binary: 1 = "Yes", 0 = 
"No" 

All 

51 

Declined for a loan** 

11_loan 

finance 

Binary: 1 = "Yes", 0 = 
"No" 

All 

52 

Overall performance in the 
experiments (irrespective 
of treatment) 

expt_overall 

non-
specific 

Scale: 0 (no correct 
answers) - 4 (4 correct 
answers) 

All 

All in UK, RO, LT, DK, 
PT. 
No data for SE, LU, 
NL, FI, DE, FR, BE, 
SK, AT, EE, CZ, IE, 
IS, NO, LV, SI, ES, 
HU, MT, HR, CY, PL, 
IT, EL, BG. 
All countries 

All countries 

Online respondents in 
all countries. 
CATI respondents 
from CY and MT who 
have an Internet 
connection at home. 
Online respondents in 
all countries. 
CATI respondents 
from CY and MT who 
have an Internet 
connection at home. 
All in UK, RO, LT, PT. 
Online respondents in 
DK. 
No data for SE, LU, 
NL, FI, DE, FR, BE, 
SK, AT, EE, CZ, IE, 
IS, NO, LV, SI, ES, 
HU, MT, HR, CY, PL, 
IT, EL, BG. 
All in DK, LT, PT, RO, 
UK. 
No data for SE, LU, 
NL, FI, DE, FR, BE, 
SK, AT, EE, CZ, IE, 
IS, NO, LV, SI, ES, 
HU, MT, HR, CY, PL, 
IT, EL, BG. 

Questions 
(Survey 
question 
numbers) 

C13 

Q21.3 

Q21.6 

Q21.4 

Q21.5 

Meaning 

Never switched main bank account in the last 5 years for 
one of the following reasons: I am in debt with my current 
provider/s so don't think I can switch; No other provider is 
available in the area where I live 

Did not switch your energy, telecommunication or banking 
supplier because of additional costs for termination of the 
contract (eg extra fees for returning the internet modem) 
Decided not to switch provider because the offer was 
bundled or because your current product is part of a bundle 
Decided not to purchase a product or service online because 
you found it too difficult to complete the purchase 

Was unable to buy a product or service online because you 
didn't have a payment card allowing you to pay over the 
internet 

Q13b 

Tried to obtain an unsecured loan or credit in the last 5 
years, but did not obtain it 

Experiments 

Total number of correct choices made in the experiments 
(across all sector experiments, rounds and treatments); 
takes a maximum value of 4 since each respondent made 4 
choices in total 

Dimension 5: Higher susceptibility to marketing practices 
53 

12_off 

non-
specific 

Scale: 1 = "Not at all" - 4 
= "To a great extent" 

Perception of own 
vulnerability due to 
complexity of offers 

All 

All countries 

Q16.4 

Extent feels vulnerable or disadvantaged when choosing and 
buying goods or services due to complexity of offers, terms 
or conditions 

70 

 
 
 
3 │ Conceptualisation of a theoretical framework and operationalisation of vulnerability 

Table 5:  Indicators of vulnerability based on the survey 

Code 
(Unique code 
matching 
output files) 

12_off_b2 

Sector 

Type 

Relevant 
consumers 

Data 
availability 

Meaning 

Questions 
(Survey 
question 
numbers) 

non-
specific 

Binary: 1 = "Yes", 0 = 
"No" 

All 

All countries 

Q16.4 

Feels vulnerable or disadvantaged when choosing and 
buying goods or services to 'a great extent' due to 
complexity of offers, terms or conditions 

#  Name 

54 

55 

Perception of own 
vulnerability due to 
complexity of offers 
In this section referred to 
as ‘Vulnerable to a great 
extent due to complexity 
of offers (own perception)’ 
Experiment choice under 
the marketing practice 
treatment - energy sector 

expt_mp_ene 

energy 

Binary: 1 = "Not correct", 
0 = "Correct" 

All 

56 

Experiment choice under 
the marketing practice 
treatment - online sector 

expt_mp_onl 

online 

Binary: 1 = "Not correct", 
0 = "Correct" 

All 

57 

Experiment choice under 
the marketing practice 
treatment – finance sector 

expt_mp_fin 

finance  

Binary: 1 = "Not correct", 
0 = "Correct" 

All 

58 

Experiment choice under 
the marketing practice 
treatment – cross-cutting 

expt_mp_cc 

Non-
specific  

Binary: 1 = "Not correct", 
0 = "Correct" 

All 

Experiments 

Whether the respondent made the correct choice of offer 
under the marketing practice treatment in both rounds of 
the energy sector experiment 

Experiments 

Whether the respondent made the correct choice of offer 
under the marketing practice treatment in both rounds of 
the online sector experiment 

Experiments 

Whether the respondent made the correct choice of offer 
under the marketing practice treatment in both rounds of 
the finance sector experiment 

Experiments 

Whether the respondent made the correct choice of offer 
under the marketing practice treatment in both rounds of 
the cross-cutting experiment 

All in DK, LT, PT, RO, 
UK. 
No data for SE, LU, 
NL, FI, DE, FR, BE, 
SK, AT, EE, CZ, IE, 
IS, NO, LV, SI, ES, 
HU, MT, HR, CY, PL, 
IT, EL, BG. 
All in DK, LT, PT, RO, 
UK. 
No data for SE, LU, 
NL, FI, DE, FR, BE, 
SK, AT, EE, CZ, IE, 
IS, NO, LV, SI, ES, 
HU, MT, HR, CY, PL, 
IT, EL, BG. 
All in DK, LT, PT, RO, 
UK. 
No data for SE, LU, 
NL, FI, DE, FR, BE, 
SK, AT, EE, CZ, IE, 
IS, NO, LV, SI, ES, 
HU, MT, HR, CY, PL, 
IT, EL, BG. 
All in DK, LT, PT, RO, 
UK. 
No data for SE, LU, 
NL, FI, DE, FR, BE, 
SK, AT, EE, CZ, IE, 
IS, NO, LV, SI, ES, 
HU, MT, HR, CY, PL, 
IT, EL, BG. 

Note: * Indicator not available for the following 11 countries: BG, HR, CY, EL, HU, IT, LV, MT, PL, SL, ES; ** Indicator only available for the following five countries (because the relevant questions were only asked in countries 
where the experiments were run): DK, LT, PT, RO, UK. 
Source: London Economics 

71 

 
 
 
3 │ Conceptualisation of a theoretical framework and operationalisation of vulnerability 

3.6.1.  Validation of the operationalisation of vulnerability 

This sub-section discusses the approach used to group the vulnerability indicators into 
the vulnerability dimensions in order to operationalise vulnerability.   

The  dimensions  of  vulnerability  set  out  in  section  3.3  are  operationalised  with 
indicators based on the literature review. However, factor analysis can provide further 
insights  regarding  the  construct  validity31  of  the  dimensions  vis-à-vis  the  indicators. 
Specifically, construct validity is used as a methodology to assess the soundness of the 
grouping of the vulnerability indicators into the 5 vulnerability dimensions. 

Factor  analysis  is  one  of  the  most  widely  used  statistical  techniques  in  social  science. 
Factor analysis is a method for expressing a number of variables in terms of a smaller 
number of ‘factors’. This approach is used to establish how the survey and experiment 
data suggest which indicators  should be grouped, and compare these results with our 
operationalisation based on the literature review. 

Details  of  the  factor  analysis  method  and  results  are  presented  in  Annex  9.  Here  we 
summarise the main findings and conclusions of the factor analysis. 

Overall  the  factor  analysis  supports  the  allocation  of  vulnerability  indicators  to 
dimensions  consistent  with  that  in  the  literature.  The  factor  analysis  supports  the 
allocation  of  the  identified  indicators  to  dimensions  1  and  2  in  particular,  and;  also 
suggests that many indicators allocated to dimensions 3 and 4 based on the literature 
do indeed relate to a common factor.  

However, all dimensions include highly unique indicators and/or indicators that share a 
common factor with indicators in other dimensions. Thus, the allocation of indicators to 
dimensions based on the data would be somewhat different to that which is based on 
the literature. 

Given the broad nature of the dimensions, one would not expect all indicators that are 
allocated to a certain dimension (based on theory) to measure the same phenomenon. 
For  example,  in  dimension  4,  failure  to  make  a  correct  product  choice  may  be  driven 
by  a  range  of  different  factors  (personal,  market-related,  etc.)  and  one  would  not 
necessarily expect these factors to be associated. 

The dimensions themselves are linked to the factors as follows (note that we comment 
on only three factors, referred to here as factors 1, 2 and 3)32: 

 

In  dimension  1,  ‘heightened  risk  of  negative  outcomes  or  impacts  on  well-
being’,  indicators  are  related  in  general  to  factor  3,  although  not  very 
convincingly. The two indicators in this dimension share this factor with several 

31 Construct validity is the extent to which a survey question or test, measures what it claims or 
purports to measure (Cronbach & Meehl, 1955). We use construct validity as a methodology 
to assess the soundness of the grouping of indicators into dimensions. 

32 The factors are reported in Table  151, Annex 9. We comment on three factors, instead of e.g. 
five  factors,  since  the  first  three  factors  capture  most  of  the  underlying  components  of 
vulnerability. In the principle factor analysis, each additional factor relates more weakly than 
the  previous  factors  to  the  indicators.  In  practice  after  the  third  factor  (as  shown  by  an 
eigenvalue  analysis  presented  Annex  9),  factors  no  longer  have  a  discernible  relationship 
with either the indicators or dimensions. 

72 

 
 
                                                 
3 │ Conceptualisation of a theoretical framework and operationalisation of vulnerability 

 

 

 

 

indicators  in  dimension  4  and  dimension  5.  In  addition,  one  of  the  two 
indicators (Indicator 1) is fairly unique. 

In  dimension  2,  ‘having  characteristics  that  limit  ability  to  maximise  well-
being’, indicators are strongly associated with factor 1. Indicators in dimension 
2  share  a  common  factor  with  several  indicators  in  dimension  4  and  one 
indicator  in  dimension  5.  Nevertheless,  the  indicators  in  dimension  2  have 
higher  factor  loadings  in  Factor  1  than  any  other  indicator  (this  holds  across 
the four analyses). In addition, no indicators in dimension 2 are highly unique. 
Overall, this reflects the soundness of the grouping of these indicators into the 
dimensions. 

In  dimension  3,  ‘having  difficulty  in  obtaining  or  assimilating  information’, 
indicators  are  generally  associated  with  factor  2.  Several  indicators  in  this 
dimension share the factor with indicators in dimension 4. However, more than 
half of the indicators in dimension 3 in all four analyses are highly unique. 

In dimension 4, ‘inability or failure to buy, choose or access suitable products’, 
indicators  are  generally  related  to  factor  2.  However,  there  are  several 
indicators  broadly  related  to  factor  2,  others  to  factor  3,  and  a  couple  (some 
just  below  the  0.3  highlight  threshold)  to  factor  1.  Indicators  in  dimension  4 
thus  share  a  factor  with  indicators  from  each  of  the  before  mentioned 
dimensions.  In  addition,  dependent  on  the  analysis,  dimension  4  has  a 
significant number of its indicators with high uniqueness. 

In  dimension  5,  ‘higher  susceptibility  to  marketing  practices’,  indicators  are, 
broadly  speaking,  associated  with  factor  1  and  factor  3.  Indicators  in  this 
dimension  share  the  factors  with  indicators  in  dimension  1,  dimension  2  and 
dimension  4.  In  addition,  dependent  on  the  analysis,  dimension  5  has  an 
indicator high in uniqueness. 

As  a  concluding  comment,  across  the  dimensions  a  significant  number  of  indicators 
have generally high uniqueness scores in our factor analysis, which relates to the high 
dimensionality of vulnerability. One interpretation is that the indicators measure many 
different  aspects  of  consumer  vulnerability.  Consumer  vulnerability 
is  widely 
acknowledged  to  be  an  extremely  multi-faceted  concept  and  this  is  to  some  extent 
reflected in the indicators. 

3.7. Operationalisation of potential drivers of vulnerability 

A  key  objective  of  the  present  study  is  to  examine  the  drivers  of  consumer 
vulnerability.  This  section  sets  out  how  the  potential  drivers  of  vulnerability  are 
operationalised using data from the consumer  survey (analysis of the impact of these 
drivers is presented in Chapter 6). 

As discussed in section 3.4, evidence from the literature suggests that a wide range of 
factors  may  be  linked  to  consumer  vulnerability.  These  can  be  divided  into  five 
categories: 

  Personal and demographic characteristics 
  Behavioural drivers of vulnerability 
  Market-related drivers of vulnerability 
  Access drivers of vulnerability 
  Situational drivers of vulnerability 

The  potential  drivers  of  vulnerability  that  are  operationalised  in  the  analysis  within 
each  category  are  summarised  in  Table  6  below.  The  precise  coding  of  the  variables 

73 

 
 
3 │ Conceptualisation of a theoretical framework and operationalisation of vulnerability 

which  represent  these  potential  drivers  is  set  out  in  section  3.7.1.  Some  relevant 
variables  are  not  included  in  the  operationalisation.  For  example,  no  questions  were 
asked in the survey on hearing or visual impairment  for practical reasons; individuals 
with visual impairments are not logged in to Ipsos’ panel, and individuals with hearing 
impairments cannot be contacted via telephone. 

Table 6:  Potential drivers of vulnerability 

Category 

Drivers operationalised in the analysis 

Personal and 
demographic 
characteristics 

  Age 

  Gender 

 

Population density of the respondent’s region of residence 

  Household size 

  Education level 

  Whether the respondent’s mother tongue is different from the official 

language(s) spoken in their country of residence 

Behavioural 
drivers of 
vulnerability 

  Trust in others (2 measures) 

  Credulity  (2 measures, one of which is a test) 

  Willingness to take risks 

 

Impulsiveness (4 measures) 

  Tests of a computational ability (2 measures) 

  Knowledge of terms relating to the energy and online sectors and 
ability to identify the best interest rate for a savings account (3 
measures) 

Market-related 
divers of 
vulnerability 

  Respondent’s knowledge of their contract (in each sector) 

  Being unable to read the terms and conditions of a contract because of 

small print 

 

Frequency with which the respondent compares deals (in each sector) 

  Whether the respondent read the last bill or communication from their 

provider (in each sector) 

  How easy the respondent found it to read the last bill or communication 

from their provider (in each sector) 

Access drivers of 
vulnerability 

 

Frequency of internet use for the purposes of online search, comparison 
of prices, online banking, online purchases, online selling, social media, 
and email 

  The number of purposes (listed above) the respondent uses internet for 

Situational drivers’ 
of vulnerability 

at least once a month 

  Occupational status 

  Whether the respondent finds it easy to ‘make ends meet’ (a proxy for 

the state of their finances) 

 

Personal situation (married, remarried, not married living with a 
partner, single, divorced or separated, widowed or other) 

  Number of dependent children 

  Whether the respondent is a single parent 

  Measures describing the respondent’s social circles (having friends who 

buy on-line, buy on credit, or can’t make ends meet) 

74 

 
 
3 │ Conceptualisation of a theoretical framework and operationalisation of vulnerability 

3.7.1.   Coding variables to represent potential drivers of vulnerability 

The  explanatory  variables  examined  in  the  analysis  in  Chapter  6  (on  the  drivers  of 
consumer  vulnerability)  are  all  coded  as  dummy  variables  (i.e.  taking  values  0  or  1). 
These include sets of mutually exclusive dummies, which represent where respondents 
lie  on  a  scale.  For  example,  the  behavioural  characteristic  of  ‘trust’33  is  coded  as  four 
mutually  exclusive  dummies  representing  the  levels  ‘very  low’,  ‘low’,  ‘high’  and  ‘very 
high’ (rather than as a scale variable taking values from 1 to 4).  

Coding  the  variables  in  this  way  means  that  the  effect  of  a  scale  variable  on  the 
dependent variable in regression analysis does not have to be linear, i.e. the difference 
between one pair of adjacent levels on a scale (e.g. ‘low’ and ‘high’) does not have to 
be the same as the difference between another pair of adjacent levels (e.g. ‘high’ and 
‘very high’). Moreover, this coding means that any level on a scale (including levels in 
the middle of the scale) can have the greatest impact on the dependent variable. 

In  addition,  this  coding  approach  provides  a  solution  to  a  problem  with  the  data, 
namely  that  not  all  respondents  answered  all  survey  questions.34  Using  this  coding 
approach we can code ‘no response’ to a particular question as a separate dummy, and 
so  keep  all  other  (more  useful)  information  from  the  respondent  in  the  analysis 
(otherwise, if information were missing for a respondent for just a single question, all 
information from that respondent would be lost). Consequently, some results reported 
in  Chapter  6  relate  to  explanatory  variables  which  signify  that  respondents  did  not 
answer  a  particular  survey  question  (e.g.  no  response  dummies  for  questions  on 
comparing deals, knowing contract terms and reading bills and communications). 

A  further  consequence  of  coding  the  explanatory  variables  in  this  way  is  that  one 
dummy  variable  from  each  set  of  mutually  exclusive  dummies  must  be  used  as  the 
‘base’, meaning that the impacts of all other variables in the set are measured relative 
to  this  base.  For  example,  in  the  case  of  the  ‘trust’  characteristic,  the  dummy 
representing ‘very low’ trust is used as the base, and the impacts of all other levels of 
trust are measured relative to very low trust. 

The full set of explanatory variables examined in the analysis in Chapter 6 is presented 
in  Table  7  below.35  This  table  also  indicates  which  variables  (i.e.  which  levels  or 
categories) are used as bases in the analysis. 

33 A measure that is based on the extent that respondents agree with the statement “you need 

to be very careful in dealing with people”. 

34  Some  questions  were  not  asked  depending  on  the  respondent’s  country  and  what  survey 

mode (online, CAPI or CATI) was used to survey them. 

35  The full set of explanatory variables examined in the G-S analysis is also presented in  Table 

153 in Annex 8. 

75 

 
 
                                                 
3 │ Conceptualisation of a theoretical framework and operationalisation of vulnerability 

Table 7: 

Explanatory variables included in regressions 

Name/label 

Description 

Personal and demographic characteristics 

Age 16-24 

Age 25-34 

Age 45-54 

Age 55-64 

Age 65-74 

Age 75+ 

Male 

Low density region 

Medium density region 

Household size 

Low education 

Medium education 

Age group from 16 to 24 

Age group from 25 to 34 

Age group from 45 to 54 

Age group from 55 to 64 

Age group from 65 to 74 

Age group above 75 

Male respondent 

Respondent lives in low density region 

Respondent lives in medium density region 

Number of individuals in the household 

Low education level  

Medium education level 

Base category 

Age group 35 to 44 

Female 

High density region 

N/A 

High education level 

Non-native tongue, no difficulty 

Mother tongue different from the official language, but this does not cause difficulty 

Native speaker 

Non-native tongue, difficulty 

Behavioural characteristics 

Trust 1: Low 

Trust 1: High 

Trust 1: Very high 

Trust 2: Low 

Trust 2: High 

Trust 2:Very high 

Credulity test: Incorrect 

Credulity test: Not taken 

Credulity: Low  

Credulity: High 

Credulity: Very high 

Impulsiveness 1: Low  

Impulsiveness 1: High 

Mother tongue different from the official language, and this causes difficulty 

Low trust in people; Tends to agree with the statement "you need to be very careful in dealing with people" 

Very low trust 

High trust in people; Tends to disagree with the statement "you need to be very careful in dealing with 
people" 

Very high trust in people; Strongly disagrees with the statement "you need to be very careful in dealing with 
people" 

Low trust in people; Tends to disagree with the statement "I believe most people can be trusted" 

Very low trust 

High trust in people, Tends to agree with the statement "I believe most people can be trusted" 

Very high trust in people; Strongly agrees with the statement "I believe most people can be trusted" 

Answered credulity test incorrectly  

Did not take credulity test 

Answered credulity test correctly  

Low credulity; Tends to disagree  with the statement "Most advertisements report objective fact, I trust most 
of the information provided in advertisements" 

Very low credulity 

High credulity; Tends to agree with the statement "Most advertisements report objective fact, I trust most of 
the information provided in advertisements" 

Very high credulity; Strongly agrees with the statement "Most advertisements report objective fact, I trust 
most of the information provided in advertisements" 

Low impulsiveness; Tends to disagree with the statement "I have a hard time breaking bad habits" 

Very low impulsiveness 

High impulsiveness; Tends to agree with the statement "I have a hard time breaking bad habits" 

Impulsiveness 1: Very high 

Very high impulsiveness; Strongly agrees with the statement "I have a hard time breaking bad habits" 

76 

 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
3 │ Conceptualisation of a theoretical framework and operationalisation of vulnerability 

Table 7: 

Explanatory variables included in regressions 

Name/label 

Impulsiveness 2: Low  

Impulsiveness 2: High 

Impulsiveness 2: Very high 

Impulsiveness 3: Low  

Impulsiveness 3: High 

Impulsiveness 3: Very high 

Impulsiveness 4: Low  

Impulsiveness 4: High 

Impulsiveness 4: Very high 

Risk taking: Not very  

Risk taking: Fairly 

Risk taking: Very 

Risk taking: No response  

Computation test 1 correct  

Computation test 2 correct  

Knows meaning of kWh 

Knows meaning of Mbps 

Description 

Base category 

Low impulsiveness; Tends to agree  with the statement “I’m good at resisting temptation” 

Very low impulsiveness 

High impulsiveness; Tends to disagree with the statement “I’m good at resisting temptation” 

Very high impulsiveness; Strongly disagrees with the statement “I’m good at resisting temptation” 

Low impulsiveness; Tends to agree  with the statement “People would say that I have very strong self-
discipline” 

Very low impulsiveness 

High impulsiveness; Tends to disagree with the statement  “People would say that I have very strong self-
discipline” 

Very high impulsiveness; Strongly disagrees with the statement  “People would say that I have very strong 
self-discipline” 

Low impulsiveness; Tends to disagree  with the statement “I’m impulsive in the purchase decisions I take” 

Very low impulsiveness 

High impulsiveness; Tends to agree with the statement “I’m impulsive in the purchase decisions I take” 

Very high impulsiveness; Strongly agrees with the statement “I’m impulsive in the purchase decisions I take” 

Not very willing to take risks 

Fairly willing to take risks 

Very willing to take risks 

Did not answer the question on willingness to take risks 

Answered first computation test correctly 

Answered second computation test correctly 

Knows the meaning of the term kWh 

Knows the meaning of the term Mbps 

Not at all willing to take risks 

Answered incorrectly 

Answered incorrectly 

Answered incorrectly 

Answered incorrectly 

Answered incorrectly 

Identified best interest rate 

Identified best interest rate for a savings account 

Market-related drivers and experience in markets 

Unable to read T&Cs due to small print 

Unable to read energy, telecommunication or banking contract terms and conditions due to overly small print  Able to read contract Ts&Cs 

Compare energy deals: When need to renew 

Compares energy deals from internet providers "but only when I need to renew my contract" 

Compare energy deals: Sporadically 

Compares energy deals "but only sporadically" 

Compare energy deals: Only the first time 

Compares energy deals from internet providers only "the first time I needed to choose a provider" 

Compare energy deals: Never 

Never compared energy deals 

Compare internet deals: When need to renew 

Compares deals from internet providers "but only when I need to renew my contract" 

Compare internet deals: Sporadically 

Compares deals from internet providers "but only sporadically" 

Compare internet deals: Only the first time 

Compares deals from internet providers only "the first time I needed to choose a provider" 

Compare internet deals: Never 

Never compared deals from internet providers 

Compare internet deals: No response 

Did not answer the question on frequency of comparing internet deals 

Compares deals "from time to 
time"  

Compares deals "from time to 
time" 

77 

 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
3 │ Conceptualisation of a theoretical framework and operationalisation of vulnerability 

Table 7: 

Explanatory variables included in regressions 

Name/label 

Description 

Compare deals from banks: When notified 

Compares deals from banks "but only when I am notified that the conditions of my contract will be changed" 

Compare deals from banks: Sporadically 

Compares deals from banks "but only sporadically" 

Compare deals from banks: Only the first time 

Compares deals from banks only "the first time I needed to choose a bank" 

Compare deals from banks: Never 

Never compared deals from banks 

Know energy contract: Not at all 

Does not know energy contract conditions "at all" 

Know energy contract: Not very 

Does not know energy contract conditions "very much" 

Know energy contract: Fair amount 

Knows energy contract conditions a "fair amount" 

Know energy contract/Read bill: No response 

Does not have a gas or electricity contract (so no response to question on knowledge of contract/read last 
bill) 

Know internet contract conditions: Not at all 

Does not know internet contract conditions "at all" 

Know internet contract conditions: Not very 

Does not know internet contract conditions "very much" 

Know internet contract conditions: Fair amount 

Knows internet contract conditions a "fair amount" 

Know internet contract/read communication: 
No response 

Does not have internet contract (so no response to question on knowledge of contract/read communication) 

Know bank contract conditions: Not at all 

Does not know bank contract conditions "at all" 

Know bank contract conditions: Not very 

Does not know bank contract conditions "very much" 

Know bank contract conditions: Fair amount 

Knows bank contract conditions a "fair amount" 

Base category 

Compares deals "from time to 
time" 

Know contract conditions 
completely 

Know contract conditions 
completely 

Know contract conditions 
completely 

Know bank contract/read communication: No 
response 

Does not have bank contract (so no response to question on knowledge of contract/read communication) 

Read energy bill: Glanced or skim read 

Read last bill from energy supplier, but "glanced over it or skim read it" 

Read last bill in detail 

Read energy bill: Looked at total price 

Read last bill from energy supplier, but "only looked at total price" 

Read energy bill: Not at all 

Read energy bill: Don’t know 

Read energy bill: No response 

Did not read last bill from energy supplier 

Does not know or remember if read last bill from energy supplier 

Does not have a gas or electricity contract  

Read internet communication: Glanced or skim 
read 

Read last internet provider communication, but "glanced over it or skim read it" 

Read communication in detail 

Read internet communication: Saw what it was 

Read last internet provider communication, but "only saw what it was (i.e. looked at the numbers)" 

Read internet communication: Not read it at all 

Did not read last internet provider communication "at all" 

Read internet communication: Don’t know 

Does not know or remember if read last internet provider communication 

Read internet communication: No response 

Does not have internet contract  

Read bank communication: Glanced or skim 
read 

Read last bank communication, but "glanced over it or skim read it" 

Read communication in detail 

Read bank communication: Saw what it was 

Read last bank communication and "only saw what it was (i.e. looked at the numbers)" 

Read bank communication: Not read it at all 

Did not read last bank communication "at all" 

78 

 
 
  
  
  
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
3 │ Conceptualisation of a theoretical framework and operationalisation of vulnerability 

Table 7: 

Explanatory variables included in regressions 

Name/label 

Description 

Read bank communication: Don't know 

Does not know or remember if read last bank communication 

Read bank communication: No response 

Does not have bank contract 

Read energy bill: Easy 

Read energy bill: Difficult 

Reading last bill from energy supplier was easy 

Reading last bill from energy supplier was difficult 

Read energy bill: Very difficult 

Reading last bill from energy supplier was very difficult 

Base category 

Very easy 

Did not read energy bill 

Did not read the last bill from the energy supplier in detail, glanced over or skim read it (or has no energy 
supplier) 

Read internet communication: Easy 

Reading last internet provider communication was easy 

Very easy 

Read internet communication: Difficult 

Reading last internet provider communication was difficult 

Read internet communication: Very difficult 

Reading last internet provider communication was very difficult 

Did not read internet communication 

Did not read the internet provider communication in detail, glanced over or skim read it (or has no internet 
provider) 

Read bank communication: Easy 

Reading last bank communication was easy 

Read bank communication: Difficult 

Reading last bank communication was difficult 

Read bank communication: Very difficult 

Reading last bank communication was very difficult 

Very easy 

Did not read bank communication 

Did not read the bank communication in detail, glanced over or skim read it (or has no bank account) 

Access-related drivers 

Monthly internet activities: 7 

Monthly internet activities: 6 

Monthly internet activities: 5 

Monthly internet activities: 4 

Monthly internet activities: 3 

Monthly internet activities: 2 

Monthly internet activities: 1 

Uses internet at least once per month for 7 out of 7 activities (Search, Price comparison, Online banking , 
Online purchases, Online sales, Social network,  Email) 

Zero monthly internet activities 

Uses internet at least once per month for 6 out of 7 activities (Search, Price comparison, Online banking , 
Online purchases, Online sales, Social network,  Email) 

Uses internet at least once per month for 5 out of 7 activities (Search, Price comparison, Online banking , 
Online purchases, Online sales, Social network,  Email) 

Uses internet at least once per month for 4 out of 7 activities (Search, Price comparison, Online banking , 
Online purchases, Online sales, Social network,  Email) 

Uses internet at least once per month for 3 out of 7 activities (Search, Price comparison, Online banking , 
Online purchases, Online sales, Social network,  Email) 

Uses internet at least once per month for 2 out of 7 activities (Search, Price comparison, Online banking , 
Online purchases, Online sales, Social network,  Email) 

Uses internet at least once per month for 1 out of 7 activities (Search, Price comparison, Online banking , 
Online purchases, Online sales, Social network,  Email) 

Frequent internet use: Search 

Uses internet at least once a month for: Online search 

Frequent internet use: Compare prices 

Uses internet at least once a month for: Price comparison 

Frequent internet use: Banking 

Uses internet at least once a month for: Online banking 

Frequent internet use: Purchases 

Uses internet at least once a month for: Online purchases 

Frequent internet use: Selling 

Uses internet at least once a month for: Online sales 

Does not... 

Does not... 

Does not... 

Does not... 

Does not... 

79 

 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
3 │ Conceptualisation of a theoretical framework and operationalisation of vulnerability 

Table 7: 

Explanatory variables included in regressions 

Name/label 

Frequent internet use: Social 

Frequent internet use: Email 

Situational drivers 

Employed part-time 

Self-employed full-time 

Self-employed part-time 

Unemployed & looking 

Unemployed & not looking 

Long-term sick or disabled 

Housewife/Homemaker 

Retired 

In full time education 

Studying with part-time job 

Description 

Uses internet at least once a month for: Social network 

Uses internet at least once a month for: Email 

Employed part-time 

Self-employed full-time 

Self-employed part-time 

Unemployed & looking 

Unemployed & not looking 

Long-term sick or disabled 

Housewife/Homemaker 

Retired 

In full time education 

Studying with part-time job 

Unemployed & looking 5+ years 

Has been unemployed & looking for work for at least 5 years 

Unemployed & not looking 5+ years 

Has been unemployed & not looking for at least 5 years 

Long-term sick or disabled 5+ years 

Has been long-term sick or disabled for at least 5 years 

Housewife/Homemaker 5+ years 

Has been a housewife or homemaker for at least 5 years 

Retired 5+ years 

Remarried 

Living with a partner 

Single 

Divorced or separated 

Widowed 

Other 

Single parent 

1 dependent child 

2 dependent children 

3+ dependent children 

Single person household 

Friends buy online 

Friends buy on credit 

Friends can’t make ends meet 

Has been retired for at least 5 years 

Marital status: remarried 

Marital status: not married living with a partner 

Marital status: single 

Marital status: divorced or separated 

Marital status: widowed 

Marital status: other 

Is a single parent 

One child in the household  

Two children in the household 

Three or more children in the household 

Only member of the household 

Base category 

Does not... 

Does not... 

Employed full-time 

Not... 

Not... 

Not... 

Not... 

Not... 

Married 

Not... 

No dependent children 

Agree or strongly agree that "Most of my friends and relatives buy goods and services online" 

Agree or strongly agree that "Most of my friends and relatives buy goods and services on credit" 

Agree or strongly agree that "Most of my friends and relatives find it difficult to make ends meet every 
month" 

Does not... 

Does not... 

Does not... 

Making ends meet: Fairly easy 

Making ends meet is fairly easy 

Making ends meet is easy 

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3 │ Conceptualisation of a theoretical framework and operationalisation of vulnerability 

Table 7: 

Explanatory variables included in regressions 

Name/label 

Description 

Making ends meet: Fairly difficult 

Making ends meet is fairly difficult 

Making ends meet: Very difficult 

Making ends meet is very difficult 

Making ends meet: Prefer not to say 

Prefers not to answer the statement 

Base category 

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4 │ Incidence of consumer vulnerability 

4.  INCIDENCE OF CONSUMER VULNERABILITY 

Box 2:  Summary of Chapter 4 

The  present  chapter  reviews  the  incidence  of  a  number  of  vulnerability  indicators 
reflecting 5 different vulnerability dimensions, namely: 

  Dimension 1: Heightened risk of negative outcomes or impacts on well-being. 
  Dimension 2: Having characteristics that limit ability to maximise well-being. 
  Dimension 3: Having difficulty in obtaining or assimilating information. 
  Dimension 4: Inability or failure to buy, choose or access suitable products. 
  Dimension 5: Higher susceptibility to marketing practices. 

Each dimension regroups a number  of vulnerability indicators which were populated 
by  the  responses  from  the  consumer  survey  and  the  results  of  the  consumer 
experiments run as part of the present study. The indicators used in the first part of 
the chapter to identify the aspects  of vulnerability with a particularly high incidence 
across the EU28 and then examine the extent to which such incidence of vulnerability 
varies across the EU28. 

The  second  part  of  the  chapter  examines  whether  individuals  who  are  vulnerable 
according  to  one  dimension  of  vulnerability  are  also  vulnerable  according  to  an 
additional  dimensions  or  perhaps  even  according  to  several  other  dimension.  The 
purpose of such an analysis is to gain a deeper understanding of the extent to which 
vulnerability has multiple facets or is more linked to one particular factor. 

The main findings of the incidence of vulnerability in the EU28 are that  

1.  The highest vulnerability incidence rates are observed EU-wide for the 
indicators  of  dimension  5  of  vulnerability,  namely  “Higher  susceptibility 
to marketing practices”. These indicators reflect the inability of consumers of 
choosing  the  optimal  deal  in  the  experiments  and  show  an  incidence  rate  of 
53%  in  the  online  sector,  66%  in  the  cross-cutting  sector  and  85%  in  the 
energy  sector.  As  not  all  consumers  face  the  particular  marketing  situations 
tested in the experiments, these incidence rates are likely to be a somewhat 
upward  biased  estimate  of  the  actual  incidence  of  dimension  5  vulnerability 
among the overall population. 

2.  The  second  highest  incidence  rates  are  observed  in  the  case  of 
dimension  4  vulnerability.  In  particular,  vulnerability  arises  because 
consumers  do  not  compare  deals  (57%  of  consumers  in  the  finance  sector 
and 52% in the energy sector) or have problems comparing deals because of 
market-related  factors  or  personal  factors.  Overall,  almost  75%  of  EU28 
consumers exhibit at least one such dimension vulnerability. 

3.  Individual 

indicators 

in  Dimension  2,  namely 

individual  personal 
characteristics, do not, individually, show particularly high vulnerability 
incidence  rates.  However,  overall,  about  23%  of  consumers  are  vulnerable 
on  the  basis  of  one  or  several  personal  characteristics.  Among  the  personal 
characteristics,  financial  and  employment  circumstances  show  the  highest 
incidence rate. 

4.  The  incidence  of  dimension  1  and  3  vulnerability  indicators  is 
relatively  low,  generally  10%  or  less  and  never  exceeds  15%.  The  only 
exceptions  are  the  dimension  3  indicators  of  “having  problems  comparing 
deals  due  to  information-related  factors”,  especially  in  the  energy  sector, 

82 

 
 
4 │ Incidence of consumer vulnerability 

where the EU28 average incidence rate is 15%. 

With  regards  to  the  variation  in  the  incidence  of  vulnerability  across  the  EU28,  it  is 
worth  noting  that  among  the  54  vulnerability  indicators  considered,  4  indicators 
based  on  the  survey  results  show  considerable  cross-country  variation  in  incidence 
rates. These relate to having problems comparing deals in the energy  sector due to 
access related factors; finding it difficult comparing deals in the online sector; having 
problems  comparing deals because of access  related factors in the online sector; or 
using only information from advertisements to compare deals in the online sector. 

Overall, both the analysis of the incidence of  vulnerability across the EU as  a whole 
and  of  the  variation  of  such  vulnerability  across  EU  Member  States  highlights  that 
marketing  practices,  lack  of  use  of  information  or  imperfect  information  and  access 
issues are the most frequent causes of vulnerability. 

Moreover, the analysis of the variation of the incidence of vulnerability across the EU 
also  shows  that  Member  States  typically  exhibit  a  combination  of  higher-than-EU28 
and  lower-than-EU28  incidence  rates  across  the  different  vulnerability  indicators 
without any systematic pattern. 

However,  Austria,  the  Czech  Republic,  Estonia,  Finland,  Germany,  Poland  and  the 
United  Kingdom  never  show  rates  of  vulnerability  that  are  much  higher  than  the 
EU28  average,  on  any  of  the  indicators.  On  the  other  hand,  Belgium,  Cyprus, 
Hungary,  Italy,  Latvia  and  Spain  never  show  incidence  rates  that  are  much  lower 
than the EU28 average. 

Moreover, Germany, the Netherlands and Norway are the countries which most often 
show  a  markedly  lower  vulnerability  incidence  than  the  EU-28  average,  while  the 
opposite is true for Cyprus and, to a somewhat lesser extent, Croatia and Romania. 

In terms of the multi-dimensionality of vulnerability in the EU28, the analysis shows 
that  the  dimension  4  vulnerability  is  the  one  most  frequently  experienced  by  EU28 
consumers.  Almost  43%  of  EU  consumers  exhibit  some  type  dimension  4 
vulnerability  without  any  other  type  of  vulnerability  and  another  30%  of  consumers 
experience a combination of dimension 4 and other dimension vulnerabilities. 

The  second  most  frequently  experienced  vulnerability  is  dimension  2,  with  23%  of 
consumers  exhibiting such vulnerability (under dimension 2 alone or in combination 
with another dimension). 

The present chapter provides an overview of the incidence of consumer vulnerability in 
the  EU28  based  on  a  descriptive  statistical  analysis  of  those  vulnerability  indicators 
operationalised  in  section  3.6  of  the  previous  chapter,  using  data  from  the  consumer 
survey and the results from the experiments.36  

The first section of the chapter discusses the incidence of consumer vulnerability in the 
EU28,  in  individual  Member  States,  and  in  Norway  and  Iceland.  Such  discussion 

36  The  present  chapter  only  utilises  the  results  of  the  experiment  with  the  marketing  practice 
treatment,  and  only  in  the  form  of  four  indicators  of  vulnerability  –  indicators  55  to  58, 
‘Experiment choice under the marketing practice treatment [...]’, as defined in Table 5. 

83 

 
 
 
                                                 
4 │ Incidence of consumer vulnerability 

proceeds  indicator  by  indicator  for  each  of  the  vulnerability  dimensions.  The  second 
section  provides  a  descriptive  analysis  of  the  incidence  of  consumer  vulnerability 
across multiple dimensions of vulnerability, and the third section provides a summary 
of the key findings from the present chapter. 

For each indicator, the incidence of consumer vulnerability is defined as the proportion 
of  consumers  who  are  classified  as  `vulnerable’.  In  other  words,  in  the  present 
chapter,  a  binary  scale  is  used  whereby  each  survey  respondent  or  experiment 
participant is classified as being either ‘vulnerable’ or ‘not vulnerable’ according to the 
respective  survey  question.  Therefore,  the  indicators  listed  in  Table  5 which  are  scale 
indicators37  and  are  analysed  in  subsequent  chapters  of  the  report  are  only  used  in  a 
summary binary form in the current chapter. 

Furthermore,  six  additional  indicators  have  been  created  as  part  of  Dimension  4 
(inability  or  failure  to  buy,  choose  or  access  suitable  products)  specifically  for  the 
purposes of the present chapter38, namely: 

  Does not compare product deals - energy sector; 
  Finds it very difficult to compare product deals - energy sector; 
  Does not compare product deals - online sector; 
  Finds it very difficult to compare product deals - online sector; 
  Does not compare product deals - finance sector; and 
  Finds it very difficult to compare product deals - finance sector. 

These  additional  indicators  are  based  on  the  questions  preceding  those  used  in 
indicators 29-37 (‘Has problems comparing deals due to...’), and therefore capture the 
whole  survey  sample39,  which  allows  for  the  comparison  of  vulnerability  across 
dimensions presented in section 4.2.40 

A  similar  reasoning  has  required  the  re-design  of  a  number  of  indicators  in  order  to 
more accurately capture the vulnerability incidence in the population: 

 

Indicator  ‘1.  Unassertive  (took  no  action)  when  experienced  a  problem’ 
considers  only  consumers  who  have  experienced  a  problem.  However,  in  the 
survey  not  the  whole  sample  indicated  having  experienced  problems  when 
purchasing or using goods or services.  Therefore, to  extrapolate the  incidence 
of vulnerability to the whole sample, another version of this indicator has been 
created for the analysis in Chapter 4, namely ‘Has experienced a problem and 

37 These scale indicators are: 3. ‘Perception of own vulnerability due to personal characteristics’; 
12. ‘Does not feel informed about prices etc.’; 52. ‘Overall  performance  in the  experiments 
(irrespective of treatment)’; 53. ‘Perception of own vulnerability due to complexity of offers’; 
55.  ‘Experiment  choice  under  the  marketing  practice  treatment  -  energy  sector’;  56. 
‘Experiment  choice  under  the  marketing  practice  treatment  -  online  sector’;  and  57. 
‘Experiment choice under the marketing practice treatment - finance sector’. 

38 These additional indicators have also been used in the cluster analysis presented in Annex 7. 
39  The respective indicators for the online sector  (25 and 26) do not capture the whole sample 
due to survey mode restrictions. Hence, only the energy- and finance-related indicators (23 
and 24, and 27 and 28, respectively)  have  been used  for the cross-dimensional analysis of 
vulnerability. 

40  As  indicators  29-37  are  closer  aligned  with  those  conceptualised  in  the  operationalisation  of 
vulnerability,  they  have  been  analysed  whenever  cross-dimensional  comparison  is  not  the 
topic of interest. 

84 

 
 
                                                 
4 │ Incidence of consumer vulnerability 

took no action’. This version of the indicator includes all survey respondents as 
the reference base. 

  The  incidence  of  vulnerability  for  indicators  `14.  Compares  product  deals  by 
using  information  from  advertisements  only  -  energy  sector’;  `15.  Compares 
product  deals  by  using  information  from  advertisements  only  -  online  sector’; 
and  `16.  Compares  product  deals  by  using  information  from  advertisements 
only - finance sector’ has also been calculated to include all respondents as the 
reference  population,  rather  than  only  those  respondents  who  have  compared 
deals.41 

Different approaches to weighting the survey results have been adopted in the present 
chapter:42 

  When presenting country-level results, results have been weighted using within 
country  weights,  which  ensure  that  the  sample  is  representative  of  each 
country’s population in terms of its demographic composition43; 

  When comparing country-level results, the comparison has been done based on 
a  simple  arithmetic  average  of  country-level  results,  which  have  been 
computed using the within country weights;  

  When  discussing  EU28-level  results  (or  the  overall  results  based  on  a  sub-set 
of  countries),  a  set  of  cross-country  weights  has  been  used  which  not  only 
accounts  for  the  demographic  composition  of  each  country,  but  also  for  the 
differences in population levels between countries. 

However, when the EU28 average across countries is reported, only the within country 
weights are used. 

4.1. Incidence of various vulnerability dimensions in the EU28 and Norway and 

Iceland 

The  present  section  discusses  the  incidence  of  consumer  vulnerability  in  the  EU28,  in 
individual  Member  States  and  Iceland  and  Norway.  First,  the  analysis  focuses  on  the 
EU28-level of incidence and then takes a closer look at the variation in the incidence of 
vulnerability  across  countries  and  identifies  countries  which  appear  to  be  outliers  in 
terms  of  vulnerability  incidence.  The  second  part  of  the  chapter  then  proceeds  to 
investigate the incidence of vulnerability at Member State level by indicator in each of 
the five vulnerability dimensions.44 

4.1.1.  Analysis of overall incidence of vulnerability – all vulnerability 
dimensions EU28 and Norway and Iceland 

The present sub-section provides for each of the indicators a snapshot of the share of 
vulnerable consumers in the EU28 population, and an overview of the extent to which 
vulnerability incidence varies across Member States.   

41  The  ‘Gets  information  from  adverts  only’  versions  of  these  indicators  are  based  only  on 

respondents who have compared deals. 

42 Unless otherwise specified. 
43  In  terms  of  gender,  age,  urbanisation  rate,  region,  work  status  and  educational  attainment. 

For further details on the construction of weights, see section A2.9 in Annex 2. 

44 The indicators and dimensions are defined in Chapter 3. 

85 

 
 
 
                                                 
4 │ Incidence of consumer vulnerability 

A  key  finding  of  such  EU-wide  analysis  is  that  the  incidence  of  vulnerability  varies 
greatly across vulnerability indicators (Figure 7). 

  The  group  of  vulnerability  indicators  with  the  highest  incidence  rates  includes 
indicators of Dimension 5, ‘Higher susceptibility to marketing practices’. These 
indicators  reflect  the  results  from  the  experiments  under  the  marketing 
practice  treatment.  The  inability  of  experiment  participants  to  choose  the 
optimal  deal  under  a  current  marketing  practice  results  in  a  vulnerability 
incidence  rate  of  85%  in  the  energy  sector,  66%  in  the  cross-cutting 
experiment and 53% in the online sector.   

 

 

It  should  be  noted,  however,  that  such  high  incidence  is  influenced  by 
the fact that the marketing practices considered in the experiment were 
chosen  because  they  are  known  to  be  particularly  problematic  in  the 
respective  sector  (as  elaborated  in  section  2.4).  Therefore,  one  could 
expect  that  consumers  find  it  difficult  to  make  the  optimal  choice  in 
either  round  of  the  experiment  and,  therefore,  are  classified  as 
vulnerable.  
In  addition,  the  experiment-based  indicators  reflect  the  incidence  of 
vulnerability  amongst  consumers  who  face  the  particular  marketing 
practices  considered  in  the  experiment.  However,  in  reality,  not  all 
consumers  face  the  particular  marketing  practice.  Whilst  certain 
vulnerability indicators from other dimensions45 incorporate a ‘correction’ 
to  address  the  fact  that  a  particular  aspect  of  vulnerability  may  not  be 
experienced by the whole population, it is not possible to do so with the 
experiment-based indicators.   

  The  second-highest  incidence  of  vulnerability  is  observed  in  the  indicators 
related  to  Dimension  4,  in  particular  those  which  relate  to  the  comparison  of 
deals. Vulnerability arises because either: 

  Consumers  do  not  compare  deals  (57%  of  survey  respondents  in  the 
case of financial products and 52% in the case of energy products); or 
  Consumers  have  problems  comparing  deals  because  of  market-related 

factors (33% in the case of both financial and energy products); or, 
  Consumers  have  problems  comparing  deals  because  of  personal  factors 
(31%  in  the  case  of  financial  products  and  28%  in  the  case  of  energy 
products). 

  Other  Dimension  4  vulnerability  indicators  showing  relatively  high  incidence 
rates  are  having  problems  comparing  deals  online  due  to  market-related 
factors (23% of survey respondents) and lack of switching because of bundling 
(22%). 

  While each of the personal characteristics (Dimension 2) is not associated with 
a  particularly  high  incidence  of  vulnerability,  in  total  23%  of  the  survey 
respondents show vulnerability for at least one of the personal characteristics. 

  The incidence of vulnerability indicators of dimensions 1, 3, and 5 is relatively 

low. It never exceeds 15% and often remains well below 10%. 

Besides  providing  information  on  the  incidence  rate  of  the  various  vulnerability 
indicators in the EU28 (as well as the EU and Norway and Iceland)46, Table 8 overleaf 

45 Indicators 1, 14, 15 and 16 (see Table 5), as described at the beginning of the chapter. 
46 Weighted by cross-country weights. 

86 

 
 
                                                 
4 │ Incidence of consumer vulnerability 

also  shows,  for  each  indicator,  the  minimum,  maximum,  median,  mean  and  standard 
deviation of the distribution of the Member States incidence rates.47 

The EU28 incidence rate differs slightly from the mean of the incidence rates across the 
EU28 Member States as the latter measure is a simple (i.e. unweighted) average of the 
incidence in each Member State48 while the former measure is implicitly a population-
weighted average (as the individual responses have been weighted to take account of 
country  population-size  differences).  The  mean  of  the  Member  States  incidence  rates 
has  been  computed  for  the  purpose  of  between-country  comparison  of  vulnerability 
incidence rates. 

47 Based on country-level incidence results,  which have been weighted by country demographic 

weights but not by cross-country population weights.  

48 Weighted by country weights. 

87 

 
 
 
                                                 
4 │ Incidence of consumer vulnerability 

Figure 7:  

Incidence of various vulnerability indicators – EU28 

Note: Results weighed using cross-country weights. Sample size varies between indicators. For more details, 
see  Table  8. Incidence of  each  indicator  is calculated  as  a proportion of  all respondents who  answered  the 
relevant survey question, with the exception of ‘Has experienced problems and took no action’ and the three 
sector indicators ‘Compares product deals by using information from advertisements only’, which have been 
calculated as a percentage of the whole sample. 
Source: London Economics 

88 

10%11%5%14%11%5%3%4%4%23%4%2%4%2%15%8%10%2%0%1%52%14%31%4%57%13%28%16%31%33%23%33%10%4%2%10%3%7%11%6%13%12%3%2%16%22%19%15%4%10%85%53%27%66%Has experienced a problem and took no action (n=25,151)Overpaid for services due to being unable to use certain payment methods (n=25,151)Feels vulnerable to 'a great extent' due to health problems (n=25,151)Feels vulnerable to 'a great extent' due to financial circumstances (n=25,151)Feels vulnerable to 'a great extent' due to employment situation (n=25,151)Feels vulnerable to 'a great extent' due to age (n=25,151)Feels vulnerable to 'a great extent' due to belonging to a minority group (n=25,151)Feels vulnerable to 'a great extent' due to personal issues (n=25,151)Feels vulnerable to 'a great extent' due to other reasons (n=25,151)Feels vulnerable to 'a great extent' due to any personal characteristic (n=25,151)Feels 'not at all' informed ... when choosing and buying goods and services (n=25,151)Compares product deals by using information from advertisements only -energy sector (n=14,074)Compares product deals by using information from advertisements only -online sector  (n=18,506)Compares product deals by using information from advertisements only -finance sector  (n=14,074)Has problems comparing deals due to information-related factors -energy sector (n=14,074)Has problems comparing deals due to information-related factors -online sector (n=18,506)Has problems comparing deals due to information-related factors -finance sector (n=14,074)Has an electricity supplier and has not switched due to information-related factors -energy sector (n=4,782)Has an internet provider and has not switched due to information-related factors -online sector (n=3,483)Has a bank account and has not switched due to information-related factors -financial sector (n=4,368)Does not compare product deals -energy sector (n=25,151)Finds it very difficult to compare product deals -energy sector (n=25,136)Does not compare product deals –online sector (n=18,506)Finds it very difficult to compare product deals -online sector (n=18,506)Does not compare product deals -finance sector (n=25,151)Finds it very difficult to compare product deals -finance sector (n=25,151)Has problems comparing deals due to personal factors -energy sector (n=14,074)Has problems comparing deals due to personal factors -online sector (n=18,506)Has problems comparing deals due to personal factors -financial sector (n=14,074)Has problems comparing deals due to market-related factors -energy sector (n=14,074)Has problems comparing deals due to market-related factors -online sector (n=18,506)Has problems comparing deals due to market-related factors -financial sector (n=14,074)Has problems comparing deals due to access-related factors -energy sector (n=14,074)Has problems comparing deals due to access-related factors –online sector (n=18,506)Has problems comparing deals due to access-related factors -financial sector (n=14,074)Has an electricity supplier and has not switched due to personal factors -energy sector (n=4,782)Has an internet provider and has not switched due to personal factors -online sector (n=3,483)Has a bank account and has not switched due to personal factors -financial sector (n=4,368)Has an electricity supplier and has not switched due to market-related factors -energy sector (n=4,782)Has an internet provider and has not switched due to market-related factors -online sector (n=3,483)Has a bank account and has not switched due to market-related factors -financial sector (n=4,368)Has an electricity supplier and has not switched due to access-related factors -energy sector (n=4,782)Has an internet provider and has not switched due to access-related factors -online sector (n=3,483)Has a bank account and has not switched due to access-related factors -financial sector (n=4,368)Has not switched due to termination costs (n=25,151)Has not switched due to bundling (n=25,151)Excluded from e-commerce due to difficulty of the process (n=18,550)Excluded from e-commerce due to not having a payment card (n=18,550)Declined for a loan (n=4,971)Vulnerable to a great extent due to complexity of offers (own perception) (n=25,151)Experiment choice under the marketing practice treatment -energy sector (n=1,263)Experiment choice under the marketing practice treatment -online sector (n=1,254)Experiment choice under the marketing practice treatment -finance sector (n=1,252)Experiment choice under the marketing practice treatment -cross-cutting (n=1,282)Dimension 1Dimension 5Dimension 3Dimension 4Dimension 2 
 
 
 
4 │ Incidence of consumer vulnerability 

Table 8: Distribution of incidence of various indicators of vulnerability across EU28 Member States and IS and NO 
Outliers 
- Low - 
EU28 
MSs + 
NO and 
IS 

Outliers 
- High - 
EU28 
MSs + 
NO and 
IS 

Normalised. 
standard 
deviation - 
EU28 MSs 

Incidence 
- EU28 + 
NO and 
IS 

Standard 
deviation 
- EU28 
MSs 

Maximum 
- EU28 
MSs 

Minimum 
- EU28 
MSs 

Mean 
-
EU28 
MSs 

Median 
- EU28 
MSs 

Incidence 
- EU28 

Observations 
EU28 total 

Dimension 1 : Heightened risk of negative outcomes or impacts on well-being 

1. Has experienced a problem and 
took no action 

10% 

10% 

5% 

28% 

10% 

11% 

6% 

51% 

BG, 
HU, 
RO 

FR, NL 

25,151 

2. Overpaid for services due to being 
unable to use certain payment 
methods 
Dimension 2 : Having characteristics that limit ability to maximise well-being 

11% 

11% 

23% 

6% 

12% 

13% 

5% 

38% 

CY, IE, 
PL, RO 

CZ, 
FR, NL 

25,151 

4. Feels vulnerable to 'a great  extent' 
due to health problems 

5% 

5% 

2% 

11% 

5% 

5% 

2% 

44% 

14% 

14% 

5% 

35% 

18% 

16% 

8% 

51% 

HR, 
CY, 
LV, PT 

CY, 
EL, LT 

CY, 
EL, ES 

BG, 
CY, 
RO 

LU, IS 

25,151 

AT, 
CZ, 
DE, 
LU, 
NL, IS, 
NO 
AT, 
CZ, 
DE, 
LU, 
NL, IS, 
NO 

25,151 

25,151 

NO 

25,151 

11% 

11% 

2% 

27% 

12% 

12% 

6% 

55% 

5% 

5% 

2% 

13% 

5% 

5% 

3% 

50% 

3% 

4% 

3% 

4% 

1% 

2% 

10% 

3% 

3% 

2% 

55% 

CY, PT 

NO 

25,151 

8% 

4% 

4% 

2% 

40% 

CY, IT, 
PT 

AT, 
CZ, 

25,151 

5. Feels vulnerable to 'a great  extent' 
due to financial circumstances 

6. Feels vulnerable to 'a great  extent' 
due to employment situation 

7. Feels vulnerable to 'a great  extent' 
due to age 
8. Feels vulnerable to 'a great  extent' 
due to belonging to a minority group 
9. Feels vulnerable to 'a great  extent' 
due to personal issues 

89 

 
 
 
 
4 │ Incidence of consumer vulnerability 

Incidence 
- EU28 

Minimum 
- EU28 
MSs 

Incidence 
- EU28 + 
NO and 
IS 

Table 8: Distribution of incidence of various indicators of vulnerability across EU28 Member States and IS and NO 
Outliers 
- Low - 
EU28 
MSs + 
NO and 
IS 
NL, IS, 
NO 
AT, 
CZ, 
IS, NO 
AT, 
CZ, 
DE, 
LU, 
NL, IS, 
NO 

10.  Feels  vulnerable  to 
extent' due to other reasons 

Outliers 
- High - 
EU28 
MSs + 
NO and 
IS 

Normalised. 
standard 
deviation - 
EU28 MSs 

Standard 
deviation 
- EU28 
MSs 

Maximum 
- EU28 
MSs 

Mean 
-
EU28 
MSs 

'a  great 
personal 

Median 
- EU28 
MSs 

CY, 
EL, RO 

HR, 
CY, 
RO 

'a  great 

44% 

23% 

23% 

10% 

47% 

27% 

25% 

11% 

42% 

4% 

3% 

2% 

8% 

3% 

4% 

2% 

to 

Observations 
EU28 total 

25,151 

25,151 

1% 

4% 

3% 

11.  Feels  vulnerable  to 
extent' 
any 
due 
characteristic 
Dimension 3 : Having difficulty in obtaining or assimilating information 
13. Feels 'not at all' informed about 
product price, quality, conditions, etc 
when choosing and buying goods and 
services 
14. Compares product deals by using 
information from advertisements only 
- energy sector*  
15. Compares product deals by using 
information from advertisements only 
- online sector  
16. Compares product deals by using 
information from advertisements only 
- finance sector * 
17. Has problems comparing deals 
due to information-related factors - 
energy sector* 
18. Has problems comparing deals 
due to information-related factors - 

15% 

15% 

2% 

4% 

2% 

1% 

2% 

4% 

1% 

1% 

2% 

3% 

8% 

8% 

9% 

6% 

5% 

29% 

16% 

10% 

18% 

3% 

4% 

2% 

57% 

BE, 
HR, 
CY, LT 

- 

25,151 

3% 

3% 

1% 

49% 

PT 

IS 

14,074 

4% 

6% 

4% 

68% 

CY, LU 
LV, MT 

- 

18,506 

2% 

2% 

1% 

50% 

LU 

SE, NL 

14,074 

16% 

17% 

6% 

36% 

7% 

7% 

3% 

41% 

FR, 
LU, 
RO, IS 
HR, 
LU, 

DE 

14,074 

BG, LT 

18,506 

90 

 
 
 
4 │ Incidence of consumer vulnerability 

Table 8: Distribution of incidence of various indicators of vulnerability across EU28 Member States and IS and NO 
Outliers 
- Low - 
EU28 
MSs + 
NO and 
IS 

Normalised. 
standard 
deviation - 
EU28 MSs 

Incidence 
- EU28 + 
NO and 
IS 

Standard 
deviation 
- EU28 
MSs 

Maximum 
- EU28 
MSs 

Minimum 
- EU28 
MSs 

Mean 
-
EU28 
MSs 

Median 
- EU28 
MSs 

Incidence 
- EU28 

Observations 
EU28 total 

Outliers 
- High - 
EU28 
MSs + 
NO and 
IS 
ES, IS 

7% 

10% 

10% 

online sector 
19. Has problems comparing deals 
due to information-related factors - 
finance sector* 
20. Has an electricity supplier and has 
not switched due to information-
related factors - energy sector** 
21. Has an internet provider and has 
not switched due to information-
related factors - online sector** 
22. Has a bank account and has not 
switched due to information-related 
factors - financial sector** 
Dimension 4 : Inability or failure to buy, choose or access suitable products 

19% 

0% 

2% 

1% 

1% 

2% 

0% 

1% 

1% 

3% 

0% 

0% 

2% 

12% 

12% 

3% 

24% 

DK, IS  LT, UK 

14,074 

2% 

2% 

1% 

43% 

0% 

0% 

0% 

95% 

1% 

1% 

1% 

67% 

- 

- 

- 

- 

- 

- 

4,782 

3,483 

4,368 

23. Does not compare product deals - 
energy sector 

52% 

52% 

33% 

87% 

53% 

57% 

17% 

30% 

24. Finds it very difficult to compare 
product deals - energy sector 

14% 

14% 

7% 

47% 

16% 

17% 

9% 

52% 

25. Does not compare product deals – 
online sector 

31% 

31% 

12% 

53% 

30% 

31% 

12% 

39% 

BG, 
CY, 
FR, 
LT, 
LU, 
MT, 
RO, IS 
BG, 
CY, 
MT, IS 
BE, 
LU, 
MT, 
SE, 
NL, 
IS, NO 

DE, 
IE, SI, 
UK 

25,151 

DE 

25,136 

BG, 
HR, 
EL, PT, 
SI 

18,506 

91 

 
 
 
 
Table 8: Distribution of incidence of various indicators of vulnerability across EU28 Member States and IS and NO 
Outliers 
- Low - 
EU28 
MSs + 
NO and 
IS 

Normalised. 
standard 
deviation - 
EU28 MSs 

Incidence 
- EU28 + 
NO and 
IS 

Standard 
deviation 
- EU28 
MSs 

Maximum 
- EU28 
MSs 

Minimum 
- EU28 
MSs 

Mean 
-
EU28 
MSs 

Median 
- EU28 
MSs 

Incidence 
- EU28 

Observations 
EU28 total 

4% 

4% 

0% 

17% 

3% 

4% 

4% 

81% 

57% 

57% 

43% 

73% 

60% 

58% 

7% 

13% 

13% 

13% 

7% 

23% 

13% 

13% 

5% 

39% 

28% 

29% 

17% 

49% 

34% 

32% 

8% 

25% 

16% 

16% 

6% 

31% 

17% 

17% 

6% 

35% 

Outliers 
- High - 
EU28 
MSs + 
NO and 
IS 
CY, 
MT, IS 
FR, 
LT, 
RO, 
NL, IS 

HR, 
CY, IT, 
MT, PT 

FR, 
LU, 
SK, IS 
BE, 
CY, 
LU, 
SE, 
IS, NO 

18,506 

25,151 

25,151 

14,074 

AT, 
CZ, 
PL, 
SK, SI 
CZ,  
EE, 
DE, 
NO 

DE, 
IE,  

BG, 
EL, PT 

18,506 

4 │ Incidence of consumer vulnerability 

26. Finds it very difficult to compare 
product deals - online sector 

27. Does not compare product deals - 
finance sector 

28. Finds it very difficult to compare 
product deals - finance sector 

29. Has problems comparing deals 
due to personal factors - energy 
sector* 

30. Has problems comparing deals 
due to personal factors - online sector 

31. Has problems comparing deals 
due to personal factors - financial 
sector* 
32. Has problems comparing deals 
due to market-related factors - 
energy sector* 

33. Has problems comparing deals 
due to market-related factors - online 
sector 

34. Has problems comparing deals 
due to market-related factors - 

31% 

31% 

20% 

45% 

34% 

33% 

7% 

21% 

LT, RO  AT, DE 

14,074 

33% 

33% 

15% 

47% 

34% 

33% 

8% 

24% 

FR, LU 

LT, IS 

14,074 

23% 

23% 

8% 

39% 

21% 

20% 

8% 

37% 

33% 

33% 

26% 

46% 

35% 

35% 

6% 

16% 

BE, 
FR, 
LU, IS 

DK, 
FR,  

BG, 
EL, 
MT, 
RO 
NL, 
UK, 

18,506 

14,074 

92 

 
 
 
 
4 │ Incidence of consumer vulnerability 

Table 8: Distribution of incidence of various indicators of vulnerability across EU28 Member States and IS and NO 
Outliers 
- Low - 
EU28 
MSs + 
NO and 
IS 
NO 

Outliers 
- High - 
EU28 
MSs + 
NO and 
IS 

Normalised. 
standard 
deviation - 
EU28 MSs 

Incidence 
- EU28 + 
NO and 
IS 

Standard 
deviation 
- EU28 
MSs 

Maximum 
- EU28 
MSs 

Minimum 
- EU28 
MSs 

Mean 
-
EU28 
MSs 

Median 
- EU28 
MSs 

Incidence 
- EU28 

Observations 
EU28 total 

financial sector* 
35. Has problems comparing deals 
due to access-related factors - energy 
sector* 
36. Has problems comparing deals 
due to access-related factors – online 
sector 
37. Has problems comparing deals 
due to access-related factors - 
financial sector* 
38. Has an electricity supplier and has 
not switched due to personal factors - 
energy sector** 
39. Has an internet provider and has 
not switched due to personal factors - 
online sector** 
40. Has a bank account and has not 
switched due to personal factors - 
financial sector** 
41. Has an electricity supplier and has 
not switched due to market-related 
factors - energy sector** 
42. Has an internet provider and has 
not switched due to market-related 
factors - online sector** 
43. Has a bank account and has not 
switched due to market-related 
factors - financial sector** 
44. Has an electricity supplier and has 
not switched due to access-related 

93 

10% 

10% 

3% 

55% 

10% 

15% 

15% 

98% 

LT, RO 

- 

14,074 

4% 

4% 

0% 

16% 

3% 

4% 

3% 

67% 

SE, 
NO,  

MT 

18,506 

2% 

2% 

0% 

6% 

2% 

2% 

1% 

62% 

LT, IS 

- 

14,074 

10% 

10% 

5% 

22% 

11% 

14% 

8% 

56% 

RO 

UK 

4,782 

3% 

3% 

1% 

4% 

3% 

3% 

1% 

38% 

- 

7% 

7% 

5% 

14% 

8% 

9% 

4% 

49% 

RO 

- 

- 

3,483 

4,368 

11% 

11% 

4% 

15% 

11% 

10% 

4% 

38% 

6% 

6% 

2% 

7% 

5% 

5% 

2% 

43% 

- 

- 

LT 

4,782 

LT 

3,483 

13% 

13% 

10% 

14% 

12% 

12% 

2% 

13% 

DK 

RO 

4,368 

12% 

12% 

3% 

42% 

10% 

20% 

18% 

92% 

LT 

- 

4,782 

 
 
 
 
4 │ Incidence of consumer vulnerability 

Table 8: Distribution of incidence of various indicators of vulnerability across EU28 Member States and IS and NO 
Outliers 
- Low - 
EU28 
MSs + 
NO and 
IS 

Outliers 
- High - 
EU28 
MSs + 
NO and 
IS 

Normalised. 
standard 
deviation - 
EU28 MSs 

Incidence 
- EU28 + 
NO and 
IS 

Standard 
deviation 
- EU28 
MSs 

Maximum 
- EU28 
MSs 

Minimum 
- EU28 
MSs 

Mean 
-
EU28 
MSs 

Median 
- EU28 
MSs 

Incidence 
- EU28 

Observations 
EU28 total 

factors - energy sector** 
45. Has an internet provider and has 
not switched due to access-related 
factors - online sector** 
46. Has a bank account and has not 
switched due to access-related factors 
- financial sector** 

3% 

3% 

2% 

7% 

4% 

4% 

2% 

47% 

2% 

2% 

2% 

5% 

3% 

3% 

1% 

39% 

- 

- 

- 

- 

3,483 

4,368 

47. Has not switched due to 
termination costs 

16% 

16% 

6% 

27% 

17% 

17% 

5% 

32% 

HR, 
EL, RO 

48. Has not switched due to bundling 

22% 

22% 

14% 

38% 

26% 

24% 

6% 

24% 

HR, SI 

49. Excluded from e-commerce due to 
difficulty of the process 

19% 

19% 

10% 

39% 

24% 

23% 

6% 

27% 

50. Excluded from e-commerce due to 
not having a payment card 

15% 

15% 

6% 

41% 

17% 

19% 

9% 

49% 

HR, 
CY, EL 

HR, 
EL, 
RO, SI 

51. Declined for a loan** 
Dimension 5 : Higher susceptibility to marketing practices 
54. Vulnerable to a great extent due 

10% 

10% 

4% 

4% 

4% 

1% 

8% 

3% 

4% 

3% 

64% 

RO 

DK, 
FI, DE, 
MT, 
SE, 
NL, IS, 
NO 
DE, 
LT, 
SE, 
NL, 
UK, IS 
DE, 
MT, 
NL, UK 
FR, 
MT, 
SE, 
NL, 
UK, 
NO 
- 

25,151 

25,151 

18,550 

18,550 

4,971 

26% 

10%  10% 

5% 

44% 

CY, ES 

AT, 

25,151 

94 

 
 
 
4 │ Incidence of consumer vulnerability 

Table 8: Distribution of incidence of various indicators of vulnerability across EU28 Member States and IS and NO 
Outliers 
- Low - 
EU28 
MSs + 
NO and 
IS 
DE, 
NL, IS, 
NO 

to complexity of offers (own 
perception) 

Outliers 
- High - 
EU28 
MSs + 
NO and 
IS 

Normalised. 
standard 
deviation - 
EU28 MSs 

Incidence 
- EU28 + 
NO and 
IS 

Standard 
deviation 
- EU28 
MSs 

Maximum 
- EU28 
MSs 

Minimum 
- EU28 
MSs 

Mean 
-
EU28 
MSs 

Median 
- EU28 
MSs 

Incidence 
- EU28 

Observations 
EU28 total 

4% 

82% 

53% 

90% 

85% 

88% 

85% 

90% 

48% 

53% 

55. Experiment choice under the 
marketing practice treatment - energy 
sector** 
56. Experiment choice under the 
marketing practice treatment - online 
sector** 
57. Experiment choice under the 
marketing practice treatment - 
finance sector** 
58. Experiment choice under the 
marketing practice treatment - cross-
cutting** 
Notes: Results weighted using cross-country weights for the EU28 and EU28 + NO and IS incidence rates and country weights for the remaining measures.  
Normalised standard deviation = standard deviation/mean. Outliers are countries with incidence rate that is either greater than the EU28 mean + 1 standard deviation (high 
outliers) or lower than the EU28 mean - 1 standard deviation (low outliers) (with a 0.5 percentage point buffer above/below the cut-off). The absence of outliers indicates 
that there is clustering of countries at around the 1 standard deviation cut-off. As a result these countries are not identified as outliers on the basis of the outlier criteria 
described in this footnote. 
* Responses for only 19 countries; **Responses only for 5 countries in which experiments were run. 
Source: London Economics 

1,263 

1,254 

1,252 

1,282 

66% 

64% 

57% 

55% 

12% 

27% 

27% 

20% 

35% 

26% 

26% 

24% 

66% 

60% 

78% 

73% 

70% 

10% 

7% 

6% 

7% 

4% 

RO 

RO 

UK 

UK 

PT 

- 

- 

- 

95 

 
 
 
 
4 │ Incidence of consumer vulnerability 

4.1.2.  Analysis of cross-country variation of vulnerability incidence 

This  subsection  proceeds  by  examining  the  cross-country  variation  of  vulnerability 
incidence focusing on how this incidence differs between indicators and dimensions. It 
then  identifies  countries  which  are  frequently  outliers  according  to  all  vulnerability 
indicators  compared  to  the  EU28  as  a  whole  and  whether  countries  characterise 
outliers differently based on the individual dimensions of vulnerability.    

Overall variation across Member States of vulnerability incidence 

The degree of variation across countries in the vulnerability incidence indicators differs 
markedly as shown by Figure 8, which presents the normalised standard deviation rate 
(i.e.  the  standard  deviation  rate  divided  by  the  mean)  for  each  indicator  across  the 
EU28. 

 

 

In the case of 37 indicators (out of 54 indicators), the normalised standard 
deviation is less than or equal to 50%, meaning that the standard deviation is 
relatively low - at most half of the mean of the indicator. 
In contrast, in the case of 7 indicators, the cross-country variation is large with 
the standard deviation ranging from about 75% to close to 100% of the mean. 
These seven vulnerability indicators are the following: 

  Has problems comparing deals due to access-related factors - energy 

sector (normalised standard deviation of 98% - question was asked in 19 
countries) 

  Has an internet provider and has not switched due to information-related 
factors - online sector (normalised standard deviation of 95%  - question 
was only asked in 5 countries in which experiments were run) 

  Has an electricity supplier and has not switched due to access-related 

factors - energy sector (normalised standard deviation of 92% - question 
was only asked in 5 countries in which experiments were run) 
  Finds it very difficult to compare product deals - online sector 

(normalised standard deviation of 81% - question was asked in all 
countries) 

  Compares product deals by using information from advertisements only - 
online sector  (normalised standard deviation of 68% - question was 
asked in all countries) 

  Has a bank account and has not switched due to information-related 
factors - financial sector (normalised standard deviation of 67% - 
question was only asked in 5 countries in which experiments were run) 
 Has problems comparing deals due to access-related factors – online 
sector (normalised standard deviation of 67% - question was asked in all 
countries) 

 

96 

 
 
4 │ Incidence of consumer vulnerability 

Figure 8: Variation in the incidence of the vulnerability indicators across 

EU28 Member States 

Note: The reported variation is of the normalised standard deviation (standard deviation/mean). Results are 
based on weighted results of country incidence rates, using country demographic weights. Sample size varies 
between indicators. For more details, see Table 8. 
Source: London Economics 

Outlier analysis  

Table  8  also  identifies  for  each  vulnerability  indicator  the  countries  which  can  be 
considered as outliers in terms of vulnerability incidence.49 Countries are considered to 
be “high outliers” if the  incidence rate they show is greater than the  EU28 mean plus 
one standard deviation, and “low outliers” if their incidence rate is lower than the mean 
minus one standard deviation.  

49  Not all questions have been asked in  all  countries. This  would only  skew the probability of a 
country  being  identified  as  an  outlier  if  all  countries  had  the  same  probability  of  being  an 
outlier in each indicator, and the probabilities of being an outlier in different indicators were 
independent. However, there is no indication that this is the case.  

97 

51%38%44%51%55%50%55%40%44%42%57%49%68%50%36%41%24%43%95%67%30%52%39%81%13%39%25%35%21%24%37%16%98%67%62%56%38%49%38%43%13%92%47%39%32%24%27%49%64%44%4%12%24%10%Has experienced a problem and took no action (n=25,151)Overpaid for services due to being unable to use certain payment methods (n=25,151)Feels vulnerable to 'a great extent' due to health problems (n=25,151)Feels vulnerable to 'a great extent' due to financial circumstances (n=25,151)Feels vulnerable to 'a great extent' due to employment situation (n=25,151)Feels vulnerable to 'a great extent' due to age (n=25,151)Feels vulnerable to 'a great extent' due to belonging to a minority group (n=25,151)Feels vulnerable to 'a great extent' due to personal issues (n=25,151)Feels vulnerable to 'a great extent' due to other reasons (n=25,151)Feels vulnerable to 'a great extent' due to any personal characteristic (n=25,151)Feels 'not at all' informed ... when choosing and buying goods and services (n=25,151)Compares product deals by using information from advertisements only -energy sector (n=14,074)Compares product deals by using information from advertisements only -online sector  (n=18,506)Compares product deals by using information from advertisements only -finance sector  (n=14,074)Has problems comparing deals due to information-related factors -energy sector (n=14,074)Has problems comparing deals due to information-related factors -online sector (n=18,506)Has problems comparing deals due to information-related factors -finance sector (n=14,074)Has an electricity supplier and has not switched due to information-related factors -energy sector (n=4,782)Has an internet provider and has not switched due to information-related factors -online sector (n=3,483)Has a bank account and has not switched due to information-related factors -financial sector (n=4,368)Does not compare product deals -energy sector (n=25,151)Finds it very difficult to compare product deals -energy sector (n=25,136)Does not compare product deals –online sector (n=18,506)Finds it very difficult to compare product deals -online sector (n=18,506)Does not compare product deals -finance sector (n=25,151)Finds it very difficult to compare product deals -finance sector (n=25,151)Has problems comparing deals due to personal factors -energy sector (n=14,074)Has problems comparing deals due to personal factors -online sector (n=18,506)Has problems comparing deals due to personal factors -financial sector (n=14,074)Has problems comparing deals due to market-related factors -energy sector (n=14,074)Has problems comparing deals due to market-related factors -online sector (n=18,506)Has problems comparing deals due to market-related factors -financial sector (n=14,074)Has problems comparing deals due to access-related factors -energy sector (n=14,074)Has problems comparing deals due to access-related factors –online sector (n=18,506)Has problems comparing deals due to access-related factors -financial sector (n=14,074)Has an electricity supplier and has not switched due to personal factors -energy sector (n=4,782)Has an internet provider and has not switched due to personal factors -online sector (n=3,483)Has a bank account and has not switched due to personal factors -financial sector (n=4,368)Has an electricity supplier and has not switched due to market-related factors -energy sector (n=4,782)Has an internet provider and has not switched due to market-related factors -online sector (n=3,483)Has a bank account and has not switched due to market-related factors -financial sector (n=4,368)Has an electricity supplier and has not switched due to access-related factors -energy sector (n=4,782)Has an internet provider and has not switched due to access-related factors -online sector (n=3,483)Has a bank account and has not switched due to access-related factors -financial sector (n=4,368)Has not switched due to termination costs (n=25,151)Has not switched due to bundling (n=25,151)Excluded from e-commerce due to difficulty of the process (n=18,550)Excluded from e-commerce due to not having a payment card (n=18,550)Declined for a loan (n=4,971)Vulnerable to a great extent due to complexity of offers (own perception) (n=25,151)Experiment choice under the marketing practice treatment -energy sector (n=1,263)Experiment choice under the marketing practice treatment -online sector (n=1,254)Experiment choice under the marketing practice treatment -finance sector (n=1,252)Experiment choice under the marketing practice treatment -cross-cutting (n=1,282)Dimension 1Dimension 5Dimension 3Dimension 4Dimension 2 
 
 
 
                                                 
4 │ Incidence of consumer vulnerability 

Such an outlier analysis is useful for identifying countries (if any) which stand out from 
the  overall  EU28  picture  according  to  a  particular  vulnerability  indicator  and  helps  to 
identify  countries  where  a  particular  indicator  of  consumer  vulnerability  is  more 
prevalent  than  the  average  across  the  EU28.  The  findings  from  such  an  analysis  may 
help policy-makers to identify consumer issues requiring particular policy attention and 
assist  them  in  targeting  policy  efforts  and  public  resources  at  specific  dimensions  of 
consumer vulnerability. 

Moreover,  the  analysis  below  also  identifies  countries  which  are  frequently  outliers 
across  a  range  of  indicators.  Countries  which  are  frequently  low  outliers  could  be  a 
source  of  good  practices  which  other  countries  could  consider  and  implement.  In 
contrast, the fact that a country is a high outlier signals to domestic policy-makers the 
need to address consumer vulnerability much more vigorously.  

Among the 28 Member States and Norway and Iceland: 

  Most countries are a combination of being in some cases “high outliers” and in 

some other cases “low outliers”. The exceptions are: 

 

 

In the case of being only “low outliers”: Austria, the Czech Republic, 
Estonia, Finland, Germany, Poland and the United Kingdom; 
In the case of being only “high outliers”: Belgium, Cyprus, Hungary, 
Italy, Latvia and Spain. 

  The frequency of being a “high outlier” or a “low outlier” varies greatly across 

Member States (Figure 9): 

  A few countries show a relatively high frequency50 of being a “High 

outlier” – this is especially the case for Cyprus, and to a somewhat lesser 
extent, Croatia and Romania. 

  Only three countries show a relatively high frequency of being a “low 
outlier” and rarely a “high outlier”. This is the case for Germany, the 
Netherlands and Norway. 
Iceland is relatively frequently both a “low outlier” and a “high outlier”.  

 
  Finally, the frequency of being an outlier (either a “low outlier” or a “high 

outlier” or a combination of both) is relatively low (15% or less) in most 
of the remaining countries. 

50  A frequency of  30% or above has been  arbitrarily considered  as high frequency, based  on a 

visual inspection of the outliers’ frequency scatter plot in Figure 9.  

98 

 
 
                                                 
4 │ Incidence of consumer vulnerability 

Figure 9: Frequency of being a “high outlier” or a “low outlier”  

Notes:  Based  on  weighted  results.  The  frequency  is  the  ratio  of  the  number  of  times  a  country  is  a  “high 
outlier” or a “low outlier” to the total number of indicators for which survey data were collected (see Table 8 
for  details).  A  “high  outlier”  has  an  incidence  rate  greater  than  the  mean  incidence  rate  +  1  standard 
deviation  of  the  incidence  rate  across  the  EU28  +  NO  and  IS  while  a  “low  outlier”  has  an  incidence  rate 
smaller than mean incidence rate  - 1 standard deviation of the incidence rate across the EU28 + NO and IS.  
Source: London Economics 

The  analysis  in  Figure  9  attaches  the  same  importance  to  each  indicator  of 
vulnerability.51  However,  different  numbers  of  indicators  are  included  in  each  of  the 
five  dimensions.  In  order  to  examine  which  aspects  of  vulnerability  drive  certain 
countries  to  be  frequently  high,  frequently  low  or  a  mix  of  high  and  low  outliers,  the 
analysis  reported  above  has  been  performed  for  each  dimension  (see  Figure  10  to 
Figure 14 below).  

51 Each indicator populated for a given country. 

99 

ATBEBGHRCYCZDKEEFIFRDEELHUIEITLVLTLUMTPLPTROSKSIESSENLUKISNO0%10%20%30%40%50%60%70%0%10%20%30%40%50%60%70%Frequency of being a low outlier countryFrequency of being a high outlier country  
 
 
 
                                                 
4 │ Incidence of consumer vulnerability 

Outliers in Dimension 1 

Figure 10: Frequency of being a “high outlier” or a “low outlier” in 

Dimension 1  

Notes:  Based  on  weighted  results  for  indicators  in  Dimension  1  only.  The  frequency  is  the  ratio  of  the 
number of  times  a  country  is  a  “high  outlier” or  a “low  outlier”  to  the  total  number  of  indicators for which 
survey data were collected (see Table 8 for details). A “high outlier” has an incidence rate greater than the 
mean incidence rate + 1 standard deviation of the incidence rate across the EU28 + NO and IS while a “low 
outlier” has an incidence rate smaller than mean incidence rate - 1 standard deviation of the incidence rate 
across the EU28 + NO and IS.  
Source: London Economics 

As  Dimension  1  (‘heightened  risk  of  negative  outcomes  or  impacts  on  well-being’) 
includes  only  two  indicators,  there  are  very  few  possible  combinations  of  high  outlier 
and low outlier frequencies for each country.  

Most  countries  are  neither  high  nor  low  outliers  for  any  of  the  two  indicators  in 
Dimension 1 (Figure 10).   

  Romania is the only country which is a high outlier in terms of both the share 
of respondents  who  experienced a problem and took no action, and the share 
of  respondents  who  overpaid  for  services  due  to  being  unable  to  use  certain 
payment methods. 

  France  and  the  Netherlands  are  the  only  countries  which  are  ‘low  outliers’ 

according to both indicators. 

  None of the countries are polarised outliers, i.e. a high outlier according to one 

of the indicators and a low outlier according to the other indicator. 

100 

0%10%20%30%40%50%60%70%80%90%100%0%10%20%30%40%50%60%70%80%90%100%Frequency of being a low outlier countryFrequency of being a high outlier country CZROAT, BE, HR, DK, EE, FI, DE,EL, IT, LV, LT, LU, MT, PL, PT, SK, SI, ES, SE, UK, IS, NOBG, CY,HU, IE FR, NL 
 
 
4 │ Incidence of consumer vulnerability 

Outliers in Dimension 2 

Figure 11: Frequency of being a “high outlier” or a “low outlier” in 

Dimension 2  

Notes:  Based  on  weighted  results  for  indicators  in  Dimension  2  only.  The  frequency  is  the  ratio  of  the 
number of  times  a  country  is  a  “high  outlier” or  a “low  outlier”  to  the  total  number  of  indicators for which 
survey data were collected (see Table 8 for details). A “high outlier” has an incidence rate greater than the 
mean incidence rate + 1 standard deviation of the incidence rate across the EU28 + NO and IS while a “low 
outlier” has an incidence rate smaller than mean incidence rate - 1 standard deviation of the incidence rate 
across the EU28 + NO and IS.  
Source: London Economics 

Dimension  2  (‘having  characteristics  that  limit  ability  to  maximise  well-being’)  is 
captured  by  eight  indicators,  all  of  which  are  populated  with  data  for  all  countries. 
There  is  a  slightly  higher  level  of  variation  in  the  countries’  frequency  of  being  an 
outlier, but overall, if a country is a high outlier according to any indicators, it is not a 
low  outlier  according  to  any  of  the  indicators  in  Dimension  2,  and  vice  versa  (Figure 
11). Notably, 

  Cyprus is a high outlier in the incidence of vulnerability according to almost all 

indicators in this dimension; 

  Greece,  Portugal  and  Romania  are  high  outliers  according  to  a  third  of  the 

indicators in Dimension 2; 

  Norway  is  a  low  outlier  according  to  the  majority  of  the  indicators  in  this 
dimension as are, to a less extent, Iceland, Austria and the Czech Republic. 

101 

ATBEBGHRCYCZDKEEFIFRDEELHUIEITLVLTLUMTPLPTROSKSIESSENLUKISNO0%10%20%30%40%50%60%70%80%90%100%0%10%20%30%40%50%60%70%80%90%100%Frequency of being a low outlier countryFrequency of being a high outlier country  
 
 
 
4 │ Incidence of consumer vulnerability 

Outliers in Dimension 3 

Figure 12: Frequency of being a “high outlier” or a “low outlier” in 

Dimension 3  

Notes:  Based  on  weighted  results  for  indicators  in  Dimension  3  only.  The  frequency  is  the  ratio  of  the 
number of  times  a  country  is  a  “high  outlier” or  a “low  outlier”  to  the  total  number  of  indicators for which 
survey data were collected (see Table 8 for details). A “high outlier” has an incidence rate greater than the 
mean incidence rate + 1 standard deviation of the incidence rate across the EU28 + NO and IS while a “low 
outlier” has an incidence rate smaller than mean incidence rate - 1 standard deviation of the incidence rate 
across the EU28 + NO and IS.  
Source: London Economics 

Dimension  3  (‘having  difficulty 
is 
characterised by ten indicators, of which three have data for all 30 countries, and three 
have data only for the experiment countries.52  

in  obtaining  or  assimilating 

information’) 

  Cyprus and Croatia are high outliers according to the majority of the indicators 
for which data are available53; Luxembourg, is a high outlier according to more 
than half of the indicators (Figure 12).   

52  The  countries  in  which  the  experiment  was  conducted  are  Denmark  (DK),  Lithuania  (LT), 

Portugal (PT), Romania (RO) and the United Kingdom (UK). 

53 Data are available for only three indicators in the case of Cyprus and Croatia: 13. ‘Feels 'not at 
all' informed about product price, quality,  conditions, etc.  when choosing and buying  goods 
and services’; 15. ‘compares product deals by using information from advertisements only - 
online  sector’;  and  18.  ‘has  problems  comparing  deals  due  to  information-related  factors  – 
on-line sector’ 

102 

ATBEBGHRCYCZDKEEFIFRDEELHUIEITLVLTLUMTPLPTROSKSIESSENLUKISNO0%10%20%30%40%50%60%70%0%10%20%30%40%50%60%70%Frequency of being a low outlier countryFrequency of being a high outlier country  
 
 
                                                 
4 │ Incidence of consumer vulnerability 

 

Iceland  stands  out  as  a  country  which  is  a  high  outlier  on  a  relatively  large 
proportion of its indicators in Dimension 3, while at the same time being a low 
outlier in the case of a number of other indicators. 

  No  country  is  frequently  a  low  outlier  across  the  indicators  of  Dimension  3  – 
Bulgaria  is  the  country  which  is  a  low  outlier  with  regards  to  the  largest 
number of indicators (a third).54 

Outliers in Dimension 4 

Figure 13: Frequency of being a “high outlier” or a “low outlier” in 

Dimension 4  

Notes:  Based  on  weighted  results  for  indicators  in  Dimension  4  only.  The  frequency  is  the  ratio  of  the 
number of  times  a  country  is  a  “high  outlier” or  a “low  outlier”  to  the  total  number  of  indicators for which 
survey data were collected (see Table 8 for details). A “high outlier” has an incidence rate greater than the 
mean incidence rate + 1 standard deviation of the incidence rate across the EU28 + NO and IS while a “low 
outlier” has an incidence rate smaller than mean incidence rate - 1 standard deviation of the incidence rate 
across the EU28 + NO and IS.  
Source: London Economics 

Dimension  4,  ‘Inability  or  failure  to  buy,  choose  or  access  suitable  products’,  is  the 
dimension which is operationalised by the largest number of indicators  – 29. Thirteen 
indicators have information for all 30 countries, six have information for 19 countries, 
and  ten  contain  information  for  the  five  experiment  countries  only.  The  variability  of 

54  Data  are  available  for  only  three  indicators  in  the  case  of  Bulgaria:  13.  ‘Feels  'not  at  all' 
informed about product price, quality, conditions, etc. when choosing and buying goods and 
services’;  15.  ‘compares  product  deals  by  using  information  from  advertisements  only  - 
online  sector’;  and  18.  ‘has  problems  comparing  deals  due  to  information-related  factors  – 
on-line sector’   

103 

ATBEBGHRCYCZDKEEFIFRDEELHUIEITLVLTLUMTPLPTROSKSIESSENLUKISNO0%10%20%30%40%50%0%5%10%15%20%25%30%35%40%45%50%Frequency of being a low outlier countryFrequency of being a high outlier country  
 
 
 
                                                 
4 │ Incidence of consumer vulnerability 

high  and  low  outlier  is  the  highest  amongst  all  dimensions,  and  this  dimension  is  the 
one which contributes the most to the overall outlier results presented in Figure 9.   

 

Iceland is a high outlier with the highest frequency. However, it is also a low 
outlier for some indicators.  

  Cyprus is a high outlier in most cases and not a low outlier in the case of any of 

the indicators. 

  Germany, which is sometimes a low outlier in the case of the other dimensions, 
stands out as the country which is most often a low outlier and never a high 
outlier according to Dimension 4. 

  Malta is a high outlier fairly frequently but just as frequently it is a low outlier in 
Dimension 4. This is also the case, although with a lower frequency, for Greece.  

Outliers in Dimension 5 

Figure 14: Frequency of being a “high outlier” or a “low outlier” in 

Dimension 5  

Notes:  Based  on  weighted  results  for  indicators  in  Dimension  5  only.  The  frequency  is  the  ratio  of  the 
number of  times  a  country  is  a  “high  outlier” or  a “low  outlier”  to  the  total  number  of  indicators for which 
survey data were collected (see Table 8 for details). A “high outlier” has an incidence rate greater than the 
mean incidence rate + 1 standard deviation of the incidence rate across the EU28 + NO and IS while a “low 
outlier” has an incidence rate smaller than mean incidence rate - 1 standard deviation of the incidence rate 
across the EU28 + NO and IS.  
Source: London Economics 

Consumer  vulnerability  according  to  Dimension  5,  ‘Higher  susceptibility  to  marketing 
practices’, is operationalised by: 

  Only  one  survey-based  indicator  (`54.  Vulnerable  to  a  great  extent  due  to 
complexity  of  offers  (own  perception)’),  which  has  data  for  all  respondents  in 
each country; and  

104 

0%10%20%30%40%50%60%70%80%90%100%0%10%20%30%40%50%60%70%80%90%100%Frequency of being a low outlier countryFrequency of being a high outlier country AT,DE, NL, IS, NOBE, BG, HR, CZ, DK, EE, FI, FR, EL, HU, IE, IT, LV,LT, LU, MT, PL, SK, SI, SEPTROCY, ESUK 
 
 
4 │ Incidence of consumer vulnerability 

  Four  indicators  constructed  on  the  basis  of  the  four  marketing  practice 
experiments  (one  experiment 
for  each  sector  and  one  cross-cutting 
experiment) for which data are only available for the five experiment countries.  

Additionally,  three  of  the  four  experiment-based  indicators  are  characterised  by  very 
high  consumer  vulnerability,  and  a  very  low  normalised  standard  deviation.  This  fact 
together  with  the  fact  that  four  out  of  the  five  indicators  are  populated  only  in  five 
countries leads to fewer frequent outlier countries according to this dimension.  

  Cyprus and Spain are high outliers on the indicator for which data are available 
for  all  countries.55  Romania  is  also  a  country  which  is  relatively  frequently  a 
high outlier. 

  Austria, Germany, the Netherlands, Iceland and Norway are low outliers on the 
indicator  for  which  data  are  available  for  all  countries.  The  UK  is  another 
country which is relatively frequently a low outlier in this dimension. 

Country  differences  in  the  incidence  of  the  different  vulnerability  indicators  are 
discussed  in  greater  details  in  the  following  sections,  organised  by  dimension  of 
vulnerability.  

4.1.3.  Analysis of incidence of vulnerability – vulnerability Dimension 1 in EU28 
countries, Iceland and Norway 

The share of respondents who  experienced a problem and did not take action is 10% 
across the EU28 (see Table 9), and ranges from 5% in France to 28% in Bulgaria.  

  Besides Bulgaria, many other central European countries are characterised by a 
high  share  of  consumers  not  taking  any  actions  when  having  experienced  a 
problem: Croatia – 15%, Estonia – 17%, Hungary – 26%, Latvia - 16%, Poland 
- 15% and Romania – 19%. 

 

In  contrast, in many western  European  countries, the share of consumers not 
taking any action when having experience a problem is relatively low (less than 
10%): Austria – 8%, Belgium – 7%, Denmark – 7%, Finland – 9%, Italy – 7%, 
Luxembourg – 7%, Malta – 6%, Netherlands  – 5%,  Norway – 8%,  Portugal – 
9%, Spain – 9%, Sweden – 6% and United Kingdom 6%). 

Moreover,  at  11%,  the  share  of  consumers  who  overpaid  for  services  because  they 
were  unable  to  use  a  certain  payment  method  is  also  relatively  low  in  the  EU28. 
However, in a limited number of countries, a much larger proportion of consumers face 
this problem. 

  This is  particularly  the  case  in  Cyprus  –  23%,  Ireland  –  22%,  and  Romania  – 

23%; and, 

  To a slightly lesser extent in Greece – 18%, Latvia – 18% and Slovenia – 16%. 

These  two  indicators,  which  are  used  to  characterise  the  first  vulnerability  dimension, 
represent  different  vulnerability  aspects  as,  at  the  individual  respondent  level,  the 
correlation between the two indicators in this dimension is low (0.073). 

55 Indicator 54, ‘Vulnerable to a great extent due to complexity of offers (own perception)’. 

105 

 
 
 
                                                 
4 │ Incidence of consumer vulnerability 

Table 9:  

Dimension 1 vulnerability indicators in EU28 Member States and 

IS and NO 

Observations 

1. Has experienced a 
problem and took no 
action56 

EU28 
AT 
BE 
BG 
HR 
CY 
CZ 
DK 
EE 
FI 
FR 
DE 
EL 
HU 
IE 
IT 
LV 
LT 
LU 
MT 
PL 
PT 
RO 
SK 
SI 
ES 
SE 
NL 
UK 
IS 
NO 
Note:  Results  weighted  using  cross-country  weights  for  the  EU28  measure  and  country  weights  for  the 
country measures.  
Source: London Economics 

2. Overpaid for 
services due to being 
unable to use certain 
payment methods 
11% 
10% 
11% 
15% 
18% 
23% 
6% 
10% 
10% 
11% 
8% 
10% 
18% 
13% 
22% 
12% 
18% 
13% 
12% 
8% 
18% 
14% 
23% 
13% 
16% 
9% 
8% 
7% 
9% 
8% 
8% 

25,151 
1,001 
1002 
1,000 
1,002 
5,00 
1,000 
1,002 
500 
1,004 
1,002 
1,006 
1,004 
1,003 
1,011 
1,001 
500 
1,009 
503 
524 
1,009 
1,012 
1,001 
1,005 
502 
1,000 
1,002 
1,019 
1,027 
500 
1,002 

10% 
8% 
7% 
28% 
15% 
12% 
10% 
7% 
17% 
9% 
5% 
12% 
15% 
26% 
11% 
7% 
16% 
11% 
7% 
6% 
15% 
9% 
19% 
10% 
9% 
9% 
6% 
5% 
6% 
12% 
8% 

The  country-level  results  of  the  proportion  of  consumers  who  took  action  when  they 
experienced  a  problem  can  be  compared  with  similar  results  from  the  Consumer 
Conditions  Scoreboard  (CCS)  produced  by  the  European  Commission  (2015),  which 
reports  the  proportion  of  consumers  who  took  action  when  they  encountered 
problems.57 At country level, the results from the survey are correlated with those from 

56  This  indicator  is  based  on  the  ‘Unassertive  (took  no  action)  when  experienced  a  problem’ 
indicator ‘1’ in Table 5, but the base is all respondents, not just those who have experienced 
a problem. 

57 The latest CCS can be found here: 

http://ec.europa.eu/consumers/consumer_evidence/consumer_scoreboards/11_edition/docs
/ccs2015scoreboard_en.pdf .  
The question in both surveys asked: In the past 12 months, have you experienced any 
problem when buying or using any goods or services in [respondent’s country] where you 

106 

 
 
 
                                                 
 
4 │ Incidence of consumer vulnerability 

the  latest  CCS,  with  a  correlation  coefficient  of  0.53,  although,  in  general,  the  CCS 
found  that  a  higher  share  of  consumers  took  action  when  faced  with  a  problem  than 
found by the survey (see Figure 15).58 

Figure 15:  Share of consumers who took action when they encountered 

problems – country level results from the Consumer Conditions 
Scoreboard and from survey undertaken for the present study  

Note:  Results  from  the  present  study  weighted  using  country  weights.  The  share  of  consumers  who  took 
action when having experienced a problem differs from the share reported in the previous table as the latter 
share  reflects  the  share  of  consumers  who  experienced  problems  and  took  action.  (EU28  sample  size: 
Consumer Conditions Scoreboard - 5,975; survey for present study – 7,346) 
Source: Consumer Conditions Scoreboard 11th (2015) edition and London Economics  

In  order  to  examine  in  greater  detail  the  relationship  between  the  findings  from  the 
CCS and the present study, a simple model which relates the country-level proportion 
of consumers who took action from the survey undertaken for the present study to the 
country-level proportion from the CCS was estimated.59 

The  estimation  results  of  the  model  across  all  the  countries  shows  that  France  and 
Hungary  are  the  countries  where  the  predicted  proportion  of  consumers  who  took  no 
action  is  much  higher  than  the  proportion  shown  by  the  survey.  When  the  same 

thought you had a legitimate cause for complaint? Answers: 1) Yes, and you took action to 
solve the problem; 2) Yes, but you did not do anything; 3) No.  

58 A comparison of survey results by mode suggests that the difference in results from the two 
surveys could stem from a difference in survey modes used. Face-to-face respondents might 
be  less  inclined  to  admit  that  they  experienced  problems  and  did  nothing,  for  social-
desirability reasons.  

59 Present survey estimated incidence rate = 0.4827 x CCS incidence rate + 0.2556 ; R Square 

= 0.277 estimated cross-sectionally for all EU28 Member States + Iceland + Norway 

107 

ATBEBGHRCYCZDKEEFIFRDEELHUIEITLVLTLUMTPLPTROSKSIESSENLUKISNO20%30%40%50%60%70%80%90%100%20%30%40%50%60%70%80%90%100%Survey for present studyConsumer Conditions Scoreboard 
 
 
 
                                                                                                                                                     
4 │ Incidence of consumer vulnerability 

analysis  is  reproduced  excluding  France  and  Hungary60,  the  correlation  coefficient 
increases to 0.68. 

Another  potential  explanation  for  the  difference  between  the  findings  of  the  present 
study  and  the  CCS is  that  the  survey  undertaken  for  the  present  study  used  a  mixed 
mode approach (online, CAPI and CATI), whereas the survey on which the CCS based 
(Flash Eurobarometer 397) was a telephone survey. 

4.1.4.  Analysis of incidence of vulnerability – vulnerability Dimension 2 in EU28 
countries, Iceland and Norway 

The  incidence  of  perceived  vulnerability  varies  both  across  aspects  of  perceived 
vulnerability  and  across  countries.  In  terms  of  personal  characteristics,  financial  and 
employment  circumstances  show  the  highest  incidence  rate.  Among  the  EU  Member 
States  and  Norway  and  Iceland,  Cyprus  and,  to  a  lesser  extent,  Greece  and  Portugal 
stand out as having always or often a much higher incidence rate than the EU28 as a 
whole. 

Table 10:  Dimension 2 vulnerability indicators in EU28 Member States and 

IS and NO 

a

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O

EU28 
AT 
BE 
BG 
HR 
CY 
CZ 
DK 
EE 
FI 
FR 
DE 
EL 
HU 
IE 
IT 
LV 
LT 
LU 

5% 
3% 
5% 
9% 
6% 

3% 
14%  11% 
5% 
3% 
2% 
6% 
3% 
3% 
11% 
9% 
6% 
2% 
4% 
21%  14% 
3% 
11%  19%  13% 
33%  25%  13%  10% 
9% 
4% 
2% 
7% 
3% 
2% 
6% 
8% 
4% 
3% 
19% 
9% 
7% 
2% 
16%  11% 
4% 
4% 
19%  14% 
5% 
2% 
6% 
3% 
2% 
2% 
35%  27% 
6% 
5% 
22%  12% 
8% 
3% 
14%  13% 
3% 
4% 
5% 
20%  17% 
4% 
10%  21%  14% 
3% 
25%  17% 
8% 
3% 
4% 
7% 
2% 

3% 
3% 
5% 
3% 
6% 
3% 
6% 
7% 
4% 
6% 
8% 
7% 
3% 

4% 
2% 
4% 
6% 
6% 
8% 
2% 
2% 
4% 
3% 
4% 
3% 
5% 
7% 
3% 
7% 
5% 
5% 
3% 

4% 
2% 
3% 
6% 
6% 
8% 
2% 
2% 
4% 
3% 
5% 
2% 
3% 
6% 
3% 
5% 
5% 
5% 
3% 

23%  25,151 
1,001 
10% 
1002 
19% 
1,000 
33% 
1,002 
31% 
5,00 
47% 
1,000 
12% 
1,002 
14% 
500 
29% 
1,004 
22% 
1,002 
28% 
1,006 
10% 
1,004 
46% 
1,003 
34% 
1,011 
25% 
1,001 
30% 
500 
34% 
1,009 
34% 
503 
14% 

60 Present survey estimated incidence rate = 0.5958 x CCS incidence rate + 0.1698 ; R Square 

= 0.468 

108 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                                 
4 │ Incidence of consumer vulnerability 

Table 10:  Dimension 2 vulnerability indicators in EU28 Member States and 

IS and NO 

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524 
PL 
1,009 
PT 
1,012 
RO 
1,001 
SK 
1,005 
SI 
502 
ES 
1,000 
SE 
1,002 
NL 
1,019 
UK 
1,027 
IS 
500 
1,002 
NO 
Note:  Results  weighted  using  cross-country  weights  for  the  EU28  measure  and  country  weights  for  the 
country measures.  
Source: London Economics 

5% 
9% 
6% 
5% 
12%  10% 
7% 
24%  17% 
21%  14%  11% 
3% 
11%  10% 
3% 
18%  14% 
8% 
20%  19% 
3% 
6% 
8% 
2% 
3% 
5% 
5% 
8% 
9% 
3% 
2% 
6% 
2% 
3% 
7% 

18% 
22% 
34% 
36% 
21% 
27% 
32% 
14% 
10% 
17% 
10% 
12% 

4% 
3% 
6% 
6% 
3% 
3% 
5% 
3% 
2% 
2% 
1% 
1% 

3% 
3% 
6% 
4% 
2% 
1% 
5% 
2% 
2% 
2% 
2% 
1% 

4% 
6% 
8% 
6% 
5% 
6% 
5% 
3% 
3% 
3% 
2% 
3% 

4% 
4% 
8% 
6% 
4% 
3% 
5% 
3% 
2% 
4% 
0% 
1% 

While the table above  provided information about the incidence  of  vulnerability based 
on  the  proportion  of  survey  respondents  who  indicated  that  they  felt  vulnerable  to  a 
great  extent,  the  table  below  provides  more  granular  information  by  taking  into 
account the full set of responses to the relevant survey questions. This table shows the 
average rating on a scale of 1 (not at all) to 4 (to a great extent) given by respondents 
to indicate the extent to which they ‘feel vulnerable’ due to health problems, financial 
circumstances, employment situation, age, being a minority, personal issues and other 
reasons,  as  well  as  a  combined  score  across  these  factors  (the  sum  across  factors). 
The  combined  score  was  12.3  on  average  for  the  full  sample  (out  of  a  maximum  of 
28), ranging from 9.7 for Iceland to 14.1 for Portugal and Romania.  

Two key points emerge from the data reported in the table below: 

1.  The  mean  score  does  not  vary  greatly  across  vulnerability  perception,  ranging 
from  1.47  out  of  4  for  “feeling  vulnerable  because  belonging  to  a  minority”  to 
2.21 for “feeling vulnerable because of health reasons”; and 

2.  The EU28 cross-country variation in the mean rating is very low for each of the 

characteristics (see Figure 16). 

Moreover,  the  cross-country  variation  in  the  aggregate  mean  rating  across  all  7 
dimensions  is  very  low,  with  Iceland  showing  the  lowest  overall  rating  of  9.7  and 
Poland and Portugal the highest mean rating of 14.1.  Central and Southern European 
countries tend to cluster towards the higher end of the range while Nordic and Western 
European countries tend to cluster towards the lower end (see Figure 17). 

109 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4 │ Incidence of consumer vulnerability 

Figure 16: Standard deviation of vulnerability perception factor (rated on a 

scale of 1 to 4) by personal characteristics – EU28 

Note: Results weighted using cross-country weights.  EU28 N =25,151.  
Source: London Economics 

Table 11:  Mean rating of extent to which consumers feel vulnerable on a 
scale of 1 "not at all" to 4 "to a great extent" - EU28 Member States 
and IS and NO 

Perception of own vulnerability due to personal characteristics due to: 

Health 

Finance
s 

Employ
-ment 

Age 

Minority  Personal  Other 

Combin
ed 

Observ-
ations 

1.60 
1.47 
1.65 
1.57 
1.68 
1.63 
1.4 
1.46 
1.7 
1.54 
1.52 
1.58 
1.56 
1.72 
1.51 
1.6 
1.9 
1.8 
1.52 
1.41 
1.84 
1.75 
1.8 
1.51 
1.64 
1.66 

2.21 
1.78 
2.06 
2.53 
2.4 
2.73 
1.92 
1.79 
2.57 
2.25 
2.34 
1.9 
2.95 
2.43 
2.31 
2.41 
2.57 
2.64 
1.85 
1.89 
2.4 
2.71 
2.63 
2.16 
2.59 
2.44 

2.01 
1.46 
1.88 
2.17 
2.08 
2.49 
1.64 
1.62 
2.03 
1.99 
2.05 
1.65 
2.69 
1.99 
2.2 
2.28 
2.26 
2.34 
1.63 
1.63 
2.17 
2.4 
2.33 
1.96 
2.28 
2.38 

1.75 
1.47 
1.73 
1.73 
1.7 
1.93 
1.51 
1.52 
1.71 
1.61 
1.75 
1.62 
1.82 
1.77 
1.77 
1.74 
1.96 
1.8 
1.65 
1.53 
1.94 
1.85 
2.07 
1.62 
1.69 
1.92 

1.47 
1.34 
1.5 
1.18 
1.3 
1.66 
1.25 
1.3 
1.47 
1.3 
1.43 
1.45 
1.39 
1.4 
1.5 
1.53 
1.53 
1.47 
1.45 
1.42 
1.52 
1.7 
1.58 
1.32 
1.31 
1.8 

1.66 
1.51 
1.64 
1.61 
1.67 
1.66 
1.39 
1.37 
1.74 
1.59 
1.58 
1.63 
1.72 
1.66 
1.66 
1.78 
1.84 
1.8 
1.52 
1.36 
1.79 
1.9 
1.92 
1.5 
1.62 
1.8 

1.58 
1.39 
1.56 
1.58 
1.64 
1.58 
1.25 
1.4 
1.64 
1.57 
1.55 
1.56 
1.63 
1.61 
1.64 
1.61 
1.75 
1.75 
1.48 
1.38 
1.66 
1.77 
1.78 
1.33 
1.61 
1.78 

12.3 
10.4 
12 
12.4 
12.5 
13.7 
10.4 
10.5 
12.9 
11.8 
12.2 
11.4 
13.8 
12.6 
12.6 
12.9 
13.8 
13.6 
11.1 
10.6 
13.3 
14.1 
14.1 
11.4 
12.7 
13.8 

25,151 
1,001 
1,002 
1,000 
1,002 
500 
1,000 
1,002 
500 
1,004 
1,002 
1,006 
1,004 
1,003 
1,011 
1,001 
500 
1,009 
503 
524 
1,009 
1,012 
1,001 
1,005 
502 
1,000 

EU28 
AT 
BE 
BG 
HR 
CY 
CZ 
DK 
EE 
FI 
FR 
DE 
EL 
HU 
IE 
IT 
LV 
LT 
LU 
MT 
PL 
PT 
RO 
SK 
SI 
ES 

110 

0.130.350.330.160.140.150.14 
 
 
 
 
 
4 │ Incidence of consumer vulnerability 

Table 11:  Mean rating of extent to which consumers feel vulnerable on a 
scale of 1 "not at all" to 4 "to a great extent" - EU28 Member States 
and IS and NO 

Perception of own vulnerability due to personal characteristics due to: 

Health 

Finance
s 

Employ
-ment 

Age 

Minority  Personal  Other 

1.43 
1.49 
1.51 
1.31 
1.4 

1.8 
1.75 
1.9 
1.72 
1.82 

1.61 
1.58 
1.8 
1.44 
1.61 

1.53 
1.52 
1.77 
1.39 
1.5 

1.33 
1.38 
1.36 
1.3 
1.16 

1.49 
1.51 
1.5 
1.29 
1.39 

1.5 
1.44 
1.45 
1.27 
1.36 

Combin
ed 

Observ-
ations 

10.7 
10.7 
11.3 
9.7 
10.2 

1,002 
1,019 
1,027 
500 
1,002 

1.59 

2.24 

1.99 

1.71 

1.42 

1.62 

1.55 

12.1 

SE 
NL 
UK 
IS 
NO 
Average of 
mean 
ratings 
across EU28 
Member 
States* 

0.34 

0.14 

0.36 

0.16 

Standard 
deviation 
(EU28)**  
Note:  Results  weighted  using  cross-country  weights  for  the  EU28  measure  and  country  weights  for  the 
country  measures.  *The  average  of  the  weighted  mean  scores  across  the  EU28  countries  is  a  simple 
average,  not  weighted  by  the  cross-country  weight.  **The  standard  deviation  here  is  calculated  based  on 
the  average  of  the  mean  scores,  in  order  to  illustrate  between-country  differences  in  country  weighted 
means. 
Results  are  based  on  the  same  survey  questions  used  for  the  construction  of  indicators  4  –  11  (‘Feel 
vulnerable to a great extent due to...’). 
Source: London Economics 

0.16 

0.15 

0.14 

1.3 

Figure 17:  Mean combined rating of extent to which consumers feel 

vulnerable - EU28 Member States and IS and NO 

Note:  Results  weighted  using  cross-country  weights  for  the  EU28  measure  and  country  weights  for  the 
country measures.  EU28 + NO and IS N = 26,653. 
Source: London Economics analysis of survey data 

The  two  tables  and  figures  above  presented  two  alternative  ways  of  representing  a 
same  indicator,  using  the  share  of  consumers  who  feel  “vulnerable  to  a  great  extent” 
or the average rating on the scale of 1 to 4. However, these two indicators are highly 

111 

10.410.410.510.610.710.711.111.311.411.411.81212.212.312.412.512.612.612.712.912.913.313.613.713.813.813.814.114.19.710.2 
 
 
 
 
 
 
 
 
4 │ Incidence of consumer vulnerability 

correlated.  For  example,  the  correlation  coefficient  between  the  two  overall  measures 
of vulnerability is 0.89, and a strong link between these two measures can be seen at 
country  level  in  Figure  18  below.  The  countries  where  the  relationship  between  the 
mean  combined  rating and  the  incidence  of  vulnerability  ‘for  any  reason’  is  weakest61 
are  Cyprus,  Poland  and  Greece,  indicating  a  larger  dispersion  of  the  mean  combined 
vulnerability  rating  in  these  countries.  Thus,  there  is  no  concern  that  the  use  of  the 
binary indicator will result in erroneous assessment of the incidence of vulnerability. 

Figure 18:  Country level share of consumers who feel vulnerable "to a great 
extent" for any reason and country level combined mean rating of the 
extent to which that consumers feel vulnerable 

Note: Results weighted using country weights. EU28 + NO and IS N = 26,653. 
Source: London Economics  

The  remainder  of  the  present  sub-section  discusses  in  further  detail  the  incidence  of 
consumer vulnerability for each one of the reported reasons: financial and employment 
circumstances;  health  and    age  factors;  being a  minority,  personal  and  other  factors, 
grouped thematically and according to the associated vulnerability incidence. 

Financial and employment circumstances 

The  factors  due  to  which  survey  respondents  were  most  likely  to  feel  vulnerable  to  a 
great  extent  were  their  financial  circumstances  and  employment  situation,  with  14% 
and 11% of the whole EU28 sample feeling vulnerable due to these factors.  

61 These three countries have the largest predicted residuals when the incidence of vulnerability 
is  predicted  using  the  mean  combined  rating  of  the  extent  for  which  consumers  feel 
vulnerable.  

112 

ATBEBGHRCYCZDKEEFIFRDEELHUISIEITLVLTLUMTNOPLPTROSKSIESSENLUK0%5%10%15%20%25%30%35%40%45%50%45678910111213141516Share who feel vulnerable for any reasonMean combined rating of extent that consumers feel vulnerable 
 
 
                                                 
4 │ Incidence of consumer vulnerability 

For  these  two  specific  factors,  reported  scores  were  particularly  high  (i.e.  consumers 
felt on average more vulnerable) in many of the financial crisis countries (such as, for 
example, Greece and Cyprus) and in a number of Central European countries. 

Figure 19:  Mean rating and share of consumers who feel vulnerable due to 
financial and employment reasons - EU28 Member States and IS and 
NO 

Note:  Results  weighted  using  cross-country  weights  for  the  EU28  measure  and  country  weights  for  the 
country measures. EU28 + NO and IS N = 26,653. 
Source: London Economics  

Health and age factors 

In  the  case  of  the  health  and  age  factors,  the  EU28-wide  shares  of  consumers  who 
indicated that they felt vulnerable to a great extent are low (5% in each case). 

However, a few countries stand out (in terms of much higher shares): 

113 

0%5%10%15%20%25%30%35%40%Share (%)FinancesEmployment0.00.51.01.52.02.53.03.5Mean scoreFinancesEmployment 
 
 
 
4 │ Incidence of consumer vulnerability 

  Croatia and Latvia in the case of health (11% and 10% respectively); and 
  Cyprus and Romania in the case of age (13% and 11% respectively). 

Figure 20:  Mean rating and share of consumers who feel vulnerable due to 
health and age reasons - EU28 Member States and IS and NO 

Note:  Results  weighted  using  cross-country  weights  for  the  EU28  measure  and  country  weights  for  the 
country measures. EU28 + NO and IS N = 26,653. 
Source: London Economics analysis  

Being a minority, personal and other factors 

In  the  case  of  feeling  vulnerable  for  being  a  minority,  personal  or  other  reasons,  the 
EU28-wide  shares  of  consumers  feeling  vulnerable  for  these  reasons  is  low  as  well 
(3%,  4%  and  4%  respectively).  But,  in  contrast  to  the  case  of  the  personal 
characteristics  discussed  above,  the  incidence  of  these  factors  varies  little  across 
countries. The main exception is “being vulnerable due to being a minority” in Cyprus 
where 10% of consumers feel very vulnerable for this reason. 

114 

0%2%4%6%8%10%12%14%16%Share (%)HealthAge0.00.51.01.52.02.5Mean scoreHealthAge 
 
 
4 │ Incidence of consumer vulnerability 

Figure 21:  Mean rating and share of consumers who feel vulnerable due to 
being a minority and personal reasons - EU28 Member States and IS 
and NO 

Note:  Results  weighted  using  cross-country  weights  for  the  EU28  measure  and  country  weights  for  the 
country measures. EU28 + NO and IS N = 26,653. 
Source: London Economics  

4.1.5.  Analysis of incidence of vulnerability – vulnerability Dimension 3 in EU28 
countries, Iceland and Norway 

The third dimension of consumer vulnerability relates to the difficulty which consumers 
experience  when  obtaining  or  processing  information  during  their  decision  making 
process.  A  number  of  indicators  have  been  constructed  to  evaluate  this  dimension  of 
consumer vulnerability among survey respondents, namely: 

115 

0%2%4%6%8%10%12%Share (%)MinorityPersonal0.00.51.01.52.02.5Mean scoreMinorityPersonal 
 
 
 
4 │ Incidence of consumer vulnerability 

  A  self-reported  measure  about  whether  consumers  feel  generally  uninformed 

about product characteristics;  

  The information sources and how many sources at a time consumers use when 

selecting and buying a product or service62; 

  The  extent  to  which  consumers  fail  to  compare  deals  for  information-related 

reasons; and, 

  The  extent  to  which  consumers  fail  to  switch  service  providers  due  to 

information-related factors. 

In the majority of cases, the incidence of the indicators of the third dimension of 
vulnerability is mostly very low, ranging from 2% to 4% (indicators 11 to 14 and 18 to 
20).  

The only exceptions are the indicators of “having problems comparing deals due to 
information-related factors”, especially in the energy sector, where the EU28 average 
incidence rate is 15%. In the case of the online and financial products and service, the 
EU28-wide incidence rate is somewhat lower at 8% and 10% respectively.  

Table 12:   Dimension 3 vulnerability indicators in EU28 Member States and 

IS and NO 

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(n= 
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2% 
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3% 
9% 
10% 
3% 
3% 
5% 
2% 
4% 
2% 

2% 
(n= 
14,074) 
3% 
2% 

1% 
2% 
3% 
4% 
3% 
2% 

4% 
(n= 
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6% 
3% 
4% 
2% 
18% 
3% 
3% 
6% 
4% 
4% 
3% 

EU28 
AT 
BE 
BG 
HR 
CY 
CZ 
DK 
EE 
FI 
FR 
DE 

2% 
(n= 
14,074) 
2% 
3% 

15% 
(n= 
14,074) 
14% 
16% 

3% 
2% 
2% 
3% 
1% 
2% 

16% 
20% 
13% 
11% 
27% 
9% 

8% 
(n= 
18,506) 
8% 
11% 
3% 
11% 
9% 
4% 
7% 
5% 
8% 
10% 
8% 

10% 
(n= 
14,074) 
12% 
15% 

9% 
19% 
11% 
13% 
12% 
9% 

2% 
(n= 
4,782) 

0% 
(n= 
3,483) 

1% 
(n= 
4,368) 

2% 

0% 

1% 

62 Further information on the various information sources which consumers use when buying or 

selection products can be found in Annex 5 or the report. 

63 See Table 8 for more details on this indicator. 
64 See Table 8 for more details on this indicator. 
65 See Table 8 for more details on this indicator. 

116 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                                 
4 │ Incidence of consumer vulnerability 

Table 12:   Dimension 3 vulnerability indicators in EU28 Member States and 

IS and NO 

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29% 

6% 
1% 
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4% 
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8% 
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7% 
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1% 
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3% 
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EL 
HU 
IE 
IT 
LV 
LT 
LU 
MT 
PL 
PT 
RO 
SK 
SI 
ES 
SE 
NL 
UK 
IS 
NO 
Ovserv
ations 
(EU28 
+NO  + 
IS) 
Note:  Results  weighted  using  cross-country  weights  for  the  EU28  measure  and  country  weights  for  the 
country measures. Missing data is due to particular survey questions not being asked in all countries.  
Source: London Economics  

5% 
7% 
8% 
11% 
7% 
3% 
16% 
6% 
9% 
4% 
4% 
7% 
7% 
13% 
7% 
5% 
6% 
15% 
9% 

26,653  15,481  19,913  15,481  15,481  19,913  15,481 

11% 
13% 
11% 
32% 
16% 

9% 
12% 
7% 
20% 
13% 

1% 
1% 
2% 
1% 
2% 

1% 
1% 
3% 
1% 
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25% 
21% 

12% 
14% 
12% 

2% 
3% 
3% 

3% 
3% 

0% 
1% 

2% 
2% 

4,782 

3,483 

4,368 

1% 

0% 

1% 

Among  the  various  Member  States,  only  Luxembourg,  and  to  a  lesser  extent  Cyprus, 
stand  out  as  having  more  often  than  other  countries  a  higher  incidence  rate  across 
these Dimension 3 vulnerability indicators.  

As  already  noted,  the  survey  asked  consumers  to  assess  how  uninformed  they  felt in 
general  when  choosing  and  purchasing  goods  and  services,  for  instance  in  terms  of 
their price, quality or the purchase conditions. 

Figure  22  presents  the  average  score  (from  1  meaning  not  at  all  uninformed  to  4 
meaning  highly  uninformed),  as  well  as  the  share  of  consumers  in  each  country  who 
reported  they  felt  highly  uninformed.  The  mean  score  of  the  ‘feeling  uninformed’ 
indicator in the total sample was 2, ranging from 1.8 in Luxembourg to 2.3 in Lithuania 
and Poland. Cyprus was the country with the highest proportion (10%) of respondents 

117 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4 │ Incidence of consumer vulnerability 

who  said  they  felt  highly  uninformed,  whereas  Sweden  had  the  lowest  share  of 
vulnerable consumers in this aspect, only 1%. 

Figure 22: 

Indicators of ‘feeling uninformed about prices etc’ - EU28 

Member States and IS and NO 

Note:  Results  weighted  using  cross-country  weights  for  the  EU28  measure  and  country  weights  for  the 
country measures. EU28 + NO and IS N = 26,653. 
Source: London Economics analysis  

Figure 23:  Average rating of feeling informed (1 = very well informed, 4 -= 

not informed at all) - EU28 Member States and IS and NO 

Note:  Results  weighted  using  cross-country  weights  for  the  EU28  measure  and  country  weights  for  the 
country measures. EU28 + NO and IS N = 26,653. 
Source: London Economics analysis  

118 

00.511.522.50%2%4%6%8%10%12%SEFISIDEUKATLVELROLUITBGCZSKDKEU28NLFRHUPLEEIEPTESMTBELTHRCYISNOScoreShare (%)Share of consumers who felt very uninformedAverage score1.81.81.81.81.91.91.91.91.91.91.91.91.91.91.9222222.12.12.12.12.12.22.22.32.31.92.1ATFIIELUBEHRFRELITPTROSISENLUKCZDKDEMTEU28CYEEHULVSKBGESLTPLNOIS 
 
 
 
 
4 │ Incidence of consumer vulnerability 

The  remaining  indicators  in  Dimension  3  are  sector-specific.  An  overview  of  the 
findings  is  provided  below,  and  a  more  detailed  discussion  based  on  the  vulnerability 
incidence of these factors is presented in Chapter 9. 

Vulnerability according to Dimension 3 in the three sectors 

Survey  respondents  were  also  asked  which  sources  of  information  they  use  when 
selecting  and  purchasing  products  in  the  energy,  online  and  financial  sectors  -  online 
search,  comparison  tools,  national  authorities,  consumer  organisations,  friends  and 
family, advertisements or other sources.  

The key findings are that: 

 

In  all  three  sectors,  online  searches  are  the  most  common  source  of 
information when comparing product deals. However, compared to the energy 
sector, a smaller proportion of consumers report using comparison sites in the 
online sector, and an even smaller proportion – in the financial sector. 

  Friends and family are the second most common source, except in the case of 
finance where a bank or similar institution is the second most important source 
of information. 

  Only  4%  of  respondents  in  the  EU28  who  do  compare  deals  are  considered 
vulnerable  for  using  only  advertisements  when  comparing  deals in  the  energy 
sector.  The  corresponding  proportions  are  5%  in  the  online  sector  and  3%  in 
the financial sector. Countries where this share is larger than the average are: 

  Luxembourg (9%) and Portugal (9%) for the energy sector; 
  Cyprus  (24%),  Malta  (18%),  Luxembourg  (17%)  and  Latvia  (13%)  for 

the online sector; and 

  Luxembourg (8%) for the financial sector. 

A  further  indicator  of  information-related  consumer  vulnerability  is  the  extent  to 
which  consumers  fail  to  compare  deals  due  to  information-related  factors.  On 
average across all countries66, more consumers do not compare, or find it difficult 
to compare, deals for information-related reasons in the energy sector (15%) and 
the finance sector (10%) than in the online sector (8%).  

In  the  energy  sector,  Iceland  is  the  country  with  the  highest  proportion  of 
respondents  (32%)  who  said  they  do  not  compare  or  find  it difficult  to  compare 
lowest 
deals  due  to 
corresponding  share  is  Germany  (9%).  Iceland  is  also  the  country  with  highest 
prevalence  of  consumers  who  do  not  compare  financial  products  deals  (20%).  In 
the online sector, the highest proportion of vulnerable consumers according to this 
indicator (16%), was found in Luxemburg.  

information-related  reasons.  The  country  with  the 

Further discussion of sectoral differences is provided in Chapter 9. 

66 In countries where the respective question was asked. 

119 

 
 
 
                                                 
4 │ Incidence of consumer vulnerability 

4.1.6.  Analysis of incidence of vulnerability – vulnerability dimension 4 in EU28 
countries, Iceland and Norway 

The fourth dimension of consumer vulnerability – the inability or failure to buy, choose 
or access suitable products – incorporates a number of indicators67: 

  Does not compare deals (for each of the energy, online and finance sectors); 

  Finds  it  difficult  to  compare  deals  (for  each  of  the  energy,  online  and  finance 

sectors); 

  Has  problems68  comparing  deals  (for  each  of  the  energy,  online  and  finance 

sectors) due to: 

  Personal factors; 
  Market-related factors; and 
  Access-related factors; 

  Has not recently switched service provider (for each of the energy, online and 

finance sectors) due to: 

  Personal factors; 
  Market-related factors;  
  Access-related factors;  

  Has not recently switched service provider due to: 

  Termination costs;  
  Bundling; 

  Exclusion from e-commerce: share of consumers who have not made an online 

purchase in the last year 

  Because of the difficulty of the process; 
  Because they don’t have a payment card; 

  Tried but failed to obtain a loan.  

The EU28-wide incidence of vulnerability across the different Dimension 4 indicators is 
generally much higher than in the case of almost all other vulnerability dimensions and 
indicators. 

These Dimension 4 indicators focus mainly on the actions of actually comparing deals 
and switching providers. The incidence rates reported below suggest that a significant 
proportion of consumers may be very or to relatively highly vulnerable in these aspects 
of their marketplace interactions. 

The findings are that: 

  A  majority  of  consumers  do  not  at  all  compare  deals  –  the  two  respective 
indicators for the finance and energy sectors show the highest incidence rate of 
all  Dimension  4 indicators  (57%  for  financial products  and  52%  in  the  energy 
sector). 

67 Further details on how these indicators were constructed can be found in Table 5. 
68  Never  compare  deals  or  finds  it  difficult  to  compare  deals.  Further  details  on  how  these 

indicators were constructed can be found in Table 5. 

120 

 
 
                                                 
4 │ Incidence of consumer vulnerability 

  Seven  indicators  show  a  somewhat  lower  but  still  high  incidence  rate  of 
between 20% and 35%. Most of these indicators relate to problems comparing 
product deals:  

  Having problems comparing market deals: 

–  Due to market-related factors: 

In the energy sector (incidence rate of 33%); 

In the finance sector (33%); 

In the online sector (23%); 

–  Due to personal reasons: 

In the finance sector (31%); 

In the energy sector (28%); 

–  For personal reasons in the energy and finance sectors;  

  Not comparing product deals in the online sector (31%); 

  Lack of switching due to bundling (22%). 

  Moreover,  a  further  set  of  11  indicators  show  a  still  relatively  substantial 

incidence rate of 10% to 20%. These indicators relate to: 

  Being excluded from e-commerce due to:  

–  The difficulty of the process (19%); and  
–  Not having a payment card (15%); 

  Having problems comparing deals: 

for personal reasons in the online sector (16%);  and  

– 
–  For access reason in the energy sector (10%); 

  Difficulties comparing deals in the energy and financial sectors (14% and 

13% respectively); 

  Lack of switching because of: 

–  Termination costs69 (16%);  
–  Market-related reasons 

in the finance sector (13%); and  

in the energy sector (11%); 

–  Access-related reasons in the energy sector (12%); and 
–  Personal reasons in the finance sector (10%).  

69  The  ‘Has  not  switching  due  to  termination  costs’  indicator  was  not  structured  as  a  sector-
specific  indicator  in  the  survey.  It  relates  to  all  respondents.  Further  details  on  how  these 
indicators were constructed can be found in Table 5. 

121 

 
 
 
 
 
 
 
 
 
 
 
                                                 
4 │ Incidence of consumer vulnerability 

Figure 24:   EU28-wide incidence of Dimension 4 vulnerability indicators  

Note:  Results  weighted  using  cross-country  weights  for  the  EU28  measure  and  country  weights  for  the 
country measures. Sample size varies between indicators. For more details, see Table 8. 
Source: London Economics 

In terms of not comparing product deals at all, a few countries show exceptionally high 
incidence rates in the energy sector compared to the EU28 average of 52%. These are 
Bulgaria (with an incidence rate of 87%), Cyprus (85%), Lithuania (83%), Luxembourg 
(82%), Malta (85%), Romania (78%), and Iceland (91%). 

Here,  though,  it  should  be  taken  into  account  that  in  some  Member  States  (e.g. 
Bulgaria,  Cyprus  and  Malta)  the  retail  energy  market  is  not  liberalised  or  was  only 
recently  liberalised.  This  may  explain  the  high  incidence  rates  in  these  countries  on 
some  indicators  (i.e.  indicators  relating  to  comparing  and  switching)  in  the  energy 
sector,  since  it  means  that  de  facto  there  are  few  or  no  options  for  consumers  to 
compare. 

122 

2%2%3%3%4%4%4%6%7%10%10%11%12%13%13%14%15%16%16%19%22%23%28%31%31%33%33%52%57%Has problems comparing deals due to access-related factors -financial sector (n=14,074)Has a bank account and has not switched due to access-related factors -financial sector (n=4,368)Has an internet provider and has not switched due to personal factors -online sector (n=3,483)Has an internet provider and has not switched due to access-related factors -online sector (n=3,483)Has problems comparing deals due to access-related factors –online sector (n=18,506)Finds it very difficult to compare product deals -online sector (n=18,506)Declined for a loan (n=4,971)Has an internet provider and has not switched due to market-related factors -online sector (n=3,483)Has a bank account and has not switched due to personal factors -financial sector (n=4,368)Has an electricity supplier and has not switched due to personal factors -energy sector (n=4,782)Has problems comparing deals due to access-related factors -energy sector (n=14,074)Has an electricity supplier and has not switched due to market-related factors -energy sector (n=4,782)Has an electricity supplier and has not switched due to access-related factors -energy sector (n=4,782)Has a bank account and has not switched due to market-related factors -financial sector (n=4,368)Finds it very difficult to compare product deals -finance sector (n=25,151)Finds it very difficult to compare product deals -energy sector (n=25,136)Excluded from e-commerce due to not having a payment card (n=18,550)Has not switched due to termination costs (n=25,151)Has problems comparing deals due to personal factors -online sector (n=18,506)Excluded from e-commerce due to difficulty of the process (n=18,550)Has not switched due to bundling (n=25,151)Has problems comparing deals due to market-related factors -online sector (n=18,506)Has problems comparing deals due to personal factors -energy sector (n=14,074)Does not compare product deals –online sector (n=18,506)Has problems comparing deals due to personal factors -financial sector (n=14,074)Has problems comparing deals due to market-related factors -energy sector (n=14,074)Has problems comparing deals due to market-related factors -financial sector (n=14,074)Does not compare product deals -energy sector (n=25,151)Does not compare product deals -finance sector (n=25,151) 
 
 
4 │ Incidence of consumer vulnerability 

More  generally,  as  can  be  seen  in  Table  13  to  Table  16,  Romania,  Cyprus,  Lithuania 
and  Luxembourg  stand  out  as  being  the  most  frequent  “high  outliers”  according  to 
Dimension 4 (‘Inability or failure to buy, choose or access suitable products’). 

Table 13:  Dimension 4 vulnerability indicators in EU28 Member States and 

IS and NO  – Problems comparing deals, part 1 

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52% 
(n=25,151) 
43% 
(n=1,001) 
48% 
(n=1,002) 
87% 
(n=1,000) 
52% 
(n=1,002) 
85% 
(n=500) 
43% 
(n=1,000) 
53% 
(n=1,002) 
47% 
(n=500) 
45% 
(n=1,004) 
76% 
(n=1,002) 
36% 
(n=1,006) 
67% 
(n=1,004) 
72% 
(n=1,003) 
33% 
(n=1,011) 
48% 
(n=1,001) 
59% 
(n=500) 
83% 

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.
4
2

-

s
l
a
e
d

t
c
u
d
o
r
p

e
r
a
p
m
o
c

1
7
r
o
t
c
e
s

y
g
r
e
n
e

14% 
(n=25,136) 
9% 
(n=1,001) 
16% 
(n=1,002) 
37% 
(n=1,000) 
23% 
(n=1,002) 
47% 
(n=500) 
9% 
(n=1,000) 
19% 
(n=1,002) 
9% 
(n=485) 
12% 
(n=1,004) 
17% 
(n=1,002) 
7% 
(n=1,006) 
16% 
(n=1,004) 
19% 
(n=1,003) 
9% 
(n=1,011) 
22% 
(n=1,001) 
15% 
(n=500) 
24% 

EU28 

AT 

BE 

BG 

HR 

CY 

CZ 

DK 

EE 

FI 

FR 

DE 

EL 

HU 

IE 

IT 

LV 
LT 

t
c
u
d
o
r
p

e
r
a
p
m
o
c

t
o
n

o
D

.
5
2

2
7
r
o
t
c
e
s

e
n

i
l

n
o

-

s
l
a
e
d

o
t

t
l
u
c
i
f
f
i
d

y
r
e
v

t
i

d
n
F

i

.
6
2

-

s
l
a
e
d

t
c
u
d
o
r
p

e
r
a
p
m
o
c

3
7
r
o
t
c
e
s

e
n

i
l

n
o

31% 
(n=18,506) 
29% 
(n=791) 
49% 
(n=780) 
17% 
(n=510) 
15% 
(n=662) 
36% 
(n=310) 
38% 
(n=710) 
35% 
(n=922) 
37% 
(n=375) 
36% 
(n=874) 
43% 
(n=800) 
30% 
(n=853) 
12% 
(n=544) 
26% 
(n=693) 
20% 
(n=820) 
22% 
(n=631) 
32% 
(n=345) 
24% 

4% 
(n=18,506) 
5% 
(n=791) 
8% 
(n=780) 
1% 
(n=510) 
4% 
(n=662) 
11% 
(n=310) 
2% 
(n=710) 
6% 
(n=922) 
2% 
(n=375) 
7% 
(n=874) 
8% 
(n=800) 
3% 
(n=853) 
0% 
(n=544) 
2% 
(n=693) 
4% 
(n=820) 
5% 
(n=631) 
2% 
(n=345) 
1% 

t
c
u
d
o
r
p

e
r
a
p
m
o
c

t
o
n

o
D

.
7
2

4
7
r
o
t
c
e
s

e
c
n
a
n
i
f

-

s
l
a
e
d

57% 
(n=25,151) 
46% 
(n=1,001) 
62% 
(n=1,002) 
61% 
(n=1,000) 
62% 
(n=1,002) 
65% 
(n=500) 
43% 
(n=1,000) 
55% 
(n=1,002) 
56% 
(n=500) 
55% 
(n=1,004)) 
67% 
(n=1,002) 
50% 
(n=1,006) 
61% 
(n=1,004) 
54% 
(n=1,003) 
54% 
(n=1,011) 
60% 
(n=1,001) 
63% 
(n=500) 
66% 

o
t

t
l
u
c
i
f
f
i
d

y
r
e
v

t
i

d
n
F

i

.
8
2

-

s
l
a
e
d

t
c
u
d
o
r
p

e
r
a
p
m
o
c

5
7
r
o
t
c
e
s

e
c
n
a
n
i
f

13% 
(n=25,136) 
8% 
(n=1,001) 
16% 
(n=1,002) 
18% 
(n=1,000) 
23% 
(n=1,002) 
21% 
(n=500) 
7% 
(n=1,000) 
15% 
(n=1,002) 
7% 
(n=485) 
9% 
(n=1,004) 
15% 
(n=1,002) 
7% 
(n=1,006) 
15% 
(n=1,004) 
16% 
(n=1,003) 
13% 
(n=1,011) 
22% 
(n=1,001) 
10% 
(n=500) 
11% 

70 See Table 8 for more details on this indicator. 
71 See Table 8 for more details on this indicator. 
72 See Table 8 for more details on this indicator. 
73 See Table 8 for more details on this indicator. 
74 See Table 8 for more details on this indicator. 
75 See Table 8 for more details on this indicator. 

123 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                                 
4 │ Incidence of consumer vulnerability 

Table 13:  Dimension 4 vulnerability indicators in EU28 Member States and 

IS and NO  – Problems comparing deals, part 1 

t
c
u
d
o
r
p

e
r
a
p
m
o
c

t
o
n

s
e
o
D
.
3
2

0
7
r
o
t
c
e
s

y
g
r
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n
e

-

s
l
a
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d

o
t

t
l
u
c
i
f
f
i
d

y
r
e
v

t
i

s
d
n
F

i

.
4
2

-

s
l
a
e
d

t
c
u
d
o
r
p

e
r
a
p
m
o
c

1
7
r
o
t
c
e
s

y
g
r
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n
e

t
c
u
d
o
r
p

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r
a
p
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t
o
n

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D

.
5
2

2
7
r
o
t
c
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s

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n

i
l

n
o

-

s
l
a
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d

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t

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l
u
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i
f
f
i
d

y
r
e
v

t
i

d
n
F

i

.
6
2

-

s
l
a
e
d

t
c
u
d
o
r
p

e
r
a
p
m
o
c

3
7
r
o
t
c
e
s

e
n

i
l

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o

t
c
u
d
o
r
p

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r
a
p
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t
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n

o
D

.
7
2

4
7
r
o
t
c
e
s

e
c
n
a
n
i
f

-

s
l
a
e
d

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t

t
l
u
c
i
f
f
i
d

y
r
e
v

t
i

d
n
F

i

.
8
2

-

s
l
a
e
d

t
c
u
d
o
r
p

e
r
a
p
m
o
c

5
7
r
o
t
c
e
s

e
c
n
a
n
i
f

SI 

PL 

PT 

LU 

SK 

MT 

RO 

(n=1,009) 
82% 
(n=503) 
85% 
(n=524) 
61% 
(n=1,009) 
46% 
(n=1,012) 
78% 
(n=1,001) 
55% 
(n=1,005) 
35% 
(n=502) 
53% 
(n=1,000) 
50% 
(n=1,002) 
46% 
(n=1,019) 
38% 
(n=1,027) 
91% 
(n=500) 
55% 
(n=1,002) 

(n=625) 
51% 
(n=468) 
51% 
(n=414) 
20% 
(n=703) 
13% 
(n=621) 
30% 
(n=541) 
30% 
(n=752) 
18% 
(n=372) 
23% 
(n=680) 
53% 
(n=921) 
46% 
(n=959) 
33% 
(n=830) 
48% 
(n=475) 
58% 
NO 
(n=932) 
Note:  Results  weighted  using  cross-country  weights  for  the  EU28  measure  and  country  weights  for  the 
country measures. 
Source: London Economics 

(n=1,009) 
62% 
(n=503) 
57% 
(n=524) 
49% 
(n=1,009) 
63% 
(n=1,012) 
66% 
(n=1,001) 
45% 
(n=1,005) 
47% 
(n=502) 
55% 
(n=1,000) 
58% 
(n=1,002) 
73% 
(n=1,019) 
60% 
(n=1,027) 
71% 
(n=500) 
52% 
(n=1,002) 

(n=1,009) 
19% 
(n=503) 
29% 
(n=524) 
12% 
(n=1,009) 
15% 
(n=1,012) 
21% 
(n=1,001) 
11% 
(n=1,005) 
12% 
(n=502) 
19% 
(n=1,000) 
13% 
(n=1,002) 
12% 
(n=1,019) 
10% 
(n=1,027) 
40% 
(n=500) 
9% 
(n=1,002) 

(n=1,009) 
8% 
(n=503) 
19% 
(n=524) 
8% 
(n=1,009) 
20% 
(n=1,012) 
18% 
(n=1,001) 
10% 
(n=1,005) 
12% 
(n=502) 
16% 
(n=1,000) 
8% 
(n=1,002) 
8% 
(n=1,019) 
8% 
(n=1,027) 
17% 
(n=500) 
7% 
(n=1,002) 

(n=625) 
8% 
(n=468) 
17% 
(n=414) 
2% 
(n=703) 
1% 
(n=621) 
2% 
(n=541) 
2% 
(n=752) 
2% 
(n=372) 
3% 
(n=680) 
6% 
(n=921) 
4% 
(n=959) 
3% 
(n=830) 
14% 
(n=475) 
7% 
(n=932) 

UK 

ES 

SE 

NL 

IS 

Table 14:  Dimension 4 vulnerability indicators in EU28 Member States and 

IS and NO – Problems comparing deals, part 2 

g
n
i
r
a
p
m
o
c

l

s
m
e
b
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r
p

s
a
H

.
9
2

-

s
r
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t
c
a
f

l

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r
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p

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t

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u
d

r
o
t
c
e
s

y
g
r
e
n
e

s
l
a
e
d
28% 
(n= 

EU28 

g
n
i
r
a
p
m
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c

l

s
m
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b
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r
p

s
a
H

.
0
3

-

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u
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s

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n

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l

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o

s
l
a
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d
16% 
(n= 

r
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s

l

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m
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b
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r
p

s
a
H

.
1
3

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a
n
i
f

-

s
r
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c
a
f

l

a
n
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s
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p

31% 
(n= 

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n
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s
m
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b
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s
a
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.

2
3

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f

33% 
(n= 

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a
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.
3
3

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t

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23% 
(n= 

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s

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n

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a
f

33% 
(n= 

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b
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a
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a
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10% 
(n= 

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b
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a
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6
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r
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t
c
a
f

4% 
(n= 

g
n
i
r
a
p
m
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c

l

s
m
e
b
o
r
p

s
a
H

.
7
3

l

d
e
t
a
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r
-
s
s
e
c
c
a

o
t

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u
d

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c
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s

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a
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a
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r
o
t
c
a
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2% 
(n= 

124 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4 │ Incidence of consumer vulnerability 

Table 14:  Dimension 4 vulnerability indicators in EU28 Member States and 

IS and NO – Problems comparing deals, part 2 

l

s
m
e
b
o
r
p

s
a
H

.
1
3

o
t

e
u
d

s
l
a
e
d

g
n
i
r
a
p
m
o
c

l

a
i
c
n
a
n
i
f

-

s
r
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t
c
a
f

l

a
n
o
s
r
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p

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o
t
c
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s

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p

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a
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.

2
3

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d
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r
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l
a
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o
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c
e
s

y
g
r
e
n
e

-

s
r
o
t
c
a
f

14,074) 
20% 
(n=791) 
31% 
(n=780) 

14,074) 
32% 
(n=791) 
40% 
(n=780) 

27% 
(n=710) 
34% 
(n=922) 
39% 
(n=375) 
38% 
(n=874) 
34% 
(n=800) 
22% 
(n=853) 

38% 
(n=710) 
42% 
(n=922) 
36% 
(n=375) 
34% 
(n=874) 
47% 
(n=800) 
26% 
(n=853) 

28% 
(n=820) 

26% 
(n=820) 

-

s
r
o
t
c
a
f

l

a
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s
r
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p

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s
m
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b
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r
p

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a
H

.
0
3

s
l
a
e
d
18,506) 
17% 
(n=791) 
26% 
(n=780) 
7% 
(n=510) 
13% 
(n=662) 
31% 
(n=310) 
16% 
(n=710) 
18% 
(n=922) 
20% 
(n=375) 
21% 
(n=874) 
19% 
(n=800) 
15% 
(n=853) 
7% 
(n=544) 
17% 
(n=693) 
11% 
(n=820) 
16% 
(n=631) 
18% 
(n=345) 

12% 
(n=625) 
29% 
(n=468) 
19% 
(n=414) 
16% 
(n=703) 

6% 
(n=621) 

12% 
(n=541) 
18% 
(n=752) 

45% 
(n= 
1,009) 
29% 
(n=468) 

15% 
(n= 
1,009) 
42% 
(n=468) 

39% 
(n= 
1,012) 
44% 
(n= 
1,001) 
27% 
(n=752) 

25% 
(n= 
1,012) 
26% 
(n= 
1,001) 
40% 
(n=752) 

l

s
m
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b
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a
H

.
3
3

o
t

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u
d

s
l
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a
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c
a
f

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a
m

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o
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c
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s

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n

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l

n
o

18,506) 
27% 
(n=791) 
32% 
(n=780) 
10% 
(n=510) 
22% 
(n=662) 
17% 
(n=310) 
15% 
(n=710) 
28% 
(n=922) 
15% 
(n=375) 
22% 
(n=874) 
30% 
(n=800) 
26% 
(n=853) 
10% 
(n=544) 
22% 
(n=693) 
16% 
(n=820) 
23% 
(n=631) 
18% 
(n=345) 

8% 
(n=625) 
39% 
(n=468) 
12% 
(n=414) 
16% 
(n=703) 

13% 
(n=621) 

10% 
(n=541) 
17% 
(n=752) 

g
n
i
r
a
p
m
o
c

l

s
m
e
b
o
r
p

s
a
H

.
4
3

l

d
e
t
a
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t

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u
d

s
l
a
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d

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o
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c
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s

l

a
i
c
n
a
n
i
f

-

s
r
o
t
c
a
f

g
n
i
r
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p
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l

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m
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b
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r
p

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a
H

.
5
3

l

d
e
t
a
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r
-
s
s
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c
c
a

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t

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u
d

s
l
a
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d

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o
t
c
e
s

y
g
r
e
n
e

-

s
r
o
t
c
a
f

14,074) 
41% 
(n=791) 
38% 
(n=780) 

14,074) 
12% 
(n=791) 
3% 
(n=780) 

33% 
(n=710) 
46% 
(n=922) 
36% 
(n=375) 
36% 
(n=874) 
46% 
(n=800) 
31% 
(n=853) 

9% 
(n=710) 
15% 
(n=922) 
17% 
(n=375) 
9% 
(n=874) 
10% 
(n=800) 
6% 
(n=853) 

35% 
(n=820) 

6% 
(n=820) 

30% 
(n= 
1,009) 
38% 
(n=468) 

55% 
(n= 
1,009) 
29% 
(n=468) 

35% 
(n= 
1,012) 
29% 
(n= 
1,001) 
36% 
(n=752) 

10% 
(n= 
1,012) 
44% 
(n= 
1,001) 
11% 
(n=752) 

g
n
i
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a
p
m
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c

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b
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p

s
a
H

.
6
3

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c
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s

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n

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l

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o

-

s
r
o
t
c
a
f

18,506) 
3% 
(n=791) 
2% 
(n=780) 
3% 
(n=510) 
3% 
(n=662) 
2% 
(n=310) 
7% 
(n=710) 
7% 
(n=922) 
5% 
(n=375) 
7% 
(n=874) 
2% 
(n=800) 
3% 
(n=853) 
2% 
(n=544) 
7% 
(n=693) 
6% 
(n=820) 
3% 
(n=631) 
6% 
(n=345) 

4% 
(n=625) 
3% 
(n=468) 
0% 
(n=414) 
4% 
(n=703) 

2% 
(n=621) 

5% 
(n=541) 
4% 
(n=752) 

g
n
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a
p
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c

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b
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a
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.
7
3

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s

l

a
i
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n
a
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f

-

s
r
o
t
c
a
f

14,074) 
1% 
(n=791) 
2% 
(n=780) 

2% 
(n=710) 
2% 
(n=922) 
2% 
(n=375) 
3% 
(n=874) 
1% 
(n=800) 
1% 
(n=853) 

3% 
(n=820) 

6% 
(n= 
1,009) 
0% 
(n=468) 

3% 
(n= 
1,012) 
3% 
(n= 
1,001) 
1% 
(n=752) 

-

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c
a
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.
9
2

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l
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d
14,074) 
28% 
(n=791) 
32% 
(n=780) 

32% 
(n=710) 
35% 
(n=922) 
35% 
(n=375) 
34% 
(n=874) 
41% 
(n=800) 
18% 
(n=853) 

17% 
(n=820) 

33% 
(n= 
1,009) 
49% 
(n=468) 

28% 
(n= 
1,012) 
37% 
(n= 
1,001) 
42% 
(n=752) 

AT 

BE 

BG 

HR 

CY 

CZ 

DK 

EE 

FI 

FR 

DE 

EL 

HU 

IE 

IT 

LV 

LT 

LU 

MT 

PL 

PT 

RO 

SK 

125 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
g
n
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.
9
2

-

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g
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SI 

ES 

-

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l

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l

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b
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p

s
a
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.
0
3

s
l
a
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d
19% 
(n=372) 
16% 
(n=680) 
24% 
(n=921) 
21% 
(n=959) 

4 │ Incidence of consumer vulnerability 

Table 14:  Dimension 4 vulnerability indicators in EU28 Member States and 

IS and NO – Problems comparing deals, part 2 

l

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27% 
(n=372) 
25% 
(n=680) 
26% 
(n=921) 
23% 
(n=959) 

5% 
(n=372) 
2% 
(n=680) 
16% 
(n=921) 
3% 
(n=959) 

NL 

SE 

UK 

35% 
(n=921) 
29% 
(n=959) 
24% 
(n= 
1,027) 
51% 
(n=475) 
35% 
(n=932) 

1% 
(n=921) 
2% 
(n=959) 
1% 
(n= 
1,027) 
5% 
(n=475) 
1% 
NO 
(n=932) 
Note:  Results  weighted  using  cross-country  weights  for  the  EU28  measure  and  country  weights  for  the 
country measures. Missing data is due to particular survey questions not being asked in all countries via all 
modes.  
Source: London Economics 

10% 
(n=921) 
3% 
(n=959) 
4% 
(n= 
1,027) 
27% 
(n=475) 
12% 
(n=932) 

31% 
(n=921) 
34% 
(n=959) 
31% 
(n= 
1,027) 
23% 
(n=475) 
31% 
(n=932) 

35% 
(n=921) 
34% 
(n=959) 
33% 
(n= 
1,027) 
36% 
(n=475) 
31% 
(n=932) 

33% 
(n=921) 
27% 
(n=959) 
26% 
(n= 
1,027) 
41% 
(n=475) 
27% 
(n=932) 

13% 
(n=830) 
25% 
(n=475) 
25% 
(n=932) 

21% 
(n=830) 
37% 
(n=475) 
18% 
(n=932) 

3% 
(n=830) 
2% 
(n=475) 
21% 
(n=932) 

IS 

Table 15:   Dimension 4 vulnerability indicators in EU28 Member States and 

IS and NO – Problems switching providers 

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10% 
(n= 
4,782) 

3% 
(n= 
3,483) 

7% 
(n= 
4,368) 

11% 
(n= 
4,782) 

6% 
(n= 
3,483) 

EU28 

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13% 
(n= 
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12% 
(n= 
4,782) 

3% 
(n= 
3,483) 

2% 
(n= 
4,368) 

16% 
(n= 
25,151) 

22% 
(n= 
25,151) 

AT 

BE 

BG 

HR 

CY 

CZ 

DK 

EE 

9% 

4% 

5% 

15% 

5% 

14% 

10% 

4% 

3% 

16% 

13% 

18% 

25% 

17% 

15% 

9% 

19% 

19% 

24% 

28% 

38% 

30% 

20% 

19% 

26% 

126 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4 │ Incidence of consumer vulnerability 

Table 15:   Dimension 4 vulnerability indicators in EU28 Member States and 

IS and NO – Problems switching providers 

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i

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s

t
o
n

s
a
H

r
o
t
c
e
s

y
g
r
e
n
e

-

s
r
o
t
c
a
f

d
e
t
a
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r

l

d
n
a

i

r
e
d
v
o
r
p

t
e
n
r
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t
n

i

n
a

s
a
H

.
5
4

-
s
s
e
c
c
a

o
t

e
u
d

d
e
h
c
t
i

w
s

t
o
n

s
a
H

r
o
t
c
e
s

e
n

i
l

n
o

-

s
r
o
t
c
a
f

d
e
t
a
e
r

l

t
o
n

s
a
H
d
n
a

t
n
u
o
c
c
a

k
n
a
b

a

s
a
H
.
6
4

l

d
e
t
a
e
r
-
s
s
e
c
c
a

o
t

e
u
d

d
e
h
c
t
i

w
s

r
o
t
c
e
s

l

a
i
c
n
a
n
i
f

-

s
r
o
t
c
a
f

FI 

FR 

DE 

EL 

HU 

IE 

IT 

LV 

LT 

LU 

MT 

PL 

PT 

RO 

SK 

SI 

ES 

SE 

NL 

UK 

IS 

21% 

3% 

8% 

4% 

2% 

12% 

42% 

6% 

4% 

11% 

22% 

1% 

4% 

12% 

14% 

12% 

10% 

6% 

3% 

12% 

10% 

7% 

36% 

2% 

7% 

3% 

5% 

5% 

3% 

5% 

11% 

7% 

13% 

3% 

2% 

2% 

o
t

e
u
d

d
e
h
c
t
i

w
s

t
o
n

s
a
H

.
7
4

s
t
s
o
c

n
o
i
t
a
n
m
r
e
t

i

9% 

16% 

10% 

25% 

22% 

21% 

22% 

22% 

17% 

19% 

10% 

21% 

18% 

27% 

17% 

22% 

16% 

6% 

10% 

12% 

9% 

o
t

e
u
d

d
e
h
c
t
i

w
s

t
o
n

s
a
H

.
8
4

g
n

i
l

d
n
u
b

24% 

24% 

17% 

30% 

24% 

28% 

26% 

23% 

18% 

26% 

26% 

29% 

27% 

30% 

26% 

35% 

23% 

14% 

15% 

14% 

17% 

NO 
Note:  Results  weighted  using  cross-country  weights  for  the  EU28  measure  and  country  weights  for  the 
country measures. Missing data is due to particular survey questions not being asked in all countries.  
Source: London Economics 

22% 

7% 

Table 16:   Dimension 4 vulnerability indicators in EU28 Member States and 

IS and NO - Exclusion 

49. Excluded from e-
commerce due to 
difficulty of the 
process 

50.Excluded from e-
commerce due to not 
having a payment 
card 

19% 
(n=18,550) 
17% 
20% 
29% 
39% 
31% 
25% 
19% 

15% 
(n=18,550) 
27% 
18% 
24% 
35% 
28% 
17% 
10% 

51. Declined for a 
loan 

4% 
(n=4,971) 

3% 

EU28 
AT 
BE 
BG 
HR 
CY 
CZ 
DK 

127 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4 │ Incidence of consumer vulnerability 

Table 16:   Dimension 4 vulnerability indicators in EU28 Member States and 

IS and NO - Exclusion 

49. Excluded from e-
commerce due to 
difficulty of the 
process 

50.Excluded from e-
commerce due to not 
having a payment 
card 

51. Declined for a 
loan 

EE 
FI 
FR 
DE 
EL 
HU 
IE 
IT 
LV 
LT 
LU 
MT 
PL 
PT 
RO 
SK 
SI 
ES 
SE 
NL 
UK 
IS 
NO 
Note:  Results  weighted  using  cross-country  weights  for  the  EU28  measure  and  country  weights  for  the 
country measures. Missing data is due to particular survey questions not being asked in all countries via all 
modes.  
Source: London Economics 

16% 
13% 
8% 
17% 
36% 
20% 
15% 
19% 
15% 
20% 
14% 
9% 
22% 
27% 
35% 
18% 
41% 
12% 
8% 
6% 
8% 
15% 
6% 

24% 
26% 
21% 
14% 
31% 
24% 
26% 
19% 
28% 
21% 
29% 
13% 
21% 
23% 
28% 
28% 
26% 
19% 
21% 
10% 
15% 
19% 
20% 

5% 
8% 

1% 

3% 

4.1.7.   Analysis of incidence of vulnerability – vulnerability Dimension 5 in EU28 
countries, Iceland and Norway 

Dimension  5,  ‘Higher  susceptibility  to  marketing  practices’,  is  characterised  by  five 
indicators  (see  Table  17),  of  which  four  are  based  on  the  experiments  under  the 
marketing practice treatment and are, therefore, populated with data for only the five 
experiment countries.   

Table 17:  Dimension 5 vulnerability indicator in EU28 Member States and 

IS and NO  

54. 
Vulnerable to 
a great extent 
due to 
complexity of 
offers 

10% 
(n=25,151) 
4% 
(n=1,001) 
7% 

EU28 

AT 
BE 

55. 
Experiment 
choice under 
the marketing 
practice 
treatment - 
energy sector 
85% 
(n=1,263) 

56. 
Experiment 
choice under 
the marketing 
practice 
treatment - 
online sector 
53% 
(n=1,254) 

57. 
Experiment 
choice under 
the marketing 
practice 
treatment – 
finance sector 
27% 
(n=1,252) 

58. 
Experiment 
choice under 
the marketing 
practice 
treatment – 
cross-cutting 
66% 
(n=1,282) 

128 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4 │ Incidence of consumer vulnerability 

Table 17:  Dimension 5 vulnerability indicator in EU28 Member States and 

IS and NO  

54. 
Vulnerable to 
a great extent 
due to 
complexity of 
offers 

55. 
Experiment 
choice under 
the marketing 
practice 
treatment - 
energy sector 

56. 
Experiment 
choice under 
the marketing 
practice 
treatment - 
online sector 

57. 
Experiment 
choice under 
the marketing 
practice 
treatment – 
finance sector 

58. 
Experiment 
choice under 
the marketing 
practice 
treatment – 
cross-cutting 

(n=1,002) 
13% 
(n=1,000) 
9% 
(n=1,002) 
26% 
(n=500) 
8% 
(n=1,000) 
7% 
(n=1,002) 
8% 
(n=500) 
6% 
(n=1,004) 
12% 
(n=1,002) 
4% 
(n=1,006) 
13% 
(n=1,004) 
8% 
(n=1,003) 
9% 
(n=1,011) 
12% 
(n=1,001) 
12% 
(n=500) 
15% 
(n=1,009) 
9% 
(n=503) 
13% 
(n=524) 
12% 
(n=1,009) 
14% 
(n=1,012) 
15% 
(n=1,001) 
12% 
(n=1,005) 
7% 
(n=502) 
15% 
(n=1,000) 
7% 
(n=1,002) 

BG 

HR 

CY 

CZ 

DK 

EE 

FI 

FR 

DE 

EL 

HU 

IE 

IT 

LV 

LT 

LU 

MT 

PL 

PT 

RO 

SK 

SI 

ES 

SE 

129 

85% 
(n=249) 

49% 
(n=252) 

20% 
(n=250) 

64% 
(n=251) 

90% 
(n=255) 

57% 
(n=251) 

20% 
(n=255) 

73% 
(n=248) 

90% 
(n=246) 
90% 
(n=250) 

58% 
(n=247) 
64% 
(n=250) 

35% 
(n=244) 
27% 
(n=250) 

74% 
(n=275) 
78% 
(n=251) 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4 │ Incidence of consumer vulnerability 

Table 17:  Dimension 5 vulnerability indicator in EU28 Member States and 

IS and NO  

54. 
Vulnerable to 
a great extent 
due to 
complexity of 
offers 

55. 
Experiment 
choice under 
the marketing 
practice 
treatment - 
energy sector 

56. 
Experiment 
choice under 
the marketing 
practice 
treatment - 
online sector 

57. 
Experiment 
choice under 
the marketing 
practice 
treatment – 
finance sector 

58. 
Experiment 
choice under 
the marketing 
practice 
treatment – 
cross-cutting 

82% 
(n=263) 

48% 
(n=254) 

26% 
(n=253) 

60% 
(n=257) 

NL 

UK 

IS 

4% 
(n=1,019) 
10% 
(n=1,027) 
4% 
(n=500) 
5% 
(n=1,002) 

NO 
Note: Results weighted using cross-country weights for the EU28/experiment countries measure and country 
weights for the country measures. LT is not identified as an outlier when a figure with 1 decimal is used. 
Source: London Economics 

The  EU28-wide  incidence  of  vulnerability  due  to  the  complexity  of  offers  is  10%  and 
varies  to  some  extent  across  the  EU28.  In  particular,  Cyprus  shows  a  much  higher 
incidence rate of 26% (and to a smaller extent Spain with an incidence rate of 15%), 
which is more than twice as high as the EU28 average. 

In  terms  of  the  rating  of  the  complexity  of  offers  (on  a  scale  from  1  to  4),  the  mean 
rating is equal to 2 or less in 6 countries among the EU28 Member States, Iceland and 
Norway. Additionally, the mean rating never exceeds 2.6 (see Figure 25). 

These  findings  suggest  that,  on  average,  in  the  majority  of  EU28  Member  States  and 
Iceland  and  Norway,  consumers  find  the  offers  in  the  market  place  only  very 
marginally  complex.  However,  in  each  country,  a  certain  proportion  of  the  population 
(ranging  from  4%  in  Iceland  to  26%  in  Cyprus)  feels  very  vulnerable  because  of  the 
complexity of offers.  

In  contrast,  the  experiment  results  point  at  the  opposite,  namely  that  consumer 
vulnerability due to failure to make the right choices under current marketing practices 
is amongst the highest out of all indicators of consumer vulnerability: 

  The majority of consumers across the five experiment countries – 85% – fail to 

choose optimally76 under the marketing practice in the energy sector; 
  Very high shares of consumers are vulnerable also under the cross-cutting 

experiment (66%) and in the online sector (53%);  

  Experiment participants performed much better under the finance experiment – 
only 27% of them have failed to choose the right option in both rounds of the 
experiment – however this is still a relatively high incidence compared to most 
indicators in all other dimensions. 

A  caveat  to  these  findings  is  that the  experiments  were  designed  to mimic  marketing 
practices  are  known  to  be  particularly  problematic.  In  reality,  not  all  consumers  face 

76 In both rounds of the experiment. 

130 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                                 
4 │ Incidence of consumer vulnerability 

these  marketing  practices,  and  therefore  it  would  be  inaccurate  to  extrapolate  the 
vulnerability  incidence  estimated  from  the  experiments  to  the  general  population.  A 
limitation  of  the  approach  is  that,  unlike  for  other  indicators,  it  is  not  possible  to  re-
base  the  experiment  indicators  to  account  for  the  proportion  of  the  population  which 
faces  such  marketing  practices.  The  results  from  the  experiments  are  discussed  in 
greater detail in Chapter 6 of the report.   

Figure 25:  Mean rating of complexity of offers ( 1= not complex, 4=very 

complex) in EU28 Member States and IS and NO 

Note:  Results  weighted  using  cross-country  weights  for  the  EU28  measure  and  country  weights  for  the 
country measures. EU28 + NO and IS N = 26,653. 
Source: London Economics 

4.2. Extent that consumers are vulnerable across a number of dimensions 

While the previous section provides useful information on the incidence of vulnerability 
on  an  indicator-by-indicator  basis,  it  did  not  shed  any  light  on  whether  particular 
individuals  exhibit  vulnerability  along  a  number  of  vulnerability  dimensions.  The 
present section aims to address this information gap through a two-pronged approach: 

  First,  the  section  presents  information  on  the  extent  to  which  survey 
participants  who  can  be  considered  vulnerable  according  to  one  of  the  five 
vulnerability  dimensions  are  also  vulnerable  according  to  one  or  more  of  the 
other vulnerability dimensions; 

  Second,  the  section  provides  detailed  information  on  the  distribution  of  the 
overall survey population according to two or more vulnerability indicators at a 
time.  This  analysis  differs  from  the  previous  as  it  also  takes  into  account 
survey respondents who are not vulnerable. 

4.2.1.  Incidence of multi-dimensional vulnerability 

Multi-dimensional  vulnerability  refers  to  the  extent  to  which  survey  participants  who 
can be considered vulnerable according to one of the five vulnerability dimensions are 
also vulnerable according to one or more of the other vulnerability dimensions. 

For the purpose of the present analysis of multi-dimensional vulnerability, each survey 
respondent  was  allocated  to  a  particular  dimension  if  at  least  one  of  the  vulnerability 
indicators of the dimension signalled that the survey respondent was vulnerable. Thus 
any survey respondent could be allocated to more than one dimension. 

131 

1.81.81.91.91.922.12.12.12.12.12.12.22.22.22.22.32.32.32.32.32.42.42.42.42.52.52.62.61.82ATNLDKDESEBEHRCZFIHULUUKEEIEMTEU28BGFRITSKSIELLVLTPLPTESCYROISNO 
 
 
 
4 │ Incidence of consumer vulnerability 

It should be noted that the complex survey structure does not allow this section of the 
analysis to capture each and every vulnerability indicator which has been tested in the 
survey as the analysis is restricted to those indicators of vulnerability which are based 
on answers by all survey respondents. This is a limitation of the survey design, and all 
indicators have been used to the extent possible.   

In  the  case  of  Dimension  4,  ‘Inability  or  failure  to  buy,  choose  or  access  suitable 
products’,  none  of  the  indicators  constructed  for    analysis  of  the  drivers  of 
vulnerability77  are  based  on  questions  answered  by  all  survey  respondents.  To 
overcome  this  limitation,  four  new  vulnerability  indicators  were  constructed.  They 
identify respondents who do not compare product deals or who find comparing product 
deals  very  difficult,  without  examining  in  detail  the  reasons  why  respondents  exhibit 
this behaviour. 

The  complete  set  of  vulnerability indicators  which  satisfy  the  condition  of  having  data 
for is the following:  

  Dimension  1  (Heightened  risk  of  negative  outcomes  or  impacts  on  well-

being):  

 

‘Overpaid  for  services  due  to  being  unable  to  use  certain  payment 
methods’  

  `Has experienced a problem and took no action’ 

  Dimension  2  (Having  characteristics  that  limit  the  ability  to  maximise  well-

being): 

 

 

 

 

 

 

 

‘Perception  of  own  vulnerability  due  to  personal  characteristics:  Feels 
vulnerable to 'a great extent' due to age’ 
‘Perception  of  own  vulnerability  due  to  personal  characteristics:  Feels 
vulnerable to 'a great extent' due to employment situation’ 
‘Perception  of  own  vulnerability  due  to  personal  characteristics:  Feels 
vulnerable to 'a great extent' due to financial circumstances’ 
‘Perception  of  own  vulnerability  due  to  personal  characteristics:  Feels 
vulnerable to 'a great extent' due to health problems’ 
‘Perception  of  own  vulnerability  due  to  personal  characteristics:  Feels 
vulnerable to 'a great extent' due to belonging to a minority group’ 
‘Perception  of  own  vulnerability  due  to  personal  characteristics:  Feels 
vulnerable to 'a great extent' due to personal issues’ 
‘  Perception  of  own  vulnerability  due  to  personal  characteristics:  Feels 
vulnerable to 'a great extent' due to other reasons’  

  Dimension 3 (Having difficulty in obtaining or assimilating information): 

 

‘Feels  'not  at  all'  informed  about  product  price,  quality,  conditions,  etc 
when choosing and buying goods and services’ 

  Dimension  478  (Inability  or  failure  to  buy,  choose  or  access  suitable 

products): 

 

 

‘Does not compare product deals - energy sector’ 
‘ Does not compare product deals - finance sector’ 

77 Presented in Chapter 6. 
78 The information related to the online sector is not used in this particular part of the analysis 
because  information  is  not  available  for  all  respondents.  However,  previous  analysis  in  this 
report showed that overall the incidence of vulnerability according to this indicator was lower 
in  the  online  sector  than  in  the  energy  and  finance  sectors.  Therefore,  the  omission  of  the 
online sector is unlikely to result in a significant under-estimation of vulnerability.  

132 

 
 
                                                 
4 │ Incidence of consumer vulnerability 

 

 

‘ Finds it very difficult to compare product deals - energy sector’ 
‘ Finds it very difficult to compare product deals - finance sector’ 

  Dimension 5 (Higher susceptibility to marketing practices): 

 

 

‘Feels  vulnerable  to  a  great  extent  due  to  complexity  of  offers  (own 
perception)’ 
‘Experiment choice under the marketing practice treatment’ 

A survey respondent has been classified as vulnerable according to a particular 
dimension if they are considered vulnerable according to any of the indicators of a 
particular dimension. The distribution of all the survey respondents in the EU28, 
Iceland and Norway across the various combinations of vulnerability dimensions is 
shown in Table 18. The key points to note are that: 

Only 17.2% of survey respondents did not show any indication of vulnerability. 
The most populated single dimension is  Dimension 4 on its own with 42.7% of 
survey  respondent  showing  a  sign  of  vulnerability  on  the  basis  of  at  least  one 
indicator of Dimension 4 of vulnerability, the ‘inability or failure to buy, choose 
or access suitable products’, and not in combination with any other vulnerability 
dimension. 
In  fact,  73%  of  survey  respondents  showed  sign  of  vulnerability  under 
Dimension  4  alone  or  in  combination  with  other  vulnerability  dimensions  (see 
Figure 26). 
The next most common occurrence is the combination of Dimensions 2 and 4: 
9.6%  of  all  respondents  are  vulnerable  only in  terms  of  ‘having  characteristics 
that  limit  the  ability  to  maximise  well-being’  and  in  terms  of  the  ‘inability  or 
failure to buy, choose or access suitable products’ at the same time. 
The third most common occurrence is the combination of Dimensions 1 and 4: 
7.1%  of survey  respondents are vulnerable at the same time in terms of their 
‘heightened risk of negative outcomes or impacts on well-being’ and in terms of 
the ‘inability or failure to buy, choose or access suitable products’. 
None  of  the  other  single  dimensions  or  combination  of  dimensions  account  for 
an  important  share  of  survey  respondents  with  the  most  populated  cell  of  this 
group  being  Dimension  2  on  its  own  which  accounts  for  2.3%  of  survey 
respondents.  However,  in  total  23%  of  survey  respondents  showed  signs  of 
vulnerability  under  Dimension  2,  on  its  own  or  in  combination  with  other 
vulnerability dimensions (see Figure 26). 
The  sum  of  the  percentages  in  each  single  dimension  and  combination  of 
dimension exceeds 100 as a single survey respondent may belong to more than 
1  cell  if  she/he  is  characterised  by  multi-dimensional  vulnerability.  The  extent 
to  which  this  sum  exceeds  100  provides  an  indication  of  the  extent  of  such 
multi-dimensional vulnerability. Overall: 

  49.6% of respondents showed sign of vulnerability according to only one 

dimension; 

  33.3%  of  respondents  are  categorised  as  vulnerable  according  to 

multiple dimensions; and 

  17.2% are not vulnerable according to any of the indicators in any of the 

dimensions.  

Therefore,  the  incidence  of  consumer  vulnerability  is  not  concentrated  in  one 
single  dimension,  but  is  dispersed  across  multiple  concepts,  such  that  the 
majority  of  consumers  can  be  classified  as  vulnerable  according  to  some 
dimension. 

 

 

 

 

 

 

 

 

133 

 
 
 
 
4 │ Incidence of consumer vulnerability 

Figure 26:  Share of survey respondents reporting particular dimension of 
vulnerability (allocation is not mutually exclusive) – EU28 

Note: Results weighted using cross-country weights and based on the EU28 (N = 25,151). The sum of shares 
is  greater  than  100%,  as  one  respondent  can  be  vulnerable  according  to  more  than  one  dimension.  The 
shares in the chart sum to 140%.  
Source: London Economics 

134 

Dimension 1: 19%Dimension 2: 23%Dimension 3: 3%Dimension 4: 73%Dimension 5: 10%No dimension: 17%0%10%20%30%40%50%60%70%80%Dimension 1Dimension 2Dimension 3Dimension 4Dimension 5No dimension 
 
 
Table 18:  Distribution of survey respondents by number of vulnerability dimensions 

4 │ Incidence of consumer vulnerability 

Dimen-
sions 

4 only 
4 & 5 only 

42.7% 
2.3% 

Dimen-
sions 

5 only 

0.5% 

No 
dimen-
sion 

17.2% 

Dimen-
sions 

3 only 
3 & 4 only 

3 & 5 only 
3, 4 & 5 
only 

0.3% 
1.7% 

0.0% 

0.1% 

Dimen-
sions 

2 only 
2 & 3 only 

2 & 4 only 

2 & 5 only 
2, 3 & 4 
only 
2, 3 & 5 
only 
2, 4 & 5 
only 
2, 3, 4 & 5 
only 

2.3% 
0.0% 

9.6% 

0.6% 

0.3% 

0.0% 

3.3% 

0.3% 

Dimen-
sions 

1 only 
1 & 2 only 

1 & 3 only 

3.7% 
1.5% 

0.1% 

1 & 4 only 

7.1% 

1 & 5 only 
1, 2 & 3 
only 
1, 2 & 4 
only 
1, 2 & 5 
only 
1, 3 & 4 
only 
1, 3 & 5 
only 
1, 4 & 5 
only 
1, 2, 3 & 4 
only 
1, 2, 3 & 5 
only 
1, 2, 4 & 5 
only 
1, 3, 4 & 5 
only 
1, 2, 3, 4 & 
5 

0.1% 

0.0% 

3.1% 

0.5% 

0.3% 

0.0% 

0.7% 

0.1% 

0.0% 

1.3% 

0.1% 

0.1% 

Note: Results weighted using cross-country weights and based on the EU28 countries. N = 25,151  
Source: London Economics 

135 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4 │ Incidence of consumer vulnerability 

Among the EU28 Member States and Iceland and Norway, Austria, the Czech Republic, 
Germany, Slovenia, the United Kingdom and Norway stand out as outliers in terms of 
having the largest proportion of consumers who are not vulnerable according to any of 
the  vulnerability  dimensions.  These  are  the  countries  with  the  smallest  share  of 
vulnerable consumers according to any of the indicators serving as proxies for the five 
dimensions (listed at the beginning of this section).  

On  the  contrary,  countries  with  only  a  small  proportion  of  respondents  who  are  not 
classified  as  vulnerable  according  to  any  of  the  aforementioned  indicators  are  Cyprus 
(2.3%), Iceland (3.5%), Bulgaria (4%), Malta (5.5%), Romania (4.8%) and Lithuania 
(6%).79 

Moreover, Cyprus  stands out as being systematically a “high outlier” in terms  of each 
of  the  five  vulnerability  dimensions.  Among  the  other  outliers,  only  Romania  is  an 
outlier in three dimensions, and Bulgaria and Lithuania are outliers in two dimensions. 
The remaining countries are outliers only with regards to a single dimension, with the 
exception  of  Denmark,  Estonia,  Finland,  Poland,  Portugal,  Slovakia,  Sweden  and  the 
Netherlands, which are not outliers in any dimension.80 

Table 19: 

Vulnerability by dimension across EU28 Member States and IS 

and NO 

Proportion 
of country 
respondents 
vulnerable 
according 
to any 
indicator in 
Dimension 
1 
18.7% 
15.7% 
16.5% 
35.4% 
28.2% 
31.3% 
15.1% 
14.9% 
24.9% 
17.6% 
11.5% 
18.3% 
29.2% 
34.4% 
29.9% 
16.8% 
30.3% 
21.5% 
16.6% 
14.2% 

Proportion 
of country 
respondents 
vulnerable 
according 
to any 
indicator in 
Dimension 
2 
23.1% 
10.3% 
19.2% 
32.6% 
31.4% 
47.4% 
12.5% 
14.4% 
29.4% 
22.2% 
28.4% 
10.3% 
46.5% 
33.5% 
24.5% 
30.0% 
33.7% 
34.3% 
13.6% 
17.7% 

Proportion 
of country 
respondents 
vulnerable 
according 
to any 
indicator in 
Dimension 
3  
3.5% 
2.3% 
8.0% 
2.8% 
8.5% 
10.2% 
2.8% 
3.4% 
4.7% 
1.8% 
4.0% 
2.0% 
2.4% 
4.3% 
4.9% 
2.7% 
2.4% 
8.4% 
2.6% 
6.6% 

Proportion 
of country 
respondents 
vulnerable 
according 
to any 
indicator in 
Dimension 
4  
73.2% 
63.9% 
75.0% 
90.7% 
78.4% 
93.6% 
61.2% 
75.2% 
72.0% 
68.6% 
89.1% 
61.3% 
79.7% 
82.8% 
61.6% 
74.1% 
77.3% 
89.1% 
88.6% 
91.2% 

Proportion 
of country 
respondents 
vulnerable 
according 
to any 
indicator in 
Dimension 
5  
10.0% 
4.4% 
6.7% 
13.0% 
9.2% 
26.0% 
8.1% 
6.7% 
8.2% 
6.3% 
12.3% 
3.5% 
13.0% 
7.7% 
8.7% 
12.4% 
11.9% 
14.6% 
8.7% 
13.2% 

Not 
vulnerable 
according 
to any 
dimension* 

17.2% 
27.7% 
16.5% 
4.0% 
10.9% 
2.3% 
29.0% 
18.6% 
12.0% 
19.6% 
7.7% 
28.5% 
7.3% 
7.7% 
21.3% 
13.8% 
10.7% 
6.0% 
7.0% 
5.5% 

EU28 
AT 
BE 
BG 
HR 
CY 
CZ 
DK 
EE 
FI 
FR 
DE 
EL 
HU 
IE 
IT 
LV 
LT 
LU 
MT 

79  Percentage shares  represent the share of  respondents in  the  respective  country who  are  not 

classified as vulnerable according to any of the dimensions. 

80 As defined by the sub-set of indicators listed at the beginning of the section.  

136 

 
 
 
                                                 
4 │ Incidence of consumer vulnerability 

Table 19: 

Vulnerability by dimension across EU28 Member States and IS 

and NO 

Not 
vulnerable 
according 
to any 
dimension* 

Proportion 
of country 
respondents 
vulnerable 
according 
to any 
indicator in 
Dimension 
2 
22.0% 
34.2% 
36.3% 
21.1% 
26.7% 
32.5% 
14.2% 
10.0% 
16.9% 
10.0% 
11.7% 

Proportion 
of country 
respondents 
vulnerable 
according 
to any 
indicator in 
Dimension 
1 
28.6% 
20.7% 
35.2% 
21.2% 
21.8% 
16.0% 
12.5% 
10.8% 
14.4% 
17.8% 
14.5% 

Proportion 
of country 
respondents 
vulnerable 
according 
to any 
indicator in 
Dimension 
3  
4.4% 
PL 
5.6% 
PT 
2.5% 
RO 
3.0% 
SK 
1.8% 
SI 
6.4% 
ES 
1.5% 
SE 
3.7% 
NL 
2.2% 
UK 
1.8% 
IS 
NO 
2.7% 
Note:  Results  weighted  using  cross-country  weights  for  the  EU28  measure  and  country  weights  for  the 
country measures. Data based on the full sample in each country (EU28 + NO and IS N = 26,653). The sum 
of shares is greater than 100%, as one respondent can be vulnerable according to more than one dimension. 
*Based on the indicators included in the multi-dimensional analysis only. 
Source: London Economics 

Proportion 
of country 
respondents 
vulnerable 
according 
to any 
indicator in 
Dimension 
4  
71.7% 
73.3% 
87.0% 
69.6% 
58.4% 
72.0% 
70.7% 
80.1% 
67.1% 
95.6% 
69.5% 

Proportion 
of country 
respondents 
vulnerable 
according 
to any 
indicator in 
Dimension 
5  
11.6% 
14.0% 
14.8% 
11.8% 
7.5% 
15.5% 
6.6% 
4.4% 
9.5% 
4.2% 
5.1% 

13.9% 
13.5% 
4.8% 
19.8% 
26.4% 
15.8% 
20.5% 
14.6% 
24.1% 
3.5% 
24.9% 

137 

 
 
 
 
5 │ Concept of the ‘average consumer’ 

5.  CONCEPT OF THE ‘AVERAGE CONSUMER’ 

Box 3:  Summary of Chapter 5 

This  chapter  examines  insights  from  the  literature  review  and  stakeholder 
consultation  relevant  to  the  concept  of  the  average  consumer  and  explores  the 
vulnerability  characteristics  of  the  average  consumer  based  on  the  present  study’s 
survey data. 

The main vulnerability definition at EU level has its source in the UCPD: vulnerability 
may arise out of the consumers’ "mental or physical infirmity, age or credulity”. This 
is  to  be  distinguished  from  the  notion  of  the  average  consumer,  which  in  the 
Directive  is  understood  as  a  consumer  “who  is  reasonably  well-informed  and 
reasonably  observant  and  circumspect,  taking  into  account  social,  cultural  and 
linguistic factors”. 

This  definition  has  been  criticised  for  setting  the  bar  too  high in  terms  of  consumer 
protection  and  reflecting  an  unrealistic  benchmark.  Experts  and  stakeholders  have 
asked for the concept to be interpreted more flexibly or even abandoned.  

The  first  part  of  the  chapter  considers  the  extent  to  which  this  definition  has 
impacted  on  national-level  enforcement  of  consumer  protection  legislation.  It  is 
found  that  the  terms  “average  consumer”  and  “vulnerable  consumer”  have  been 
used  across  a  number  of  cases,  but  only  a  limited  number  of  cases  explored  the 
implications of the terms as such.  

 “Average consumer” groups have in cases been considered to be sufficiently familiar 
with a specific sector not to be misled by exaggerated claims from advertisers, and in 
others,  particular  difficulties  such  as  in  reading  small  print,  were  considered  to  be 
characteristics that could be found in the “average consumer”. 

Groups  that  have  been  identified  as  vulnerable  include  consumers  banned  by  credit 
institutions,  people  affected  by  a  serious  illness  and  consumers  in  a  certain 
demographic  group  targeted  by  particular  marketing  practices.  Yet,  the  only 
identified case where consumer vulnerability was central to the decision is a German 
case  concerning  payment  methods  offered  by  a  German  gas  supplier.  The  only  two 
alternatives consisted of a monthly Direct Debit or a yearly bank transfer. On appeal 
it  was  accepted  that  insufficient  options  were  being  offered  as  certain  vulnerable 
consumers would not be able to transfer a year’s worth of charges in advance and/or 
might not have access to a bank account. 

In the second part of the chapter, the profile of the average  consumer is defined in 
terms of median levels for the explanatory variables that have been identified as the 
main  drivers  and  indicators  of  vulnerability.  The  definition  of  the  average  consumer 
on  this  basis  means  it  can  be  linked  to  vulnerability.  Medians  are  calculated  from 
survey responses.  

Overall,  for  each  of  the  vulnerability  indicators,  the  “average  consumer,”  as 
represented by the median consumer, exhibits few signs of vulnerability. 

However, when the responses are combined to capture the maximum score assigned 
to  “health”,  “age”,  “minority  status”,  “personal  issues”,  “employment  status”  and 
“financial  situation”,  the  median  response  is  ‘feel  vulnerable  to  some  extent’,  i.e.  a 

138 

 
 
5 │ Concept of the ‘average consumer’ 

value  of  3  on  the  1  to  4  scale.  This  means  that  overall,  half  the  population  of 
respondents  declared  some  (mild)  vulnerability  in  at  least  one  of  the  aspects 
surveyed  under  Dimension  2.  In  respect  of  Dimension  4,  the  average  consumer 
shows some vulnerability: the average consumer compares deals offered by internet 
providers but never compares deals offered by banks or energy providers. 

Most countries do not differ from the overall average consumer profile or differ only 
slightly.    However,  consumers  in  7  countries  are  more  vulnerable  than  the  median 
full-sample consumer in more than 3 indicators: Bulgaria, Cyprus, Greece, Hungary, 
Latvia,  Portugal,  Romania  and  Spain.  In  contrast,  countries  where  the  average 
consumer shows somewhat less vulnerability than the average across the EU28 are: 
Austria,  Belgium,  the  Czech  Republic,  Finland,  Germany,  Iceland,  Ireland, 
Luxembourg,  Malta,  Norway,  Poland,  Slovakia,  Slovenia,  Sweden,  the  Netherlands 
and the United Kingdom. 

This  chapter  comprises  of  two  parts.  The  first  part  examines  insights  from  the 
literature  review  and  stakeholder  consultation  relevant  to  the  concept  of  the  average 
consumer.  The  second  part  explores  the  vulnerability  characteristics  of  the  average 
consumer  based  on  the  survey  data.  In  doing so,  the  chapter  aims  to  respond  to  the 
research  questions  set  out  in  section  1.4  which  relate  to  the  concept  of  the  average 
consumer (RQ1 and RQ8). 

5.1. Insights from the literature review and stakeholder consultation 

In order to understand the concept of the ‘average consumer’ it is important to place it 
within the broader context of the definitions and approaches to consumer vulnerability 
in the EU and in national law. The following sections discuss the approach to consumer 
vulnerability in individual Member States, including an overview of case law.  

5.1.1.  Definitions and approaches to consumer vulnerability in the EU and 
national law 

This section discusses the definitions of vulnerability in use in the EU and national law, 
as well as the national approaches to vulnerability. 

Consumer vulnerability in EU law 

As noted previously, the main vulnerability definition at EU level has its source in the 
UCPD,  which  notes  that  vulnerability  may  arise  out  of  the  consumers’  "mental  or 
physical infirmity, age or  credulity”. This is to be distinguished from the notion of the 
average  consumer,  which  is  in  the  Directive  understood  as  a  consumer  “who  is 
reasonably  well-informed  and  reasonably  observant  and  circumspect,  taking  into 
account  social,  cultural  and  linguistic  factors”.  Although  the  UCPD  definition  of 
vulnerability  serves  as  the  basis  for  this  study,  there  are  other  “consumer  images” 
present in EU legislation which could be linked to vulnerability. Stuyck (2014) notes for 
instance that the concept of vulnerability in relation to energy poverty is present in the 
Electricity  and  Gas  market  Directives,  as  well  as  in  the  Directive  on  universal  service 
and  users'  rights  relating  to  electronic  communications  networks  and  services 
(Universal Service Directive), where it focuses on accessibility, affordability and choice 
for consumers with disabilities.  

Approaches to consumer vulnerability on national level 

139 

 
 
 
5 │ Concept of the ‘average consumer’ 

In  order  to  understand  how  the  concept  of  consumer  vulnerability  is  approached  at 
Member State level one should consider three main questions: 

 

 

Is the concept of consumer vulnerability present in consumer protection 
legislation or related legislation? 
Is the concept of consumer vulnerability used in practice by national bodies 
responsible for consumer protection, as well as consumer organisations and 
other civil society bodies? 

  How does the use of the concept differ across different institutional contexts 

and sectors? 

It is also important to note that the concept can be in use even if it does not exist as 
such  in  any  legislative  provisions.  For  instance  in  Germany,  the  term  “vulnerable 
consumers”  (verletzliche  Verbraucher)  is  not  present  in  the  legislation  but  is  used  in 
practice by consumer organisations.  

Overall,  the  research  conducted  to  date  presents  a  varied  picture  with  regard  to  the 
above  questions.  In  terms  of  use  in  legislation,  the  transposition  of  the  UCPD  has 
meant  that  the  term  is  present  in  consumer  protection  legislation  in  most  Member 
States,  with  18  Member  States  making  use  of  the  term.  Some  Member  States  have 
used  modified  wording.  The  word  “susceptible”  is  used  in  Hungary,  Netherlands,  and 
Estonia, while selected Member States (Croatia, Finland, France, Germany, and Latvia) 
have  relied  on  other  wording.  Some  Member  States  have  also  modified  the  list  of 
potential  sources  of  vulnerability  highlighted  in  the  UCPD.  For  instance  in  Poland  the 
term  “credulity”  is  not  used,  while  in  Germany  commercial  inexperience  and  fear  are 
added to the provisions.  

Even  if  the  concept  of  vulnerability  is  present  in  the  legislation,  it  is  important  to 
remember that the UCPD concept of vulnerability is a more narrowly defined one than 
what has been outlined in the previous sections of this report. In addition, the concept 
appears  to  be  used  more  broadly  within  the  national  consumer  protection  framework 
only in selected Member States. A broader approach to vulnerability has been identified 
in  the  Czech  Republic,  Finland,  Hungary,  Ireland,  Poland,  Romania,  and  the  UK, 
although  in  some  instances  this  relates  only  to  certain  consumer  groups  being 
highlighted in strategic policy documents (i.e. in Poland). In Germany, as noted above, 
the concept of vulnerability appears to play a larger part in the work of the consumer 
organisations than that of public authorities.  

One  area  where  consumer  vulnerability  is  more  universally  recognised  within  the 
consumer  protection  framework  is  the  energy  sector,  where,  as  noted  by  the  2009 
European  Regulators  Group  for  Electricity  and  Gas  report,  eight  Member  States  make 
use of the vulnerable consumer concept, namely Belgium, Bulgaria, Greece, Hungary, 
Ireland, Italy, Slovenia, and the United Kingdom.  

The  sectoral  dimension  is  another  important  aspect  of  understanding  national 
approaches to vulnerability. Where the concept of consumer vulnerability is in use, it is 
likely  to  be  limited  to  a  specific  sector  (for  instance  energy),  or  be  used  differently 
across  sectors  and  contexts.  In  Belgium,  for  example,  in  addition  to  the  concept  of 
vulnerability stemming from the UCPD, a notion of consumers in a precarious situation 
is  used  in  the  energy  sector.  In  Portugal  two  other  terms  are  used  in  addition  to  the 
UCPD  term  “vulnerable”,  namely  “economically  vulnerable  consumers”  (consumidores 
economicamente vulneráveis) and “consumers with special needs” (consumidores com 
necessidades  especiais).  Even  in  the  UK,  which  is  the  Member  State  with  the  most 

140 

 
 
5 │ Concept of the ‘average consumer’ 

developed  approach  to  vulnerability,  there  is  a  range  of  terms  and  definitions  in  use 
across individual sectors and organisations.  

At the same time, it is important to stress the fact that a lack of a single definition of 
vulnerability  or  a  single  unified  strategy  to  address  consumer  vulnerability  does  not 
necessarily  reflect  negatively  on  the  consumer  protection  framework  across  the 
Member States. As noted in the previous section, consumer vulnerability is a complex 
and dynamic concept. Hence, a more case-by-case sector-specific approach could be of 
value.  In  addition,  some  of  the  interviewed  stakeholders  stressed  the  importance  of 
ensuring  that  consumer  policy  addresses  all  consumers  and  questioned  the  value  of 
trying to identify and target specific vulnerable groups.  

Approaches to the average consumer concept  

The  concept  of  average  consumer  is  of  particular  relevance  to  the  discussion  of 
consumer  vulnerability.  As  the  2013  European  Consumer  Consultative  Group  (ECCG) 
opinion  on  consumers  and  vulnerability  notes,  the  notion  of  average  consumer 
introduced by the European Court of Justice case law and which is present in the UCPD 
sets  the  bar  very  high  in  terms  of  consumer  protection  by  using  an  unrealistic 
benchmark.  Similarly,  Incardona  and  Poncibo  (2007)  conclude  that  the  ‘‘reasonably 
well-informed and reasonably observant and circumspect” average consumer does not 
reflect  reality  and  the  concept  should  be  interpreted  more  flexibly  or  abandoned. 
Therefore,  understanding  how  the  concept  is  used  is  an  important  aspect  of 
understanding how consumer vulnerability is tackled in individual jurisdictions, since a 
vulnerable consumer is unlikely to be an average consumer. 

The  term  was  almost  universally  transposed  into  national  legislation  as  part  of  the 
UCPD.  Only  in  four  Member  States  (Czech  Republic,  Denmark,  Hungary,  Sweden)  the 
reference  to  an  “average”  consumer  was  not  transposed.  However,  the  interviewees 
(for  instance  Lithuania  and  Poland)  were  able  to  elaborate  on  the  concept  only  in 
selected instances, which in most cases can be attributed to limited experience of using 
the  concept  in  practice.  In  France  the  usage  appears  to  differ  by  sector.  In  Germany 
the  term  is  seen  by  the  main  consumer  organisation  as  an  effective  lowering  of 
protection standards, since the “average consumer” is viewed as a more informed one 
than  previously  assumed  in  German  legislation.  As  a  result,  practices  that  would 
previously be interpreted as misleading could now no longer be considered as such to a 
more informed “average consumer”. 

Overall, however, the individual countries appear to have had limited experience with 
the concept. This appears to be in line with the case law review, which shows that in 
most  cases  the  average  consumer  concept  has  not  been  an  important  element  of  the 
decisions, nor is it addressed in depth.  

5.1.2.  Case law on consumer vulnerability 

In addition to existing evidence from literature and interviews concerning  vulnerability 
drivers, indicators and practices, it is important to also take into account existing case 
law  that  addresses  the  issue  of  vulnerability.  This is  of  value  since  it  not  only  further 
enhances the understanding of problematic practices, but also shows how the question 
of consumer vulnerability is addressed in practice in a legal context. 

141 

 
 
 
5 │ Concept of the ‘average consumer’ 

The  study  team  examined  the  European  Commission’s  UCPD  legal  database81  and 
asked interviewed organisations about relevant national case law. In addition, we have 
consulted  our  expert  Professor  Peter  Rott  concerning  specific  case  law  focusing  on 
vulnerability.  

Although there is a wide range of case law related to unfair commercial practices, only 
a limited number of cases have been identified where the notion of vulnerability or that 
of  “average  consumer”  has  been  directly  addressed.  The  following  table  provides  an 
overview  of  the  relevant  cases  included  in  the  European  Commission  UCPD  legal 
database associated with keywords related to vulnerability. 

Table 20: 

Case law overview 

Keyword  Number 

of 
cases 

8 

Vulnerable 
consumer 

Member 
Sates 

Greece (1) 
Hungary 
(1) 
Italy (3) 
Poland (1) 
UK (2) 

Summary 

Three  cases  make  direct  reference  to  vulnerable 
consumers: 

  A Hungarian case (Vj-5/2011/73) concerning a 
financial service provider targeting consumers 
banned by credit institutions and therefore 
identified as vulnerable.  

  An Italian case (PS6980) of using misleading 

health claims on products, with the practice 
considered particularly serious due to misleading 
vulnerable consumers (in this case such as 
people affected by a serious illness). 

  An Italian case (PS649) o using health claims on 
products that can mislead vulnerable consumers 
(in this case identified to be women between the 
ages of 40 and 60 targeted by the product). 

Most  other  cases  are  categorised  as  concerning 
vulnerable consumers or make reference to the term but 
do not specifically define it: 

  One Greek case concerning newspapers 
providing pornographic content as gifts. 

  One Italian case concerning “premium” mobile 
services with concealed open-end contracts. 
  One UK case concerning an antisocial behaviour 
order imposed on an individual convicted of 
aggressive marketing practices targeting older 
consumers. 

  One UK case concerning misleading practices 

targeting involved consumers. 

  One Polish case concerning an advertising 

campaign resembling an official government 
campaign. 

Average 

49 

Austria (1)  Most  cases  use  the  term  “average  consumer”  without 

81 See https://webgate.ec.europa.eu/ucp/public/index.cfm?event=public.home.show 

142 

 
 
 
 
 
                                                 
5 │ Concept of the ‘average consumer’ 

Table 20: 

Case law overview 

Keyword  Number 

of 
cases 

consumer 

Member 
Sates 

Belgium (4) 
Bulgaria (1) 
Germany 
(7) 
Estonia (1) 
Greece (3) 
France (2) 
Hungary 
(1) 
Italy (3) 
Lithuania 
(4)  
Latvia (1) 
Netherlands 
(2) 
Poland (3) 
Portugal 
(9) 
Slovenia 
(1) 
Slovakia 
(4) 
UK (2) 

Summary 

addressing the term as such. Specific cases that address 
the term in more detail involve: 

  A Greek case (2130/2013) where the average 

consumer of medical machinery and devices was 
deemed to be sufficiently informed so as to 
understand and not be misled by exaggerated 
claims made by advertisers of such products. 
  One Latvian case (Administratīvās rajona tiesas 
spriedums lietā Nr. A420592710) addressed the 
question of whether the term “average 
consumer” can be defined by a national authority 
in relation to a specific sector. The decision 
confirms that this can be done. 

  A decision in one German case (4 U 141/11) 

stated that people with impaired eyesight can 
also be considered average consumers and 
printing information in a very small font can be 
considered a misleading commercial practice. 
Similar decision was taken in a Slovak case 
(Slovak Trade Inspection, 30/11/2010 ref. code: 
P/0359/07/2010). 

  A decision in one UK case ([2011] EWHC 106 

(Ch)) stated that the term relates to “consumers 
who take reasonable care of themselves, rather 
than the ignorant, careless or over-hasty”. It 
also concludes that one cannot assume that the 
average consumer will read small print on 
promotional documents.  

Age 

2 

Belgium (1) 
Greece (1) 

  One Greek case concerning newspapers 
providing pornographic content as gifts. 

  One Belgian case (AR nr. 2008/AR/2917) where 
a practice of offering discounts on organised 
holidays for consumers over 55, was deemed 
discriminatory and an unfair commercial practice. 

The relevant cases include:  

 

Two Austrian cases ruling that advertisement 
directed at children are not an aggressive 
practice if other characteristics of unfair practices 
are not present (4Ob244/12d), nor are indirect 
exhortations (4Ob95/13v). 

  One Austrian case (4 Ob 57/08y) ruling that 

practices that mislead children given their limited 
intellectual capacity should be considered as 
aggressive.  

  A Latvian case (Consumer Rights Protection 
Centre Decision Nr. E03-RIG-511) deeming 
“providing young children with SIM cards, 

Children 

5 

Austria (3) 
Germany 
(1) 
Greece (1) 
Italy (1) 
Latvia (1) 
Sweden (1) 

143 

 
 
 
 
 
 
 
5 │ Concept of the ‘average consumer’ 

Table 20: 

Case law overview 

Keyword  Number 

of 
cases 

Member 
Sates 

Summary 

inciting them to use mobile phone services, and 
giving them information regarding prepayment 
cards” to be an aggressive commercial practice. 

  A German case (103 0 171/08) stating that 

advertising that directly calls on children to buy a 
product is an unfair commercial practice.  

  A Swedish case (MD 2012:14) deeming 

statements such as "Buy", "Buy more", "buy 
here", "upgrade", "upgrade now" in online 
gaming communities, as well as “buy before it's 
too late" in email communications are direct 
exhortations to buy a product and hence 
aggressive practices. 

Credulity 

1 

Finland 

Single case identified: 

 

In one Finnish case (MAO:157/11) the plaintiff 
claimed the defendant was using consumers’ 
credulity and environmental concern, by making 
misleading claims concerning environmental 
impact of product purchases. 

- 
Cases  concern  claims  about  product  properties  with 
regard  to  curing  illnesses.  Illness  as  a  source  of 
vulnerability is generally not tackled. With the exception 
of the aforementioned Italian case (PS6980).  

No cases relevant to consumer vulnerability. 

- 

Disability 
Illness 

- 
8 

Income 

6 

Infirmity  

- 

- 
Italy (2) 
Lithuania 
(6) 
Slovakia 
(1) 
Lithuania 
(1) 
Latvia (2) 
Poland (1) 
Slovenia 
(1) 
EU (1) 
- 

Although,  as  can  be  seen  in  the  above  table,  the  term  “average  consumer”  has  been 
used across a number  of cases, the term as such has only been  explored further in a 
few  instances.  In  the  Greek  case  outlined  above,  the  average  consumer  group  is 
considered  to  be  sufficiently  familiar  with  a  specific  sector  (medical  devices  and 
machinery) so as to be able to understand and not be misled by exaggerations used in 
advertising.  Similarly,  the  aforementioned  UK  case  defines  “average  consumers”  as 
“consumers who take reasonable care of themselves, rather than the ignorant, careless 
or  over-hasty”,  while  the  German  case  notes  that  consumers  with  impaired  eyesight 
can also be considered “average”. In most other cases the term “average consumer” is 
used to aid the final decision, without being elaborated on as such. 

Other  cases  concerning  the  “average  consumer”  concept  beyond  the  ones  identified 
above include the following examples: 

144 

 
 
 
 
5 │ Concept of the ‘average consumer’ 

  A Greek case (Ombudsman of the Consumer 16th of December 2009 - Protocol 
No  3990)  addressed  the  question  whether  an  “average  consumer  [can]  be 
expected  to  know  that  an  insurance  company's  liability  for  a  vehicle's  own 
damage  is  calculated  in  such  a  way  that  any  amount  paid  during  each 
insurance period is subtracted from the insured amount”. The decision deemed 
the  omission  of  the  information  on  behalf  of  the  insurance  company  an  unfair 
practice  by  arguing  that  understanding  of  the  calculation  would  require 
“specialised” knowledge.  

  A  German  case  (17  HK  O  3216/13)  questioning  whether  a  fast  food 
advertisement  misleadingly  listed  in  an  overview  of  all  contents  of  a 
cheeseburger  only  beef,  cucumbers,  mustard,  ketchup,  and  cheddar  cheese, 
while  in  fact  the  product  included  a  wide  range  of  other  ingredients.  The 
decision  stated  that  the  average 
food  consumer  to  which  the 
advertisement  was  directed  would  be  aware  that  industrially  produced  food 
products will include a wider range of contents.  

fast 

Both the above cases show that, like in most cases, the average consumer concept is 
used in practice but not always addressed in detail nor explicitly linked to the concept 
of vulnerability.  

The  UCPD  legal  database  revealed  only  a  small  number  of  cases  directly  addressing 
consumer vulnerability, with identified vulnerable groups including: 

  Consumers banned by credit institutions (Hungarian case Vj-5/2011/73); 
  People affected by a serious illness (Italian case PS6980); or 
  Consumers in a certain demographic, such as women aged between 40 and 60 

(Italian case PS649). 

This  in  turn  suggests  that  the  interpretations  of  consumer  vulnerability  in  European 
case  law  are  varied  and  highly  dependent  on  the  specific  market  context.  One  can 
imagine  that  women  aged  between  40  and  60  would  not  necessarily  be  considered 
vulnerable  in  a  number  of  situations,  but  were  deemed  vulnerable  when  targeted  by 
advertising  for  a  product  with  particular  health  benefits.  It  is  however  important  to 
note  that  in  the  above  cases  vulnerability  was  generally  an  exacerbating  factor,  with 
the  practices  in  question  already  being  potentially  misleading:  in  the  Hungarian  case 
the  information  provided  to  the  consumers  was  deemed  incomplete,  while  the  claims 
made in the two Italian cases were deemed false or misleading regardless of targeted 
consumers.  

The only identified case where consumer vulnerability (relating to income) was directly 
tackled  and  central  to  the  decision  is  a  German  case  VIII  ZR  131/12.  The  case 
concerned a gas supplier’s practice of offering two payment methods, a monthly Direct 
Debit  or  a  yearly  bank  transfer.  The  practice  was  challenged  by  a  German  consumer 
organisation  on  the  basis  that  it  does  not  effectively  provide  consumers  with  a  wide 
range  of  payment  methods,  as  stipulated  by  the  transposition  of  the  Directive 
2009/73/EG concerning common rules for the internal market in natural gas. Although 
in the first instance the case was unsuccessful, as the two different methods were seen 
as  sufficient  in  offering  multiple  payment  options  to  consumers,  it  was  later 
successfully appealed on the basis that certain consumers will not be able to transfer a 
year’s worth of charges in advance, meaning that their payment options are limited to 
just one method, or to none at all, if they have no access to a bank account.  

The  above  case  shows  that  vulnerability  of  lower-income  consumers  and  practices 
restricting  access  to  services  can  be  effectively  identified  in  legal  proceedings. 

145 

 
 
 
5 │ Concept of the ‘average consumer’ 

Nevertheless,  the  fact  that  the  initial  ruling  did  not  fully  consider  consumer 
vulnerability  shows  that,  in  practice,  making  use  of  this  concept  may  prove 
challenging.   

Beyond  the  cases  addressing  directly  the  concepts  of  “consumer  vulnerability”, 
“average  consumer”,  or  specific  vulnerability indicators,  there  are  also  other  concepts 
in  use  in  selected  Member  States  that  are  linked  to  consumer  vulnerability.  For 
instance recent case law in the financial services sector in Norway has used terms such 
as  “non-professional  investors”  (Paragraph  125)  and  “small  savers  and  inexperienced 
investors”  (Paragraph  128)  to  refer  to  consumers  that  are  more  vulnerable  in  the 
financial market.   

Overall, the limited number  of cases addressing vulnerability suggests that it remains 
an issue that is relatively unexplored in a legal setting across the Member States. The 
limited  engagement  with  consumer  vulnerability  and  the  average  consumer  concept 
within  national  case  law  means  that  it  is  important  to  also  look  at  examples  from 
beyond  the  EU.  In  particular,  the  European  Consumer  Consultative  Group  opinion  on 
consumers and vulnerability noted a recent example of Canadian case law, which has 
effectively  modified  the  existing  concept  of  an  average  consumer,  similar  to  the  one 
present  in  the  EU,  to  account  for  the  fact  that  a  wide  range  of  consumers  can  be 
susceptible  to  particular  practices.  The  judgement  sees  an  average  consumer  as  a 
person  “who  is  credulous  and inexperienced  and  takes  no  more  than  ordinary  care  to 
observe  that  which  is  staring  him  or  her  in  the  face  upon  first  entering  into  contact 
with an entire advertisement” (Richard v. Time Inc. 2012 SCC 8 in ECCG 2013). Given 
the  findings  concerning  vulnerability  drivers  and  practices,  the  Canadian  notion  of  an 
average consumer does in fact appear to better reflect market realities. 

Beyond  case  law,  one  area  where  the  existing  notions  of  vulnerability  and  average 
consumer  are  challenged  is  in  the  legal  literature.  As  noted  above,  Incardona  and 
Poncibo (2007) argue that the existing notion of an average consumer neither reflects 
human  behaviour,  nor  constitutes  an  appropriate  basis  for  judicial  decisions.  Lennard 
(2009)  draws  a  similar  conclusion  about  the  concept  of  vulnerability  in  the  UCPD, 
arguing  that  the  approach  based  on  the  notions  of  age,  credulity,  and  infirmity  is  an 
out-dated and an overly static one, which can lead to stereotyping of particular groups 
and proposes instead a risk factor-based approach, where particular characteristics can 
be seen as risk factors potentially contributing to vulnerability.   

Conversely, Stuyck (2014) argues that the average consumer in the UCPD is never an 
actual  average  consumer,  but  instead  a  consumer  targeted  by  specific  practices.  As 
such,  this  average  consumer  does  not  necessarily  need  to  be  a  well-informed  and 
circumspect one. Furthermore, with regard to the indicators of vulnerability outlined in 
the UCPD, Stuyck argues that these are observable indicators that should be viewed as 
examples rather than a comprehensive definition of vulnerability. 

Concerning the use of the average consumer concept across the EU, Van Dam (2009) 
stresses  the  importance  of  pluralism  in  applying  the  concept,  arguing  that,  given 
national differences, there is no “average euroconsumer” and the development of such 
a  consumer  model  should  also  not  be  promoted.  Similarly,  Meli  (2011)  highlights  the 
need for the notion of average consumer to be re-constructed on the basis of general 
social  and  cultural  factors,  with  a  case-by-case  approach  needed  in  order  to  identify 
consumer categories. 

In addition to academic debate at the European level, the concepts of vulnerability and 
average  consumers  are  also  discussed  at  national  level.  In  the  Netherlands,  Wouters 

146 

 
 
5 │ Concept of the ‘average consumer’ 

(2011)  notes  that  in  practice  both  the  terms  “average  consumer”  and  “vulnerable 
consumer” are of limited relevance, since the “black list” of unfair practices included in 
the  UCPD  and  transposed  into  Dutch  legislation  effectively  makes  certain  practices 
illegal  regardless  of  the  consumer  to  which  they  are  addressed.  For  remaining 
practices, Wouters sees all consumers as the weaker party in Dutch law, but concludes 
that  socially  vulnerable  groups,  although  such  do  exist,  are  not  specifically  protected 
under  Dutch  law.  Schonewille  and  Verhage  (2012)  note  that  in  the  financial  sector  in 
the  Netherlands,  the  actual  average  consumer  generally  has  a  lower  level  of  financial 
literacy than an average consumer as outlined in the law resulting in challenges when 
applying existing legislation.   

In  France,  Nicod  (2013)  notes  that  the  notion  of  an  average  consumer  differs  in 
practice between its use in European law and its use by French judges, who would view 
consumers as possessing average levels of intelligence and attentiveness and at least 
minimal  critical  sense.  This  in  turn  means  that  the  latter  “average  consumer”  is  not 
expected  to  seek  out  information  in  order  to  make  decisions,  resulting  in  a  potential 
disparity between the European and the French notion of an average consumer.  

In  relation  to  Nordic  countries,  Bärlund  (2011)  notes  that  the  consumer  protection 
framework  in  the  Nordic  countries  assumes  that  consumers  are  the  weaker  part  in 
their relation to businesses and therefore they merit stronger protection. Since Nordic 
consumer  protection  is  not  built  around  the  concept  of  the  homo  economicus  in 
contrast  to  the  average  consumer  that  the  legal  EU  definition  is  built  around,  the  EU 
definition puts a greater responsibility on the consumer than the Nordic perception.  

Finally,  in  Germany  current  debates  focus  on  the  existing  consumer  models.  In  2010 
the  Scientific  Advisory  Council  to  the  Ministry  of  Food  and  Agriculture  produced  a 
position  paper  distinguishing  between  the  trusting,  vulnerable,  and  responsible 
consumers and  calling for a more differentiated consumer policy, while in the context 
of  the  food  sector,  Schwan  (2009)  identifies  the  emerging  model  of  an  informed 
consumer.  

While  these  criticisms  of  the  UCPD  concepts  of  consumer  vulnerability  and  average 
consumer  appear  to  reflect  the  findings  of  the  study  to  date  with  regard  to  drivers, 
indicators,  and  problematic  practices,  limited  case  law  in  the  area  suggests  relatively 
little  engagement  with  the  concepts  in  practice.  Similarly,  the  national  debates 
concerning these concepts show that there appears to be a degree of tension between 
national  approaches  to  consumer  “models”  and  ones  introduced  in  the  UCPD,  but  the 
debates are mainly theoretical with no practical implications being highlighted to date. 
Overall, the degree to which these concepts are appropriate is also dependent on how 
flexibly  they  are  interpreted,  with  a  more  flexible  interpretation,  similar  to  one 
suggested by Stuyck (2014), being a potential reason for the concepts not being more 
widely contested in existing case law.  

5.2. Vulnerability characteristics of the “average” consumer based on the 

survey data 

5.2.1.  General considerations 

This part of the analysis examines what characteristics, skills and behaviour define the 
average consumer. 

The  preparatory  review  found  that  an  average  consumer  who  is  “reasonably  well 
informed  and  reasonably  observant  and  circumspect”,  referred  to  in  the  Unfair 

147 

 
 
 
5 │ Concept of the ‘average consumer’ 

Commercial  Practices  Directive  (UCPD),  might  not  reflect  reality  (e.g.  Incardona  and 
Poncibo  2007),  and  that  although  the  term  average  consumer  is  used  in  case  law  it 
seems to be rarely explored or further defined. 

In the present analysis, the profile of an average consumer is defined in terms of the 
explanatory  variables  that  are  identified  as  the  main  drivers  and  indicators  of 
vulnerability,  specifically  as  an  individual  at  the  median  of  these  variables.  It  is 
appropriate  to  define  the  average  consumer  based  on  variables  that  are  strongly 
related  to  the  operationalisations  of  vulnerability,  since  the  definition  of  the  average 
consumer on this basis means it is possible to explore the levels of vulnerability of the 
average consumer. 

It  is  also  worthwhile  to  construct  the  profile  of  an  average  consumer  in  terms  of  the 
dependent  and  explanatory  variables  that  satisfy  the  definition  of  someone  who  is 
“reasonably well informed and reasonably observant and circumspect”, as described in 
the Unfair Commercial Practices Directive. 

Both  approaches  to  generating  a  profile  for  the  average  consumer  depend,  to  some 
extent, on variables that are binary indicators. This means that the average consumer 
will  either  take  one  state  or  another  depending  on  the  median  response  to  that 
particular question.  

An  initial  step  in  this  part  of  the  analysis  involved  assessing,  via  simple  frequency 
analysis,  whether  there  are  any  differences  in consumers  with  such  a profile  between 
the full sample of EU Member States and Norway and Iceland and the EU 28 Member 
States  only.  This  initial  analysis  is  followed  by  a  more  in-depth  analysis  at  Member 
State level.  

5.2.2.  Vulnerability characteristics of the “average” consumer  

As  discussed  earlier  in  this  chapter,  the  various  dimensions  of  consumer  vulnerability 
identified  in  the  literature  review  have  been  translated  into  indicators,  which  in  turn 
have  been  mapped  to  questions  and  variables  from  our  survey  and  experiment.  This 
list  of  dimensions  and  indicators  has  been  reproduced  in  Table  21,  along  with  the 
median  responses  from  the  full  sample  of  all  EU28  Member  States  and  Norway  and 
Iceland and the median responses from EU 28 Member States only. In all cases where 
a binary variable was constructed from a scale variable, the original scale variable was 
used. This approach was chosen to allow more variability in responses to emerge.   

Overall, for each of the vulnerability indicators, the “average consumer” as represented 
by  the  median  consumer  exhibits  few  signs  of  vulnerability.  The  points  below  report 
the incidence of vulnerability in the average consumer according to each dimension.  

  Dimension  1:  no  signs  of  vulnerability  emerge,  as  the  median 
respondent  took  action  when  experiencing  a  problem  and  did  not 
overpay for services due to being unable to pay with certain methods. 

  Dimension  2:  when  focusing  on  specific  factors  such  as  age  or  health,  the 
median  consumer  declares  not  to  feel  vulnerable.  In  particular,  in  the  case  of 
“health”,  “age”,  “minority  status”,  “personal  issues”  or  “other  issues”  all 
median  scores  take  the  lowest  value,  i.e.  there  is  no  self-perceived 
vulnerability  (“not  at  all”).  In  the  case  of  employment  status  and  financial 
circumstances,  the  median  response  is  ‘hardly  at  all  vulnerable’.  However, 
when the responses are  combined to capture  the maximum score  assigned to 
any  of  these  questions,  the  median  response  is  ‘feel  vulnerable  to  some 

148 

 
 
5 │ Concept of the ‘average consumer’ 

extent’, i.e. a value of 3 on the 1 to 4 scale. This means that overall, half the 
population  of  respondents  declared  some  (mild)  vulnerability  in  at 
least one of the aspects listed above.  

  Dimension  3:  no  vulnerability  is  observed  in  this  dimension.  Overall,  the 
average  consumer  reports  no  problems  in  obtaining  or  assimilating 
information. 

  Dimension  4:  in  this  dimension  the  average  consumer  shows  some 
vulnerability.  In  fact,  the  average  consumer  declares  to  compare  deals 
offered  by  internet  providers,  but  never  compares  deals  offered  by 
banks  or  energy  providers.  When  asked  about  the  difficulties  in  comparing 
offers,  the  responses  are  fully  consistent  with  the  previous  question.  The 
average  consumer  finds  it  difficult  to  compare  energy  deals  and 
finance  deals,  but  reports  no  difficulty  in  comparing  internet  provider 
deals.  

  Dimension  5:  no  signs  of  vulnerability  are  reported  in  the  dimension 

regarding susceptibility to marketing practices.  

Table 21:   Mapping median responses from the survey to the dimensions 
and indicators identified as the main drivers of vulnerability 

Indicators (code) 

i) Unassertive when experienced a problem 
buying or using goods or services (0=no, 
1=yes) 
(code 1)  

Median response 
– EEA sample 

Median response 
– EU 28 sample 

Took action when 
experienced a problem 

Took action when 
experienced a problem. 

ii) Overpaid for services due to being unable 
to use certain payment methods (0=no, 
1=yes) 
(code 2)  

Has not overpaid due 
to being unable to use 
certain payment 
methods 

Has not overpaid due 
to being unable to use 
certain payment 
methods 

iii) Perception of own vulnerability due to 
personal characteristics: 1 to 4 scale (1=not 
at all, 2=hardly at all, 3=, 4=very) 
(code 3)  

iii) Perception of own vulnerability due to 
personal characteristics – combined 
perception with all personal characteristics: 
1 to 4 scale  (1=not at all, 2=hardly at all, 
3=quite, 4=very) 
(code 3)  

Does not feel 
vulnerable at all due to 
health problems, age, 
belonging to a minority 
group, personal issues 
or other reasons.  
Feels hardly at all 
vulnerable due to 
employment situation 
or financial 
circumstances 

Does not feel 
vulnerable at all due to 
health problems, age, 
belonging to a minority 
group, personal issues 
or other reasons.  
Feels hardly at all 
vulnerable due to 
employment situation 
or financial 
circumstances 

Feel vulnerable to 
some extent due to 
any of the above issues 

Feel vulnerable to some 
extent due to any of 
the above issues 

Dimen
sion 

1. 
Heighten
ed risk 
of 
negative 
outcome
s or 
impacts 
on well-
being 

2. 
Having 
characte
ristics 
that limit 
ability to 
maximis
e well-
being 

149 

 
 
 
 
5 │ Concept of the ‘average consumer’ 

Table 21:   Mapping median responses from the survey to the dimensions 
and indicators identified as the main drivers of vulnerability 

Indicators (code) 

iv) Does not feel informed about prices 
(1=very, 2=quite, 3=, 4=not) 
(code 4)  

Median response 
– EEA sample 

Median response 
– EU 28 sample 

Feels quite informed 
about prices 

Feels quite informed 
about prices 

Dimen
sion 

3. 
Having 
difficulty 
in 
obtainin
g or 
assimilat
ing 
informati
on 

v) Gets information from adverts only 
(0=no, 1=yes) (energy, online , finance)  
(code 5_adv_ene; 5_adv_onl;5_adv_fin) 

vi) Has problems comparing deals due to 
information-related factors (0=no, 1=yes)  
(code 6_ene; 6_onl; 6_fin) 

vii) Has not recently switched due to being 
unsure about where to get information 
(0=no, 1=yes)  
(code 7_ene; 7_onl; 7_fin) 

4. 
Inability 
or failure 
to buy, 
choose 
or 
access 
suitable 
products 

viii a) Does not compare product deals  

offered by different gas and/or electricity 
providers:(0=no, 1=yes)    
(code 8_1_ene) 

offered by different internet service 
providers: (0=no, 1=yes)    
(code 8_1_onl) 

offered by different banks: (0=no, 1=yes)   
(code 8_1_fin) 

Does not get 
information only from 
adverts  in energy, 
online or finance sector 

Does not get 
information only from 
adverts in energy, 
online or finance sector 

Does not have 
problems comparing 
deals due to 
information-related 
factors in energy, 
online or finance sector 

Does not have 
problems comparing 
deals due to 
information-related 
factors in energy, 
online or finance sector 

Has not been 
prevented from 
switching due to being 
unsure about where to 
get information in 
energy, online or 
finance sector 

Has not been prevented 
from switching due to 
due to being unsure 
about where to get 
information in energy, 
online or finance sector 

Never compares deals 
offered by different gas 
and/or electricity 
providers 

Never compares deals 
offered by different gas 
and/or electricity 
providers 

Compares services 
offered by different 
internet service 
providers 

Never compares 
services offered by 
different banks 

Compares services 
offered by different 
internet service 
providers 

Never compares 
services offered by 
different banks 

viii b) Extent that consumer finds it difficult to compare product deals  

offered by different gas and/or electricity 
providers: (1=not at all, 2=, 3=fairly 
4=very) 
(code 8_2_ene) 

Finds it fairly difficult to 
compare deals offered 
by different gas and/or 
electricity providers 

Finds it fairly difficult to 
compare deals offered 
by different gas and/or 
electricity providers 

offered by different internet service 
providers: (1=not at all, 2=, 3=fairly 
4=very) 
(code 8_2_onl) 

offered by different banks: (1=not at all, 
2=, 3=fairly 4=very) 
(code 8_2_fin) 

Does not find it difficult 
to compare services 
offered by different 
internet service 
providers 

Does not find it difficult 
to compare services 
offered by different 
internet service 
providers 

Does not find it difficult 
to compare services 
offered by different 
banks 

Does not find it difficult 
to compare services 
offered by different 
banks 

150 

 
 
 
 
 
 
5 │ Concept of the ‘average consumer’ 

Table 21:   Mapping median responses from the survey to the dimensions 
and indicators identified as the main drivers of vulnerability 

Dimen
sion 

Indicators (code) 

Has problems in comparing deals due to 
personal factors (0=no, 1=yes) 
 (code 8a_ene; 8a_onl; 8a_fin) 

Has problems in comparing deals due to 
market-related factors (0=no, 1= yes) 
(code 8b_ene; 8b_onl; 8b_fin) 

Has problems in comparing deals due to 
access-related factors (0=no, 1=yes) 
(code 8c_ene; 8c_onl;8c_fin) 

Median response 
– EEA sample 

Median response 
– EU 28 sample 

Does not have 
problems comparing 
deals due to personal 
factors in energy, 
online or finance sector 

Does not have 
problems comparing 
deals due to personal 
factors in energy, 
online or finance sector 

Does not have 
problems comparing 
due to market-related  
factors in energy, 
online or finance sector 

Does not have 
problems comparing 
due to market-related  
factors in energy, 
online or finance sector 

Does not have 
problems comparing 
due to access-related  
factors in energy, 
online or finance sector 

Does not have 
problems comparing 
due to access-related 
factors in energy, 
online or finance sector 

ix) Has not recently switched due to a) 
personal factors, b) market-related factors, 
c) access-related factors, d) termination 
costs and e) bundling of offers: (0=no, 
1=yes) (code personal:9a_ene; 9a_onl; 
9a_fin; market:  9b_ene; 9b_onl; 9b_fin; 
access:  9c_ene; 9c_onl; 9c_fin; 
termination: 9d_ter; bundling: 9e_bun) 

Has not been 
prevented from 
switching due to  a) 
personal, b) market-
related, c) access-
related factors, d) 
termination costs or e) 
bundling of offers 

Has not been prevented 
from switching due to  
a) personal, b) market-
related, c) access-
related factors, d) 
termination costs or e) 
bundling of offers  

x) Excluded from e-commerce due to 
difficulty of the process or due to not having 
a credit card (0=no, 1=yes) (code 10a; 
10b) 

Was not excluded from 
e-commerce due to 
difficulty of the process 
or due to not having a 
credit card 

Was not excluded from 
e-commerce due to 
difficulty of the process 
or due to not having a 
credit card 

xi) Declined a loan: binary (code 11_loan) 

Loan not declined 

Loan not declined 

xii) Perception of own vulnerability due to 
complexity of offers : (1=not at all, 
2=hardly at all, 3= 4)  (12_off) 

Feels hardly at all 
vulnerable to 
marketing practices 

Feels hardly at all 
vulnerable to marketing 
practices 

5. 
Higher 
suscepti
bility to 
marketin
g 
practices 

Perception of own vulnerability due to 
complexity of offers (1=not at all, 2= 3=4)  
(code 12_off) 

Feels hardly at all 
vulnerable due to 
complexity of offers 

Feels hardly at all 
vulnerable due to 
complexity of offers 

Perception of own vulnerability due to 
complexity of offers In this section referred 
to as ‘Vulnerable to a great extent due to 
complexity of offers (own 
perception)’  (0=not at all or hardly at all, 
1=to a great extent) (code 12_off_b2) 

Does not feel 
vulnerable to a great 
extent 

Does not feel 
vulnerable to a great 
extent 

The  degree  of  vulnerability  of  the  average  consumer  in  each  country  could  slightly 
differ  from  the  average  consumer  identified  above  at  the  European  level.  Table  22 
shows  the  median  response  at  country  level.  Medians  that  depart  from  the  overall 
European  level  median  are  highlighted  with  colours.  Green  values  denote  less 
vulnerability, red values denote higher vulnerability. Most countries do not differ from 
the overall average consumer profile or differ only slightly.  Only 7 countries are more 

151 

 
 
 
 
 
 
 
 
 
 
 
5 │ Concept of the ‘average consumer’ 

vulnerable  than  the  median  consumer  in  more  than  3  indicators  (Bulgaria,  Cyprus, 
Greece, Hungary, Latvia, Portugal, Romania and Spain). 

  Dimension  1:  While  at  EU  level  no  signs  of  vulnerability  emerge,  in  6 
countries  (Bulgaria,  Estonia,  Hungary,  Latvia,  Lithuania  and  Romania)  the 
median  respondent  did  not  take  action  when  experiencing  a  problem.  In 
contrast, survey  respondents in no country  showed a tendency to  overpay for 
services due to being unable to pay with certain methods. 

  Dimension  2:  This  dimension  presents  the  highest  variation  across 
countries.  For  example,  in  the  case  of  “financial  circumstances”,  the  median 
response  is  ‘hardly  at  all  vulnerable’,  but  the  median  respondent  in  13 
countries  declares  to  feel  ‘vulnerable  to  some  extent’,  and  the  median 
respondents  in  8  countries  declare  not  to  feel  vulnerable  at  all.  Similar 
variation is also observed with regards to the “employment situation” question. 
In  the  case  of  “age”,  median  survey  participants  in  a  number  of  countries 
(Greece,  Latvia,  Poland,  Portugal,  Romania  and  Spain)  appear  to  be  more 
vulnerable  than  on  average  across  the  EU28.  Median  survey  respondents  in 
Poland,  Portugal,  Romania,  and  Spain  are  also  more  vulnerable  than  on 
average across the EU28 in terms of “personal issues”. In the case of “health”, 
“minority status” or “other reasons”, there are no differences.  

  Dimension 3: No vulnerability of the median survey respondent is observed 
for  this  dimension.  Overall,  across  the  EU28  the  median  consumer  reports  no 
problems  in  obtaining  or  assimilating  information  and  this  observation  also 
holds in all Member States. 

  Dimension  4:  This  dimension  shows  some  variability  across  countries, 
particularly in the energy and finance sectors. Overall in the EU28, the median 
consumer reports comparing deals offered by internet providers, but this is not 
the  case  in  3  countries  (Malta,  Norway  and  Sweden).  Across  the  EU28,  the 
median  consumer  never  compares  deals  offered  by  energy  providers,  but,  in 
fact,  the  median  consumer  does  so  in  14  countries.  When  asked  about  the 
difficulties  in  comparing  offers,  the  median  consumer  in  14  countries  declares 
having some difficulty in the energy sector and in 11 countries he/she declares 
having some difficulty in the finance sector.  There are no differences between 
the median consumers  in the different Member States with regards to access-
related  factors  and  personal  factors  for  which  the  median  survey  respondent 
does not report any degree of vulnerability. 

  Dimension 5: no signs of vulnerability are  reported by the median survey 
participants with regards to the susceptibility to marketing practices. However, 
median  survey  participants  in  6  countries  (Cyprus,  Greece,  Latvia,  Portugal, 
Romania  and  Spain)  report  that  they  feel  vulnerable  to  some  extent  with 
regards to indicator focusing on the complexity of offers. 

Overall, the vast majority of the indicators (37 out of 51) show a completely consistent 
picture  of  the  average  consumer  across  EU28  Member  States.  However,  the  average 
consumer is more or less vulnerable than on average across the EU28 in the case of a 
few  indicators  (a  maximum  of  6  deviations)  in  a  few  countries.  The  number  of  times 
this is the case in each country is shown in Table 23. The countries where the average 
consumer  shows  somewhat  less  vulnerability  than  the  average  across  the  EU28  are: 
Austria, Belgium, the Czech Republic, Finland, Germany, Iceland, Ireland, Luxembourg, 
Malta,  Norway,  Poland,  Slovakia,  Slovenia,  Sweden,  the  Netherlands  and  the  United 
Kingdom.  In  contrast,  countries  where  the  average  consumer  is  slightly  more 
vulnerable  than  on  average  across  the  EU28  are  Bulgaria,  Cyprus,  Greece,  Hungary, 
Latvia,  Lithuania,  Portugal,  Romania,  and  Spain.  In  the  remaining  countries,  the 

152 

 
 
5 │ Concept of the ‘average consumer’ 

average consumer’s vulnerability pattern is very similar to the EU-wide pattern. These 
countries are Croatia, Denmark, Estonia, France, and Italy.   

153 

 
 
 
 
5 │ Concept of the ‘average consumer’ 

Table 22:   Mapping median responses from the survey to the dimensions and indicators identified as the main drivers of 

vulnerability  by country 

Dimensio
n 

Indicators (code) 

i) Unassertive when 
experienced a problem buying 
or using goods or services 
(0=experienced a problem and 
took action; 1=experienced a 
problem and did not take action) 

ii) Overpaid for services (0= 
did not overpay for services due 
to being unable to pay with 
certain methods; 1= overpaid for 
services due to being unable to 
pay with certain methods)    

1. 
Heightene
d risk of 
negative 
outcomes 
or impacts 
on well-
being 

2. Having 
characteri
stics that 
limit 
ability to 
maximise 
well-being 

e
s
n
o
p
s
e
r

e
g
a
r
e
v
A

a
i
r
t
s
u
A

i

m
u
g
l
e
B

a
i
r
a
g
u
B

l

a
i
t
a
o
r
C

s
u
r
p
y
C

c
i
l

b
u
p
e
R
h
c
e
z
C

k
r
a
m
n
e
D

y
n
a
m
r
e
G

y
r
a
g
n
u
H

e
c
e
e
r
G

e
c
n
a
r
F

d
n
a
l
n
i
F

d
n
a
l
e
c
I

d
n
a
l
e
r
I

a
i
v
t
a
L

y
l
a
t
I

a
i
n
o
t
s
E

g
r
u
o
b
m
e
x
u
L

a
i
n
a
u
h
t
i
L

y
a
w
r
o
N

d
n
a
l

o
P

a
t
l
a
M

l
a
g
u
t
r
o
P

a
i
n
a
m
o
R

a
i
k
a
v
o
S

l

a
i
n
e
v
o
S

l

n
e
d
e
w
S

n
i
a
p
S

i

m
o
d
g
n
K
d
e
t
i
n
U

s
d
n
a
l
r
e
h
t
e
N

0 

0 

0 

1 

0 

0 

0 

0 

1 

0 

0  0  0  1  0  0  0  1  1  0  0  0  0 

0 

1 

0  0  0  0  0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0  0  0  0  0  0  0  0  0  0  0  0  0 

0 

0 

0  0  0  0  0 

0 

iii) Perception of own vulnerability due to personal characteristics (1= not at all; 4=very vulnerable) 

health issues 

financial issues 

employment issues 

age 

minority status 

personal issues 

other issues 

1 

2 

2 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

2 

1 

1 

1 

1 

1 

1 

3 

2 

1 

1 

1 

1 

1 

3 

2 

1 

1 

1 

1 

1 

3 

3 

1 

1 

1 

1 

1 

2 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

3 

2 

1 

1 

1 

1 

1 

2 

2 

1 

1 

1 

1 

1  1  1  1  1  1  1  1  1  1  1  1  1 

2  2  3  3  1  2  2  3  3  1  1  1  3 

2  1  3  2  1  2  2  2  2  1  1  1  2 

1  1  2  1  1  1  1  2  1  1  1  1  2 

1  1  1  1  1  1  1  1  1  1  1  1  1 

1  1  1  1  1  1  1  1  1  1  1  1  2 

1  1  1  1  1  1  1  1  1  1  1  1  1 

1 

3 

3 

2 

1 

2 

1 

1 

3 

2 

2 

1 

2 

1 

1  1  1  1  1 

2  3  3  1  1 

2  2  2  1  1 

1  1  2  1  1 

1  1  1  1  1 

1  1  2  1  1 

1  1  1  1  1 

1 

2 

1 

1 

1 

1 

1 

154 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5 │ Concept of the ‘average consumer’ 

Table 22:   Mapping median responses from the survey to the dimensions and indicators identified as the main drivers of 

vulnerability  by country 

Dimensio
n 

Indicators (code) 

iii) Perception of own 
vulnerability due to personal 
characteristics – combined 
perception with all personal 
characteristics (1=not at all 
vulnerable; 4=very vulnerable) 

iv) Does not feel informed 
about prices (1=very informed; 
4=not at all informed) 

e
s
n
o
p
s
e
r

e
g
a
r
e
v
A

a
i
r
t
s
u
A

i

m
u
g
l
e
B

a
i
r
a
g
u
B

l

a
i
t
a
o
r
C

s
u
r
p
y
C

c
i
l

b
u
p
e
R
h
c
e
z
C

k
r
a
m
n
e
D

y
n
a
m
r
e
G

y
r
a
g
n
u
H

e
c
e
e
r
G

e
c
n
a
r
F

d
n
a
l
n
i
F

d
n
a
l
e
c
I

d
n
a
l
e
r
I

a
i
v
t
a
L

y
l
a
t
I

a
i
n
o
t
s
E

g
r
u
o
b
m
e
x
u
L

a
i
n
a
u
h
t
i
L

y
a
w
r
o
N

d
n
a
l

o
P

a
t
l
a
M

l
a
g
u
t
r
o
P

a
i
n
a
m
o
R

a
i
k
a
v
o
S

l

a
i
n
e
v
o
S

l

n
e
d
e
w
S

n
i
a
p
S

i

m
o
d
g
n
K
d
e
t
i
n
U

s
d
n
a
l
r
e
h
t
e
N

3 

2 

2 

3 

3 

3 

2 

2 

3 

3 

3  2  3  3  2  3  3  3  3  2  3  2  3 

3 

3 

3  3  3  2  2 

3 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2  2  2  2  2  2  2  2  2  2  2  2  2 

2 

2 

2  2  2  2  2 

2 

3. Having 
difficulty 
in 
obtaining 
or 
assimilatin
g 
informatio
n 

v) Gets information from adverts only (0=not adverts only; 1=adverts only) 

Energy  

Finance 

Online 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0  0 

0  0 

0  0 

0 

0  0  0  0  0  0  0  0  0  0  0  0  0 

0  0 

0  0 

0  0 

vi) Has problems comparing deals due to information-related factors  (0= has no problem; 1=has problems) 

Energy  

Finance 

Online 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0  0 

0  0 

0  0 

0  0  0  0  0  0  0  0  0  0  0  0  0 

0  0 

0  0 

0  0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0  0 

0  0  0  0  0 

0 

0 

0  0 

0  0 

0  0  0  0  0 

0 

0  0 

vii) Has not recently switched due to being unsure about where to get information (0=no; 1=yes, has not recently switched due to being unsure) 

Energy  

155 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5 │ Concept of the ‘average consumer’ 

Table 22:   Mapping median responses from the survey to the dimensions and indicators identified as the main drivers of 

vulnerability  by country 

Dimensio
n 

Indicators (code) 

Online 

Finance 

e
s
n
o
p
s
e
r

e
g
a
r
e
v
A

0 

0 

a
i
r
t
s
u
A

i

m
u
g
l
e
B

a
i
r
a
g
u
B

l

a
i
t
a
o
r
C

s
u
r
p
y
C

c
i
l

b
u
p
e
R
h
c
e
z
C

k
r
a
m
n
e
D

0 

0 

y
n
a
m
r
e
G

y
r
a
g
n
u
H

e
c
e
e
r
G

e
c
n
a
r
F

d
n
a
l
n
i
F

d
n
a
l
e
c
I

d
n
a
l
e
r
I

a
i
v
t
a
L

y
l
a
t
I

a
i
n
o
t
s
E

y
a
w
r
o
N

d
n
a
l

o
P

a
t
l
a
M

g
r
u
o
b
m
e
x
u
L

a
i
n
a
u
h
t
i
L

0 

0 

4. Inability 
or failure 
to buy, 
choose or 
access 
suitable 
products 

viii a) Does not compare product deals (0=no; 1=yes, does not compare deals) 
offered by different gas and/or 
electricity providers: binary  

1 

0 

0 

1 

0 

1 

0 

1 

offered by different internet 
service providers: binary  
offered by different banks: binary  

0 

1 

0 

0 

0 

1 

0 

1 

0 

1 

0 

1 

0 

0 

0 

1 

0 

0 

1 

0 

0 

1 

1  0  1  1  1  0  0  1  1  1  1  1  1 

0  0  0  0  0  0  0  0  0  0  1  1  0 

1  1  1  1  1  1  1  1  1  1  1  0  0 

l
a
g
u
t
r
o
P

0 

0 

0 

0 

1 

viii b) Extent that the consumer finds it difficult to compare deals (1=not at all difficult; 4=very difficult) 

offered by different gas and/or 
electricity providers 

offered by different internet 
service providers 

offered by different banks 

2 

2 

2 

3 

2 

3 

3 

2 

2 

3 

2 

3 

3 

2 

3 

2 

2 

2 

3 

2 

3 

2 

2 

2 

2 

2 

2 

3 

2 

3 

2  2  3  3  2  3  3  3  3  2  2  3  3 

3 

2  2  2  2  2  2  2  2  3  2  2  2  2 

2  3  3  3  2  3  2  2  2  2  2  2  3 

Has problems in comparing deals due to personal factors (0= has no problem; 1= has problems) 

Energy 

Online 

Finance 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0  0 

0  0 

0  0 

0 

0  0  0  0  0  0  0  0  0  0  0  0  0 

0  0 

0  0 

0  0 

0 

Has problems in comparing deals due to market-related factors (0= has no problem; 1= has problems) 

2 

2 

0 

0 

0 

a
i
n
a
m
o
R

0 

0 

1 

0 

1 

2 

2 

2 

0 

0 

0 

a
i
k
a
v
o
S

l

a
i
n
e
v
o
S

l

n
e
d
e
w
S

n
i
a
p
S

s
d
n
a
l
r
e
h
t
e
N

1  0  1  0  0 

0  0  0  1  0 

0  0  1  1  1 

i

m
o
d
g
n
K
d
e
t
i
n
U

0 

0 

0 

0 

1 

2  3  2  2  2 

2 

2  2  2  2  2 

2  3  2  2  2 

0 

0  0 

0  0  0  0  0 

0 

0  0 

2 

2 

0 

0 

0 

156 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 22:   Mapping median responses from the survey to the dimensions and indicators identified as the main drivers of 

5 │ Concept of the ‘average consumer’ 

vulnerability  by country 

Dimensio
n 

Indicators (code) 

Energy  

Online 

Finance 

e
s
n
o
p
s
e
r

e
g
a
r
e
v
A
0 

0 

0 

a
i
r
t
s
u
A
0 

0 

0 

i

m
u
g
l
e
B
0 

0 

0 

a
i
r
a
g
u
B

l

a
i
t
a
o
r
C

s
u
r
p
y
C

0 

0 

0 

c
i
l

b
u
p
e
R
h
c
e
z
C
0 

0 

0 

k
r
a
m
n
e
D
0 

0 

0 

a
i
n
o
t
s
E
0 

0 

0 

d
n
a
l
n
i
F
0 

0 

0 

y
r
a
g
n
u
H

e
c
e
e
r
G

y
n
e
a
c
m
n
r
a
e
r
G
F
0  0 

d
d
n
n
a
a
l
l
e
e
c
r
I
I
0  0 

a
i
v
t
a
L

y
l
a
t
I

g
r
u
a
o
i
b
n
m
a
u
e
h
x
t
u
i
L
L
0  0 

a
t
l
a
M

d
n
a
l

o
P

y
a
w
r
o
N
0 

0  0  0  0  0  0  0  0  0  0  0  0  0 

0  0 

0  0 

0  0 

s
d
n
a
n
l
r
e
e
d
h
e
t
w
e
N
S
0  0 

a
i
n
e
v
o
S

l

n
i
a
p
S

a
i
k
a
v
o
S
0 

l

0  0  0  0  0 

0 

0 

0  0 

0  0 

0  0  0  0  0 

a
i
n
a
m
o
R
0 

0 

0 

0 

0 

l
a
g
u
t
r
o
P
0 

0 

0 

0 

0 

0 

0 

Has problems in comparing deals due to access-related factors (0= has no problem; 1= has problems) 

Energy 

Online 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0  0 

0  0 

1  0 

0  0  0  0  0  0  0  0  0  0  0  0  0 

Finance 

0  0 
ix) Has not recently switched due to a) personal factors, b) market-related factors, c) access-related factors, d) termination costs and e) bundling of 
offers   (0=no; 1=yes, has not recently switched due to the factors) 

0  0 

0  0 

0  0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

Personal (energy) 

Personal (online) 

Personal (finance) 

Market (energy) 

Market (online) 

Market (finance) 

Access (energy) 

Access (online) 

Access (finance) 

Termination costs 

157 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0  0  0  0  0  0  0  0  0  0  0  0  0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0  0  0  0  0 

i

m
o
d
g
n
K
d
e
t
i
n
U
0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5 │ Concept of the ‘average consumer’ 

Table 22:   Mapping median responses from the survey to the dimensions and indicators identified as the main drivers of 

vulnerability  by country 

Dimensio
n 

Indicators (code) 

Bundling of offers 

x) Excluded from e-commerce 
due 
the 
to  difficulty  of 
process  or  due  to  not  having 
a credit card (0= no; 1=yes) 

xi) Declined a loan (0= no; 
1=yes) 

5. Higher 
susceptibilit
y to 
marketing 
practices 

xii) Perception of own 
vulnerability due to 
complexity of offers (1=not at 
all vulnerable; 4=very 
vulnerable) 

Perception of own 
vulnerability due to 
complexity of offers 
‘Vulnerable to a great extent due 
to complexity of offers’ (own 
perception : 0=no ; 1=yes) 

e
s
n
o
p
s
e
r

e
g
a
r
e
v
A
0 

a
i
r
t
s
u
A
0 

i

m
u
g
l
e
B
0 

a
i
r
a
g
u
B
0 

l

a
i
t
a
o
r
C
0 

s
u
r
p
y
C
0 

c
i
l

b
u
p
e
R
h
c
e
z
C
0 

k
r
a
m
n
e
D
0 

a
i
n
o
t
s
E
0 

d
n
a
l
n
i
F
0 

y
n
a
m
r
e
G

y
r
a
g
n
u
H

e
c
e
e
r
G

e
c
y
n
l
a
a
o
r
t
P
F
I
0  0  0  0  0  0  0  0  0  0  0  0  0 

a
i
v
t
a
L

a
t
l
a
M

d
n
a
l

d
n
a
l
e
r
I

d
n
a
l
e
c
I

y
a
w
r
o
N

a
i
n
a
u
h
t
i
L

g
r
u
o
b
m
e
x
u
L

s
d
n
a
a
l
i
r
k
e
n
a
h
i
v
a
t
o
e
p
N
S
S
0  0  0  0  0 

a
i
n
e
v
o
S

n
e
d
e
w
S

l

l

l
a
g
u
t
r
o
P
0 

a
i
n
a
m
o
R
0 

i

m
o
d
g
n
K
d
e
t
i
n
U
0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0  0  0  0  0  0  0  0  0  0  0  0  0 

0 

0 

0  0  0  0  0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0  0  0  0  0  0  0  0  0  0  0  0  0 

0 

0 

0  0  0  0  0 

0 

2 

2 

2 

2 

2 

3 

2 

2 

2 

2 

2  2  3  2  2  2  2  3  2  2  2  2  2 

3 

3 

2  2  3  2  2 

2 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0  0  0  0  0  0  0  0  0  0  0  0  0 

0 

0 

0  0  0  0  0 

0 

Note: empty cells imply that question was not administered in the country. In the average response column, values in green imply that the median respondent (in the full 
sample) is not vulnerable, whereas values in red imply that the median respondent (in the full sample) is vulnerable. In the  country-level columns, values in green (red) 
imply that the median respondent in the relevant country is less (more) vulnerable than the median respondent in the full sample. 

158 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5 │ Concept of the ‘average consumer’ 

Table 23:  Mapping median responses from the survey to the dimensions and indicators identified as the main 

drivers of vulnerability  - number of deviations from the median by country 

Number of indicators 
differing from median 

Number of indicators with 
median more vulnerable than 
the average consumer  

Number of indicators with 
median  less vulnerable than 
the average consumer  

a
i
r
t
s
u
A

0 

i

m
u
g
l
e
B

1 

a
i
r
a
g
u
B

l

2 

a
i
t
a
o
r
C

2 

s
u
r
p
y
C

4 

c
i
l

b
u
p
e
R
h
c
e
z
C

0 

k
r
a
m
n
e
D

1 

a
i
n
o
t
s
E

2 

d
n
a
l
n
i
F

0 

y
n
a
m
r
e
G

y
r
a
g
n
u
H

e
c
e
e
r
G

e
c
n
a
r
F

d
n
a
l
e
c
I

d
n
a
l
e
r
I

a
i
v
t
a
L

y
l
a
t
I

g
r
u
o
b
m
e
x
u
L

a
i
n
a
u
h
t
i
L

y
a
w
r
o
N

d
n
a
l

o
P

a
t
l
a
M

l
a
g
u
t
r
o
P

a
i
n
a
m
o
R

a
i
k
a
v
o
S

l

a
i
n
e
v
o
S

l

n
e
d
e
w
S

n
i
a
p
S

i

m
o
d
g
n
K
d
e
t
i
n
U

s
d
n
a
l
r
e
h
t
e
N

1  0  4  3  1  0  1  3  2  1  1  1  1  6 

5  0  1  4  1  0  0 

6 

3 

0 

1 

0 

5 

2 

2 

2 

0  4  1  0  3  2  1  0  0  3  3  5  3  1 

0  2  3  1  5  5  3 

159 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5 │ Concept of the ‘average consumer’ 

5.2.3.  Prevalence of the “average” consumer as defined by the UCPD 

The  existing  definition  of  the  average  consumer  in  the  UCPD  refers  to  being  “well 
informed”, “observant” and “circumspect”. The appropriate explanatory and dependent 
variables  which  contribute  to  such  a  definition  have  been  mapped  against  two 
dimensions:  “well  informed”  and  “observant  and  circumspect”.  The  list  of  indicators 
and median responses from the EU 28 and Norway and Iceland sample and the EU 28 
sample are provided in Table 24. 

Most  of  the  indicators  used  to  form  a  definition  of  the  average  consumer  in  Table  24 
can be termed as measures of respondents’ self-assessed character traits and tests of 
respondents’ character  and ability, which might also be considered ‘behaviour drivers’ 
of vulnerability. For all scale indicators, the median responses are moderate  – e.g. for 
indicators  iv)  to  viii)  where  there  are  four  response  options,  none  of  the  median 
responses are at the two end points of the scale, but rather clustered around the two 
middle values. This conforms to expectations of median values from self-assessment of 
various behavioural traits.  

  Dimension 1: Overall, the average consumer reads communication from 

energy or internet providers or banks, but declares to have ‘glanced over or 
skim read it’. The average consumer also feels quite informed and does not 
only get information from adverts.   

  Dimension 2: The questions defining personality traits such as credulity and 
risk aversion cannot easily be related to vulnerability. In general, the average 
consumer tends to agree on being careful when dealing with people and when 
taking decisions, and does not trust advertisements as objective sources. The 
average consumer is also not willing to take risks. On average, consumers 
were able to answer correctly all questions related to the key market 
terminology and credulity questions.  

Overall,  therefore,  the  average  consumer  appears  to  be  well  informed  (although  not 
too thorough in reading communication from providers) and circumspect.  

Table 24:  Mapping median responses from the survey to the dimensions 

and indicators identified as part of the UCPD definition of the average 
consumer 

Dimension 

Indicators 

1. Well 
informed 

i)  Reads 
communication 
from  providers  (1=  Read  it 
in  detail,  2=Glanced  over  it 
or  skim  read  it,  3=Only 
looked  at  the  total  price, 
4=Did not read it at all) 

ii)  Does  not  feel  informed 
about 
(1=very  
2=quite,  3=not  very  4=not 
at all)  

prices 

Median response – 
EEA sample 

Median response – 
EU 28 sample 

Read  communication 
from internet, banking 
or energy providers, 
but glanced over or 
skim read it 

Read  communication 
from internet, banking 
or energy providers, 
but glanced over or 
skim read it 

Feels quite informed 
about prices 

Feels quite informed 
about prices 

160 

 
 
 
 
 
 
5 │ Concept of the ‘average consumer’ 

Table 24:  Mapping median responses from the survey to the dimensions 

and indicators identified as part of the UCPD definition of the average 
consumer 

Dimension 

Indicators 

2. Observant 
and 
circumspect 

iii) Gets information from 
adverts only (0=not adverts 
only; 1=adverts only) 
(energy, finance, online 
5_adv_ene; 5_adv_fin; 
5_adv_onl) 

iv)  Careful  in  dealing  with 
people  (1=Strongly  agree, 
2=Tend to agree, 3= 
Tend to disagree, 4= 
Strongly disagree) 

v) Credulous in assessing 
objectivity in advertising 
(1=strongly disagree, 
2=tend to disagree, 3=tend 
to agree, 4=strongly agree) 

vi) Careful in making 
decisions (1=very  2=quite, 
3=not very 4=not at all)  

vii)  Willing  to  take  risks 
(1=not  at  all  willing,  2=not 
very willing, 3=fairly willing, 
4=very willing) 

viii)  Believes  people  can  be 
trusted  (1=Strongly  agree, 
2=Tend to agree, 3= 
Tend to disagree, 4= 
Strongly disagree) 

Median response – 
EEA sample 

Median response – 
EU 28 sample 

  Does not get 
information only from 
adverts in energy, 
online or finance sector 

Does not get 
information only from 
adverts in energy, 
online or finance sector 

Tends to agree 

Tends to agree 

Tends to disagree that 
advertisements report 
objective fact 

Tends to disagree that 
advertisements report 
objective fact 

Quite careful in making 
decisions 

Quite careful in making 
decisions 

Not very willing to take 
risks 

Not very willing to take 
risks 

Tends to disagree 

Tends to disagree 

ix) Test questions on 
understanding of key 
market terminology (kWh, 
Mbps, interest rate) 

Was able to correctly 
identify terminology for 
energy, internet and 
finance provision 

Was able to correctly 
identify terminology for 
energy, internet and 
finance provision 

Test question  for 
measuring understanding of 
cheapest option 

Was able to identify 
both answers correctly 

Was able to identify 
both answers correctly 

Test question for measuring 
credulity 

Was able to answer 
correctly 

Was able to answer 
correctly 

Note that the responses from the full sample with all EEA member states were the same as the 
responses from the EU 28 member state only. 

Looking  at  country  patterns,  there  is  little  variation  from  the  average  consumer  at 
European level (Table 25) in terms of UCPD dimensions.  

  Dimension  1:  A  few  countries  (Bulgaria,  Cyprus,  Estonia,  Lithuania, 
Luxembourg  and  Romania)  declared  on  average  to  have  only  looked  at  the 
total price in the latest communication from energy providers, while in Iceland, 

161 

 
 
 
 
 
 
 
 
 
5 │ Concept of the ‘average consumer’ 

the median response was ‘not read it at all’. There is no variation in the extent 
to which consumers feel informed about prices, and in all countries the average 
respondent does not rely only on adverts for information.  

  Dimension  2:  In  the  case  of  behavioural  traits  such  as  credulity  and  trust, 
there  are  some  minor  differences,  but  in  most  cases  the  answers  do  not 
contradict  the  median  response.  The  same  countries  that  exhibited  more 
vulnerability  in  the  previous  analysis  (Bulgaria,  Cyprus,  Greece,  Hungary, 
Latvia, Romania), as well as Malta and Slovakia, declared they strongly agreed 
that  one  needs  to  be  careful  when  dealing  with  people.  Interestingly,  the 
contradiction is found in Iceland and Sweden, where the median response was 
a disagreement with the statement. In the case of the statement ‘most people 
can  be  trusted’,  while  the  average  consumer  tends  to  disagree,  the  average 
consumer  in  Denmark,  Estonia,  Finland,  Iceland,  Norway,  Sweden,  the 
Netherlands  and  the  United  Kingdom  responded  with  mild  agreement.  On  the 
other  hand,  no  country  had  an  average  consumer  who  believes  advertising  to 
report objective facts.  

162 

 
 
 
5 │ Concept of the ‘average consumer’ 

Table 25:  Mapping median responses from the survey to the dimensions and indicators identified as part of the UCPD 

definition of the average consumer, by country 

Dimensi
on 

Indicat
ors 

e
s
n
o
p
s
e
r

e
g
a
r
e
v
A

c
i
l

b
u
p
e
R
h
c
e
z
C

a
i
r
t
s
u
A

i

m
u
g
l
e
B

a
i
r
a
g
u
B

l

a
i
t
a
o
r
C

s
u
r
p
y
C

k
r
a
m
n
e
D

a
i
n
o
t
s
E

d
n
a
l
n
i
F

e
c
n
a
r
F

y
n
a
m
r
e
G

y
r
a
g
n
u
H

e
c
e
e
r
G

d
n
a
l
e
c
I

d
n
a
l
e
r
I

a
i
v
t
a
L

y
l
a
t
I

g
r
u
o
b
m
e
x
u
L

a
i
n
a
u
h
t
i
L

y
a
w
r
o
N

d
n
a
l

o
P

a
t
l
a
M

l
a
g
u
t
r
o
P

a
i
n
a
m
o
R

a
i
k
a
v
o
S

l

a
i
n
e
v
o
S

l

n
e
d
e
w
S

n
i
a
p
S

s
d
n
a
l
r
e
h
t
e
N

1. Well 
informed 

i) Reads communication from providers (energy, internet or bank) (1= Read it in detail, 2=Glanced over it or skim read it, 3=Only looked at the total price, 
4=Did not read it at all) 

2 

2 

2 

2 

2 

1 

2 

2 

2 

3 

2 

1 

2 

2 

2 

3 

3 

2 

2 

2 

2 

2 

2 

2 

3 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

4 

3 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

3 

2 

2 

3 

2 

2 

2 

3 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

3 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

i

m
o
d
g
n
K
d
e
t
i
n
U

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

Energy 

Online 

Finance 

ii)  Does 
not 
feel 
informed 
(1=very  
2=quite, 
3=not 
very 
4=not  at 
all)  

iii) Gets information from adverts only, or also other sources: (0=not adverts only; 1=adverts only) (energy, finance, online) 

Energy 

Online 

Finance 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

0 

163 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5 │ Concept of the ‘average consumer’ 

Table 25:  Mapping median responses from the survey to the dimensions and indicators identified as part of the UCPD 

definition of the average consumer, by country 

e
s
n
o
p
s
e
r

e
g
a
r
e
v
A

c
i
l

b
u
p
e
R
h
c
e
z
C

a
i
r
t
s
u
A

i

m
u
g
l
e
B

a
i
r
a
g
u
B

l

a
i
t
a
o
r
C

s
u
r
p
y
C

k
r
a
m
n
e
D

a
i
n
o
t
s
E

d
n
a
l
n
i
F

e
c
n
a
r
F

y
n
a
m
r
e
G

y
r
a
g
n
u
H

e
c
e
e
r
G

d
n
a
l
e
c
I

d
n
a
l
e
r
I

a
i
v
t
a
L

y
l
a
t
I

g
r
u
o
b
m
e
x
u
L

a
i
n
a
u
h
t
i
L

y
a
w
r
o
N

d
n
a
l

o
P

a
t
l
a
M

l
a
g
u
t
r
o
P

a
i
n
a
m
o
R

a
i
k
a
v
o
S

l

a
i
n
e
v
o
S

l

n
e
d
e
w
S

n
i
a
p
S

i

m
o
d
g
n
K
d
e
t
i
n
U

s
d
n
a
l
r
e
h
t
e
N

2 

2 

2 

1 

2 

1 

1 

2 

2 

2 

2 

2 

1 

1 

3 

2 

2 

1 

2 

2 

1 

2 

2 

2 

1 

1 

2 

2 

3 

2 

2 

Dimensi
on 

Indicat
ors 

2. 
Observa
nt and 
circumsp
ect 

iv) 
Careful in 
dealing 
with 
people 
(1=Stron
gly 
agree, 
2=Tend 
to agree, 
3= 
Tend to 
disagree, 
4= 
Strongly 
disagree) 

164 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5 │ Concept of the ‘average consumer’ 

Table 25:  Mapping median responses from the survey to the dimensions and indicators identified as part of the UCPD 

definition of the average consumer, by country 

Dimensi
on 

Indicat
ors 

e
s
n
o
p
s
e
r

e
g
a
r
e
v
A

c
i
l

b
u
p
e
R
h
c
e
z
C

a
i
r
t
s
u
A

i

m
u
g
l
e
B

a
i
r
a
g
u
B

l

a
i
t
a
o
r
C

s
u
r
p
y
C

k
r
a
m
n
e
D

a
i
n
o
t
s
E

d
n
a
l
n
i
F

e
c
n
a
r
F

y
n
a
m
r
e
G

y
r
a
g
n
u
H

e
c
e
e
r
G

d
n
a
l
e
c
I

d
n
a
l
e
r
I

a
i
v
t
a
L

y
l
a
t
I

g
r
u
o
b
m
e
x
u
L

a
i
n
a
u
h
t
i
L

y
a
w
r
o
N

d
n
a
l

o
P

a
t
l
a
M

l
a
g
u
t
r
o
P

a
i
n
a
m
o
R

a
i
k
a
v
o
S

l

a
i
n
e
v
o
S

l

n
e
d
e
w
S

n
i
a
p
S

i

m
o
d
g
n
K
d
e
t
i
n
U

s
d
n
a
l
r
e
h
t
e
N

v) 
Credulou
s in 
assessing 
objectivit
y in 
advertisin
g 
1=strongl
y 
disagree, 
2=tend 
to 
disagree, 
3=tend 
to agree, 
4=strongl
y agree 

vi) 
Careful in 
making 
decisions 
(1=very  
2=quite, 
3=not 
very 
4=not at 
all)  

165 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

1 

2 

2 

2 

2 

2 

1 

2 

1 

2 

2 

1 

1 

2 

2 

2 

1 

2 

2 

1 

2 

2 

2 

2 

2 

2 

1 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5 │ Concept of the ‘average consumer’ 

Table 25:  Mapping median responses from the survey to the dimensions and indicators identified as part of the UCPD 

definition of the average consumer, by country 

Dimensi
on 

Indicat
ors 

vii) 
Willing to 
take risks 
(1=not at 
all 
willing, 
2=not 
very 
willing, 
3=fairly 
willing, 
4=very 
willing) 

viii) 
Believes 
people 
can be 
trusted 
(1=stron
gly 
disagree, 
2=tend 
to 
disagree, 
3=tend 
to agree, 
4=strongl
y agree 

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2 

2 

3 

2 

2 

2 

2 

2 

2 

3 

3 

3 

166 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5 │ Concept of the ‘average consumer’ 

Table 25:  Mapping median responses from the survey to the dimensions and indicators identified as part of the UCPD 

definition of the average consumer, by country 

Dimensi
on 

Indicat
ors 

Test 
question 
for 
measurin
g 
credulity 
2 

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2 

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2 

2 

2 

2 

2 

2 

2 

2 

2 

1 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

ix) Test questions on understanding of key market terminology (kWh, Mbps, interest rate) 

kWh 

Mbps 

Interest 
rate 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

0 

1 

1 

1 

1 

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1 

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1 

1 

1 

167 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

6.  DRIVERS OF AND FACTORS LINKED TO CONSUMER VULNERABILITY 

Box 4:  Summary of Chapter 6 

This chapter examines evidence relating to the drivers of consumer vulnerability. In 
particular, the chapter investigates which variables are linked with the dimensions of 
vulnerability set out in chapter 3: 

  Dimension 1: Heightened risk of negative outcomes or impacts on well-being 

  Dimension 2: Having characteristics that limit ability to maximise well-being 

  Dimension 3: Having difficulty in obtaining or assimilating information 

  Dimension 4: Inability or failure to buy, choose or access suitable products 

  Dimension 5: Higher susceptibility to marketing practices 

Each dimension is operationalised via one or more indicators, as set out in chapter 3.  

The  following  types  of  drivers  are  examined  (Chapter  3  explains  how  these  drivers 
are operationalised): 

  Personal and demographic characteristics 

  Behavioural drivers/characteristics 

  Market-related drivers 

  Access drivers 

  Situational drivers 

Market-related  drivers  are  found  to  be  particularly  important,  since  these  are  often 
consistently linked with many vulnerability indicators across multiple dimensions, and 
often  have  the  strongest  effects  on  individual  indicators  (see  below  for  details). 
However, other types of drivers are also important, especially with respect to certain 
dimensions and vulnerability indicators. For example, among the behavioural drivers, 
being  trusting  has  particularly  strong  effects  on  feeling  uniformed  about  prices, 
quality, etc. and feeling vulnerable due to the complexity of offers; among the access 
drivers,  using  the  internet  to  search  for  information  has  a  strong  impact  on  being 
able to choose between offers; and among the situational drivers,  finding it hard to 
make ends meet is linked with vulnerability across many dimensions (details below). 

Overall  across  the  indicators  examined  for  each  dimension,  the  strongest  links 
between the drivers and dimensions are: 

Personal and demographic characteristics 

  Young  age  is  associated  with  a  higher  likelihood  of  vulnerability  in 

dimensions 1 and 2. 

  Older  age  is  linked  with  an  increased  likelihood  of  vulnerability  in  terms  of 
certain aspects of dimension 4, in particular having problems comparing and 
choosing between82 deals. However, older age is also associated with a lower 
likelihood  of  vulnerability  in  terms  of  other  aspects  of  dimension  4,  namely 
exclusion from e-commerce and being declined a loan. 

  Gender,  specifically  being  male,  is  associated  with  a  lower  likelihood  of 
vulnerability in terms of having problems comparing deals (in dimension 4). 

82 Insights regarding choosing between deals are based on the experiment results. 

168 

 
 
                                                 
6 │ Drivers of and factors linked to consumer vulnerability 

  Living  in  a  low  density  region  is  related  to  a  higher  likelihood  of 
vulnerability in terms of being excluded from e-commerce (in dimension 4). 

  Being  low  educated  is  linked  with  a  higher  likelihood  of  vulnerability  in 

terms of having problems choosing between deals (in dimension 4). 

  Being  a  non-native  speaker83  is  related  to  a  higher  likelihood  of 
vulnerability  in  dimensions  1  and  2,  in  terms  of  having  problems  choosing 
between deals (in dimension 4), and in dimension 5. Regarding dimension 5, 
this  driver  has  one  of  the  strongest  effects  on  feeling  vulnerable  due  to  the 
complexity of offers (the effect being to increase the feeling of vulnerability). 

Behavioural drivers/characteristics 

  Having  higher  trust  in  people84  is  associated  with  a  lower  likelihood  of 
vulnerability in dimensions 2 and 5, but a higher likelihood of vulnerability in 
terms of feeling uniformed about prices, quality, etc. (in dimension 3). With 
respect to dimension 5, this driver has one of the strongest effects on feeling 
vulnerable  due  to  the  complexity  of  offers  (the  effect  being  to  reduce  the 
feeling of vulnerability). 

  Having higher credulity85 is linked with a higher likelihood of vulnerability 
in  dimensions  1  and  2,  but  a  lower  likelihood  of  vulnerability  in  terms  of 
having  problems  comparing  deals  (in  dimension  4).  Furthermore,  failing  the 
credulity  test86  is  associated  with  a  higher  likelihood  of  having  problems 
choosing between deals (in dimension 4). 

  Being  more  impulsive87  is  related  to  a  higher  likelihood  of  vulnerability  in 
terms of feeling uniformed about prices, quality, etc (in dimension 3), having 
problems comparing deals (in dimension 4), and exclusion from e-commerce 
and being declined a loan (in dimension 4). 

  Being  more  willing  to  take  risks  is  linked  with  a  lower  likelihood  of 
vulnerability  in  dimension  2,  in  terms  of  feeling  uniformed  about  prices, 
quality, etc (in dimension 3), and in dimension 5. However, this characteristic 
is  associated  with  a  higher  likelihood  of  vulnerability  in  terms  of  having 
problems choosing between deals (in dimension 4). 

  The  effects  of  high  impulsiveness  and  being  willing  to  take  risks  on 
feeling uniformed about prices, quality, etc are the largest magnitude effects 
for that indicator (in opposite directions). 

83 That is, having a mother tongue different to the official language of their country. 
84  This  measure  is  based  on  the  extent  that  respondents  agree  with  the  statement  that  “you 

need to be very careful in dealing with people”. 

85  This  measure  is  based  on  the  extent  that  respondents  agree  with  the  statement  “most 
advertisements  report  objective  fact,  I  trust  most  of  the  information  provided  in 
advertisements”. 

86  The  credulity  test  asked  respondents  whether  the  radio  advertisement  “Sign-up  to  BEACH 
BREAKS and receive a free pair of sunglasses. Sunglasses delivered when you purchase one 
of our beach holidays” 1) offers free sunglasses to all who sign-up to beach breaks, 2) offers 
sunglasses  only  to  those  who  buy  a  holiday  from  beach  breaks,  or  3)  they  do  not  know 
which  answer  is  correct.  Those  who  identified  the  second  answer  as  the  correct  response 
answered the credulity test question correctly. 

87  These  findings  are  based  on  the  results  for  3  impulsiveness  measures.  These  measures  are 
based  on  the  extent  that  respondents  agree  with  the  statements:  “I  have  a  hard  time 
breaking  bad  habits”,  “people  would  say  that  I  have  very  strong  self-discipline”  and  “I’m 
impulsive in the purchase decisions I take”. 

169 

 
 
 
                                                 
6 │ Drivers of and factors linked to consumer vulnerability 

  Having  better  computational  ability  is  linked  with  a  lower  likelihood  of 
vulnerability  in  terms  of  being  unassertive  when  problems  occur  (in 
dimension  1)  and  in  terms  of  having  problems  choosing  between  deals  (in 
dimension 4). 

Market related drivers 

  Being unable to read contract terms and conditions due to overly small 
print is  associated  with  a  higher  likelihood  of  vulnerability in  all  dimensions. 
Together with finding it difficult to make ends meet, this driver is one of two 
that  are  consistently  linked  with  vulnerability  across  the  dimensions.  It  also 
has  the  strongest  (i.e.  largest  magnitude)  relationship  with  being  excluded 
from e-commerce (in dimension 4). 

  Rarely  comparing  deals  from  providers  (i.e.  energy  suppliers,  internet 
providers  or  banks)  is  linked  with  an  increased  likelihood  of  vulnerability  in 
several  dimensions.  In  particular,  rarely  comparing  deals  from  energy 
suppliers  is  related  to  a  higher  likelihood  of  vulnerability  in  terms  of  being 
unassertive  when  problems  occur  (in  dimension  1),  and  in  terms  of  having 
problems  comparing  deals  and  not  switching  deals  (in  dimension  4).  Rarely 
comparing deals from energy suppliers is the driver with the strongest effect 
on any single indicator relating to not switching (in dimension 4). 

  Not  knowing  your  contract  conditions  (i.e.  your  energy  supply,  internet 
provider or bank contract conditions) is associated with a higher likelihood of 
vulnerability  across  multiple  dimensions.  In  particular,  not  knowing  your 
bank contract is linked with a higher likelihood of vulnerability in dimension 1 
and  in  terms  of  having  problems  comparing  offers  and  not  switching  (in 
dimension 4). 

  Not  reading  communications  from  your  provider  (i.e.  your  energy 
supplier,  internet  provider  or  bank)  and  finding  it  hard  to  understand 
communications  from  your  provider  is  related  to  an  increased  likelihood 
of  vulnerability  in  most  dimensions.  In  particular,  finding  it  difficult  to 
understand  communications  from  your  internet  provider  or  bank  is  linked 
with  a  higher  likelihood  of  vulnerability  in  dimensions  1,  3  and  4.  Finding  it 
hard to understand communications from your internet provider is the driver 
with  the  strongest  (i.e.  largest  magnitude)  relationship  with  vulnerability  in 
dimension  1,  and  the  strongest  effect  on  any  single  indicator  relating  to 
having problems comparing deals (in dimension 4). 

Access drivers 

  Using the internet more overall (in terms of the number of activities it is 
used  for  at  least  once  per  month)  is  linked  with  a  lower  likelihood  of 
vulnerability  in  terms  of  overpaying  for  services  due  to  being  unable  to  use 
certain  payment  methods  (in  dimension  1),  feeling  vulnerable  due  to 
personal  characteristics  (in  dimension  2),  and  being  declined  a  loan  (in 
dimension 4). 

  Using the internet to search for information (at least once per month) is 
associated  with  a  lower  likelihood  of  vulnerability  in  terms  of  being  able  to 
choose  between  offers88  (in  dimension  4),  and,  furthermore,  this  driver  has 
the largest magnitude effect on this indicator. 

88 That is, those who search for information online were more likely to choose the best offer in 

the experiments. 

170 

 
 
                                                 
6 │ Drivers of and factors linked to consumer vulnerability 

Situational drivers 

  Finding  it  difficult  to  make  ends  meet  is  associated  with  an  increased 
likelihood  of  vulnerability  across  all  dimensions.  Together  with  being  unable 
to read contract terms and conditions due to small print, this driver is one of 
two  that  are  consistently  linked  with  vulnerability  across  the  dimensions.  It 
also  has  the  strongest  (i.e.  largest  magnitude)  relationship  with  feeling 
vulnerable due to personal characteristics (in dimension 2). 

  Having  friends  who  find  it  difficult  to  make  ends  meet  is  also  linked 
with  multiple  dimensions  of  vulnerability;  in  particular,  this  driver  is 
associated with a higher likelihood of vulnerability in dimensions 1 and 2, in 
terms of having problems choosing between deals (in dimension 4), in terms 
of being excluded from e-commerce (in dimension 4), and in dimension 5. 

  Being  long-term  sick  or  disabled  has  one  of  the  strongest  (i.e.  largest 
to  personal 

feeling  vulnerable  due 

relationships  with 

magnitude) 
characteristics (in dimension 2). 

  Most  other  situational  drivers  are  not  consistently  related  with  higher  or 
lower vulnerability across the dimensions; for example,  being unemployed 
and  not  seeking  employment  is  associated  with  a  lower  likelihood  of 
vulnerability  in  terms  of  being  unassertive  when  problems  occur  (in 
dimension  1),  but  a  higher  likelihood  of  vulnerability  in  terms  of  feeling 
uniformed  about  prices,  quality,  etc.  (in  dimension  3)  and  being  excluded 
from e-commerce (in dimension 4). 

This chapter examines the drivers of and factors linked to consumer vulnerability based 
on  insights  from  the  literature  review  and  stakeholder  consultation,  and  by 
investigating which explanatory variables are linked with the indicators of vulnerability 
developed in Chapter 3 based on the survey and experiment data. 

A number of issues from the research questions set out in section 1.4 (specifically RQ1, 
RQ2,  RQ6  and  RQ7)  are  examined  in  this  chapter,  such  as  what  consumer 
characteristics  and  skills  are  needed  to  avoid  vulnerability,  which  characteristics  point 
to a higher risk of vulnerability, and the implications of having multiple characteristics 
that are linked to vulnerability. Some of the analysis examines these issues by sector, 
whereas  other  parts  of  the  analysis  are  cross-cutting  across  sectors  (complexities  of 
vulnerability across sectors are examined in more detail in Chapter 9). 

Evidence  from  the  literature  review,  consumer  survey  and  behavioural  experiment 
suggests  that  a  wide  range  of  factors  are  linked  to  consumer  vulnerability.  These 
factors can be divided into five categories: 

1)  Personal and demographic characteristics. 
2)  Consumers character traits and ability, which might be considered behavioural 

drivers of vulnerability.89 

3)  Market-related  drivers  of  vulnerability,  including  the  marketing  practices  and 
remedies examined in the experiments, and consumers’ experience in the key 
markets examined in the study. 

89  Consumers’  character  traits  and  ability  are  measured  through  the  survey.  See  Table  7  in 

Section 3.7. 

171 

 
 
 
                                                 
6 │ Drivers of and factors linked to consumer vulnerability 

4)  Access drivers of vulnerability. 
5)  Situational drivers of vulnerability. 

The  following  subsection  gives  an  overview  of  how  the  survey  and  experiment  data 
analysis  supporting  this  chapter  was  conducted.  Then,  each  of  the  five  categories  of 
drivers and factors listed above are examined in turn. 

6.1.  Overview of the survey and experiment data analysis supporting 
this chapter 

To investigate the factors linked to consumer vulnerability, a wide-ranging analysis has 
been  undertaken  based  on  the  survey  and  experiment  data.  This  includes  analysis  of 
the  correlations  between  a  large  number  of  variables  and  regressions  between  many 
dependent  and  explanatory  variables.  The  analysis  conducted  is  explained  in  full  in 
Annex  8  and  complete  tables  of  results  are  available in  Excel  files  provided  alongside 
the report. The most useful and important results are drawn out and discussed below. 

This  chapter  draws  on  the  results  of  the  general-to-specific  (G-S)  econometric 
approach,  which  includes  a  wide  range  of  explanatory  variables  in  regressions  of 
indicators  of  vulnerability.  The  G-S  methodology  is  described  in  A8.1.1.  We  focus  on 
the G-S results (rather than, for example, pair-wise correlation coefficients) since this 
approach  assesses  whether  relationships  between  variables  are  statistically  significant 
in  a  multivariate  setting,  where  other  factors  are  controlled  for.90  Apparent 
relationships between pairs of variables (e.g. as shown by correlation coefficients) may 
not hold in a multivariate setting. 

The  G-S  approach  produces  a  single  final  regression  model  for  each  dependent 
variable,  which includes  statistically  significant explanatory  variables.  The  explanatory 
variables  examined  in  the  analysis  include  personal  and  demographic  characteristics, 
behavioural drivers, market-related drivers, access drivers and situational drivers. The 
explanatory variables are set out in Table 7 in Section 3.7. 

The dependent variables are the indicators of vulnerability established in Chapter 3 and 
respondents’  performance  in  the  experiments.  Thirty-nine  dependent  variables  are 
examined  in  this  analysis  (38  indicators  based  on  the  survey  questions,  plus 
respondents’ overall performance in the experiments). These are set out in section 3.6. 

The results presented in sections 6.2 to 6.6 are based on the full sample available for 
each  indicator.  However,  the  drivers  of  vulnerability  have  also  been  examined  at 
country  and  country  group  level.  The  country  and  country  group  level  analysis  is 
discussed in section 6.7. 

6.1.1.  Types of regression model and output 

The type of regression model estimated, and the type of results presented, depends on 
whether the dependent variable in question is a binary variable (i.e. taking values 0 or 
1) or a scale variable (e.g. taking values 1, 2, 3 or 4): 

90  Apparent  relationships  between  pairs  of  variables  (e.g.  as  shown  by  correlation  coefficients) 

may not hold in a multivariate setting. 

172 

 
 
                                                 
6 │ Drivers of and factors linked to consumer vulnerability 

  For  binary  dependent  variables,  logistic  regression  models  are  estimated 
and the results presented are marginal effects. Each marginal effect represents 
the  change  in  the  likelihood  that  an  individual  is  vulnerable  on  the  relevant 
indicator91 if that individual is in a certain group, relative to a ‘base group’. For 
example, if the marginal effect for a particular group is 0.05, being a member 
of  this  group  rather  than  the  base  group  increases  the  probability  of  being 
vulnerable  on  the  relevant  indicator  by  5  percentage  points  (pp).  That  is, 
values  above  zero  imply  higher  probability  of  vulnerability,  whereas 
values below zero imply lower probability of vulnerability. 

  For  scale  dependent  variables,  ordered  logistic  regression  models  are 
estimated  and  the  results  presented  are  odds  ratios.  Each  odds  ratio  shows 
whether members of a certain group have higher or lower odds (i.e. ‘chance’) 
of having a higher value on the dependent variable scale, compared to a base 
group.92 For example, if the odds ratio for a particular group is 1.5, the odds of 
having a higher value on the dependent variable scale are 1.5 times higher for 
this  group  than  for  the  base  group.  That  is,  an  odds  ratio  above  one 
implies  higher  vulnerability,  whereas  an  odds  ratio  of  less  than  one 
implies lower vulnerability. 

6.1.2.  Dependent variables 

The  G-S  regression  analysis  examines  the  relationships  between  the  explanatory 
variables  set  out  in  Table  7  in  Section  3.7  and  the  survey-based  indicators  of 
vulnerability  listed  below  (which  are  the  dependent  variables  in  the  regressions).  In 
total,  38  indicators  based  on  the  survey  questions  as  well  as  respondents’  overall 
performance  in  the  experiments  are  examined  in  this  analysis.  These  indicators  are 
defined  in  Table  5  in  section  3.6  (the  codes  in  parentheses  in  the  list  below  match 
those in Table 5, as well as those in the following results sections). 

Dimension 1: 

 

 

Indicator 1. Unassertive when experienced a problem (1). 

Indicator 2. Overpaid for services due to being unable to use certain payment 
methods (2). 

Dimension 2: 

 

Indicator 3. Perception of own vulnerability due to personal characteristics (3). 

Dimension 3: 

 

 

 

Indicator 4. Does not feel informed about prices etc. (4). 

Indicator 5. Gets information from advertisements only; separately for the 
energy (5_adv_ene), online (5_adv_onl) and finance (5_adv_fin) sectors. 

Indicator 6. Has problems comparing deals due to information-related factors; 
separately for the energy (6_ene), online (6_onl) and finance (6_fin) sectors. 

91 That is, the likelihood that the dependent variable is equal to 1. 
92 Put another way, for any point on the dependent variable scale, an odds ratio is the ratio of 
the odds that a certain group is above that point, to the odds that the base group is above 
that  point.  The  odds  ratio  applies  in  the  same  way  at  all  points  on  the  dependent  variable 
scale. 

173 

 
 
 
                                                 
6 │ Drivers of and factors linked to consumer vulnerability 

 

Indicator 7. Has not switched due to information-related factors; separately for 
the energy (7_ene), online (7_onl) and finance (7_fin) sectors. 

Dimension 4: 

 

 

 

 

 

 

 

 

 

 

 

Indicator 8a. Has problems comparing deals due to personal factors; separately 
for the energy (8a_ene), online (8a_onl) and finance (8a_fin) sectors. 

Indicator 8b. Has problems comparing deals due to market-related factors; 
separately for the energy (8b_ene), online (8b_onl) and finance (8b_fin) 
sectors. 

Indicator 8c. Has problems comparing deals due to access-related factors; 
separately for the energy (8c_ene), online (8c_onl) and finance (8c_fin) 
sectors. 

Indicator 9a. Has not switched due to personal factors; separately for the 
energy (9a_ene), online (9a_onl) and finance (9a_fin) sectors. 

Indicator 9b. Has not switched due to market-related factors; separately for 
the energy (9b_ene), online (9b_onl) and finance (9b_fin) sectors. 

Indicator 9c. Has not switched due to access-related factors; separately for the 
energy (9c_ene), online (9c_onl) and finance (9c_fin) sectors. 

Indicator 9d. Has not switched due to termination costs. 

Indicator 9e. Has not switched due to bundling. 

Indicator 10a. Excluded from e-commerce due to difficulty of the process. 

Indicator 10b. Excluded from e-commerce due to not having a payment card. 

Indicator 11. Declined for a loan. 

Dimension 5: 

 

Indicator 12. Perception of own vulnerability due to complexity of offers. 

  Experiment performance. Respondents’ overall performance in the 

experiments. 

Fewer  indicators  are  examined  in  this  chapter  than  in  the  previous  chapter  on  the 
incidence  of  vulnerability93  since  some  indicators  examined  in  Chapter  4 were  created 
specifically to proxy for the fourth dimension of vulnerability in the analysis reported in 
section  3.3,  so  these  indicators  are  not  examined  elsewhere  in  the  report.94  In 
addition, the analysis in this chapter is far more complex, which places constraints on 
the  number  of  indicators  that  can  be  examined  given  the  computing  time  available, 
and  produces  many  results  per  indicator,  so  to  keep  the  results  reasonably  concise 
only a selection of indicators are examined.95 

93 Thirty-eight indicators are examined in this chapter, compared to 48 in the previous chapter. 
94 This specifically refers to indicators 23 to 28 in Table 5 in section 3.6. These indicators do not 

strictly match any indicators that are conceptualised in section 6.4. 

95 For example, the indicators of consumers’ perceptions of their own vulnerability due to specific 
factors  (i.e.  indicators  3  to  10  Table  5  in  section  3.6)  are  not  examined in  this  chapter.  In 
the  analysis  in  this  chapter  these  indicators  are  replaced  with  a  single  indicator  (based  on 
the same survey question) signifying the extent (on a scale from 1 to 4) that consumers feel 
vulnerable  due  to  any  factor  mentioned  in  the  survey  question.  This  replacement  is  made 

174 

 
 
                                                 
 
6 │ Drivers of and factors linked to consumer vulnerability 

Furthermore,  there  are  no  results  for  indicator  7  for  the  online  sector  since  the  G-S 
process could not be run for this indicator due to the low total number of respondents 
who  are  vulnerable  on  this  indicator.  Hence,  in  the  results  tables  in  the  following 
sections no results are presented for this indicator. 

In  addition,  a  further  indicator  that  is  examined  in  this  chapter  is  respondents’ 
performance  in  the  experiments.  Specifically,  the  analysis  examines  the  relationships 
between  the  explanatory  variables  and  respondents’  overall  performance  in  the 
experiments. 

6.2.  Personal and demographic characteristics 

Although,  as  noted  in  the  previous  sections,  vulnerability  is  a  dynamic  concept  and 
should  not  simply  be  linked  to  selected  consumer  groups  (see  Chapter  3),  there  is 
value in recognising the consumer groups at greatest risk of vulnerability. As Commuri 
and  Ekici  (2008)  point  out,  while  it  is  important  to  be  aware  of  the  complexity  of 
consumer vulnerability, identifying specific consumers groups can help policymakers in 
designing potential interventions.  

Conversely, economic operators can also use these consumer characteristics to target 
practices  to  specific  consumer  groups.  Harrison  and  Gray  (2010)  highlight  the  risk  of 
increasing  ability  by  financial  service  providers  to  profile  their  customers,  while  a 
recent  review  of  the  data  broker  industry  conducted  by  the  US  Senate  Committee  on 
Commerce,  Science  and  Transportation  (2013)  shows  how  “big  data”  can  allow 
businesses to target their offers to specific consumer groups, including those identified 
as  vulnerable.  The  report  from  the  Multi-Stakeholder  Dialogue  on  Comparison  Tools 
(2013) also highlights the fact that new technologies allow vendors to identify specific 
consumer profiles and adjust prices and access accordingly. 

It  is  therefore  important  to  be  aware  of  potential  characteristics  associated  with 
vulnerability, both in order to better design policy and to identify problematic practices. 
This section examines specific consumer characteristics which, based on evidence from 
the  literature,  the  consumer  survey  and  the  behavioural  experiments,  may  contribute 
to  vulnerability.  The  characteristics  examined  include  age,  disability,  gender,  cultural 
background and ethnicity, education and location. 

Relevant results from the survey and experiment data analysis are summarised in the 
matrix presented in Figure 49. The indicator codes across the top of the matrix match 
those in the list of indicators in section 3.6, as well as those in the table of indicators in 
Annex  8.96  The  consumer  characteristics  in  the  matrix  (down  the  left  hand  side)  are 
defined  (alongside  all  other  explanatory  variables  examined  in  the  data  analysis)  in 
Table 7 in section 3.7. 

since this chapter aims to examine the links between different factors and consumers’ overall 
feeling vulnerability (whatever the reason for that perception of vulnerability). 

96  There are  no results for Indicator 7  for the online sector since  the G-S process could not  be 
run for this indicator due to the low total number of respondents who are vulnerable on this 
indicator.  Hence,  in  the  results  tables  in  this  section  no  results  are  presented  for  this 
indicator. 

175 

 
 
 
                                                                                                                                                     
6 │ Drivers of and factors linked to consumer vulnerability 

In the matrix, red cells show that consumers with a particular characteristic are found 
to  be  more  likely  to  be  vulnerable  on  the  indicator  in  question,  whereas  green  cells 
mean  that  consumers  with  a  certain  characteristic  are  less  likely  to  be  vulnerable  on 
the corresponding indicator. Grey cells show that a characteristic was controlled for in 
the  regression,  while  white  cells  mean  that  a  characteristic  was  not  included  in  the 
regression  (based  on  the  G-S  approach).  The  right-hand  column  in  the  matrix  shows 
the  links  between  respondents’  characteristics  and  their  overall  performance  in  the 
experiments, across all experiments and rounds. 

In  summary,  as  can  be  seen  from  the  matrix,  the  results  in  which  demographic  and 
personal  characteristics  are  most  closely  linked  with  the  various  dimensions  of 
vulnerability  are  very  mixed,  with  differences  across  dimensions,  sectors  and 
indicators. For example, the results suggest that: 

  Younger age groups (aged 16-24 and 25-34)  are more likely to be vulnerable 
based  on  indicators  in dimensions  1 (heightened  risk  of  negative  outcomes  or 
impacts  on  wellbeing)  and  2  (having  characteristics  that  limit  ability  to 
maximise wellbeing); 

  Older  consumers  (aged  65-74  and  75+)  are  more  likely  to  be  vulnerable  on 
some  indicators  in  dimensions  3  (having  difficulty  obtaining  or  assimilating 
information)  and  4  (inability  to  buy,  choose  or  access  suitable  products),  but 
are also less likely to be vulnerable on other indicators in these dimensions; 

  Men  are  often  less  likely  to  be  vulnerable  than  women  on  a  number  of 
indicators in dimension 4 (inability to buy, choose or access suitable products); 
and 

  Non-native speakers are more likely to be vulnerable than native speakers  on 
at  least  one  indicator  in  every  dimension,  and  are  less  likely  to  be  vulnerable 
on relatively few indicators; this group is the only group who are more likely to 
be  vulnerable  on  more  indicators  than  the  number  of  indicators  they  are  less 
likely to be vulnerable on, in every dimension. 

Furthermore, in some areas there is also some contrast between the  results from the 
survey  data  analysis  and  the  experiments.  In  particular,  whereas  the  survey  results 
suggest that consumers with low education are less likely to be vulnerable than those 
with  high  education  on  several  indicators  in  dimension  4,  which  relates  to  inability  to 
select  suitable  products.  Those  with  low  education  were  also  less  able  to  choose  the 
best deals overall in the experiments. 

Hence,  the  overall  conclusion  is  that  no  demographic  or  personal  characteristic  is 
linked to any dimension of vulnerability consistently across all or nearly all indicators in 
the dimension, but rather it depends on the specific type and measure of vulnerability 
that is being considered. 

The  links  between  consumers’  demographic  and  personal  characteristics  and  each 
dimension  of  vulnerability  are  explored  in  more  detail  in  the  following  sub-sections, 
which  draw  on  evidence  from  the  literature  review,  survey  and  behavioural 
experiments. 

The  reporting  and  discussion  of  the  survey  and  experiment  results  in  the  text  below 
focuses only on statistically significant results (unless expressly noted otherwise). The 
full set of explanatory variables examined in the G-S analysis is presented in Table 7 in 
section  3.7.  This  table  also  indicates  which  variables  (i.e.  which  levels  or  categories) 
are used as bases in the analysis. 

176 

 
 
Figure 27:  Matrix of personal and demographic characteristics linked to survey-based indicators of vulnerability 

6 │ Drivers of and factors linked to consumer vulnerability 

177 

Dimension:D1D2D3D4D5D4Feature:Sector:----Indicator:12345675675678a8b8c8a8b8c8a8b8c9a9b9c9a9b9c9a9b9c9d9e10a10b1112b12expCharacteristic / DriverUnassertiveOverpaidFeels vulnerableFeels uninformedUses adverts onlyProbl's comparingHasn't switchedUses adverts onlyProbl's comparingHasn't switchedUses adverts onlyProbl's comparingHasn't switchedPersonal factorsMarket factorsAccess factorsPersonal factorsMarket factorsAccess factorsPersonal factorsMarket factorsAccess factorsPersonal factorsMarket factorsAccess factorsPersonal factorsMarket factorsAccess factorsPersonal factorsMarket factorsAccess factorsTermination costBundled offersDifficult processPayment cardDeclined for loanVery vulnerableVulnerable scaleExperimentAge 16-240.0700***0.0250***1.487***0.9780.00750-0.0326***-0.001820.00516-0.0133**-0.0318***-0.0523***0.0237*-0.00546-0.0385***-0.0004770.0111-0.0176-0.000516-0.00109-0.0113-0.0487***-0.0123***0.0150-0.0241**0.01150.0504***0.001291.096*Age 25-340.0429**0.0167***1.276***0.998-0.00247-0.0107-0.00774***-0.00329-0.0120***-0.0178***-0.0325***0.0474***-0.0103-0.0471***0.002120.0293**-0.00777-0.000533-0.009070.0462**-0.0363***-0.0128***0.0151**-0.009810.002430.0336***0.004211.108***Age 45-540.0290-0.008730.866***1.0110.00745-0.00701-6.26e-050.00930*0.0204***-0.008620.0379***-0.01050.0139*0.0505***0.00748-0.0188-0.00146-0.0002390.0149-0.002310.0218-0.00368-0.00984-0.0107-0.0183**-0.0143**0.002680.929**Age 55-640.0102-0.0143**0.795***1.0580.0128**0.00777-0.00966***0.0142**0.0345***0.006810.0671***0.005870.0362***0.112***0.0170***0.001680.02040.001940.0398**0.005400.0417**-0.00883***-0.0303***0.00310-0.0216**-0.0370***-0.0130***0.923*Age 65-740.0272-0.009340.727***0.934-0.0003020.0275-0.00790**0.0398***0.0514***-0.0217*0.0994***0.0356*0.0555***0.152***0.00606-0.0387**-0.00252-0.0009480.02450.0437-0.000589-0.0143***-0.0336***-0.0176-0.0232-0.0506***-0.0214***0.897*Age 75+-0.0302-0.0206*0.828*1.1770.000944-0.00266-0.00966***0.1150.119*-0.0629***-0.04490.244***0.06530.251***-0.01050.0795*-0.03920.0432-0.03180.256***-0.0246-0.0127***-0.0541***-0.004670.137*-0.00944-0.0203***0.712***Male0.0294**0.00662*0.923***0.0108*0.0139***-0.0237***-0.0253***-0.0202***-0.0229***-0.0127**-0.0224***-0.0202**-0.0374***-0.0145**-0.0180***Low density region0.00859**1.160***0.00164-0.000893-0.0107*-0.0188**0.005020.0218***0.00917***0.00991***-0.008310.0465***0.00776*0.0152**0.0159**1.067**Medium density region0.0003271.002-0.00564**-0.00648**-0.00233-0.0387***-0.0309***0.00671-0.000742-0.00195-0.0242***0.00432-0.001319.39e-050.001340.993Household size0.00242*0.00993**0.00171**-0.0124**-0.00586***-0.0114***-0.0137*-0.00383***0.00370**0.0105***0.00412**Low education-0.0147***-4.39e-050.0153**-0.0267***-0.00899-0.00459-0.0455***-0.0218**-0.0822***-0.00517-0.0241***-0.0314***-0.0275***-0.00706Medium education-0.00825**0.00697**0.00950***-0.00312-0.0323***-0.0236***-0.00178-0.00676-0.01220.00357-0.0145***-0.0293***-0.0244***0.0112**Non-native tongue, no difficulty0.0839***0.0366***1.185***1.155***0.0163**0.0164**-0.0192**-0.0328**0.0269**0.0124**-0.0227-0.00468**-0.0248*0.0909***0.02030.003420.0202**0.0234**0.0278**0.0177***1.130***Non-native tongue, difficulty0.05590.0712***2.289***1.314*0.0463-0.02670.01650.04340.0211-0.182***0.001730.0349-0.001750.0314-0.0007070.01650.0748***2.000***ExcludedOffers-FinanceEnergyOnlineFinance-e-comFinanceEnergyOnlineInformation problemsProblems comparingNot switched-EnergyOnline 
 
 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

6.2.1.  Dimension 1: Heightened risk of negative outcomes or impacts on well-
being 

Evidence  from  the  literature  and  the  survey  data  provides  insights  into  links  between 
various  personal  and  demographic  characteristics  and  the  first  dimension  of 
vulnerability,  a  heightened  risk  of  negative  outcomes  or  impacts  on  well-being.  In 
particular,  below  we  consider  the  characteristics  of  age,  gender,  language,  education 
and locality. 

The  literature  identifies  a  number  of  reasons  why  old  age97  could  increase  the  risk  of 
negative  outcomes  or  impacts  on  well-being  and  so  contribute  to  this  dimension  of 
vulnerability: 

  There  is  some  evidence  that  older  people  may  be  more  prone  to  certain 

behavioural biases (Lunn and Lyons 2010); 

  Older  consumers  are  more  likely  to  face  cognitive,  sensory  or  mobility 

limitations (Stearn 2012, Griffiths and Harmon-Kizer 2011); 

  Older consumers may find themselves in a new financial situation (Griffiths and 

Harmon-Kizer 2011); and 

  Financial products offered later in life (e.g. annuities or reverse mortgages) are 

becoming increasingly complex (Stearn 2012). 

Age is also closely linked to the concept of informed consent, where older consumers, 
especially  those  with  cognitive  or  sensory  limitations,  may  sign  consent  documents 
without  being  able  to  fully  understand,  process,  and  retain  the  information  necessary 
for a truly informed consent (Griffiths and Harmon-Kizer 2011). 

Moschis,  Mosteller,  and  Kwai  Fatt  (2011)  note  that  existing  theoretical  (aging  as  a 
biological  or  psychological  phenomenon)  and  empirical  (results  of  consumer  surveys 
and  consumer  complaint  databases)  explanations  of  older  consumers’  vulnerability  do 
not paint a clear picture. Instead, they argue that vulnerability is highly dependent on 

97  Although  the  literature  takes  different  approaches  to  defining  older  consumers,  they  are 
usually seen as people over the age of 65, and in our survey and experiment data analysis, 
we examine those aged 65-74 and those aged 75+ (as well as and in comparison to younger 
age bands). 

This is in line with the approach of the WHO, which identifies the age 65 or over as a common 

definition of “older” or “elderly” 
(http://www.who.int/healthinfo/survey/ageingdefnolder/en/), as well as the OECD, which 
uses 65+ as the definition of “elderly population” (https://data.oecd.org/pop/elderly-
population.htm). Similarly 65+ is used in statistical publications in Member States such as 
Germany 
(https://www.destatis.de/EN/Publications/Specialized/Population/OlderPeopleEU.html) or the 
United Kingdom (http://www.ons.gov.uk/ons/rel/census/2011-census-analysis/what-does-
the-2011-census-tell-us-about-older-people-/what-does-the-2011-census-tell-us-about-
older-people--short-story.html).  

At the same time, the age 50+ is also sometimes in use (i.e. “seniors” in France are classified as 

citizens over 50 years of age (http://www.entreprises.gouv.fr/secteurs-
professionnels/etudes-sur-seniors-juin-2010) and AGE Platform Europe has citizens over 50 
as its target group (http://www.age-platform.eu/about-age)), while the 80+ category is 
used as a distinct age group by Eurostat (http://ec.europa.eu/eurostat/statistics-
explained/index.php/Population_structure_and_ageing). 

178 

 
 
                                                 
6 │ Drivers of and factors linked to consumer vulnerability 

the individuals’ previous life experiences. For instance, while older consumers may find 
themselves  in  new  financial  situations  due  to  retirement,  for  consumers  who  have 
secured sufficient pensions and at that point have settled any debts, this new situation 
would not necessarily be a source of vulnerability. 

Results  from  the  survey  data  analysis  also  demonstrate  a  link  between  age  and  this 
dimension  of  vulnerability.  However,  contrary  to  the  discussion  above,  it  is  younger 
age  groups  rather  than  the  elderly  that  are  identified  as  more  at  risk  based  on  this 
analysis. 

Results  from  the  general-to-specific  (G-S)  regression  methodology  based  on  the  full 
sample  (including  all  countries)  showing  the  impacts  of  personal  and  demographic 
characteristics  on  the  indicators  of  vulnerability  in  this  dimension  are  shown  in  Table 
26 below.98 

These  results  show  that,  controlling  for  all  other  characteristics  included  in  the 
regression,  those  in  younger  age  groups  are  more  likely  to  be  vulnerable  on  the  two 
indicators  in  this  dimension  (Indicator  1  and  Indicator  2).  Specifically,  those  in  age 
groups 16-24 and 25-34 are more likely to be unassertive in terms of not taking action 
when they experience a problem and more likely to overpay for services due to being 
unable  to  use  certain  payment  methods,  compared  to  those  aged  35-44  (which  was 
used as the base age group in the analysis).99  

The impact of young age on the assertiveness indicator is among the strongest impacts 
shown in Table 26, with a marginal effect of 0.07 implying that those in the youngest 
age  group  are  around  7  percentage  points  (pp)  less  likely  to  take  action  when  they 
experience a problem than those in the base group (age 35-44). 

98  As  noted  above,  we  have  undertaken  a  wide  range  of  analysis  based  on  the  survey  and 
experiment  data  in  order  to  examine  the  factors  linked  to  consumer  vulnerability,  and  this 
chapter draws mainly on the results of the G-S approach. The output of the G-S approach is 
a single regression model for each dependent variable (i.e. the indicators of vulnerability and 
respondents’  performance  in  the  experiments),  which  includes  statistically  significant 
explanatory  variables  from  across  the  five  different  categories  (personal  and  demographic 
characteristics,  behavioural  drivers,  etc.).  In  each  section  of  this  chapter,  we  focus  on  the 
results relating to the relevant category of explanatory variables. The full set of explanatory 
variables  examined  in  the  analysis  is  presented  in  Annex  4.  The  full  set  of  explanatory 
variables included in each regression (with associated p-values) can be seen in the Excel files 
provided alongside this report. 

99  This  age  group  was  chosen  as  the  base  group  for  the  analysis  since,  based  on  an  initial 
analysis of correlation coefficients, our expectation was that this group would (often) be least 
vulnerable,  or  among  the  least  vulnerable.  In  addition,  this  group  are  old  enough  to  be 
experienced and young enough not to suffer from the effects of older age (45-54 would have 
been a similar choice in this respect). 

179 

 
 
 
                                                 
6 │ Drivers of and factors linked to consumer vulnerability 

Table 26: 

Impact of personal and demographic characteristics on indicators 

in Dimension 1 and Dimension 2 

Dimension 1 

Dimension 2 

Indicator 1: 
Unassertive if 
experiences 
problem 

Indicator 2: 
Overpaid for 
services 

Indicator 3: 
Maximum feeling 
of vulnerability 

1.487*** 
1.276*** 
0.866*** 
0.795*** 
0.727*** 
0.828* 
0.923*** 
- 
- 
- 
- 
- 

0.0700*** 
0.0429** 
0.0290 
0.0102 
0.0272 
-0.0302 
0.0294** 
- 
- 
- 
- 
- 

0.0250*** 
0.0167*** 
-0.00873 
-0.0143** 
-0.00934 
-0.0206* 
0.00662* 
0.00859** 
0.000327 
0.00242* 
-0.0147*** 
-0.00825** 

Age 16-24 
Age 25-34 
Age 45-54 
Age 55-64 
Age 65-74 
Age 75+ 
Male 
Low density region 
Medium density region 
Household size 
Low education 
Medium education 
Non-native speaker, no 
difficulty 
Non-native tongue, 
difficulty 
N 
R2 
Marginal effect / Odds ratio 
Indicator code 
Note: Values in this table are results of general to specific (G-S) regression analysis. The G-S methodology is 
described in Annex 8. “-” implies that the explanatory variable was not included in the final regression model 
established via the G-S process. */**/*** implies that a result is statistically significant at 90%/95%/99%. 
For each the indicator in this table, the R2 in the table is the Pseudo-R2 for the full regression model, which 
includes  the  explanatory  variables  shown  in  this  table  and  other  explanatory  variables  (not  shown  in  this 
table)  that  were  identified  for  the  regression  model  based  on  the  G-S  methodology.  The  indicators  are 
defined in Table 5. The explanatory variables are defined in Table 7 and A8.1.2. 
Source: London Economics analysis of the survey data 

0.0712*** 
26,653 
0.1418 
Marginal 

2.289*** 
26,653 
0.1227 
Odds ratio 

0.0559 
7,826 
0.0929 
Marginal 

0.0366*** 

0.0839*** 

1.185*** 

1 

2 

3 

Gender is a characteristic that is less frequently connected with vulnerability, although, 
as Nardo et al. (2011) note, women score lower on the consumer empowerment index 
than  men.  Gender  can  be  directly  linked  to  vulnerability  for  instance  in  situations 
where  men  control  women’s  access  to  money  (Branigan  2004  in  Singh  2005). 
Furthermore,  significant  life  changes  experienced  by  women,  such  as  pregnancy,  can 
be sources of vulnerability (The VOICE Group 2010). 

However, according to the results of the survey data analysis shown in Table 26 above, 
men are slightly more likely to not take action when they experience a problem and to 
overpay  for  services  due  to  being  unable  to  use  certain  payment  methods  (with 
marginal effects of 2.9pp and 0.7pp respectively). 

The  results  of  the  survey  data  analysis  presented  in  Table  26  suggest  that  a 
consumer’s  mother  tongue  has  an  impact  on  the  indicators  of  vulnerability  in  this 
dimension (Indicator 1 and Indicator 2). 

Those with a mother tongue that is different from the official language of their country 
of  residence  are  found  to  be  more  vulnerable  than  native  speakers,  on  these 
indicators,  whether  or  not  they  say  that  their  language  causes  them  difficulty  (the 

180 

 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

result  for  Indicator  1  for  having  a  non-native  tongue  and  this  causing  difficulty, 
although large, is not statistically significant). 

The  strongest  impact  shown  in  Table  26  for  either  of  the  two  indicators  in  this 
dimension is the effect of being a non-native speaker without this causing difficulty on 
the  assertiveness  indicator,  with  an  estimated  marginal  effect  of  8.4pp  relative  to  a 
native speaker. 

According to the literature, lower levels of education are usually associated with lower 
levels of consumer empowerment, although this does not always hold true, with Nardo 
et  al  (2011)  finding  that  in  Norway  and  Bulgaria  lower  educated  consumers  score 
higher on the empowerment metric than more educated consumers. 

Lower  education  can  be  linked  to  factors  such  as  over  indebtedness,  although  the 
relationship  is  quite  complex  and  weak  when  accounting  for  other  factors  (Civic 
Consulting  2013),  suggesting  that  the  link  between  vulnerability  and  education  is  not 
clear-cut. 

Low literacy is a clear potential source of vulnerability. Adkins and Ozanne (2005) and 
Gau  and  Viswanathan  (2008)  observe  that  consumers  with  low  literacy  level  not  only 
face  numerous  functional  problems  in  the  market,  but  they  can  also  be  subject  to  a 
low-literacy stigma, which can further compound their vulnerability. At the same time, 
they do draw on a range of skills and resources to navigate the marketplace, with Gau 
and Viswanathan (2008) highlighting the importance of social networks for low-literate 
consumers. 

There  are  broader  concepts  of  literacy,  linked  to  education,  which  should  also  be 
explored.  One  of  them  is  the  notion  of  financial  literacy.  Klapper,  Lusardi  and  Panos 
(2013)  note,  for  instance,  that  financial  literacy  appears  to  help  individuals  face 
unexpected  macroeconomic  and  income  shocks.  Similarly,  Gathergood  (2012) 
identifies a link between low financial literacy and over indebtedness. 

At the same time, financial literacy is not necessarily associated with education levels 
or age. Braunsberger, Lucas and Roach (2004) show that US college students were not 
able  to  make  informed  choices  concerning  financial  products,  but  were  also  not 
outperformed by older consumers. 

Based on the results of the survey data analysis presented in Table 26, those with low 
and  medium  education  levels  are  found  to  be  less  likely  than  those  with  a  high 
education  level  to  overpay  for  services  due  to  being  unable  to  use  certain  payment 
methods,  when  all  other  characteristics  included  in  the  regression  are  controlled  for. 
Although  this  may  seem  counterintuitive,  an  explanation  may  be  that  those  with  a 
higher education level are more aware of when they are overpaying.  

Finally,  the  results  presented  in  Table  26  show  that  those  who  live  in  low  density 
regions are slightly more likely than those in high density regions to have overpaid for 
services due to being unable to use certain payment methods. However, although this 
result is statistically significant, the magnitude is small (a marginal effect of around 1 
percentage point). 

181 

 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

6.2.2.  Dimension 2: Having characteristics that limit ability to maximise well-
being 

The  survey  data  analysis  and  evidence  from  the  literature  suggest  that  some 
demographic  and  personal  characteristics  are  related  to  the  second  dimension  of 
vulnerability, having characteristics that limit ability to maximise well-being. 

In this dimension, one indicator of vulnerability from the survey data is analysed using 
the G-S approach. This indicator, Indicator 3, is on a scale from 1 (low) to 4 (high) and 
represents  the  maximum  extent  that  respondents  feel  vulnerable  due  to  any  of  the 
personal  characteristics  mentioned  in  the  corresponding  survey  question  including 
health  problems,  financial  circumstances,  employment  situation,  age,  belonging  to  a 
minority group, personal issues, or ‘other’ reasons. 

Old age is a characteristic that, in itself, potentially fits within the second dimension of 
consumer  vulnerability.  Indeed,  the  consumer  survey  found  that,  across  the  EU28+2, 
10% of those aged 65-74 and 13% of those aged 75+ feel vulnerable to a great extent 
because of their age, compared to 5% across all age groups. 

However, examining the maximum extent that respondents feel vulnerable due to any 
of  the  personal  characteristics  mentioned  in  the  relevant  survey  question,  the  results 
presented in Table 26 above show that, controlling for other characteristics included in 
the  regression,  those  in  younger  age  groups  are  more  likely  to  feel  a  higher  level  of 
vulnerability.100 

The odds ratios are highest for the youngest (16-24) and second youngest age groups 
(25-34), at 1.49 and 1.28 respectively. These are the only age groups that are found 
to  have  a  higher  feeling  of  vulnerability  than  the  base  group  (those  aged  35-44), 
according to the G-S results.101 

In line with this finding, previous research has found that young adults and university 
students can be at risk of vulnerability in some cases. Braunsberger, Lucas and Roach 
(2004)  show  that,  in  the  case  of  US  college  students,  lack  of  financial  experience  is 
seen as a source of vulnerability when faced with financial products. 

As  noted  above,  gender  is  a  characteristic  that  is  less  commonly  associated  with 
vulnerability,  despite  the  observation  in  some  studies  that  women  score  lower  than 
men  on  the  consumer  empowerment  index  (Nardo  et  al.  2011)  and  arguments  that 
major life changes experienced by women, such as pregnancy, are potential sources of 
vulnerability (The VOICE Group 2010). 

The results of the survey data analysis presented in Table 26 above imply that men are 
slightly  less  likely  to  feel  a  higher  level  of  vulnerability,  measured  as  the  maximum 
extent  that  respondents  feel  vulnerable  due  to  any  of  the  personal  characteristics 

100 In this regression, the dependent variable is a scale from 1 (low feeling of vulnerability) to 4 
(high  feeling  of  vulnerability),  which  is  equal  to  the  highest  extent  that  respondents  feel 
vulnerable  due  to  the  individual  reasons  of  health  problems,  financial  circumstances, 
employment situation, age, belonging to a minority group, personal issues, or other reasons. 
101 Recall that an odds ratio above one implies higher vulnerability compared to the base group, 
whereas an odds ratio below one implies lower vulnerability compared to the base group. 

182 

 
 
                                                 
6 │ Drivers of and factors linked to consumer vulnerability 

mentioned in the relevant survey question (since the odds ratio for men is just below 
1, at 0.92). 

The  strongest  impacts  shown  in  Table  26  for  Indicator  3  are  the  impacts  of  being  a 
non-native  speaker.  In  particular,  those  with  a  mother  tongue  different  from    the 
official language of their country and for whom this causes difficulty have an odds ratio 
of 2.29, implying that the odds of having a higher value on the vulnerability scale are 
2.29 times larger for this group than for native speakers (the base group). 

6.2.3.  Dimension 3: Having difficulty in obtaining or assimilating information 

Evidence  from  the  survey  data  and  the  literature  suggests  that  some  personal  and 
demographic  characteristics  are  linked  to  the  third  dimension  of  vulnerability,  having 
difficulty  in  obtaining  or  assimilating  information.  The  literature  identifies  several 
reasons why old age may cause consumers to have  difficulty obtaining or assimilating 
information: 

  Fewer  elderly  consumers  are  internet  users  (Eurostat  shows  individuals in  the 
55-74  age  bracket  are  less  likely  to  use  internet  on  a  regular  basis  than  the 
average individual102); 

  Older  consumers  are  less  likely  to  have  access  to  information  and  guidance 

about markets (Stearn 2012); and 

  Older consumers can receive less marketing information and be more isolated 
from  networks  that  can  help  them  navigate  the  marketplace  (Lunn  and  Lyons 
2010). 

Results  from  the  survey  data  analysis  presented  in  Table  27  show  links  between  age 
and indicators of vulnerability in this dimension, including some interesting differences 
across sectors. Consumers in the 55+ age groups are slightly less likely than the base 
group  (35-44  year-olds)  to  have  been  prevented  from  switching  due  to  information-
related factors in the energy sector, although the marginal effects are only around 1pp 
(Indicator  7,  energy),  and  the  oldest  age  groups  (65-74  and  75+)  are  less  likely  to 
have  problems  comparing  banks  due  to  information-related  factors  (Indicator  6, 
finance). 

However, those in the 45+ age groups are more likely than the base group to only get 
information from adverts when comparing internet deals in the online sector (Indicator 
5,  online),  and  more  likely  to  have  problems  comparing  internet  deals  due  to 
information-related factors (Indicator 6, online). The estimated marginal effect for the 
75+  age  group  for  Indicator  6  for  the  online  sector  is  the  largest  in  the  table,  at 
11.9pp.  

Younger  age  groups  are  found  to  be  less  vulnerable  on  several  indicators  in  this 
dimension. For example, those aged 25-34 are less likely to have problems comparing 
deals due to information-related factors in the online and finance sectors (Indicator 6, 
online  and  finance)  and  are  less  likely  to  have  been  prevented  from  switching  due  to 
information-related factors in the energy sector (Indicator 7, energy). 

102 See  

http://epp.eurostat.ec.europa.eu/statistics_explained/index.php/Internet_use_statistics_-
_individuals 

183 

 
 
 
                                                 
6 │ Drivers of and factors linked to consumer vulnerability 

Disability  is  another  characteristic  that  may  be  associated  with  this  dimension  of 
vulnerability,  since  people  with  disabilities  can  face  accessibility  problems,  including 
inability to access  online services due to badly designed websites  (Chang et al. 2004, 
and Stearn 2012), and those with learning disabilities face challenges in understanding 
and processing information (Stearn 2012). 

Previous studies indicate that men tend to dominate the use of new technologies such 
as  internet  banking  (Pahl  1999,  Singh  and  Ryan  1999  in  Singh  2005),  resulting  in 
potential  vulnerability  in  the  online  and  financial  sectors  being  determined  along 
gender  lines.  This  is  related  to  the  third  dimension  of  vulnerability  since  new 
technologies may provide important access to certain information and products. 

However,  contrary  to  these  insights  from  the  literature,  the  results  from  the  survey 
data  analysis  presented  in  Table  27  show  that  men  are  slightly  more  likely  to  have 
problems comparing deals due to information-related factors in the energy and finance 
sectors,  with  marginal  effects  of  1.1pp  (Indicator  6,  energy)  and  1.4pp  respectively 
(Indicator  6,  finance).  This  contrast  in  findings  between  the  literature  and  the  survey 
may  be  because  the  information-related  factors  mentioned  in  the  survey  were  not 
related to the internet or technology.103 

103 The information-related factors mentioned in the survey were ‘I do not know where I can get 

this information’ and ‘suppliers do not provide enough information’. 

184 

 
 
 
                                                 
Table 27: 

Impact of personal and demographic characteristics on indicators in Dimension 3; Having difficulty obtaining 

and assimilating information 

Energy sector 

Online sector 

Finance sector 

6 │ Drivers of and factors linked to consumer vulnerability 

Indicator 
7: 

Indicator 
5: 

Indicator 
4: 

Indicator 
5: 

Feels 
uninform
ed about 
prices 

Gets info. 
from 
adverts 
only 

Indicator 
6: 

Problems 
comparin
g due to 
info. 
factors 

0.978 

0.998 
1.011 

1.058 
0.934 

1.177 
- 
1.160*** 
1.002 
- 
- 
- 

1.155*** 
1.314* 
26,653 
0.0574 

0.00750 

-0.0326*** 

-0.00247 
0.00745 

-0.0107 
-0.00701 

0.0128** 
-0.000302 

0.00777 
0.0275 

0.000944 
- 
- 
- 
- 
- 
- 

0.0163** 
- 
9,793  
0.0972 

-0.00266 
0.0108* 
- 
- 
0.00993** 
- 
- 

- 
- 
15,481  
0.0756 

Age 16-24 

Age 25-34 
Age 45-54 

Age 55-64 
Age 65-74 

Age 75+ 
Male 
Low density region 
Medium density region 
Household size 
Low education 
Medium education 
Non-native tongue, no 
difficulty 
Non-native tongue, difficulty 
N 
R2 

Not 
switched 
due to 
info. 
factors 

-0.00182 
-
0.00774*** 
-0.00000 
-
0.00966*** 
-0.00790** 
-
0.00966*** 
- 
0.00164 
-0.00564** 
- 
- 
- 

- 
- 
4,481  
0.1461 

Gets info. 
from 
adverts 
only 

Indicator 
6: 

Problems 
comparin
g due to 
info. 
factors 

Indicator 
7: 

Indicator 
5: 

Not 
switched 
due to 
info. 
factors 

Gets info. 
from 
adverts 
only 

0.00516 

-0.0133** 

-0.00329 
0.00930* 

-0.0120*** 
0.0204*** 

0.0142** 
0.0398*** 

0.0345*** 
0.0514*** 

0.115 
- 
- 
- 
- 
-0.00004 
0.00697** 

0.0164** 
0.0463 
16,451  
0.0890 

0.119* 
- 
- 
- 
- 
- 
- 

- 
- 
19,913  
0.1217 

- 

- 
- 

- 
- 

- 
- 
-0.000893 
-0.00648** 
- 
0.0153** 
0.00950*** 

- 
- 
10,228  
0.0925 

Indicator 
6: 

Problems 
comparin
g due to 
info. 
factors 

-0.0318*** 

-0.0178*** 
-0.00862 

0.00681 
-0.0217* 

-0.0629*** 
0.0139*** 
-0.0107* 
-0.00233 
- 
-0.0267*** 
-0.00312 

-0.0192** 
-0.0267 
15,481  
0.0859 

Indicator 
7: 

Not 
switched 
due to 
info. 
factors 

- 

- 
- 

- 
- 

- 
- 
- 
- 
0.00171** 
- 
- 

- 
- 
3,795  
0.1374 

Odds ratio 

Marginal 

4 

Marginal effect / Odds ratio 
Indicator code 
Note: Values in this table are results of general to specific (G-S) regression analysis. The G-S methodology is described in Annex 8. “-” implies that the explanatory variable 
was  not  included  in  the  final  regression  model  established  via  the  G-S  process.  */**/***  implies  that  a  result  is  statistically  significant  at  90%/95%/99%.  For  each  the 
indicator in this table, the R2 in the table is the Pseudo-R2 for the full regression model, which includes the explanatory variables shown in this table and other explanatory 
variables  (not  shown  in  this  table)  that  were  identified  for  the  regression  model  based  on  the  G-S  methodology.  The  indicators  are  defined  in  Table  5.  The  explanatory 
variables are defined in Table 7 and A8.1.2. There are no results for Indicator 7 for the online sector because the G-S process could not be run for this indicator due to low 
variation in the dependent variable. 
Source: London Economics analysis of the survey data 

5_adv_ene 

5_adv_onl 

5_adv_fin 

6_ene 

7_ene 

7_onl 

6_onl 

7_fin 

6_fin 

Marginal 

Marginal 

Marginal 

Marginal 

Marginal 

Marginal 

Marginal 

Marginal 

185 

 
 
 
 
 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

Some evidence suggests that cultural background and ethnicity can also be a source of 
potential  vulnerability  in  terms  of  accessing  information.  For  example,  according  to 
consultations with stakeholder organisations in Slovakia, although the Roma population 
uses  credit  and  mobile  services,  it  tends  to  have  limited  access  to  information  and 
advice concerning these services, contributing to vulnerability. 

Furthermore, the results presented in Table 27 show that a consumer’s mother tongue 
has  an  impact  on  some  indicators  of  vulnerability  in  this  dimension.  For  example, 
according  to  these  results  non-native  speakers  are  more  likely  to  feel  uninformed 
about prices, quality and conditions than native speakers, with odds ratios of 1.15 and 
1.31  depending  on  whether  being  a  non-native  speaker  causes  them  difficulty 
(Indicator 4).104 

According to the literature, a type  of literacy that is important  to take into account is 
digital  literacy.  Digital  literacy  relates  to  the  skills  consumers  need  to  effectively 
interact  with  electronic  content  and  services.  Digital  literacy  enables  consumers  to 
search  for,  access  and  assess  information  online,  which  in  turn  can  enable  them  to 
make better decisions in other sectors. 

The  results  from  the  survey  data  analysis  presented  in  Table  27  show  that  education 
does  have  an  effect  on  some  indicators  of  vulnerability  in  this  dimension,  but  the 
magnitudes of these effects are small (i.e. less than 2pp, and often less than 1pp). 

Finally,  according  to  the  results  shown  in  Table  27,  those  who  live  in  low  density 
regions  are  more  likely  than  those  in  high  density  regions  to  feel  uninformed  to  a 
greater  extent  about  prices,  quality,  and  conditions  when  and  buying  goods  and 
services, with an odds ratio of 1.16. 

6.2.4.  Dimension 4: Inability or failure to buy, choose or access suitable 
products 

Empirical evidence from the survey and experiment data analysis, in combination with 
insights 
literature,  suggest  that  some  personal  and  demographic 
characteristics are linked to the fourth dimension of consumer vulnerability, an inability 
or failure to buy, choose or access suitable products. 

from  the 

The  results  of  the  experiments  are  especially  relevant  to  this  dimension  of 
vulnerability, since the experiments tested consumers’ ability to select the optimal deal 
in simulated purchasing situations in the three key markets of the study. Therefore this 
section draws on the results of the G-S regression analysis of respondents’ answers in 
the experiments.  

There  are  several  aspects  of  old  age  that  may  contribute  to  an  inability  to  access  or 
choose  suitable  products.  For  example,  as  noted  above  (in  section  6.2.3),  older 
consumers  are  less  likely  to  be  internet  users  or  to  have  access  to  information  and 
guidance  regarding  markets  (Stearn  2012),  and  older  consumers  can  receive  less 
marketing  information  and  be  more  isolated  from  networks  that  may  help  them 
navigate markets (Lunn and Lyons 2010). 

104 An odds ratio above one implies higher vulnerability compared to the base group. 

186 

 
 
 
                                                 
6 │ Drivers of and factors linked to consumer vulnerability 

Furthermore,  some  financial  products  that  are  offered  later  in  life  (e.g.  reverse 
mortgages  and  annuities)  are  becoming  more  complicated  (Stearn  2012),  which  may 
increase the risk that older consumers do not select suitable products. 

According  to  one  stakeholder  organisation,  older  consumers  may  also  face  age  limits 
for some financial products, such as travel insurance. Although justified as being based 
on risk, these limits may be disproportionate and amount to restricting access. 

Results  of  G-S  regression  analysis  of  respondents’  choices  in  the  experiments, 
presented  in  Table  28,  show  that  those  in  the  75+  age  group  were  less  likely  than 
those  aged  35-44  to  select  the  optimal  deal  overall  across  the  various  experiments, 
and  in  particular  were  less  likely  to  select  the  best  deal  in  the  second  round  of  the 
energy sector experiment, and both rounds of the online sector experiment. This group 
has  the  lowest  odds  ratio  (0.51)  for  overall  performance  of  any  socio-demographic 
group  examined  in  the  analysis,  implying  that  this  is  the  characteristic  most  strongly 
linked with inability to select the optimal offer. In addition,  overall performance in the 
experiments was also worse among the 55-64 age group (with an odds ratio of 0.82) 
than among the base group. 

However, the results do not comprehensively imply that all older age groups were less 
likely  to  select  the  best  deal  in  the  experiments,  since  although  the  65-74  age  group 
did  perform  worse  in  the  experiments  overall  (with  an  odds  ratio  of  0.863),  the 
difference between this group and the base group was not statistically significant. 

At the young end of the age-range, the results show that those aged 16-24 performed 
worse in some experiment rounds, in particular the second round of the online sector 
experiment and the first round of the finance sector experiment. However, the overall 
performance  of  this  group,  across  all  experiments  and  rounds,  was  not  statistically 
significantly different from the base group. 

187 

 
 
 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

Table 28: 

Impact of personal and demographic characteristics on experiment performance 

Energy Sector 

Online Sector 

Finance Sector 

Cross-cutting 

Round 1 

Round 2 

Round 1 

Round 2 

Round 1 

Round 2 

Round 1 

Round 2 

Overall 

Age 16-24 
Age 25-34 
Age 45-54 
Age 55-64 
Age 65-74 
Age 75+ 
Male 
Low density region 
Medium density region 
Household size 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

Low education 

0.0369 

0.0139 
0.0284 
0.0452 
-0.0327 
-0.00597 
-0.166*** 
- 
- 
- 
- 
-
0.0835*** 

-0.00799 
-0.0289 
0.0183 
-0.00924 
-0.0700 
-0.148** 
- 
- 
- 
- 

-0.0851* 
-0.0624* 
-0.0198 
-0.0740* 
-0.0473 
-0.278*** 
- 
- 
- 
- 

-0.0662** 
-0.0113 
0.00695 
-0.00526 
-0.0113 
-0.0254 
- 
- 
- 
- 

- 
- 
- 
- 
- 
- 
0.0324** 
- 
- 
- 

-0.0227 
-
0.0614*** 

-0.00964 
-
0.0695*** 

- 

-0.0868*** 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

- 

0.0511 
0.00462 
0.0472 
0.00936 
0.0481 
-0.0339 
- 
- 
- 
- 

0.994 
0.914 
0.970 
0.821** 
0.863 
0.510*** 
- 
- 
- 
- 

-0.0185 

0.748*** 

- 

- 

- 

- 

-0.0435 

-0.0191 

-0.0334 

-0.0130 

-0.122*** 

Medium education 
Non-native tongue, no 
difficulty 
Non-native tongue, 
difficulty 
N 
2,510  
R2 
0.0823 
Marginal 
Marginal effect / Odds ratio 
Note: Values in this table are results of general to specific (G-S) regression analysis. The G-S methodology is described in Annex 8. “-” implies that the variable was not 
included in the final regression model established via the G-S process. */**/*** implies that a result is statistically significant at 90%/95%/99%. For each the indicator in 
this table, the R2 in the table is the Pseudo-R2 for the full regression model, which includes the explanatory variables shown in this table and other explanatory variables 
(not shown in this table) that were identified for the regression model based on the G-S methodology.  The explanatory variables are defined in  Table 7 and A8.1.2. The 
dependent variables in the experiment analysis signify whether the respondent chose the optimal deal. For individual experiment rounds, the dependent variable is equal to 
one if the respondent chose the optimal deal, or zero otherwise. The ‘Overall’ performance measure is the total number of correct choices made by the respondent (ranging 
from 0 to 4). Thus, positive marginal effects imply a higher likelihood of choosing the optimal deal, and odds ratios greater than one imply better overall performance. 
Source: London Economics analysis of the survey data 

-0.234** 
2,510  
0.1061 
Marginal 

- 
2,522  
0.0318 
Marginal 

5,051  
0.0421 
Odds ratio 

2,505  
0.1205 
Marginal 

2,565  
0.0628 
Marginal 

2,505  
0.1191 
Marginal 

2,522  
0.0710 
Marginal 

2,565  
0.0237 
Marginal 

-0.0513** 

-0.102*** 

0.765*** 

0.868** 

-0.109 

-0.152 

0.788 

- 

- 

- 

- 

- 

- 

- 

188 

 
 
 
 
 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

Results  from  G-S  regression  analysis  of  the  survey  data  reveal  a  very  mixed  picture 
with  respect  to  links  between  age  and  indicators  of  vulnerability  in  the  fourth 
dimension  (an  inability  or  failure  to  buy,  choose  or  access  suitable  products),  with 
different  relationships  across  the  various  indicators  and,  for  specific  indicators, 
different  relationships  between  sectors.  These  results  are  presented  in  Table  29 
(Indicator 8), Table 30 (Indicator 9) and Table 31 (Indicators 10 and 11), although the 
variation in the results is more clearly seen from the summary matrix shown in  Figure 
27 above. 

According to these results, every age group is more likely to be vulnerable on at least 
two indicators in this dimension. However, simultaneously, each age group is also  less 
likely to be vulnerable on at least two (other) indicators in the dimension.  

For  example,  the  55-64  age  group  are  more  likely  to  have  problems  comparing  deals 
due  to  market-related  factors  in  the  energy  sector105  and  due  to  personal,  market-
related  and  access-related  factors  in  the  online  sector106,  but  are  less  likely  to  be 
excluded from e-commerce due to the difficulty of the process or not having a payment 
card107, and are less likely to be declined for a loan.108 

One of the most consistent and striking results is that older consumers, in the 45+ age 
groups  and  especially  the  65+  age  groups,  are  more  likely  to  have  problems 
comparing  deals  due  to  market-related  factors  (Indicator  8b)  in  the  online  and  (to  a 
lesser extent) energy sectors. For example, the marginal effects for those aged 65-74 
and 75+ on this indicator for the online sector are 15.2pp and 25.1pp respectively, the 
latter being the largest effect of any demographic characteristic on any indicator in this 
dimension. Conversely, young age groups  are  found to be less likely to be vulnerable 
on this indicator. 

This variation in the survey results with respect to age is mirrored in the literature. For 
example,  Nardo  et  al  (2015)  find  that  while  younger  generations  tend  to  be  more 
skilled  and  engaged  consumers,  this  is  not  always  the  case,  with  Italian  consumers 
over  54  being  more  engaged,  more  aware  of  their  rights,  and  more  skilled  than 
consumers in the 15-24 age bracket. Similarly, Berg (2015) finds that older consumers 
in  Norway  are  not  necessarily  more  vulnerable  compared  to  other  consumer  groups. 
This  is  attributed  to  older  consumers  having  more  time,  as  well  as  having  more 
economic  awareness  since  they  are  more  frugal  than  younger  Norwegians.  The  UK 
consumer  empowerment  survey  also  found  that  older  and  financially  comfortable 
consumers  could  be  both  highly  engaged  “traditional  value-seekers”  (estimated  to  be 
the  largest  consumer  segment  at  24%  of  the  consumer  population),  as  well  as 
“consciously  unengaged”  consumers  (estimated  at  15%  of  the  consumer  population) 
(GfK 2015). 

105 See the results for Indicator 8b for the energy sector in Table 29. 
106 See the results for Indicators 8a, 8b and 8c for the online sector in Table 29. 
107 See the results for Indicators 10a and 10b in Table 31. 
108 See the results for Indicators 11 in Table 31. 

189 

 
 
 
 
                                                 
6 │ Drivers of and factors linked to consumer vulnerability 

Table 29: 

Impact of personal and demographic characteristics on Indicator 8 in Dimension 4; Has problems comparing 

deals due to personal factors, market related factors, and access-related factors 

Indicator 
8a: 
Personal 
factors 

- 
- 
- 
- 
- 
- 
-0.0237*** 
-0.0188** 
-0.0387*** 
-0.0124** 
-0.00899 
-0.0323*** 
- 
- 
15,481  
0.1273 

Marginal 

Energy 
Indicator 
8b: 
Market 
factors 
-0.0523*** 
-0.0325*** 
0.0379*** 
0.0671*** 
0.0994*** 
-0.0449 
-0.0253*** 
- 
- 
- 
-0.00459 
-0.0236*** 
-0.0328** 
0.0165 
15,481  
0.1084 

Marginal 

Indicator 
8c: 
Access 
factors 

0.0237* 
0.0474*** 
-0.0105 
0.00587 
0.0356* 
0.244*** 
-0.0202*** 
0.00502 
-0.0309*** 
- 
-0.0455*** 
-0.00178 
0.0269** 
0.0434 
15,481  
0.1263 

Marginal 

Indicator 
8a: 
Personal 
factors 

-0.00546 
-0.0103 
0.0139* 
0.0362*** 
0.0555*** 
0.0653 
-0.0229*** 
0.0218*** 
0.00671 
- 
- 
- 
- 
- 
19,913  
0.1442 

Marginal 

Online 
Indicator 
8b: 
Market 
factors 
-0.0385*** 
-0.0471*** 
0.0505*** 
0.112*** 
0.152*** 
0.251*** 
-0.0127** 
- 
- 
- 
-0.0218** 
-0.00676 
- 
- 
19,913  
0.1461 

Indicator 
8c: 
Access 
factors 
-0.000477 
0.00212 
0.00748 
0.0170*** 
0.00606 
-0.0105 
- 
0.00917*** 
-0.000742 
-0.00586*** 
- 
- 
0.0124** 
0.0211 
19,913  
0.0714 

Marginal 

Marginal 

Indicator 
8a: 
Personal 
factors 

0.0111 
0.0293** 
-0.0188 
0.00168 
-0.0387** 
0.0795* 
-0.0224*** 
- 
- 
-0.0114*** 
- 
- 
- 
- 
15,481  
0.1210 

Marginal 

Finance 
Indicator 
8b: 
Market 
factors 

-0.0176 
-0.00777 
-0.00146 
0.0204 
-0.00252 
-0.0392 
-0.0202** 
- 
- 
- 
- 
- 
-0.0227 
-0.182*** 
15,481  
0.1049 

Indicator 
8c: 
Access 
factors 
-0.000516 
-0.000533 
-0.000239 
0.00194 
-0.000948 
0.0432 
- 
0.00991*** 
-0.00195 
- 
- 
- 
-0.00468** 
0.00173 
15,481  
0.0933 

Marginal 

Marginal 

Age 16-24 
Age 25-34 
Age 45-54 
Age 55-64 
Age 65-74 
Age 75+ 
Male 
Low density region 
Medium density region 
Household size 
Low education 
Medium education 
Non-native tongue, difficulty 
Non-native tongue, difficulty 
N 
R2 
Marginal effect / Odds ratio 
Indicator code 

8a_ene 

8a_onl 
Note: Values in this table are results of general to specific (G-S) regression analysis. The G-S methodology is described in Annex 8. “-” implies that the explanatory variable 
was  not  included  in  the  final  regression  model  established  via  the  G-S  process.  */**/***  implies  that  a  result  is  statistically  significant  at  90%/95%/99%.  For  each  the 
indicator in this table, the R2 in the table is the Pseudo-R2 for the full regression model, which includes the explanatory variables shown in this table and other explanatory 
variables  (not  shown  in  this  table)  that  were  identified  for  the  regression  model  based  on  the  G-S  methodology.  The  indicators  are  defined  in  Table  5.  The  explanatory 
variables are defined in Table 7 and A8.1.2. 
Source: London Economics analysis of the survey data 

8b_ene 

8c_ene 

8b_onl 

8c_onl 

8b_fin 

8a_fin 

8c_fin 

190 

 
 
 
 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

Table 30: 

Impact of personal and demographic characteristics on Indicator 9 in Dimension 4; Has not switched due to 

personal factors, market-related factors, access-related factors, termination costs, and bundling 

Indicator 
9a: 
Personal 
factors 

- 
- 
- 

- 
- 
- 
- 
- 
- 

Energy 
Indicator 
9b: 
Market 
factors 

-0.00109 
-0.00907 
0.0149 

Indicator 
9c: 
Access 
factors 

-0.0113 
0.0462** 
-0.00231 

0.0398** 
0.0245 
-0.0318 
- 
-0.00831 
-0.0242*** 

0.00540 
0.0437 
0.256*** 
-0.0374*** 
0.0465*** 
0.00432 

Age 16-24 
Age 25-34 
Age 45-54 

Age 55-64 
Age 65-74 
Age 75+ 
Male 
Low density region 
Medium density region 

- 
- 
- 

- 
- 
- 
- 
- 
- 

- 
- 
- 

- 
- 
- 
- 
- 
- 

- 
- 
- 

- 
- 
- 
- 
- 
- 

Indicator 
9a: 
Personal 
factors 

Online 
Indicator 
9b: 
Market 
factors 

Indicator 
9c: 
Access 
factors 

Indicator 
9a: 
Personal 
factors 

Finance 
Indicator 
9b: 
Market 
factors 
-0.0487*** 
-0.0363*** 
0.0218 

- 
- 
- 

- 
- 
- 
-0.0145** 
- 
- 

0.0417** 
-0.000589 
-0.0246 
- 
- 
- 

- 
- 
- 
- 
- 

- 
- 
- 
- 
- 

- 
- 
- 
-0.0248* 
0.0349 

-0.0137* 
-0.0822*** 
-0.0122 
0.0909*** 
-0.00175 
4,782  

Household size 
Low education 
Medium education 
Non-native tongue, no difficulty 
Non-native tongue, difficulty 
N 
R2 
Marginal effect / Odds ratio 
Indicator code 
Note: Values in this table are results of general to specific (G-S) regression analysis. The G-S methodology is described in Annex 8. “-” implies that the explanatory variable 
was  not  included  in  the  final  regression  model  established  via  the  G-S  process.  */**/***  implies  that  a  result  is  statistically  significant  at  90%/95%/99%.  For  each  the 
indicator in this table, the R2 in the table is the Pseudo-R2 for the full regression model, which includes the explanatory variables shown in this table and other explanatory 
variables  (not  shown  in  this  table)  that  were  identified  for  the  regression  model  based  on  the  G-S  methodology.  The  indicators  are  defined  in  Table  5.  The  explanatory 
variables are defined in Table 7 and A8.1.2. 
Source: London Economics analysis of the survey data 

0.0105*** 
-0.0314*** 
-0.0293*** 
0.0234** 
-0.000707 
26,653  

0.00370** 
-0.0241*** 
-0.0145*** 
0.0202** 
0.0314 

- 
- 
- 
0.00342 
- 

- 
- 
- 
0.0203 
- 

0.0852 
Marginal 

0.0651 
Marginal 

0.2145 
Marginal 

0.2094 
Marginal 

0.0804 
Marginal 

0.1083 
Marginal 

0.0597 
Marginal 

0.1283 
Marginal 

0.1330 
Marginal 

0.1538 
Marginal 

0.1242 
Marginal 

- 
- 
- 
- 
- 

- 
- 
- 
- 
- 

26,653  

9e_bun 

9b_ene 

9a_ene 

9c_ene 

9b_onl 

9a_onl 

9d_ter 

9c_onl 

3,483  

4,782  

3,427  

3,265  

4,368  

4,095  

4,782  

4,355  

9b_fin 

9a_fin 

9c_fin 

Indicator 
9d: 
Terminat
ion costs 
0.0150 
0.0151** 
-0.00984 

Indicator 
9e: 
Bundling 

-0.0241** 
-0.00981 
-0.0107 

-0.0303*** 
-0.0336*** 
-0.0541*** 
- 
- 
- 

0.0031 
-0.0176 
-0.00467 
- 
- 
- 

Indicator 
9c: 
Access 
factors 
-0.0123*** 
-0.0128*** 
-0.00368 
-
0.00883**
* 
-0.0143*** 
-0.0127*** 
- 
0.00776* 
-0.00131 
-
0.00383**
* 
-0.00517 
0.00357 
- 
- 

191 

 
 
 
 
 
 
 
 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

Table 31: 

Impact of personal and demographic characteristics on 

Indicators 10 and 11 in Dimension 4; Excluded from e-commerce and 
declined a loan 

Excluded from e-commerce due to 

Indicator 10a: 
Difficulty of the 
process 

Indicator 10b: Not 
having payment 
card 

Indicator 11: 
Declined a loan 

0.0115 
0.00243 
-0.0183** 
-0.0216** 
-0.0232 
0.137* 
- 
0.0152** 
0.00009 
- 
-0.0275*** 
-0.0244*** 

0.00129 
0.00421 
0.00268 
-0.0130*** 
-0.0214*** 
-0.0203*** 
- 
- 
- 
- 
- 
- 

0.0504*** 
0.0336*** 
-0.0143** 
-0.0370*** 
-0.0506*** 
-0.00944 
-0.0180*** 
0.0159** 
0.00134 
0.00412** 
-0.00706 
0.0112** 

Age 16-24 
Age 25-34 
Age 45-54 
Age 55-64 
Age 65-74 
Age 75+ 
Male 
Low density region 
Medium density region 
Household size 
Low education 
Medium education 
Non-native tongue, no 
difficulty 
Non-native tongue, 
difficulty 
N 
R2 
Marginal effect / Odds ratio 
Indicator code 
Note:  Values in this table are results of general to specific (G-S) regression analysis. The G-S methodology 
is  described  in  Annex  8.  “-”  implies  that  the  explanatory  variable  was  not  included  in  the  final  regression 
model  established  via  the  G-S  process.  */**/***  implies  that  a  result  is  statistically  significant  at 
90%/95%/99%.  For  each  the  indicator  in  this  table,  the  R2  in  the  table  is  the  Pseudo-R2  for  the  full 
regression  model,  which  includes  the  explanatory  variables  shown  in  this  table  and  other  explanatory 
variables  (not  shown  in  this  table)  that  were  identified  for  the  regression  model  based  on  the  G-S 
methodology.  The  indicators  are  defined  in  Table  5.  The  explanatory  variables  are  defined  in  Table  7  and 
A8.1.2. 
Source: London Economics analysis of the survey data 

0.0165 
19,957  
0.0903 
Marginal 

- 
4,971  
0.1230 
Marginal 

19,957  
0.1188 
Marginal 

0.0278** 

11_loan 

10b 

10a 

- 

- 

- 

According  to  the  literature  on  vulnerability,  those  with  disabilities  can  suffer  from 
accessibility  problems,  such  as  being  unable  to  access  online  services  (Chang  et  al. 
2004,  Stearn  2012)  and  having  limited  physical  access  to  markets,  although  in 
discussing  disability  in  the  context  of  consumer  vulnerability,  it  is  important  to  note 
that disability is a broad concept that is not only linked to the individual but also to the 
wider environment.109 

The  results  of  the  focus  groups  show  that  different  disabilities  (i.e.  visual  impairment 
and leg  paralysis)  have  very  different  consequences  and  have  a  very  different  impact 

109 The UN Convention on the Rights of Persons with Disabilities (UN CRPD) uses the following 

definition of persons with disabilities: 
“Persons with disabilities include those who have long-term physical, mental, intellectual or 
sensory impairments which in interaction with various barriers may hinder their full and 
effective participation in society on an equal basis with others.” 
(http://www.un.org/disabilities/default.asp?id=261)   
This definition therefore implies that consumers with disabilities should be seen as a diverse 
group.  

192 

 
 
 
 
 
                                                 
6 │ Drivers of and factors linked to consumer vulnerability 

on how these consumers participate in the market. For instance, while consumers with 
restricted  mobility  tend  to  make  extensive  use  of  some  online  services  compared  to 
other  groups,  the  same  services  can  be  challenging  for  consumers  with  visual 
impairments.  This  in  turn  means  that  the  extent  to  which  those  with  disabilities 
experience vulnerability in a specific context is likely to differ significantly. 

When  combined  with  the  fact  that,  as  shown  in  the  focus  groups,  consumers  with 
disabilities  tend  to  stress  the  importance  of  being  autonomous,  one  can  see  why  the 
term  “vulnerability”  used  as  a  “blanket”  statement  in  the  context  of  disability is  often 
not  considered  to  be  appropriate  from  the  point  of  view  of  disability  organisations. 
Specifically, the European Disability Forum stresses the importance of seeing the needs 
of people with disabilities not as “vulnerabilities” but as obstacles in the environment, 
with  such  an  approach  seen  as  more  in  line  with  the  UN  CRPD  (European  Disability 
Forum 2014). 

Davis  and  Davis  and  Pechmann  (2013)  note  that  consumers  can  experience  anxiety 
and  marginalisation  in  marketplaces  where  they  do  not  understand  the  cultural 
expectations, and in some cases ethnicity is also linked to exclusion from some aspects 
of  the  market.  In  addition,  language  barriers  also  make  navigating  markets  more 
difficult (Davis and Pechmann 2013). 

These  insights  from  the  literature  are  broadly  supported  by  the  experiment  results 
presented  in  Table  28,  which  show  that  non-native  speakers  were  less  likely  than 
native  speakers  to  select  the  optimal  deal  overall  across  the  various  experiments. 
Specifically,  for  non-native  speakers  whose  language  does  not  cause  them  difficulty, 
the odds ratio for the overall performance measure is 0.765.110 

Furthermore, the results of the survey data analysis show that non-native speakers are 
more likely to be vulnerable on some indicators in this dimension (i.e. Indicator 8c and 
Indicator  9c  in  the  energy  sector,  Indicator  8c  in  the  online  sector,  Indicator  9d, 
Indicator  9e,  and  Indicator  10a),  although  these  consumers  are  also  found  to  be  less 
likely to be vulnerable on other indicators (see Table 29, Table 30 and Table 31). 

Some  literature  highlights  the  importance  of  knowledge  and  education  in  allowing 
consumers  to  access  suitable  products.  For  example,  Klapper,  Lusardi  and  Panos 
(2013)  observe  that  financial  literacy  is  associated  with  lower  use  of  informal 
borrowing sources, and the 2011 European Parliament study on consumer behaviour in 
the  digital  environment  notes  that  a  lack  of  digital literacy,  which  relates  to  the  skills 
needed to effectively interact with electronic content and services, remains a barrier to 
participating in the online economy. The interaction between different forms of literacy 
can be linked to the dynamic nature of vulnerability, since a single consumer may have 
a  high  level  of  literacy  in  one  area,  but  yet  be  potentially  vulnerable  due  to  lower 
literacy in another one. 

The  results  of  the  consumer  survey  contrast  with  those  of  the  experiments  regarding 
the  impact  of  education  on  this  dimension  of  vulnerability.  The  results  of  the  survey 
data  analysis  show  that  those  with  low  or  medium  education  are  less  likely  to  be 

110  .e.  below  one,  implying  a  higher  probability  of  lower  scores  on  the  overall  performance 
measure.  Limitations  of  the  sampling  method  with  respect  to  reaching  minorities  not 
speaking national languages are discussed in section A2.10.2. 

193 

 
 
 
                                                 
6 │ Drivers of and factors linked to consumer vulnerability 

vulnerable  than  those  with  high  education  based  on  a  number  of  indicators  in  this 
dimension. Specifically: 

  Those  with  low  education  are  less  likely  to  be  vulnerable  on  Indicator  8c  and 
Indicator 9c in the energy sector (Table 29 and Table 30), Indicator 8b in the 
online  sector  (Table  29),  Indicator  9d  (Table  30),  Indicator  9e  (Table  30)  and 
Indicator 10a (Table 31); and 

  Those  with  medium  education  are  less  likely  to  be  vulnerable  on  Indicator  8a 
and  Indicator  8b  in  the  energy  sector  (Table  29),  Indicator  9d  (Table  30), 
Indicator 9e (Table 30) and Indicator 10a (Table 31). 

In  contrast,  the  experiment  results  suggest  that  those  with  low  or  medium  education 
are  more  likely  to  be  vulnerable  in  this  dimension,  since  these  consumers  were  less 
likely  to  select  the  optimal  deal  overall  across  the  various  experiments,  with  odds 
ratios for the overall performance measure of 0.75 and 0.87 (Table 28) respectively.111 
In  particular,  those  with  low  education  were  less  likely  to  select  the  best  deal  in  the 
second  round  of  the  energy  sector  experiment  and  the  second  round  of  the  finance 
sector experiment, whereas those with medium education were less likely to select the 
best deal in both rounds of the online sector experiment and the second round of the 
cross-cutting experiment. 

A  factor  that  is  emphasised  in  the  literature  as  being  related  to  this  dimension  of 
vulnerability  is  locality,  since  a  consumer’s  location  can  result  in  reduced  physical 
access  to  goods  and  services,  as  well  as  potentially  being  worse  served  by 
telecommunication links (Chang et al. 2004). 

Reduced  physical  access  to  goods  and  services  does  not  only  mean  that  consumers 
may not obtain a certain good or service, but also that they may decide on suboptimal 
alternatives.  Andreasen  (1975,  1993)  notes  that,  in  the  financial  sector,  limited 
mobility  and  inadequate  transportation  links  can  make  it  difficult  for  consumers  to 
access  mainstream  lenders,  pushing  them  to  instead  turn  to  alternative  providers, 
where  they  may  be  subject  to  usurious  rates  (Andreasen  1975  and  1993  in  Hill  and 
Kozup 2007). 

Furthermore,  living in  areas  with  a  particular  socioeconomic  or  ethnic  profile  can  also 
mean  that  consumers  may  be  subject  to  “retail  redlining”,  where  retailers  avoid 
opening  franchises  in  disadvantaged  areas  for  reasons  other  than  objective  economic 
ones (D'Rozario and Williams 2005). 

Results  from  the  survey  data  analysis  indeed  show  that  those  in  low  density  regions 
are more likely to be vulnerable on several indicators in this dimension. These include 
having  problems  comparing  deals  due  to  access-related  factors  in  the  online  and 
finance sectors and due to personal factors in the online sector (Table 29), and being 
prevented  from  switching  due  to  access-related  factors  in  the  energy  and  finance 
sectors  (Table  30).  However,  with  marginal  effects  ranging  from  just  0.8pp  to  4.7pp, 
these effects are typically not large (although they are statistically significant). 

Finally,  although  gender  is  not  a  characteristics  that  is  frequently  connected  with 
vulnerability,  the  results  of  the  survey  data  analysis  show  that  men  are  consistently 

111 i.e. less than one, signifying a higher probability of lower scores on the overall performance 

measure. 

194 

 
 
                                                 
6 │ Drivers of and factors linked to consumer vulnerability 

less  likely  than  women  to  be  vulnerable  on  a  number  of  indicators  in  this  dimension. 
These  include  having  problems  comparing  deals  due  to  personal,  market-related  and 
access-related factors in the energy sector, due to personal and market-related factors 
in  the  online  sector,  and  due  to  personal  and  market-related  factors  in  the  finance 
sector  (Table  29),  as  well  as  being  prevented  from  switching  due  to  access-related 
factors in the energy sector (Table 30). 

6.2.5.  Dimension 5: Higher susceptibility to marketing practices 

There is evidence from the literature that old age could contribute to this dimension of 
vulnerability  since,  for  example,  more  limited  social  interaction  can  make  elderly 
consumers  susceptible  to  being  exploited  by  telemarketers  and  doorstep  selling  (Lee 
and Geistfeld, 1999).  

However,  in  contrast,  results  of  the  survey  data  analysis  presented  in  Table  32  show 
that, when other factors are controlled for in the analysis, consumers in the older age 
groups  are  less  likely  to  feel  a  high  level  of  vulnerability  due  to  the  complexity  of 
offers, terms or conditions; for example the odds ratio for the 75+ age group is 0.71. 
Conversely, those in the youngest two age groups (16-24 and 25-34) are more likely 
to feel a high level of vulnerability for this reason (with odds ratio of 1.10 and 1.11). 

The  literature  also  singles  out  children  as  category  of  consumer  who  may  be 
particularly  susceptible  to  advertising  and  marketing  directed  to  children.  Two  factors 
that are seen as making advertising to children particularly problematic are that young 
children  have  limited  understanding  of  “persuasive  intent”  while  older  children  are 
vulnerable  to  pressures  to  conform  (see  for  instance  Khadir  2007).112  It  is  however 
important  to  note  that,  as  for  other  groups,  the  relationship  between  young  age  and 
potential vulnerability is a dynamic one. For instance, while children can be vulnerable 
in the online sector, they can also be more digitally savvy than many adult consumers 
(University of Amsterdam 2010). 

The  results  of  the  survey  data  analysis  presented  in  Table  32  also  highlight  mother 
tongue as a factor that is closely related to this dimension of vulnerability. Specifically, 
non-native  speakers  are  found  to  be  considerably  more  likely  to  feel  a  high  level  of 
vulnerability  due  to  the  complexity  of  offers,  terms  or  conditions,  especially  those 
whose language causes them difficulty (with an odds ratio of 2.00).  

112  Children  are  explicitly  mentioned  in  the  UCPD  and  Annex  1  of  the  Directive  identifies 
“including in an advertisement a direct exhortation to children to buy advertised products or 
persuade their parents or other adults to buy advertised products for them” as an aggressive 
commercial practice. 

195 

 
 
 
                                                 
6 │ Drivers of and factors linked to consumer vulnerability 

Table 32: 

Impact of personal and demographic characteristics on Indicator 
12 in Dimension 5; Feeling of vulnerability due to complexity of offers 

Indicator 12: Feels vulnerable due to complexity of offers;  

Binary: “To a great extent” 

Scale: 1 (low) - 4 (high) 

Age 16-24 
Age 25-34 
Age 45-54 
Age 55-64 
Age 65-74 
Age 75+ 
Male 
Low density region 
Medium density region 
Household size 
Low education 
Medium education 
Non-native tongue, no 
difficulty 
Non-native tongue, 
difficulty 
N 
R2 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

0.0177*** 

1.096* 
1.108*** 
0.929** 
0.923* 
0.897* 
0.712*** 
- 
1.067** 
0.993 
- 
- 
- 

1.130*** 

0.0748*** 
26,653  
0.1005 
Marginal 

2.000*** 
26,653  
0.0465 
Odds ratio 

12_off_b2 

Marginal effect / Odds ratio 
Indicator code 
Note: Values in this table are results of general to specific (G-S) regression analysis. The G-S methodology is 
described in Annex 8. “-” implies that the explanatory variable was not included in the final regression model 
established via the G-S process. */**/*** implies that a result is statistically significant at 90%/95%/99%. 
For each the indicator in this table, the R2 in the table is the Pseudo-R2 for the full regression model, which 
includes  the  explanatory  variables  shown  in  this  table  and  other  explanatory  variables  (not  shown  in  this 
table)  that  were  identified  for  the  regression  model  based  on  the  G-S  methodology.  The  indicators  are 
defined in Table 5. The explanatory variables are defined in Table 7 and A8.1.2. 
Source: London Economics analysis of the survey data 

12_off 

6.3.  Behavioural drivers of vulnerability 

Behavioural  drivers  refer  primarily  to  biases  in  decision-making,  which  could  result  in 
consumers potentially finding themselves in situations of vulnerability. Lunn and Lyons 
(2010) provide a comprehensive overview of a range of relevant biases and heuristics. 
These include: 

  Status  quo  bias,  where  consumers  tend  to  choose  the  default  or  existing 
options,  which  in  turn  means  that  the  default  option  can  be  made 
disadvantageous to the consumer without the consumer switching to the more 
advantageous offer. 

  Loss  aversion  and  the  endowment  effect  refer  to  the  fact  that  consumers 
put more value on something that is lost or given away compared to something 
that is gained, potentially making them choose suboptimal options. 

  Time  discounting  relates  to  consumers  putting  more  value  on  present  than 
future  rewards,  making  the  consumers  potentially  vulnerable  to  choose  offers 
which  benefit  consumers  in  the  short-term,  but  which  may  carry  costs  in  the 
longer-term.   

  Bandwagon  effect  refers  to  biases  in  favour  of  social  norms  and  choices 
made by others, making consumers vulnerable to the influence of their peers, 
as well as to perceived popularity of offers. 

196 

 
 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

  Ambiguity  aversion  relates  to  consumers  avoiding  risks  that  cannot  be 
accurately  quantified.  Although  this  implies  that  consumers  would  avoid 
complex contracts, it also suggests that vendors oversimplifying products (i.e. 
financial products) could be exploiting this bias as a potential vulnerability.  

  Heuristics  refer  to  consumers  using  “rules  of  thumb”  instead  of  making 
complete  assessments  of  the  offers,  which  can  make  consumers  vulnerable 
when faced with complex offers.  

  Framing relates to consumer choices being influenced by the way information 
is  presented,  which  can  make  many  consumers  vulnerable  when  faced  with 
offers designed in certain ways.   

  Overconfidence  relates  to  consumers  overestimating  how  accurate  their 
judgements  are,  which  can  lead  consumers  to  badly  judge  their  future  use  of 
products or services, or repayment of loans.   

  Projection  bias  is  the  assumption  that  recent  trends  will  also  apply  in  the 
future, despite potential changes, which, as in the case of overconfidence, can 
result  in  consumers  putting  themselves  in  positions  of  vulnerability  through 
inaccurate predictions of their future situation.   

In addition to the above, consumers are also subject to cognitive limitations, namely 
limited  capacity  to  assimilate  and  process  large  amounts  of  information  in  order  to 
make  an  informed  choice  (European  Parliament  2011).  This  means  that  consumers 
may  not  be  able  to  effectively  choose  from  a  large  number  of  options,  nor  fully 
understand  complex  products  or  offers,  which  can  put  them  in  a  position  of 
vulnerability. Another related factor is “consumer attention deficit syndrome” (CADS), 
as  outlined  by  Berg  and  Gornitzka  (2012).  Berg  and  Gornitzka  (2012)  see  consumers 
as  having  a  limited  “attention  budget”,  which  can  be  easily  exhausted  in  the  modern 
marketplace. 

An  important  aspect  of  behavioural  vulnerability  drivers  is  the  fact  that  they  are 
observed across all consumer groups, which in turn means that all consumers can be 
in  a  position  of  vulnerability  depending  on  the  situations  they  find  themselves  in  and 
the way a choice is presented to them. This of course does not mean that consumers’ 
characteristics do not play a role: An individual with a background in the finance sector 
may  be  able  to  understand  more  complex  financial  product  offers  and  make  more 
informed  decisions  than  other  consumers,  but  that  consumer  may  still  be  subject  to 
cognitive  limitations  in  other  situations.  Berg  and  Gornitzka  (2012)  highlight  that 
limited  consumer  attention  means  that  consumers  focus  their  attention  on  specific 
areas  of  the  market,  meaning  that  even  if  they  are  well-informed  about  other  areas, 
they can still make suboptimal choices.  

Lunn  and  Lyons  (2010)  have  also  identified  patterns  in  existing  research  which  link 
particular biases to specific consumer groups, noting in particular that: 

  Consumers  with  lower  educational  attainment,  high  income  or  wealth,  as  well 

as older consumers are more susceptible to loss aversion; 

  People  with  lower  education  and  lower  incomes  are  more  likely  to  discount 

time; 

  People  discount  time  less  steeply  with  age  putting  increasing  value  on  future 

rewards (although this effect may reverse again in retirement); and 

  Older consumers are more susceptible to framing. 

197 

 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

In  addition,  other  research  (see  Hardesty,  Bearden  and  Carlson  2006)  shows  that, 
overall,  adult  consumers  are  more  aware  of  various  pricing  tactics,  making  them less 
susceptible to such tactics than younger consumers. 

A matrix summarising relevant results from the survey and experiment data analysis is 
presented in Figure 28 below. The indicator codes across the top of the matrix match 
those  in  the  list  of  indicators  in  section  3.6  above,  as  well  as  those  in  the  table  of 
indicators  in  Annex  3.113  The  behavioural  characteristics  in  the  matrix  (down  the  left 
hand side) are defined (alongside all other explanatory variables examined in the data 
analysis) in Table 7 in section 3.7. 

Red cells in the matrix show that consumers with a certain behavioural characteristic, 
as  measured  through  the  survey,  are  more  likely  to  be  vulnerable  on  the  relevant 
indicator, whereas green cells mean that consumers with a particular characteristic are 
less  likely  to  be  vulnerable  on  the  indicator  in  question.  Grey  cells  imply  that  a 
characteristic  was  controlled  for  in  the  analysis  but  was  not  statistically  significant, 
while  white  cells  mean  that  a  characteristic  was  excluded  from  the  regression  (based 
on  the  G-S  methodology).  The  right-hand  column  in  the  matrix  shows  the  links 
between  respondents’  behavioural  characteristics  and  their  performance  in  the 
experiments, overall across all experiments and rounds. 

The indicators examined in the analysis are set out in section  6.1.2. Further details of 
the  analysis  undertaken,  including  descriptions  of  all  the  explanatory  variables,  are 
provided in Annex 8.  

As  can  be  seen  from  Figure  28,  the  results  are  again  very  diverse,  with  statistically 
significant  results  in  both  directions  scattered  across  the  matrix.  However,  that  being 
said,  the  results  do  show  some  patterns  and  reveal  some  characteristics  that  are 
associated with indicators of vulnerability more often than others, for example: 

  Those  with  high  or  very  high  impulsiveness,  based  on  the  first  and  fourth 
‘impulsiveness’  measures114,  are  more  likely  to  be  vulnerable  on  a  relatively 
high  number  of  indicators,  including  indicators  in  all  dimensions;  based  on 
these results, these impulsiveness measures are the behavioural characteristics 
which, overall, are most strongly linked to vulnerability; 

  Those  who  are  more  trusting  of  people,  based  on  the  two  ‘trust’  measures115, 
are  less  likely  to  feel  a  high  level  of  vulnerability  due  to  the  factors  of  age, 
health  problems,  employment  situation  etc.  (Indicator  3  in  dimension  4)116, 

113 No results are available for Indicator 7 for the online sector since the G-S process could not 
be run for this indicator due to the low total number of respondents who are vulnerable on 
this  indicator.  Hence,  in  the  results  tables  in  this  section  no  results  are  presented  for  this 
indicator. 

114 These measures are labelled ‘Impulsiveness 1’ and ‘Impulsiveness 4’. They are based on the 
extent that respondents agree with the statements “I have a hard time breaking bad habits” 
and “I’m impulsive in the purchase decisions I take” respectively. 

115  These  measures  are  labelled  ‘Trust  1’  and  ‘Trust  2’.  They  are  based  on  the  extent  that 
respondents agree with the statements “you need to be very careful in dealing with people” 
and “I believe most people can be trusted”. 

116 Indicator 3 is a scale from 1 (low feeling of vulnerability) to 4 (high feeling of vulnerability), 
which  is  equal  to  the  highest  extent  that  respondents  feel  vulnerable  due  to  the  individual 
reasons  of  health  problems,  financial  circumstances,  employment  situation,  age,  belonging 
to a minority group, personal issues, or other reasons. 

198 

 
 
                                                 
6 │ Drivers of and factors linked to consumer vulnerability 

and those who are more trusting on the first trust measure (in particular) are 
also  less  likely  to  feel  a  high  level  of  vulnerability  due  to  the  complexity  of 
offers (Indicator 12 in dimension 5); 

  There  are  relationships  between  the  ‘credulity’  measure117  and  a  number  of 
indicators  of  vulnerability;  however,  these  are  often  in  opposite  directions 
across the various indicators; 

  Those who identified the best interest rate for a savings account are less likely 
to be vulnerable on a relatively high number of indicators and performed better 
in  the  experiments  overall,  as  did  those  who  answered  the  two  computation 
test questions correctly118. 

It should be noted that two variables among the explanatory variables discussed in this 
section  (‘Credulity  test:  Not  taken’  and  ‘Risk  taking:  No  response’)  are  dummy 
variables representing that respondents did not answer a particular question.119 Hence 
these  variables  have  no  meaning  with  respect  to  respondents’  behavioural 
characteristics  and  results  for  these  variables  can  be  interpreted  as  capturing  the 
effects of other characteristics that are correlated with them. 

In  the  following  subsections,  the  relationships  between  consumers’  behavioural 
characteristics  and  each  dimension  of  vulnerability  are  explored  in  more  detail, based 
on  the  survey  and  experiment  results  with  additional  insights  drawn  from  the 
literature.  As  in  the  previous  section,  reporting  and  discussion  of  the  data  analysis 
results  below  is  limited  to  statistically  significant  results  only.  The  full  set  of 
explanatory variables examined in the G-S analysis is presented Table 7 in section 3.7. 
This  table  also  indicates  which  variables  (i.e.  which  levels  or  categories)  are  used  as 
bases in the analysis. 

117 This measure is labelled ‘Credulity’. It is based on the extent that respondents agree with the 
statement  “most  advertisements  report  objective  fact,  I  trust  most  of  the  information 
provided in advertisements”. 

118  The  computation  questions  (Q23.1  and  Q23.2)  both  asked  respondents  “which  shop  is 
cheaper”  in  a  hypothetical  scenario  where  two  shops  offer  the  same  product  at  different 
prices, with and without a discount. 

119  For  example,  not  all  respondents  answered  the  credulity  test  question  since  for  online 
respondents it involved listening to an audio  recording  which some were unable to play, so 
the ‘Not taken’ variable is included in order to retain in the analysis all other (more useful) 
information from these respondents. 

199 

 
 
 
                                                 
6 │ Drivers of and factors linked to consumer vulnerability 

Figure 28:  Matrix of behavioural characteristics linked to survey-based indicators of vulnerability 

200 

Dimension:D1D2D3D4D5D4Feature:Sector:----Indicator:12345675675678a8b8c8a8b8c8a8b8c9a9b9c9a9b9c9a9b9c9d9e10a10b1112b12expCharacteristic / DriverUnassertiveOverpaidFeels vulnerableFeels uninformedUses adverts onlyProbl's comparingHasn't switchedUses adverts onlyProbl's comparingHasn't switchedUses adverts onlyProbl's comparingHasn't switchedPersonal factorsMarket factorsAccess factorsPersonal factorsMarket factorsAccess factorsPersonal factorsMarket factorsAccess factorsPersonal factorsMarket factorsAccess factorsPersonal factorsMarket factorsAccess factorsPersonal factorsMarket factorsAccess factorsTermination costBundled offersDifficult processPayment cardDeclined for loanVery vulnerableVulnerable scaleExperimentTrust 1: Low0.828***1.186***-0.0190***-0.00474*-0.0269***-0.0236***-0.000730-0.00743-0.0127-0.001250.00492-0.00656-0.0329***-0.0230***0.827***Trust 1: High0.701***1.358***-0.0225***0.00326-0.0503***-0.0256***0.00554*0.0219-0.0534***0.003860.00456-0.0280***-0.0438***-0.0251***0.640***Trust 1: Very high0.561***1.474***-0.0229-0.00126-0.0456**-0.0211*0.0107*0.0108-0.0255-0.00311-0.0203**-0.0123-0.0454***-0.0248***0.505***Trust 2: Low-0.004610.855***6.07e-05-0.00223-0.01930.001490.0138-0.00974-0.00779*Trust 2: High-0.001270.769***-0.0210**-0.000567-0.0343***0.00544-0.00526-0.00523-0.00960**Trust 2:Very high0.009030.892*-0.00437-0.0108**-0.01450.0198**-0.001100.0382**-0.000271Credulity test: Incorrect1.143***-0.0144**-0.0363***-0.0158***-0.0292***-0.0202*0.0148***-0.00791**-0.00838***0.879***Credulity test: Not taken1.174***0.0158*0.0198*0.0284***0.0237*0.001900.00974-0.0127***-0.006360.926*Credulity: Low 0.0448***0.0007000.9801.0120.00985**-0.0142**0.00825**0.0121***-0.0139***0.00609*-0.0231***-0.0199**0.00506-0.00224-0.0216***-0.00810***-0.0407***0.0213**-0.0005030.007770.00434-0.0226***1.041Credulity: High0.0855***0.0160***1.093**0.857***0.0239***-0.0368***0.0101*0.0209***-0.0291***0.0198***-0.0349***-0.0772***-0.0180**0.00720-0.0408***-0.0122***-0.0781***0.00972-0.0257**0.0232***0.0213***-0.0215***1.070**Credulity: Very high0.03880.0589***1.589***0.579***0.0418**-0.0432***0.006570.0267**-0.0173**0.0164-0.0110-0.0543**-0.004500.000706-0.0245-0.00869-0.0830***-0.0249-0.02280.0329**0.0768***0.0163**1.273***Impulsiveness 1: Low 0.001280.9960.004130.0148-0.01150.0666***-0.005740.002520.0146-0.02040.0274*-0.004150.004720.0276***0.0154-0.00172-0.005291.042Impulsiveness 1: High0.0166***1.068-0.002010.01350.0255*0.0912***-0.0243***0.0191**0.0404***0.01430.0572***-0.0360***0.0137*0.0357***0.0441***0.0202**-0.002191.131***Impulsiveness 1: Very high0.0318***1.348***-6.54e-050.0337***0.0406**0.140***-0.006850.0429***0.0661***0.0385**0.0667***0.02440.0265***0.0632***0.0772***0.0432***0.0127**1.168***Impulsiveness 2: Low 1.0161.111***-0.00177-0.0210**-0.005690.005680.00158-0.0292***-0.0217***0.966Impulsiveness 2: High1.072*1.188***0.00496-0.00276-0.0006270.0200**0.0116**-0.00772-0.0191***0.989Impulsiveness 2: Very high1.0671.243***0.0225*-0.005450.01130.00375-0.007710.0320-0.0181***0.850**Impulsiveness 3: Low 1.284***-0.0009110.0127-0.001890.00829-0.00893**0.00367-0.00221-0.0142**-0.00368-0.0112***Impulsiveness 3: High1.570***0.00207-0.0008910.004380.0294**-0.0111***0.00744**0.0105-0.0156*0.0111-0.00754*Impulsiveness 3: Very high1.661***0.0153-0.01860.008770.0243-0.009940.0108*0.02930.005720.00704-0.00627Impulsiveness 4: Low 0.0124**1.073**1.216***0.002070.003560.0109-0.000961-0.0184-0.01220.007740.0128*0.005910.009360.00944-0.006371.144***Impulsiveness 4: High0.0323***1.117***1.185***0.00940*0.01890.0174**0.00306-0.0374***0.003430.0196***0.0280***0.0244**0.0286***0.0186**0.0003511.127***Impulsiveness 4: Very high0.0479***1.279***1.0730.0254***0.0603***0.0378***-0.00981*-0.01440.01770.0349***0.0684***0.0285**0.0257**0.01010.0131**1.231***Risk taking: Not very 0.781***0.959-0.01360.0543***-0.0211-0.005950.00182-0.0374**0.0534**0.00848*0.0315**0.01390.00967**0.000757-0.0157-0.0332**-0.0227***0.677***Risk taking: Fairly0.703***0.862-0.0340*0.0638***-0.0367**-0.0162-0.00527-0.0341*0.0694***0.008220.0416**0.0326***0.0135**0.02390.0115-0.0158-0.0257***0.620***Risk taking: Very 0.641***0.415***0.05680.108***0.008680.01160.00915-0.0827***-0.01090.001050.0635*0.04580.0009650.0635*-0.0494*0.0357-0.0261**0.671***Risk taking: No response0.632***0.9180.0314-0.0509***0.01590.03190.00893-0.0353*0.0664***0.00470-0.0147-0.0387***0.003820.0104-0.0352*-0.0471***0.538***Computation test 1 correct -0.00934**0.839***-0.00995**0.0417***-0.0192**0.0270***0.0498***0.0258**-0.0182**Computation test 2 correct -0.0655***0.897***0.0164**-0.0194***0.0168***0.0248***0.0229***0.0396***-0.0340***-0.0207**Knows meaning of kWh0.9670.0387***0.0387***Knows meaning of Mbps0.937**-0.0164***-0.00608**-0.0161***-0.0516***0.0160***-0.0349***0.00907***-0.0259***-0.00350*0.0650***0.0125**-0.00759**0.932***Indentified best interest rate-0.0317**-0.0125***0.848***0.0393***-0.0199**0.0414***-0.00332*-0.0296***0.0225**-0.0212***-0.0130***-0.0150***-0.0221***-0.0150***-0.0123***-0.0119***0.924***FinanceEnergyOnlineFinanceEnergyOnlineInformation problemsProblems comparingEnergyOnline-e-com-Not switchedExcludedOffersFinance- 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

6.3.1.  Dimension 1: Heightened risk of negative outcomes or impacts on well-
being 

Suffering from the behavioural biases and characteristics outlined in section  6.3 could 
put consumers at increased risk of negative outcomes in markets. For example, biases 
that distort consumers’ choices between products, such as using rules of thumb, status 
quo  bias  or  framing  effects,  may  cause  them  to  make  import  purchasing  decisions 
suboptimally, resulting in negative outcomes and consequences for their well-being. 

Results of G-S regression analysis of the survey data, presented in Table 33, show that 
some  behavioural  characteristics  measured  via  the  survey  are  related  to  indicators  of 
vulnerability in this dimension. 

In particular, compared to the base group of those with very low credulity based on the 
‘credulity’  measure120,  those  with  low,  high  or  very  high  credulity  are  all  found  to  be 
more likely to be vulnerable on at least one indicator in this dimension. 

For  example,  those  with  high  credulity  are  more  likely  to  be  unassertive  when  they 
experience  a  problem  (Indicator  1),  with  a  marginal  effect  of  8.6  percentage  points 
(pp) relative to the base group, whereas those with very high credulity are more likely 
to overpay for services due to being unable to use certain payment methods (Indicator 
2), with a marginal effect of 5.9pp compared to the base group. 

In  addition,  those  with  high  or  very  high  impulsiveness  based  on  the  fourth 
‘impulsiveness’ measure121 are more likely than those with very low impulsiveness (the 
base  group)  to  overpay  for  services  due  to  being  unable  to  use  certain  payment 
methods  (Indicator  2),  with  marginal  effects  of  3.2pp  and  4.8pp  respectively. 
Moreover,  similar  results  (of  a  smaller  magnitude)  are  also  found  for  the  first 
‘impulsiveness’ measure.122 

Finally,  those  who  answered  some  of  the  ‘knowledge  and  ability’  questions  correctly, 
namely  the  first  and  second  computation  questions123  and  the  question  which  asked 
respondents  to  identify  the  best  interest  rate  on  a  savings  account,  are  found  to  be 
less likely to be vulnerable on at least one indicator in this dimension. 

120 This measure is labelled ‘Credulity’. It is based on the extent that respondents agree with the 
statement  “most  advertisements  report  objective  fact,  I  trust  most  of  the  information 
provided in advertisements”. 

121 This measure is labelled ‘Impulsiveness 4’. It is based on the extent that respondents agree 

with the statement “I’m impulsive in the purchase decisions I take”. 

122 This measure is labelled ‘Impulsiveness 1’. It is based on the extent that respondents agree 

with the statement “I have a hard time breaking bad habits”. 

123  The  computation  questions  (Q23.1  and  Q23.2)  both  asked  respondents  “which  shop  is 
cheaper”  in  a  hypothetical  scenario  where  two  shops  offer  the  same  product  at  different 
prices, with and without a discount. 

201 

 
 
 
                                                 
6 │ Drivers of and factors linked to consumer vulnerability 

Table 33: 

Impact of behavioural characteristics on indicators in Dimension 

1 and Dimension 2 

Dimension 1 

Dimension 2 

Indicator 1: 
Unassertive if 
experiences 
problem 

Indicator 2: 
Overpaid for 
services 

Indicator 3: 
Maximum feeling 
of vulnerability 

- 
- 
- 
- 
- 
- 
- 
- 
0.0448*** 
0.0855*** 
0.0388 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
-0.0655*** 
- 
- 
-0.0317** 
7,826 
0.0929 
Marginal 

Trust 1: Low 
Trust 1: High 
Trust 1: Very high 
Trust 2: Low 
Trust 2: High 
Trust 2: Very high 
Credulity test: Incorrect 
Credulity test: Not taken 
Credulity: Low  
Credulity: High 
Credulity: Very high 
Impulsiveness 1: Low  
Impulsiveness 1: High 
Impulsiveness 1: Very high 
Impulsiveness 2: Low  
Impulsiveness 2: High 
Impulsiveness 2: Very high 
Impulsiveness 3: Low  
Impulsiveness 3: High 
Impulsiveness 3: Very high 
Impulsiveness 4: Low  
Impulsiveness 4: High 
Impulsiveness 4: Very high 
Risk taking: Not very  
Risk taking: Fairly 
Risk taking: Very  
Risk taking: No response 
Computation test 1 correct  
Computation test 2 correct  
Knows meaning of kWh 
Knows meaning of Mbps 
Identified best interest rate 
N 
R2 
Marginal effect / Odds ratio 
Indicator code 
Note: Values in this table are results of general to specific (G-S) regression analysis. The G-S methodology is 
described in Annex 8. “-” implies that the explanatory variable was not included in the final regression model 
is  statistically  significant  at 
established  via  the  G-S  process.  */**/*** 
90%/95%/99%.For each the indicator in this table, the R2 in the table is the Pseudo-R2 for the full regression 
model,  which  includes  the  explanatory  variables  shown  in  this  table  and  other  explanatory  variables  (not 
shown  in  this  table)  that  were  identified  for  the  regression  model  based  on  the  G-S  methodology.  The 
indicators are defined in Table 5. The explanatory variables are defined in Table 7 and A8.1.2. 
Source: London Economics analysis of the survey data 

- 
- 
- 
-0.0046 
-0.0013 
0.00903 
- 
- 
0.0007 
0.0160*** 
0.0589*** 
0.0013 
0.0166*** 
0.0318*** 
- 
- 
- 
- 
- 
- 
0.0124** 
0.0323*** 
0.0479*** 
- 
- 
- 
- 
-0.00934** 
- 
- 
- 
-0.0125*** 
26,653 
0.1418 
Marginal 

0.828*** 
0.701*** 
0.561*** 
0.855*** 
0.769*** 
0.892* 
- 
- 
0.980 
1.093** 
1.589*** 
0.996 
1.068 
1.348*** 
1.016 
1.072* 
1.067 
- 
- 
- 
1.073** 
1.117*** 
1.279*** 
0.781*** 
0.703*** 
0.641*** 
0.632*** 
- 
- 
- 
- 
0.848*** 
26,653 
0.1227 
Marginal 

implies  that  a  result 

2 

3 

1 

202 

 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

6.3.2.  Dimension 2: Having characteristics that limit ability to maximise well-
being 

One survey-based indicator of vulnerability in the second dimension is analysed via the 
G-S  regression  approach.  This  indicator,  Indicator  3,  represents  the  maximum  extent 
that respondents feel vulnerable due to  any of the personal characteristics mentioned 
in  the  relevant  survey  question  (i.e.  health  problems,  financial  circumstances, 
employment  situation,  age,  belonging  to  a  minority  group,  personal  issues,  or  ‘other’ 
reasons) on a scale from 1 (low) to 4 (high). 

According to the results of the G-S regression analysis of the survey data presented in 
Table  33  above,  some  behavioural  characteristics  are  related  to  this  indicator  of 
vulnerability. 

Consumers  who  are  more  trusting  of  people  based  on  the  two  ‘trust’  measures124  are 
less  likely  to  feel  a  high  level  of  vulnerability.  For  example,  the  odds  ratio  for  those 
with  very  high  trust  on  the  first  trust  measure  is  0.56,  the  lowest  odds  ratio  for  this 
indicator among all the behavioural characteristics explored in the analysis.125 

Those  with  high  or  very  high  credulity  based  on  the  credulity  measure126  are  more 
likely  to  feel  a  high  level  of  vulnerability.  The  odds  ratio  for  those  with  very  high 
credulity  is  1.59,  the  largest  odds  ratio  of  any  behavioural  characteristic  examined  in 
the analysis.127 

Consumers  with  high  or  very  high  impulsiveness  on  the  fourth  ‘impulsiveness’ 
measure128,  and  those  with  very  high  impulsiveness  on  the  first  ‘impulsiveness’ 
measure129,  are  more  likely  to  feel  a  high  level  of  vulnerability,  with  odds  ratios  of 
1.12, 1.28  and 1.35 respectively.  

6.3.3.  Dimension 3: Having difficulty in obtaining or assimilating information 

Several  of  the  behavioural  biases  and  characteristics  reported  in  the  literature  and 
outlined  in  section  6.3  may  be  expected  to  impact  on  consumers’  ability  to  obtain, 
understand  and  use  information.  For  example,  cognitive  limitations  can  restrict 
consumers’ ability to assimilate and process information, making use of heuristics such 
as  rules  of  thumb  rather  than  fully  assessing  available  options  can  be  driven  by 
excessive or overly complex information, the bandwagon effect may cause consumers 

124  These  measures  are  labelled  ‘Trust  1’  and  ‘Trust  2’.  They  are  based  on  the  extent  that 
respondents agree with the statements “you need to be very careful in dealing with people” 
and “I believe most people can be trusted”. 

125  Recall  that  an  odds  ratio  below  one  implies  a  lower  likelihood  of  a  higher  score  on  a  scale 

dependent variable (i.e. in this case a lower likelihood of higher vulnerability). 

126 This measure is labelled ‘Credulity’. It is based on the extent that respondents agree with the 
statement  “most  advertisements  report  objective  fact,  I  trust  most  of  the  information 
provided in advertisements”. 

127  Recall  that  an  odds  ratio  above  one  implies  a  higher  likelihood  of  a  higher  score on  a  scale 

dependent variable (i.e. in this case a higher likelihood of higher vulnerability). 

128 This measure is labelled ‘Impulsiveness 4’. It is based on the extent that respondents agree 

with the statement “I’m impulsive in the purchase decisions I take”. 

129 This measure is labelled ‘Impulsiveness 1’. It is based on the extent that respondents agree 

with the statement “I have a hard time breaking bad habits”. 

203 

 
 
 
                                                 
6 │ Drivers of and factors linked to consumer vulnerability 

to  not  use  information  they  have  in  favour  of  following  social  norms,  and 
overconfidence and projection bias can distort consumers’ use of information. 

Results  of  G-S  regression  analysis  of  the  survey  data,  presented  in  Table  34  below, 
show that most of the behavioural characteristics examined in the survey have a mixed 
impact  on  indicators  of  vulnerability  in  this  dimension,  with  differences  across 
indicators  and  sectors.  This  can  be  seen  most  clearly  from  the  matrix  of  results 
presented in Figure 28 at the start of this section, as well as from the table below. 

According to these results, the indicator in this dimension with the highest number  of 
statistically significant relationships with the behavioural characteristics explored in the 
survey  is  Indicator  4,  which  represents  the  extent  that  consumers  feel  uninformed 
about prices, quality and conditions when buying and choosing products.130 

For example, the results for this indicator show that consumers who have high or very 
high  trust in  people  based  on  the  first  ‘trust’  measure131  (odds  ratios  1.36  and  1.47), 
those who answered the credulity test incorrectly (odds ratio 1.14)132, and those with 
high  or  very  high  impulsiveness  on  the  second  and  third  ‘impulsiveness’  measures 133 
(odds ratios 1.19, 1.24, 1.57 and 1.66) are more likely to feel uninformed. 

Conversely,  those  with high  or  very  high  credulity  on  the  ‘credulity’  measure134  (odds 
ratios  0.86  and  0.58),  those  who  are  very  risk  taking  on  the  risk  taking  measure135 
(odds  ratio  0.42),  those  who  answered  the  first  and  second  computation  questions136 
correctly  (odds  ratios  0.84  and  0.90),  and  those  who  know  the  meaning  of  the  term 
“Mbps” (odds ratio 0.94) are less likely to feel highly uninformed. 

High credulity on the credulity measure is the characteristic with the largest number of 
statistically  significant  relationships  with indicators  in  this  dimension  (eight  out  of  ten 
indicators).  In  addition  to  the  link  with  Indicator  4  noted  above,  those  with  high 
credulity  are  less  likely  to  have  had  problems  comparing  deals  due  to  information-

130 The G-S results reveal 19 statistically significant relationships between this indicator and the 

behavioural characteristics. 

131 This measure is labelled ‘Trust 1’. It is based on the extent that respondents agree with the 

statements “you need to be very careful in dealing with people”. 

132 The ‘credulity test’ question told respondents to consider the radio advertisement “Sign-up to 
BEACH  BREAKS  and  receive  a  free  pair  of  sunglasses.  Sunglasses  delivered  when  you 
purchase  one  of  our  beach  holidays”,  and  then  asked  them  whether:  1.  the  advertisement 
offers  free  sunglasses  to  all  who  sign-up  to  beach  breaks,  2.  the  advertisement  offers 
sunglasses only to those who buy a holiday from beach breaks, or 3. they do not know which 
answer is correct. Those who identified the second answer as the correct response answered 
the credulity test question correctly. 

133 These measures are labelled ‘Impulsiveness 2’ and ‘Impulsiveness 3’. They are based on the 
extent  that  respondents  agree  with  the  statements  “I’m  good  at  resisting  temptation”  and 
“people would say that I have very strong self-discipline” respectively. 

134 This measure is labelled ‘Credulity’. It is based on the extent that respondents agree with the 
statement  “most  advertisements  report  objective  fact,  I  trust  most  of  the  information 
provided in advertisements”. 

135 This measure is labelled ‘Risk taking’. It is based on whether respondents say that they are 

“not at all”, “not very”, “fairly” or “very” willing to take risk. 

136  The  computation  questions  (Q23.1  and  Q23.2)  both  asked  respondents  “which  shop  is 
cheaper”  in  a  hypothetical  scenario  where  two  shops  offer  the  same  product  at  different 
prices, with and without a discount. 

204 

 
 
                                                 
6 │ Drivers of and factors linked to consumer vulnerability 

related  factors  in  all  three  key  sectors  (with  marginal  effects  of  -3.7pp,  -2.9pp  and  -
3.5pp), but are more likely to only get information from adverts only when comparing 
deals in the energy, online and finance sectors (with marginal effects  of 2.4pp, 2.1pp 
and  2.0pp),  and  are  more  likely  to  have  been  prevented  from  switching  due  to 
information-related factors in the energy sector (with a marginal effect of 1.0pp). 

205 

 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

Table 34: 

Impact of behavioural characteristics on indicators in Dimension 3; Having difficulty obtaining and 

assimilating information 

Energy sector 

Online sector 

Finance sector 

Indicator 
4: 

Feels 
uninforme
d about 
prices 

Indicator 
5: 

Gets info. 
from 
adverts 
only 

Indicator 
6: 

Problems 
comparing 
due to 
info. 
factors 

Indicator 
7: 

Not 
switched 
due to 
info. 
factors 

Indicator 
5: 

Gets info. 
from 
adverts 
only 

Indicator 
6: 

Problems 
comparing 
due to 
info. 
factors 

Indicator 
7: 

Not 
switched 
due to 
info. 
factors 

Indicator 
5: 

Gets info. 
from 
adverts 
only 

Indicator 
6: 

Problems 
comparing 
due to 
info. 
factors 

Indicator 
7: 

Not 
switched 
due to 
info. 
factors 

Trust 1: Low 
Trust 1: High 
Trust 1: Very high 
Trust 2: Low 
Trust 2: High 
Trust 2:Very high 
Credulity test: Incorrect 
Credulity test: Not taken 
Credulity: Low  
Credulity: High 
Credulity: Very high 
Impulsiveness 1: Low  
Impulsiveness 1: High 
Impulsiveness 1: Very high 
Impulsiveness 2: Low  
Impulsiveness 2: High 
Impulsiveness 2: Very high 
Impulsiveness 3: Low  
Impulsiveness 3: High 
Impulsiveness 3: Very high 
Impulsiveness 4: Low  
Impulsiveness 4: High 
Impulsiveness 4: Very high 
Risk taking: Not very  
Risk taking: Fairly 
Risk taking: Very  
Risk taking: No response 
Computation test 1 correct  
Computation test 2 correct  
Knows meaning of kWh 
Knows meaning of Mbps 
Identified best interest rate 
N 
R2 

Marginal effect / Odds ratio 

Indicator code 

1.186*** 
1.358*** 
1.474*** 
- 
- 
- 
1.143*** 
1.174*** 
1.012 
0.857*** 
0.579*** 
- 
- 
- 
1.111*** 
1.188*** 
1.243*** 
1.284*** 
1.570*** 
1.661*** 
1.216*** 
1.185*** 
1.073 
0.959 
0.862 
0.415*** 
0.918 
0.839*** 
0.897*** 
0.967 
0.937** 
- 
26,653 
0.0574 

- 
- 
- 
- 
- 
- 
- 
- 
0.00985** 
0.0239*** 
0.0418** 
- 
- 
- 
-0.00177 
0.00496 
0.0225* 
-0.000911 
0.00207 
0.0153 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
-0.0164*** 
- 
9,793  
0.0972 

Odds ratio 

4 

Marginal 

5_adv_ene 

-0.0190*** 
-0.0225*** 
-0.0229 
0.00006 
-0.0210** 
-0.00437 
-0.0144** 
0.0158* 
-0.0142** 
-0.0368*** 
-0.0432*** 
- 
- 
- 
-0.0210** 
-0.00276 
-0.00545 
0.0127 
-0.000891 
-0.0186 
- 
- 
- 
- 
- 
- 
- 
- 
0.0164** 
- 
- 
- 
15,481  
0.0756 

Marginal 

6_ene 

-0.00474* 
0.00326 
-0.00126 
- 
- 
- 
- 
- 
0.00825** 
0.0101* 
0.00657 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
-0.00608** 
- 
4,481  
0.1461 

Marginal 

7_ene 

- 
- 
- 
- 
- 
- 
- 
- 
0.0121*** 
0.0209*** 
0.0267** 
- 
- 
- 
- 
- 
- 
- 
- 
- 
0.00207 
0.00940* 
0.0254*** 
- 
- 
- 
- 
- 
-0.0194*** 
- 
-0.0161*** 
- 
16,451  
0.0890 

Marginal 

5_adv_onl 

- 
- 
- 
- 
- 
- 
- 
- 
-0.0139*** 
-0.0291*** 
-0.0173** 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
19,913  
0.1217 

Marginal 

6_onl 

- 
- 
- 
-0.00223 
-0.000567 
-0.0108** 
- 
- 
0.00609* 
0.0198*** 
0.0164 
0.00413 
-0.00201 
-0.00007 
-0.00569 
-0.000627 
0.0113 
-0.00189 
0.00438 
0.00877 
- 
- 
- 
- 
- 
- 
- 
-0.00995** 
- 
- 
- 
- 
10,228  
0.0925 

Marginal 

7_onl 

Marginal 

5_adv_fin 

- 
- 
- 
- 
- 
- 
- 
- 
-0.0231*** 
-0.0349*** 
-0.0110 
0.0148 
0.0135 
0.0337*** 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
0.0168*** 
- 
- 
- 
15,481  
0.0859 

Marginal 

6_fin 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
3,795  
0.1374 

Marginal 

7_fin 

206 

 
 
 
 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

Note: Values in this table are results of general to specific (G-S) regression analysis.  The G-S methodology is described in Annex 8. “-” implies that the explanatory variable 
was  not  included  in  the  final  regression  model  established  via  the  G-S  process.  */**/***  implies  that  a  result  is  statistically  significant  at  90%/95%/99%.  For  each  the 
indicator in this table, the R2 in the table is the Pseudo-R2 for the full regression model, which includes the explanatory variables shown in this table and other explanatory 
variables  (not  shown  in  this  table)  that  were  identified  for  the  regression  model  based  on  the  G-S  methodology.  The  indicators  are  defined  in  Table  5.  The  explanatory 
variables are defined in Table 7 and A8.1.2. There are no results for Indicator 7 for the online sector because the G-S process could not be run for this indicator due to low 
variation in the dependent variable. 
Source: London Economics analysis of the survey data 

207 

 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

6.3.4.  Dimension 4: Inability or failure to buy, choose or access suitable 
products 

Many  of  the  behavioural  biases  and  characteristics  identified  in  the  literature  could 
impact on consumers’ ability to buy, choose or access suitable products in markets. For 
example, status quo bias may lead consumers to stay with their current provider when 
it  would  be  better  to  switch,  time  discounting  can  impact  on  consumers’  decisions 
concerning  products  with  costs  and  benefits  over  time  (e.g.  in  the  financial  sector), 
basing decisions on heuristics such as rules of thumb could lead to suboptimal choices, 
and overconfidence and projection bias may result in unsuitable product selections. 

The  results  of  the  behavioural  experiments  are  particularly  relevant  to  this  dimension 
of  vulnerability  since  the  experiments  tested  consumers’  ability  to  choose  the  optimal 
offer  in  simulated  purchasing  scenarios  in  the  three  key  markets  of  the  study. 
Therefore,  in  this  section  we  draw  on  the  results  of  G-S  regression  analysis  of 
respondents’ choices in the experiments, which are presented in Table 35.  

In  terms  of  overall  performance  in  the  experiments,  these  results  show  that  the 
credulity,  measured  via  the  credulity  test  question137,  is  one  of  the  most  important 
characteristics.  In  particular,  respondents  who  answered  the  credulity  test  question 
incorrectly  performed  worse  overall,  being  less  likely  to  select  the  optimal  deals  on 
average across the various rounds of the experiments (with an odds ratio of 0.82). 

Other  strong  results  from  the  experiment  data  relate  to  the  ‘knowledge  and  ability’ 
measures.  Specifically,  consumers  who  answered  the  computation  questions138 
correctly,  especially  the  second  computation  question,  and  those  who  correctly 
identified  the  best  interest  rate  for  a  savings  account  performed  better  in  the 
experiments  overall  (odds  ratios  1.15,  1.41  and  1.51),  being  more  likely  to  choose 
optimally on average over the various rounds of the experiments. 

Moreover,  respondents  who  answered  the  second  computation  question  correctly  and 
those  who  identified  the  best  interest  rate  performed  better  in  multiple  different 
experiment  rounds.  For  example,  those  who  identified  the  correct  interest  rate  were 
more  likely  to  choose  the  optimal  deal  in  the  second  round  of  the  energy  sector 
experiment,  both  rounds  of  the  online  sector  experiment,  both  rounds  of  the  finance 
sector  experiment,  and  both  rounds  of  the  cross-cutting  experiment  (i.e.  in  all 
experiment rounds except the first round of the energy sector experiment). 

137 The ‘credulity test’ question told respondents to consider the radio advertisement “Sign-up to 
BEACH  BREAKS  and  receive  a  free  pair  of  sunglasses.  Sunglasses  delivered  when  you 
purchase  one  of  our  beach  holidays”,  and  then  asked  them  whether:  1.  the  advertisement 
offers  free  sunglasses  to  all  who  sign-up  to  beach  breaks,  2.  the  advertisement  offers 
sunglasses only to those who buy a holiday from beach breaks, or 3. they do not know which 
answer is correct. Those who identified the second answer as the correct response answered 
the credulity test question correctly. 

138  The  computation  questions  (Q23.1  and  Q23.2)  both  asked  respondents  “which  shop  is 
cheaper”  in  a  hypothetical  scenario  where  two  shops  offer  the  same  product  at  different 
prices, with and without a discount. 

208 

 
 
                                                 
Table 35: 

Impact of behavioural characteristics on experiment performance 

Energy Sector 

Online Sector 

Finance Sector 

Cross-cutting 

6 │ Drivers of and factors linked to consumer vulnerability 

Round 1 
0.0425** 
-0.000678 
0.0216 
-0.0124 
-0.0116 
0.0729** 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

2,522 
0.0318 
Marginal 

Round 2 
0.0220 
0.0200 
-0.104** 
0.00942 
0.0478 
0.0176 
- 
- 
-0.00715 
-0.0250 
-0.106** 
- 
- 
- 
- 
- 
- 
- 
- 
- 
0.0184 
-0.0317 
-0.0733* 
- 
- 
- 
- 
- 
0.0544** 
- 
- 
0.0504** 
2,522 
0.0710 
Marginal 

Round 1 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
0.0897*** 
0.0616** 
- 
- 
0.0436** 
2,510 
0.1061 
Marginal 

Round 2 
- 
- 
- 
0.0616** 
0.0754** 
0.0141 
- 
- 
-0.00885 
-0.0735** 
-0.0483 
-0.00619 
0.0419 
-0.0419 
- 
- 
- 
- 
- 
- 
- 
- 
- 
-0.0211 
-0.0847** 
-0.159*** 
0.0868 
- 
0.164*** 
- 
0.0957*** 
0.0921*** 
2,510 
0.0823 
Marginal 

Round 1 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
0.0153 
0.00260 
-0.0633* 
- 
- 
- 
-0.0209 
-0.0226 
-0.114*** 
- 
- 
- 
- 
0.0416** 
0.0829*** 
- 
- 
0.0382*** 
2,505 
0.1191 
Marginal 

Round 2 
- 
- 
- 
- 
- 
- 
-0.0323** 
0.00747 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
0.00339 
0.0132 
-0.0940** 
- 
- 
- 
- 
0.0518** 
0.0592*** 
- 
- 
0.0681*** 
2,505 
0.1205 
Marginal 

Round 1 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
0.0479* 
0.00737 
-0.0179 
- 
- 
- 
- 
- 
- 
- 
- 
0.0766*** 
2,565 
0.0237 
Marginal 

Round 2 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
0.0932*** 
- 
- 
0.146*** 
2,565 
0.0628 
Marginal 

Overall 

1.161** 
0.987 
0.782 
- 
- 
- 
0.820*** 
1.020 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
1.067 
1.012 
0.826 
0.977 
0.840* 
0.861 
1.138 
1.152* 
1.411*** 
- 
- 
1.505*** 
5,051 
0.0421 
Odds ratio 

Trust 1: Low 
Trust 1: High 
Trust 1: Very high 
Trust 2: Low 
Trust 2: High 
Trust 2:Very high 
Credulity test: Incorrect 
Credulity test: Not taken 
Credulity: Low  
Credulity: High 
Credulity: Very high 
Impulsiveness 1: Low  
Impulsiveness 1: High 
Impulsiveness 1: Very high 
Impulsiveness 2: Low  
Impulsiveness 2: High 
Impulsiveness 2: Very high 
Impulsiveness 3: Low  
Impulsiveness 3: High 
Impulsiveness 3: Very high 
Impulsiveness 4: Low  
Impulsiveness 4: High 
Impulsiveness 4: Very high 
Risk taking: Not very  
Risk taking: Fairly 
Risk taking: Very  
Risk taking: No response 
Computation test 1 correct  
Computation test 2 correct  
Knows meaning of kWh 
Knows meaning of Mbps 
Identified best interest rate 
N 
R2 
Marginal effect / Odds ratio 

209 

 
 
 
 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

Note: Values in this table are results of general to specific (G-S) regression analysis. The G-S methodology is described in  Annex 8.“-” implies that the variable was not 
included in the final regression model established via the G-S process. */**/*** implies that a result is statistically significant at 90%/95%/99%. For each the indicator in 
this table, the R2 in the table is the Pseudo-R2 for the full regression model, which includes the explanatory variables shown in this table and other explanatory variables 
(not shown in this table) that were identified for the regression model based on the G-S methodology.  The indicators are defined in Table 5. The explanatory variables are 
defined in Table 7 and A8.1.2. The dependent variables in the experiment analysis signify whether the respondent chose the optimal deal. For individual experiment rounds, 
the dependent variable is equal to one if the respondent chose the optimal deal, or zero otherwise. The ‘Overall’ performance measure is the total number of correct choices 
made by the respondent (ranging from 0 to 4). Thus, positive marginal effects imply a higher likelihood of choosing the optimal deal, and odds ratios greater than one imply 
better overall performance. 
Source: London Economics analysis of the survey data 

210 

 
 
6 │ Drivers of and factors linked to consumer vulnerability 

Results from G-S regression analysis of the survey data, presented in  Table 36, Table 
37 and Table 38 below, show that some of the behavioural characteristics measured in 
the survey are linked with some indicators of vulnerability in this dimension. However, 
the overall picture is quite diverse, with few statistically significant results for a number 
of  characteristics  and  indicators.  This  can  be  seen  most  clearly  from  the  matrix 
presented in Figure 28 at the start of this section. 

Overall,  the  results  show  that  the  first  and  (to  a  lesser  extent)  fourth  impulsiveness 
measures139 are the characteristics that are most strongly linked with a higher chance 
of  vulnerability  on  indicators  in  this  dimension.  Consumers  with  either  high  or  very 
high impulsiveness on these two measures are found to be more likely to be vulnerable 
on between six and ten different indicators in this dimension.  

For  example,  those  who  are  most  impulsive  according  to  the  first  impulsiveness 
measure are more likely to have problems comparing deals due to personal factors and 
due  to  market-related  factors  in  all  three  key  sectors  (Indicator  8a  and  Indicator  8b, 
Table  36),  more  likely  to  have  not  switched  due  to  termination  costs  (Indicator  9d, 
Table  37)  and  bundling  (Indicator  9e,  Table  37),  and  to  have  been  prevented  from 
using e-commerce (Indicator 10a and Indicator 10b, Table 38). At 14pp, the marginal 
effect  for  these  consumers  for  Indicator  8b  for  the  energy  sector  (Table  36)  is  the 
largest effect among all those presented in this section. 

Conversely, those with high credulity on the credulity measure140 are often found to be 
less  likely  to  be  vulnerable  on  indicators  in  this  dimension.  Specifically,  these 
consumers  are  less  likely  to  have  problems  comparing  deals  due  to  market-related 
factors  in  all  three  key  sectors  (Indicator  8b,  Table  36),  less  likely  to  have  problems 
comparing  deals  due  to  access-related  factors  in  the  energy  and  online  sectors 
(Indicator 8c, Table 36), and less likely to have been prevented from switching in the 
energy  sector  due  to  access-related  factors  (Indicator  9c,  Table  37).  However,  these 
consumers are also found to be more likely to be vulnerable on other indicators in this 
dimension (i.e. Indicator 9d, Table 37, and Indicator 10b, Table 38). 

139 These measures are based on the extent that respondents agree with the statements “I have 
a  hard  time  breaking  bad  habits”  and  “I’m  impulsive  in  the  purchase  decisions  I  take” 
respectively.  

140 This measure is labelled ‘Credulity’. It is based on the extent that respondents agree with the 
statement  “most  advertisements  report  objective  fact,  I  trust  most  of  the  information 
provided in advertisements”. 

211 

 
 
 
 
                                                 
6 │ Drivers of and factors linked to consumer vulnerability 

Table 36: 

Impact of behavioural characteristics on Indicator 8 in Dimension 4; Has problems comparing deals due to 

personal factors, market related factors, and access-related factors 

Energy sector 

Online sector 

Finance sector 

Indicator 
8a: 
Personal 
factors 

Indicator 
8b: 
Market 
factors 

Indicator 
8c: 
Access 
factors 

Indicator 
8a: 
Personal 
factors 

Indicator 
8b: 
Market 
factors 

Indicator 
8c: 
Access 
factors 

Indicator 
8a: 
Personal 
factors 

Indicator 
8b: 
Market 
factors 

Indicator 
8c: 
Access 
factors 

- 
- 
- 
-0.0193 
-0.0343*** 
-0.0145 
-0.0363*** 
0.0198* 
- 
- 
- 
-0.0115 
0.0255* 
0.0406** 
- 
- 
- 
0.00829 
0.0294** 
0.0243 
0.00356 
0.0189 
0.0603*** 
-0.0136 
-0.0340* 
0.0568 
0.0314 
- 
- 
- 
-0.0516*** 
- 

-0.0269*** 
-0.0503*** 
-0.0456** 
- 
- 
- 
- 
- 
-0.0199** 
-0.0772*** 
-0.0543** 
0.0666*** 
0.0912*** 
0.140*** 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
0.0417*** 
- 
- 
- 
0.0393*** 

-0.0236*** 
-0.0256*** 
-0.0211* 
- 
- 
- 
-0.0158*** 
0.0284*** 
0.00506 
-0.0180** 
-0.00450 
-0.00574 
-0.0243*** 
-0.00685 
- 
- 
- 
- 
- 
- 
- 
- 
- 
0.0543*** 
0.0638*** 
0.108*** 
-0.0509*** 
- 
0.0248*** 
0.0387*** 
0.0160*** 
- 

- 
- 
- 
- 
- 
- 
- 
- 
-0.00224 
0.00720 
0.000706 
0.00252 
0.0191** 
0.0429*** 
- 
- 
- 
- 
- 
- 
0.0109 
0.0174** 
0.0378*** 
-0.0211 
-0.0367** 
0.00868 
0.0159 
-0.0192** 
- 
- 
-0.0349*** 
- 

- 
- 
- 
- 
- 
- 
- 
- 
-0.0216*** 
-0.0408*** 
-0.0245 
0.0146 
0.0404*** 
0.0661*** 
0.00568 
0.0200** 
0.00375 
- 
- 
- 
- 
- 
- 
-0.00595 
-0.0162 
0.0116 
0.0319 
0.0270*** 
0.0229*** 
- 
- 
- 

- 
- 
- 
0.00149 
0.00544 
0.0198** 
- 
- 
-0.00810*** 
-0.0122*** 
-0.00869 
- 
- 
- 
0.00158 
0.0116** 
-0.00771 
-0.00893** 
-0.0111*** 
-0.00994 
-0.00096 
0.00306 
-0.00981* 
0.00182 
-0.00527 
0.00915 
0.00893 
- 
- 
- 
0.00907*** 
- 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
-0.0204 
0.0143 
0.0385** 
-0.0292*** 
-0.00772 
0.0320 
- 
- 
- 
- 
- 
- 
-0.0374** 
-0.0341* 
-0.0827*** 
-0.0353* 
- 
- 
- 
-0.0259*** 
-0.0199** 

- 
- 
- 
- 
- 
- 
-0.0292*** 
0.0237* 
-0.0407*** 
-0.0781*** 
-0.0830*** 
0.0274* 
0.0572*** 
0.0667*** 
- 
- 
- 
- 
- 
- 
-0.0184 
-0.0374*** 
-0.0144 
0.0534** 
0.0694*** 
-0.0109 
0.0664*** 
0.0498*** 
0.0396*** 
0.0387*** 
- 
0.0414*** 

-0.000730 
0.00554* 
0.0107* 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
0.00367 
0.00744** 
0.0108* 
- 
- 
- 
0.00848* 
0.00822 
0.00105 
0.00470 
- 
- 
- 
-0.00350* 
-0.00332* 

15,481  
0.1273 

Marginal 

8a_ene 

15,481  
0.1084 

Marginal 

8b_ene 

15,481  
0.1263 

Marginal 

8c_ene 

19,913  
0.1442 

Marginal 

8a_onl 

19,913  
0.1461 

Marginal 

8b_onl 

19,913  
0.0714 

Marginal 

8c_onl 

15,481  
0.1210 

Marginal 

8a_fin 

15,481  
0.1049 

Marginal 

8b_fin 

15,481  
0.0933 

Marginal 

8c_fin 

Trust 1: Low 
Trust 1: High 
Trust 1: Very high 
Trust 2: Low 
Trust 2: High 
Trust 2:Very high 
Credulity test: Incorrect 
Credulity test: Not taken 
Credulity: Low  
Credulity: High 
Credulity: Very high 
Impulsiveness 1: Low  
Impulsiveness 1: High 
Impulsiveness 1: Very high 
Impulsiveness 2: Low  
Impulsiveness 2: High 
Impulsiveness 2: Very high 
Impulsiveness 3: Low  
Impulsiveness 3: High 
Impulsiveness 3: Very high 
Impulsiveness 4: Low  
Impulsiveness 4: High 
Impulsiveness 4: Very high 
Risk taking: Not very  
Risk taking: Fairly 
Risk taking: Very  
Risk taking: No response  
Computation test 1 correct  
Computation test 2 correct  
Knows meaning of kWh 
Knows meaning of Mbps 
Identified best interest rate 
N 
R2 
Marginal effect / Odds ratio 

Indicator code 

212 

 
 
 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

Note: Values in this table are results of general to specific (G-S) regression analysis. The G-S methodology is described in Annex 8.“-” implies that the explanatory variable 
was  not  included  in  the  final  regression  model  established  via  the  G-S  process.  */**/***  implies  that  a  result  is  statistically  significant  at  90%/95%/99%.  For  each  the 
indicator in this table, the R2 in the table is the Pseudo-R2 for the full regression model, which includes the explanatory variables shown in this table and other explanatory 
variables  (not  shown  in  this  table)  that  were  identified  for  the  regression  model  based  on  the  G-S  methodology.  The  indicators  are  defined  in  Table  5.  The  explanatory 
variables are defined in Table 7 and A8.1.2. 
Source: London Economics analysis of the survey data 

Table 37: 

Impact of behavioural characteristics on Indicator 9 in Dimension 4; Has not switched due to personal factors, 

market related factors, access- related factors, termination costs, and bundling 

Energy sector 

Online sector 

Finance sector 

Indicato
r 9a: 
Persona
l factors 

Indicato
r 9b: 
Market 
factors 

Indica
tor 9c: 
Access 
factors 

Indicato
r 9a: 
Persona
l factors 

Indica
tor 9b: 
Market 
factors 

Indicato
r 9c: 
Access 
factors 

- 
- 
- 
0.0138 
-0.00526 
-0.00110 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

-0.00743 
0.0219 
0.0108 
- 
- 
- 
- 
- 
0.0213** 
0.00972 
-0.0249 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

-0.0127 
-0.0534*** 
-0.0255 
- 
- 
- 
-0.0202* 
0.00190 
-0.000503 
-0.0257** 
-0.0228 
-0.00415 
-0.0360*** 
0.0244 
- 
- 
- 
- 
- 
- 
- 
- 
- 
0.0315** 
0.0416** 
0.0635* 
-0.0147 
- 

-0.00125 
0.00386 
-0.00311 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
-0.0122 
0.00343 
0.0177 
- 
- 
- 
- 
- 

0.00492 
0.00456 
-0.0203** 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
-0.00221 
0.0105 
0.0293 
- 
- 
- 
- 
- 
- 
- 
- 

Indica
tor 9a: 
Person
al 
factors 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
0.0139 
0.0326*** 
0.0458 
-0.0387*** 
- 

Indicato
r 9b: 
Market 
factors 

Indica
tor 9c: 
Access 
factors 

Indicato
r 9d: 
Termina
tion 
costs 

Indicato
r 9e: 
Bundlin
g 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
0.0258** 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
0.00967** 
0.0135** 
0.000965 
- 
- 

- 
- 
- 
- 
- 
- 
0.0148*** 
0.00974 
0.00777 
0.0232*** 
0.0329** 
0.00472 
0.0137* 
0.0265*** 
- 
- 
- 
- 
- 
- 
0.00774 
0.0196*** 
0.0349*** 
0.000757 
0.0239 
0.0635* 
0.00382 
- 

-0.00656 
-0.0280*** 
-0.0123 
- 
- 
- 
- 
- 
- 
- 
- 
0.0276*** 
0.0357*** 
0.0632*** 
- 
- 
- 
-0.0142** 
-0.0156* 
0.00572 
0.0128* 
0.0280*** 
0.0684*** 
-0.0157 
0.0115 
-0.0494* 
0.0104 
- 

Trust 1: Low 
Trust 1: High 
Trust 1: Very high 
Trust 2: Low 
Trust 2: High 
Trust 2:Very high 
Credulity test: Incorrect 
Credulity test: Not taken 
Credulity: Low  
Credulity: High 
Credulity: Very high 
Impulsiveness 1: Low  
Impulsiveness 1: High 
Impulsiveness 1: Very high 
Impulsiveness 2: Low  
Impulsiveness 2: High 
Impulsiveness 2: Very high 
Impulsiveness 3: Low  
Impulsiveness 3: High 
Impulsiveness 3: Very high 
Impulsiveness 4: Low  
Impulsiveness 4: High 
Impulsiveness 4: Very high 
Risk taking: Not very  
Risk taking: Fairly 
Risk taking: Very  
Risk taking: No response  
Computation test 1 correct  

213 

 
 
 
 
 
 
 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

Table 37: 

Impact of behavioural characteristics on Indicator 9 in Dimension 4; Has not switched due to personal factors, 

market related factors, access- related factors, termination costs, and bundling 

Energy sector 

Online sector 

Finance sector 

Indicato
r 9a: 
Persona
l factors 

Indicato
r 9b: 
Market 
factors 

Indica
tor 9c: 
Access 
factors 

Indicato
r 9a: 
Persona
l factors 

Indica
tor 9b: 
Market 
factors 

Indicato
r 9c: 
Access 
factors 

Computation test 2 correct  
Knows meaning of kWh 
Knows meaning of Mbps 
Identified best interest rate 
 N 
 R2 
Marginal effect / Odds ratio 

- 
- 
- 
- 

-0.0340*** 
- 
- 
-0.0296*** 

4,782  

0.1283 
Marginal 

4,782  

0.0651 
Marginal 

- 
- 
- 
- 

- 
- 
0.0650*** 
0.0225** 

4,782  

0.2145 
Marginal 

- 
- 
- 
- 

3,483  

0.2094 
Marginal 

3,427  

0.0804 
Marginal 

- 
- 
0.0125** 
- 

3,265  

0.1083 
Marginal 

Indica
tor 9a: 
Person
al 
factors 

-0.0207** 
- 
- 
-0.0212*** 

4,355  

0.1330 
Marginal 

Indicato
r 9b: 
Market 
factors 

Indica
tor 9c: 
Access 
factors 

- 
- 
- 
- 

- 
- 
- 
- 

4,368  

0.0597 
Marginal 

4,095  

0.1538 
Marginal 

Indicato
r 9d: 
Termina
tion 
costs 

- 
- 
- 
-0.0130*** 

26,653  

0.1242 
Marginal 

Indicato
r 9e: 
Bundlin
g 

- 
- 
- 
-0.0150*** 

26,653  

0.0852 
Marginal 

9a_ene 

Indicator code 
Note: Values in this table are results of general to specific (G-S) regression analysis. The G-S methodology is described in Annex 8. “-” implies that the explanatory variable 
was not included in the final regression model established via the G-S process. */**/*** implies that a result is statistically significant at 90%/95%/99%. %. For each the 
indicator in this table, the R2 in the table is the Pseudo-R2 for the full regression model, which includes the explanatory variables shown in this table and other explanatory 
variables  (not  shown  in  this  table)  that  were  identified  for  the  regression  model  based  on  the  G-S  methodology.  The  indicators  are  defined  in  Table  5.  The  explanatory 
variables are defined in Table 7 and A8.1.2. 
Source: London Economics analysis of the survey data 

9e_bun 

9b_ene 

9c_ene 

9b_onl 

9a_onl 

9d_ter 

9c_onl 

9b_fin 

9a_fin 

9c_fin 

214 

 
 
 
 
 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

Table 38: 

Impact of behavioural characteristics on Indicators 10 and 11 in 

Dimension 4; Excluded from e-commerce and declined a loan 

Excluded from e-commerce due to 

Indicator 10a: 
Difficulty of the 
process 

Indicator 10b: Not 
having payment card 

Indicator 11: 
Declined a loan 

Trust 1: Low 
Trust 1: High 
Trust 1: Very high 
Trust 2: Low 
Trust 2: High 
Trust 2:Very high 
Credulity test: Incorrect 
Credulity test: Not taken 
Credulity: Low  
Credulity: High 
Credulity: Very high 
Impulsiveness 1: Low  
Impulsiveness 1: High 
Impulsiveness 1: Very high 
Impulsiveness 2: Low  
Impulsiveness 2: High 
Impulsiveness 2: Very high 
Impulsiveness 3: Low  
Impulsiveness 3: High 
Impulsiveness 3: Very high 
Impulsiveness 4: Low  
Impulsiveness 4: High 
Impulsiveness 4: Very high 
Risk taking: Not very  
Risk taking: Fairly 
Risk taking: Very  
Risk taking: No response 
Computation test 1 correct  
Computation test 2 correct  
Knows meaning of kWh 
Knows meaning of Mbps 
Identified best interest rate 
N 
R2 
Marginal effect / Odds ratio 

-0.0329*** 
-0.0438*** 
-0.0454*** 
-0.00974 
-0.00523 
0.0382** 
- 
- 
- 
- 
- 
0.0154 
0.0441*** 
0.0772*** 
- 
- 
- 
- 
- 
- 
0.00591 
0.0244** 
0.0285** 
- 
- 
- 
- 
- 
- 
- 
- 
-0.0221*** 
19,957  
0.0903 
Marginal 

- 
- 
- 
- 
- 
- 
- 
- 
0.00434 
0.0213*** 
0.0768*** 
-0.00172 
0.0202** 
0.0432*** 
- 
- 
- 
- 
- 
- 
0.00936 
0.0286*** 
0.0257** 
-0.0332** 
-0.0158 
0.0357 
-0.0352* 
-0.0182** 
- 
- 
- 
-0.0150*** 
19,957  
0.1188 
Marginal 

- 
- 
- 
- 
- 
- 
-0.00791** 
-0.0127*** 
- 
- 
- 
- 
- 
- 
- 
- 
- 
-0.00368 
0.0111 
0.00704 
0.00944 
0.0186** 
0.0101 
- 
- 
- 
- 
- 
- 
- 
- 
-0.0123*** 

4,971  
0.1230 
Marginal 

10a 

Indicator code 
Note: Values in this table are results of general to specific (G-S) regression analysis. The G-S methodology is 
described in Annex 8. “-” implies that the explanatory variable was not included in the final regression model 
established via the G-S process. */**/*** implies that a result is statistically significant at 90%/95%/99%. 
For each the indicator in this table, the R2 in the table is the Pseudo-R2 for the full regression model, which 
includes  the  explanatory  variables  shown  in  this  table  and  other  explanatory  variables  (not  shown  in  this 
table)  that  were  identified  for  the  regression  model  based  on  the  G-S  methodology.  The  indicators  are 
defined in Table 5. The explanatory variables are defined in Table 7 and A8.1.2. 
Source: London Economics analysis of the survey data 

11_loan 

10b 

6.3.5.  Dimension 5: Higher susceptibility to marketing practices 

Several  of  the  behavioural  biases  and  characteristics  identified  in  the  literature  are 
relevant  to  this  dimension  of  vulnerability,  since  these  biases  can  interact  with 
marketing practices. For example, marketers may be able to exploit status quo bias by 
creating  default  options,  ambiguity  aversion  by  oversimplifying  products,  and  framing 
effects by manipulating the way in choices products and choices between products are 
presented to consumers. 

215 

 
 
 
 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

Results of the survey data analysis, presented in Table 39 below, show that consumers 
who  are  more  trusting  of  people  based  on  the  first  trust  measure141  are  less  likely  to 
feel a high level of vulnerability due to the complexity of offers, irrespective of how the 
dependent  variable  is  coded.142  When  this  vulnerability  indicator  is  coded  as  a  binary 
variable, similar results are also found for the second trust variable.143 

Focussing  on  the  results  when  the  vulnerability  indicator  is  coded  as  a  scale  variable, 
an  interesting  observation  is  that  some  variables  that  were  intended  to  measures 
similar  aspects  of  a  consumer’s  character  in  fact  have  opposite  relationships  with  the 
indicator. 

For example, those with very high credulity on the credulity measure144 are found to be 
more likely to feel a high level of vulnerability due to the complexity of offers, whereas 
the  opposite  result  is  found  for  those  who  answered  the  credulity  test  question 
incorrectly.145 

Similarly, those with very high impulsiveness on the second impulsiveness measure146 
are found to be less likely to feel a high level of vulnerability due to the complexity of 
offers, whereas the opposite result is found for those with very high impulsiveness on 
the fourth impulsiveness measure.147 

141  This measure is labelled ‘Trust 1’. It is based on the extent that respondents agree with the 

statement “you need to be very careful in dealing with people”. 

142 The table shows the results when the dependent variable is coded as a binary variable and as 

a scale variable. 

143  This measure is labelled ‘Trust 2’. It is based on the extent that respondents agree with the 

statement “I believe most people can be trusted”. 

144 This measure is labelled ‘Credulity’. It is based on the extent that respondents agree with the 
statement  “most  advertisements  report  objective  fact,  I  trust  most  of  the  information 
provided in advertisements”. 

145  The  ‘credulity  test’  question  instructed  respondents  to  consider  the  radio  advertisement 
“Sign-up to BEACH BREAKS and receive a free pair of sunglasses. Sunglasses delivered when 
you  purchase  one  of  our  beach  holidays”,  and  asked  them  whether  the  advertisement:  1. 
Offers free sunglasses to all who sign-up to beach breaks, 2. Offers sunglasses only to those 
who  buy  a  holiday  from  beach  breaks,  or  3.  Don’t  know.  Those  who  identified  the  second 
answer as the correct response answered the credulity test question correctly. 

146 This measure is labelled ‘Impulsiveness 2’. It is based on the extent that respondents agree 

with the statement “I’m good at resisting temptation”. 

147 This measure is labelled ‘Impulsiveness 4’. It is based on the extent that respondents agree 

with the statement “I’m impulsive in the purchase decisions I take”. 

216 

 
 
                                                 
6 │ Drivers of and factors linked to consumer vulnerability 

Table 39: 

Impact of behavioural characteristics on Indicator 12 in 
Dimension 5; Feeling of vulnerability due to complexity of offers 

Indicator 12: Feels vulnerable due to complexity of offers;  
Binary: “To a great extent” 

Scale: 1 (low) to 4 (high) 

Trust 1: Low 
Trust 1: High 
Trust 1: Very high 
Trust 2: Low 
Trust 2: High 
Trust 2:Very high 
Credulity test: Incorrect 
Credulity test: Not taken 
Credulity: Low  
Credulity: High 
Credulity: Very high 
Impulsiveness 1: Low  
Impulsiveness 1: High 
Impulsiveness 1: Very high 
Impulsiveness 2: Low  
Impulsiveness 2: High 
Impulsiveness 2: Very high 
Impulsiveness 3: Low  
Impulsiveness 3: High 
Impulsiveness 3: Very high 
Impulsiveness 4: Low  
Impulsiveness 4: High 
Impulsiveness 4: Very high 
Risk taking: Not very  
Risk taking: Fairly 
Risk taking: Very  
Risk taking: No response 
Computation test 1 correct  
Computation test 2 correct  
Knows meaning of kWh 
Knows meaning of Mbps 
Identified best interest rate 
N 
R2 
Marginal effect / Odds ratio 

-0.0230*** 
-0.0251*** 
-0.0248*** 
-0.0078* 
-0.0096** 
-0.0003 
-0.0084*** 
-0.00636 
-0.0226*** 
-0.0215*** 
0.0163** 
-0.00529 
-0.00219 
0.0127** 
-0.0217*** 
-0.0191*** 
-0.0181*** 
-0.0112*** 
-0.00754* 
-0.00627 
-0.00637 
0.000351 
0.0131** 
-0.0227*** 
-0.0257*** 
-0.0261** 
-0.0471*** 
- 
- 
- 
-0.00759** 
-0.0119*** 
26,653  
0.1005 
Marginal 

0.827*** 
0.640*** 
0.505*** 
- 
- 
- 
0.879*** 
0.926* 
1.041 
1.070** 
1.273*** 
1.042 
1.131*** 
1.168*** 
0.966 
0.989 
0.850** 
- 
- 
- 
1.144*** 
1.127*** 
1.231*** 
0.677*** 
0.620*** 
0.671*** 
0.538*** 
- 
- 
- 
0.932*** 
0.924*** 
26,653  
0.0465 
Odds ratio 

12_off_b2 

Indicator code 
Note: Values in this table are results of general to specific (G-S) regression analysis. The G-S methodology is 
described in Annex 8. “-” implies that the explanatory variable was not included in the final regression model 
established via the G-S process. */**/*** implies that a result is statistically significant at 90%/95%/99%. 
For each the indicator in this table, the R2 in the table is the Pseudo-R2 for the full regression model, which 
includes  the  explanatory  variables  shown  in  this  table  and  other  explanatory  variables  (not  shown  in  this 
table)  that  were  identified  for  the  regression  model  based  on  the  G-S  methodology.  The  indicators  are 
defined in Table 5. The explanatory variables are defined in Table 7 and A8.1.2. 
Source: London Economics analysis of the survey data 

12_off 

6.4.  Market-related drivers of vulnerability and consumers’ experience 
in markets 

Another  set  of  potential  drivers  relates  to  markets,  rather  than  to  individuals.  These 
include  information  problems,  which  could  be  linked  to  Cartwright’s  “information 
vulnerability”  (Cartwright  2011).  Information  problems  can  manifest  themselves 
through  consumers  not  having  information  to  make  informed  choices,  which  in  turn 
can make them vulnerable. This can mean both economic actors providing incomplete, 
misleading  or  inaccessible  information  about  products  or  services,  or  consumers  not 
possessing the necessary information or knowledge to make these choices despite the 
information  provided  by  economic  actors.  One  example  is  the  concept  of  financial 

217 

 
 
 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

literacy, where low levels of literacy can make a consumer vulnerable (i.e. Vandone et 
al. 2011, Stearn 2012). 

In  turn,  a  possible  cause  of  information  problems  may  be  a  lack  of  experience  and 
engagement  with  markets  by  consumers,  in  terms  of,  for  example,  rarely  comparing 
deals,  not  being  familiar  with  contract  terms  and  conditions,  or  not  reading 
communications from providers. Hence the consumer survey examined these types of 
‘experience and engagement’ factors. 

Competition  problems  are  another  market-related  vulnerability  driver.  They  refer  to 
situations where the nature of competition in the market can result in consumers being 
vulnerable. Potential ways in which competition can drive vulnerability include: 

  Situations where there is little competition in the market such that consumers 
have  few  alternatives  and  are  dependent  on  the  dominant  (or  monopoly) 
provider;  

  Situations where the nature of competition in the market makes it unviable for 
economic operators to supply certain market segments (i.e. providing banking 
services to lower income consumers); 

  Competitive pressures which may drive economic operators to exploit potential 
consumer  vulnerabilities  (i.e.  by  exploiting  behavioural  biases,  see  European 
Parliament 2011). 

Consumer  Futures  (2001)  examine  the  notion  of  “imperfect  markets”  which  are 
markets  that  “operate  in  such  a  way  as  to  reinforce  and  cause  disadvantage”.  In 
practice,  this  refers  to  markets  where  vulnerable  consumers  pay  more  for  essential 
goods and services, which is closely linked to the concept of “double disadvantage”.   

In  addition,  it  is  important  to  note  that  changes  in  the  competitive  situation  can  also 
have  an  impact  on  consumer  vulnerability,  especially  for  those  not  aware  of  the  new 
market situation, such as in the case of liberalisation of energy or telecommunications 
markets. 

More broadly, consumer vulnerability can also be driven by external economic shocks, 
such  as  the  recent  financial  crisis.  This  is  highlighted  in  a  recent  report  for  Citizens 
Advice  in  the  UK,  where  pressures  on  living  costs  stemming  from  the  economic 
situation are seen as an important driver of consumer vulnerability alongside changes 
in the way services are provided (for instance the increased  complexity of the energy 
market) (Centre for Consumers and Essential Services 2014). 

The  matrix  in  Figure  29  summarises  the  impacts  of  market-related  drivers  and 
consumers’  experience  in  markets  on  the  various  indicators  of  vulnerability,  based  on 
the results of the survey and experiment data analysis. The indicator codes across the 
top of the matrix match those in the list of indicators in section 6.1.2, as well as those 
in Table 5 in section 3.6.148 The market-related characteristics in the matrix (down the 
left  hand  side)  are  defined  (alongside  all  other  explanatory  variables  examined  in  the 
data analysis) in Table 7 in Section 3.7. 

148 There are no results for Indicator 7 for the online sector since the G-S process could not be 
run for this indicator due to the low total number of respondents who are vulnerable on this 
indicator.  Hence,  in  the  results  tables  in  this  section  no  results  are  presented  for  this 
indicator. 

218 

 
 
                                                 
6 │ Drivers of and factors linked to consumer vulnerability 

In  Figure  29,  red  cells  imply  that  consumers  with  a  certain  characteristic  are  more 
likely  to  be  vulnerable  on  the  relevant  indicator,  whereas  green  cells  show  that 
consumers with a given characteristic are less likely to be vulnerable. Grey cells show 
that  a  characteristic  was  controlled  for  in  the  regression  but  was  not  statistically 
significant,  and  white  cells  mean  that  a  characteristic  was  not  included  in  the 
regression (based on the G-S methodology).149 

The explanatory variables examined in this section include a number of variables which 
represent  consumers’  experience  of  and  engagement  with  the  three  key  markets, 
including how often they compare deals, to what extent they know their contract terms 
and  conditions,  whether  they  have  read  the  most  recent  bill  or  communication  from 
their provider, and whether they found reading this bill or communication difficult. 

As explained in section  3.7.1, these variables are coded as sets  of mutually exclusive 
dummies,  meaning  that,  for  each  set,  one  level  must  be  used  as  the  base.  In  each 
case, the level used as the base is the highest level of experience or  engagement. For 
example,  for  the  extent  to  which  respondents  know  their  contract  terms  and 
conditions, the base level used is “completely”.150 

Some results reported in this section relate to explanatory variables which signify that 
respondents  did  not  answer  a  particular  survey  question,  including  no  response 
dummies  for  questions  on  comparing  deals,  knowing  contract  terms  and  reading  bills 
and communications from suppliers. 

Some  market  experience  variables  are  not  included  in  the  regression  analysis  for 
particular  indicators  because  these  variables  are  used  to  construct  the  indicators 
themselves. Specifically, variables signifying how often respondents compare deals in a 
given sector are not used as explanatory variables for indicators representing whether 
consumers  have  problems  comparing  deals in that  sector  (i.e.  indicators  6  and  8).  In 
Figure 29, such cases are identified with crossed out cells, and in the results tables in 
the following subsections no results are reported for these variables/indicators. 

As  can  be  seen  from  the  matrix  in  Figure  29,  there  are  many  statistically  significant 
relationships  between  market-related  factors  and  the  indicators  of  vulnerability. 
Although the overall picture is very diverse and complex, several patterns can be seen 
in the results: 

  Having  been  unable  to  read  the  terms  and  conditions  of  your  energy, 
telecommunications or banking contract due to overly small print is associated 
with a higher likelihood of vulnerability on 20 out of the 38 indicators, including 
indicators  in  every  dimension,  as  well  as  worse  overall  performance  in  the 
experiments (see the first row in the matrix). 

  Being  less  experienced  and  engaged  with  a  market,  in  terms  of  knowing 
contract  terms,  reading  communications,  and  the  difficulty  of  reading 
communications,  is  associated  with  a  higher  likelihood  of  vulnerability  in  that 
particular  market  on  a  number  of  indicators,  especially  having  problems 

149  Note  that  the  indicators  examined  in  the  data  analysis  are  listed  at  the  beginning  of  this 
chapter,  while  further  details  of  the  analysis  undertaken,  including  descriptions  of  the 
explanatory variables, are provided in Annex 8. 

150 The full set of explanatory variables examined in the G-S analysis, and the variables used as 

bases, can be seen in Annex 8. 

219 

 
 
 
                                                 
6 │ Drivers of and factors linked to consumer vulnerability 

comparing  deals  in  the  sector  due  to  information-related  (Indicator  6), 
personal  (Indicator  8a),  market-related  (Indicator  8b)  and  access-related 
(Indicator 8c) factors. 

  The extent that consumers feel uninformed about prices, quality and conditions 
when  choosing  and  buying  goods  and  services  (Indicator  4  in  dimension  3)  is 
linked  to  almost  all  of  the  market-related  factors  examined  in  the  analysis151; 
and  in  most  cases  these  relationships  imply  that  the  characteristics  in  the 
matrix are associated  with a higher likelihood of vulnerability on this indicator 
(red cells), which makes intuitive sense since, as noted above, the bases used 
for  comparison  represent  the  highest  levels  of  experience  and  engagement 
with markets. 

Using  evidence  from  the  literature  review,  survey  and  behavioural  experiments,  the 
relationships  between  market-related  factors  and  consumers’  experience  in  markets 
and  each  dimension  of  vulnerability  are  examined  in  more  detail  in  the  following 
subsections.  

As in the sections above, reporting and discussion of the survey and experiment results 
in  the  text  below  is  limited  to  statistically  significant  results  only.  The  full  set  of 
explanatory  variables  examined  in  the  G-S  analysis  is  presented  in  section  3.7.  This 
table also indicates which variables (i.e. which levels or categories) are used as bases 
in the analysis. 

151 Specifically, all except whether respondents have read the most recent bill or communication 

from their provider in the energy, online or finance sectors. 

220 

 
 
                                                 
Figure 29:  Matrix of market-related characteristics linked to survey-based indicators of vulnerability 

6 │ Drivers of and factors linked to consumer vulnerability 

221 

Dimension:D1D2D3D4D5D4Feature:Sector:----Indicator:12345675675678a8b8c8a8b8c8a8b8c9a9b9c9a9b9c9a9b9c9d9e10a10b1112b12expCharacteristic / DriverUnassertiveOverpaidFeels vulnerableFeels uninformedUses adverts onlyProbl's comparingHasn't switchedUses adverts onlyProbl's comparingHasn't switchedUses adverts onlyProbl's comparingHasn't switchedPersonal factorsMarket factorsAccess factorsPersonal factorsMarket factorsAccess factorsPersonal factorsMarket factorsAccess factorsPersonal factorsMarket factorsAccess factorsPersonal factorsMarket factorsAccess factorsPersonal factorsMarket factorsAccess factorsTermination costBundled offersDifficult processPayment cardDeclined for loanVery vulnerableVulnerable scaleExperimentUnable to read Ts&Cs due to small print0.150***1.595***1.192***0.0636***0.0329***0.0143***0.0387***0.00829*0.0376***0.0617***0.0360***0.0718***0.0423***0.00819*0.199***0.209***0.244***0.185***0.0469***1.658***Compare energy deals: When need to renew0.0591***-0.0005381.108**1.178***-0.0130***-0.006880.000196-0.001340.0329***0.004380.007000.0528***0.0560***0.001020.0506*-0.006020.0264-0.00268-0.00768-0.0209**-0.001190.0183*0.0177*1.093**Compare energy deals: Sporadically0.0751***-0.0123**1.0211.226***0.0286***0.008600.004960.00979*0.0396***0.01420.0185***0.0533***0.0376**0.00689*0.03310.0713***0.0650*0.008330.00466-0.0224**0.007370.001020.001331.104**Compare energy deals: Only the first time0.127***-0.004041.074*1.261***0.006130.0118*-0.00110-0.001540.0914***0.0373***0.0365***0.119***0.0567***0.00763*0.153***0.0573***0.221***0.01010.00408-0.003910.0102-0.005870.002391.083*Compare energy deals: Never0.135***-0.0119**0.9931.391***0.0187***-0.006420.003700.0631***-0.002660.0293***0.111***0.0415***0.004550.135***-0.003920.318***0.0160*0.0209**-0.00272-0.0152**-0.0168*-0.005530.963Know energy contract: Not at all0.000310-0.0254***1.381***0.101***0.0165-0.0117***0.234***0.100***0.114***-0.0230***0.01050.0400*-0.00858***0.0887***0.0564*-0.01020.0393-0.0387***-0.0189*0.0138**1.166***Know energy contract: Not very0.0550**-0.0108*1.264***0.0839***0.0138-0.003020.211***0.166***0.111***-0.0169***0.01340.0583***-0.003720.0706***0.0524**0.02920.0111-0.00224-0.001350.004781.166***Know energy contract: Fair amount0.0247-0.0111**1.104**0.01880.00280-0.002200.0711***0.0790***0.0469***-0.00342-0.006280.0304**-0.0003080.03120.02490.0454**0.00432-0.00179-0.00586-0.005251.050Know energy contract/Read energy bill: No response0.104***-0.0139*1.455***0.0128-0.003610.296***0.161***0.378***0.0224**0.0201-0.00155-0.001340.0343-0.01420.005940.0220**1.291***Read energy bill: Glanced or skim read0.0296*1.0530.001430.00389-0.00919**-0.0209***0.0525***0.002330.00445-0.0278***0.0332***-0.0391***0.0367**0.0118-0.00842-0.00574Read energy bill: Looked at total price0.110***1.131***0.002130.0127*-0.0153***-0.0217***0.0846***0.01890.0364***-0.0406***0.0588***-0.0438***0.103***-0.0198*0.007030.00647Read energy bill: Not at all0.0792***1.213***0.001200.003840.005620.0351**0.142***0.137***0.0391***0.02190.0578***-0.002080.123***-0.00842-0.0204**-0.00327Read energy bill: Don't know0.04351.120-0.0207***0.005020.001430.0316*0.136***0.0890***0.0282*0.01550.0254-0.009110.145***-0.0254-0.0609***-0.0220*Read energy bill: No response1.395***-0.001130.005560.0158-0.0101Read energy bill: Easy1.139***1.323***0.0602***0.0131*0.0314***0.007660.125***0.0216**0.0511***-0.0100**-0.0286**0.0427***0.00631-0.0382**-0.00231-0.0135***0.0127*-0.00327-0.002701.243***Read energy bill: Difficult1.170***1.390***0.185***0.0401***0.0785***0.122***0.331***0.0415***0.133***-0.00873*0.01800.141***0.0618***-0.0605***0.0398*-0.005130.0171**0.01560.009961.424***Read energy bill: Very difficult1.233***1.468***0.293***0.0917***0.130***0.139***0.399***0.0813***0.188***0.00357-0.009920.118***0.0493-0.03720.03907.94e-050.0475***0.0367*0.0217**1.540***Did not read energy bill1.339***0.0929***0.0159-0.0185***-0.0433***0.00760-0.0112***-0.000327-0.002890.0009361.183***Compare internet deals: When need to renew1.0551.163***-0.00650**-0.0115***-0.00307-0.0136**0.02150.01200.01130.0141-0.0130-0.0179-0.02080.002720.000822-0.005070.0002330.0165**0.004830.007280.0131*Compare internet deals: Sporadically1.0001.084*0.001880.0407***0.00983*-0.0003010.0574***0.0574***0.008540.0357**0.01910.00829-0.008240.009320.0407***0.003780.00302-0.0117*-0.0142*-0.002750.0329***Compare internet deals: Only the first time0.929*1.137***0.0002040.00848*0.00363-0.0205***0.111***0.0849***0.0353***0.0929***0.0009790.00402-0.0470***0.0280**0.0275*0.00289-0.00957***-0.0182***-0.0246***-0.006610.00949Compare internet deals: Never0.900**1.161***-0.002040.00591-0.0213***0.159***0.0341**0.0243**0.109***-0.0547***0.0110-0.0713***0.0576***0.004270.0598***-0.000424-0.0323***-0.0376***-0.0252***0.00358Compare internet deals: No response0.768**1.396***-0.00489-0.00979-0.0313***0.102**-0.102***0.153***0.265***-0.0228-0.005510.02990.00299-0.0558***-0.0797***0.0501-0.0132Know internet contract conditions: Not at all1.237***-0.01870.0448***-0.0218-0.01830.152***0.0942***0.0716***-0.0473**-0.001890.0115-0.0183***0.931Know internet contract conditions: Not very1.278***0.01030.0547***0.0549***-0.007960.141***0.191***0.0243***-0.008980.004660.0191*-0.0131***1.114**Know internet contract conditions: Fair amount1.162***0.001840.0192***0.0162-0.0204**0.0410***0.0805***0.00880**0.00166-0.002230.00311-0.007591.036Know internet contract/read comm'n: No response1.056-0.0551***0.0373**0.0100-0.01490.296***0.363***0.0642**-0.0835**0.00998*0.0249-0.002930.859**Read internet communication: Glanced or skim read0.0316*-0.004790.9500.005000.00783*0.00482-0.01020.009420.0145*0.01420.0123**-0.00538-0.00665-0.0139**-0.0113-0.0181**Read internet communication: Saw what it was0.118***0.0142*1.0780.0199**0.0130**0.01010.0358**0.0259**0.116***0.188***0.0315***0.0201-0.0462***0.006910.0424***0.0282**Read internet communication: Not read it at all0.110***-0.01040.900**0.006660.0164**0.001820.0138-0.009050.146***0.158***0.0391***-0.0209-0.00673-0.0167**-0.006060.000493Read internet communication: Don't know0.0914***-0.0173**0.904*0.001900.00647-0.005230.0177-0.0208**0.118***0.183***0.0543***-0.00431-0.0264-0.00936-0.01980.0177Read internet communication: No response0.0921***0.0282***1.0840.0396*0.01760.0226-0.0417-0.0416-0.00124-0.0133Problems comparing-EnergyOnlineFinanceEnergyOnlineInformation problemsExcludedOffers-FinanceEnergyOnlineFinance-Not switchede-com 
 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

Figure 29:  Matrix of market-related characteristics linked to survey-based indicators of vulnerability 

222 

Dimension:D1D2D3D4D5D4Feature:Sector:----Indicator:12345675675678a8b8c8a8b8c8a8b8c9a9b9c9a9b9c9a9b9c9d9e10a10b1112b12expCharacteristic / DriverUnassertiveOverpaidFeels vulnerableFeels uninformedUses adverts onlyProbl's comparingHasn't switchedUses adverts onlyProbl's comparingHasn't switchedUses adverts onlyProbl's comparingHasn't switchedPersonal factorsMarket factorsAccess factorsPersonal factorsMarket factorsAccess factorsPersonal factorsMarket factorsAccess factorsPersonal factorsMarket factorsAccess factorsPersonal factorsMarket factorsAccess factorsPersonal factorsMarket factorsAccess factorsTermination costBundled offersDifficult processPayment cardDeclined for loanVery vulnerableVulnerable scaleExperimentRead internet communication: Easy0.0411**0.004401.189***0.008110.001290.0566***0.001530.003590.007230.0167*0.0855***-0.00272-0.001960.0232**0.0392***-0.0120-0.001030.005770.0199**0.01401.046Read internet communication: Difficult0.0927***0.0281**1.303***0.0569***0.03280.268***0.0203*0.0325**0.0619**0.181***0.413***0.0237**0.112***0.1140.147***-0.0003000.01430.0358**0.0459***0.0724***1.225***Read internet communication: Very difficult0.214**0.119**0.8220.173***0.285***0.02250.05230.147*0.207***0.579***0.04070.08340.07050.0920**0.05610.145**1.405Did not read internet communication1.231***0.0209*0.003790.0785***-0.001410.0286**0.001230.0368**0.0303**0.003270.0001281.064Compare deals from banks: When notified0.0651***0.0110*1.083*1.140***-0.004720.00528-0.00789**0.02230.02550.009300.01090.0236**0.00502-0.000987-0.0159**0.03370.0630**-0.005080.008420.0289***0.0231**0.01140.001931.079*Compare deals from banks: Sporadically0.0605***-0.0150***0.931*0.932*0.00664-0.0007110.0158***0.0481***0.0425***-0.002860.0180*0.0554***-0.00499-0.00469*0.00934-0.002450.0679***0.00307-0.00258-0.0007240.0222**0.00472-0.005040.958Compare deals from banks: Only the first time0.0351*-0.0244***0.926**0.874***0.0278***0.001170.005580.0816***0.0735***0.0207**0.0484***0.0804***-0.0158-0.00746***0.006830.0445**0.01450.0120*-0.0145**-0.0219***-0.00840-0.0233***-0.00858*0.967Compare deals from banks: Never0.0348*-0.0284***0.837***0.935*0.00557-0.00630*0.110***0.0550***-0.0001730.0741***0.0725***-0.0587***-0.00449*-0.009230.0509***0.004220.00872-0.0393***-0.0589***-0.0372***-0.0202***-0.0167***0.840***Know bank contract conditions: Not at all0.118***0.0328***1.218***1.322***0.01400.164***0.01010.0786***-0.0390***-0.01120.0355*0.223***0.217***0.0507*0.0615**0.111***0.01800.0390***-0.009200.0376**0.03880.0385***1.372***Know bank contract conditions: Not very0.0788***0.0170**1.320***1.219***-0.001350.107***0.009350.0612***-0.006880.0009680.0350***0.219***0.262***0.0284*0.0434***0.0826***0.0165*0.009990.0194*0.0311***0.0279**0.0247***1.328***Know bank contract conditions: Fair amount0.0352**0.001391.161***1.142***0.00924**0.0537***6.88e-050.0481***0.00841-0.0153**0.008890.0699***0.135***0.007530.0261**0.0301**0.006070.003830.003400.004650.01050.003781.115***Know bank contract/read comm'n: No response0.152***0.0355***1.897***1.264***-0.0121*0.242***0.03420.02350.00235-0.007290.349***0.265***-0.0001850.006650.008050.0639***0.0336**0.0365***2.093***Read bank communication: Glanced or skim read0.0335**0.004800.9820.00575-0.0191***0.0201*-0.0253**0.00680-0.003630.0418***-0.004210.001790.007750.00220-0.0118**0.0266**0.0390***-0.0119*-0.0110*-0.00208-0.0161***0.982Read bank communication: Saw what it was0.0829***0.0335***1.297***0.01090.0967***0.0665***0.0488***0.009940.0268**0.156***0.187***0.00154-0.00948-0.008600.0001380.0291*0.0737***0.0193*-0.01250.0164*0.009791.421***Read bank communication: Not read it at all0.008870.0183**1.116**0.01080.0714***0.02800.01375.81e-050.01580.182***0.143***0.001780.0106-0.0208**-0.004840.0504***0.0557**0.00840-0.00434-0.001030.001871.250***Read bank communication: Don't know0.0488*0.0192**1.152***0.01270.0857***0.0325*0.0003900.0287**0.01300.119***0.132***0.00742*0.0688***-0.0232***-0.0171***0.0759***0.0898***-0.0129-0.0108-0.008180.001621.222***Read bank communication: No response-0.002880.0438**0.002160.0802***-0.00545-0.00947-0.00220Read bank communication: Easy0.008631.154***1.155***0.0226**0.00874*0.005690.0661***-0.003060.0219*0.0337***0.0128*0.008910.0289**0.107***0.006280.005220.0005980.0373***-0.006480.006161.300***Read bank communication: Difficult0.0165*1.317***1.323***0.0632***0.0410***-0.002270.234***0.003260.0790***0.107***0.0385***0.0624***0.152***0.376***0.0761**0.0370***0.0260**0.0513***0.01280.0375***1.640***Read bank communication: Very Difficult0.0559***1.306*1.1960.107**0.02760.277***0.0106-0.02750.120**0.02120.04970.220***0.344***0.142*0.01360.0007890.173***0.0692**0.0494***1.855***Did not read bank communication1.342***0.0406***0.0166***0.0142**0.002420.0231**0.0225***0.0387***Problems comparing-EnergyOnlineFinanceEnergyOnlineInformation problemsExcludedOffers-FinanceEnergyOnlineFinance-Not switchede-com 
 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

6.4.1.  Dimension 1: Heightened risk of negative outcomes or impacts on well-
being 

Market-related  issues  such  as  information  and  competition  problems  and  a  lack  of 
engagement in the market by consumers, as discussed above in section 6.4, might be 
expected  to  put  consumers  at  increased  risk  of  negative  outcomes  in  markets.  For 
example,  consumers  may  pay  more  for  goods  and  services  due  to  competition 
problems  or  make  important  purchasing  decisions  sub  optimally  due  to  information 
problems. 

According  to  the  results  of  G-S  regression  analysis  of  the  survey  data  presented  in 
Table  40  below,  some  market-related  factors  are  linked  with  the  indicators  of 
vulnerability in this dimension. 

For  example,  according  to  these  results,  those  who  compare  deals  less  frequently  in 
the energy or finance sectors are more likely to be unassertive when they experience a 
problem (Indicator 1). Specifically, those who compare deals in the energy sector only 
when they need to renew their contract, only sporadically, only the first time they need 
to  choose  a  provider,  or  never,  are  more  likely  than  those  who  compare  deals  from 
time  to  time  to  be  unassertive  when  they  experience  a  problem  (Indicator  1),  with 
marginal  effects  of  5.9  percentage  points  (pp),  7.5pp,  12.7pp  and  13.3pp 
respectively.152  

In addition, those who have previously been unable to read the terms and conditions of 
their  energy,  telecommunications  or  banking  contract  due  to  overly  small  print  are 
found  to  be  more  likely  to  have  overpaid  for  services  due  to  being  unable  to  use 
certain payment methods (Indicator 2), with a marginal effect of 15pp (i.e. the largest 
effect among those reported in Table 40). 

Table 40: 

Impact of market-related characteristics on indicators in 

Dimension 1 and Dimension 2 

Dimension 1 

Dimension 2 

Indicator 1: 
Unassertive if 
experiences 
problem 

Indicator 2: 
Overpaid for 
services 

Indicator 3: 
Maximum feeling 
of vulnerability 

- 

0.150*** 

1.595*** 

0.0591*** 
0.0751*** 

0.127*** 
0.135*** 
0.000310 
0.0550** 
0.0247 
0.104*** 

-0.0005 
-0.0123** 

-0.00404 
-0.0119** 
-0.0254*** 
-0.0108* 
-0.0111** 
-0.0139* 

1.108** 
1.021 

1.074* 
0.993 
- 
- 
- 
- 

Unable to read Ts&Cs due to small 
print 
Compare energy deals: When need to 
renew 
Compare energy deals: Sporadically 
Compare energy deals: Only the first 
time 
Compare energy deals: Never 
Know energy contract: Not at all 
Know energy contract: Not very 
Know energy contract: Fair amount 
Know energy contract/Read bill: No 

152  There  are  similar  results  for  the  frequency  with  which  consumers  compare  deals  in  the 
finance  sector,  although  the  magnitudes  and  statistical  significances  of  these  results  are 
lower. 

223 

 
 
 
 
                                                 
6 │ Drivers of and factors linked to consumer vulnerability 

Table 40: 

Impact of market-related characteristics on indicators in 

Dimension 1 and Dimension 2 

Dimension 1 

Dimension 2 

Indicator 1: 
Unassertive if 
experiences 
problem 

Indicator 2: 
Overpaid for 
services 

Indicator 3: 
Maximum feeling 
of vulnerability 

response 
Read energy bill: Glanced or skim read 
Read energy bill: Looked at total price 
Read energy bill: Not at all 
Read energy bill: Don't know 
Read energy bill: No response 
Read energy bill: Easy 
Read energy bill: Difficult 
Read energy bill: Very difficult 
Did not read energy bill 
Compare internet deals: When need to 
renew 
Compare internet deals: Sporadically 
Compare internet deals: Only the first 
time 
Compare internet deals: Never 
Compare internet deals: No response 
Know internet contract conditions: Not 
at all 
Know internet contract conditions: Not 
very 
Know internet contract conditions: Fair 
amount 
Know contract/read comm'n: No 
response 
Read internet communication: Glanced 
or skim 
Read internet communication: Saw 
what it was 
Read internet communication: Not 
read it at all 
Read internet communication: Don't 
know 
Read internet communication: No 
response 
Read internet communication: Easy 
Read internet communication: Difficult 
Read internet communication: Very 
difficult 
Did not read internet communication 
Compare deals from banks: When 
notified 
Compare deals from banks: 
Sporadically 
Compare deals from banks: Only the 
first time 
Compare deals from banks: Never 
Know bank contract conditions: Not at 
all 
Know bank contract conditions: Not 
very 
Know bank contract conditions: Fair 
amount 
Know bank contract/read comm'n: No 
response 

0.0296* 
0.110*** 
0.0792*** 
0.0435 
- 
- 
- 
- 
- 

- 
- 

- 
- 
- 

- 

- 

- 

- 

- 
- 
- 
- 
- 
- 
- 
- 
- 

- 
- 

- 
- 
- 

- 

- 

- 

- 

0.0316* 

-0.00479 

0.118*** 

0.0142* 

1.053 
1.131*** 
1.213*** 
1.120 
1.395*** 
1.139*** 
1.170*** 
1.233*** 
- 

1.055 
1.000 

0.929* 
0.900** 
0.768** 

- 

- 

- 

- 

0.950 

1.078 

0.110*** 

-0.0104 

0.900** 

0.0914*** 

-0.0173** 

0.904* 

0.0921*** 
0.0411** 
0.0927*** 

0.214** 
- 

0.0282*** 
0.00440 
0.0281** 

0.119** 
- 

0.0651*** 

0.0110* 

0.0605*** 

-0.0150*** 

1.084 
- 
- 

- 
- 

1.083* 

0.931* 

0.0351* 
0.0348* 

-0.0244*** 
-0.0284*** 

0.926** 
0.837*** 

0.118*** 

0.0328*** 

1.218*** 

0.0788*** 

0.0170** 

1.320*** 

0.0352** 

0.00139 

1.161*** 

0.152*** 

0.0355*** 

1.897*** 

224 

 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

Table 40: 

Impact of market-related characteristics on indicators in 

Dimension 1 and Dimension 2 

Dimension 1 

Dimension 2 

Indicator 1: 
Unassertive if 
experiences 
problem 

Indicator 2: 
Overpaid for 
services 

Indicator 3: 
Maximum feeling 
of vulnerability 

0.0335** 

0.00480 

0.982 

0.0829*** 

0.0335*** 

1.297*** 

0.00887 

0.0488* 

- 
- 
- 

- 
- 
7,826 

0.0929 

0.0183** 

1.116** 

0.0192** 

1.152*** 

- 
0.00863 
0.0165* 

0.0559*** 
- 
26,653 

0.1418 

- 
1.154*** 
1.317*** 

1.306* 
- 

26,653 

0.1227 

Read bank communication: Glanced or 
skim  
Read bank communication: Saw what 
it was 
Read bank communication: Not read it 
at all 
Read bank communication: Don't 
know 
Read bank communication: No 
response 
Read bank communication: Easy 
Read bank communication: Difficult 
Read bank communication: Very 
Difficult 
Did not read bank communication 
N 
R2 

Marginal 

1 

Marginal effect / Odds ratio 
Indicator code 
Note: Values in this table are results of general to specific (G-S) regression analysis. The G-S methodology is 
described  in  Annex  8.    “-”  implies  that  the  explanatory  variable  was  not  included  in  the  final  regression 
model  established  via  the  G-S  process.  */**/***  implies  that  a  result  is  statistically  significant  at 
90%/95%/99%.  For  each  the  indicator  in  this  table,  the  R2  in  the  table  is  the  Pseudo-R2  for  the  full 
regression  model,  which  includes  the  explanatory  variables  shown  in  this  table  and  other  explanatory 
variables  (not  shown  in  this  table)  that  were  identified  for  the  regression  model  based  on  the  G-S 
methodology.  The  indicators  are  defined  in  Table  5.  The  explanatory  variables  are  defined  in  Table  7  and 
A8.1.2. 
Source: London Economics analysis of the survey data 

2 

3 

Odds ratio 

Marginal 

6.4.2.  Dimension 2: Having characteristics that limit ability to maximise well-
being 

The  G-S  regression  method  is  used  to  analyse  one  survey-based  indicator  of 
vulnerability in the second dimension, namely Indicator 3. This indicator stands for the 
maximum  extent  that  consumers  feel  vulnerable  due  to  any  of  the  personal 
characteristics  mentioned  in  the  relevant  survey  question  (i.e.  health  problems, 
financial  circumstances,  employment  situation,  age,  belonging  to  a  minority  group, 
personal  issues,  or  ‘other’  reasons),  on  a  scale  from  1  (low)  to  4  (high).  The  results 
presented in Table 40 above show that some market-related factors are related to this 
indicator of vulnerability. 

For example, the results show that consumers who have been unable to read the terms 
and  conditions  of  their  energy,  telecommunications  or  banking  contract  due  to  overly 
small  print  are  considerably  more  likely  to  feel  a  high  level  of  vulnerability  on  this 
indicator, with an odds ratio of 1.60. 

Furthermore, those who found it more difficult (i.e. who found it easy, difficult or very 
difficult,  rather  than  very  easy)  to  read  the  last  bill  from  their  energy  supplier  or  the 

225 

 
 
 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

last  communication  from  their  bank  (with  odds  ratios  ranging  from  1.14  to  1.32)  are 
more likely to feel highly vulnerable on this indicator. 

Conversely, those who compare deals less often (i.e. ‘only the first time’ they need to 
choose a provider or ‘never’, rather than ‘from time to time’) in the online and finance 
sectors are less likely to feel a high level of vulnerability on this indicator (odds ratios 
ranging from 0.84 to 0.93). 

6.4.3.  Dimension 3: Having difficulty in obtaining or assimilating information 

As  discussed  in  section  6.4  above,  market-related  drivers  include  information 
problems, which can refer either to incomplete, misleading or inaccessible provision of 
information by economic actors (e.g. firms), or to consumers not possessing necessary 
information  or  knowledge  to  make  proper  choices,  irrespective  of  the  information 
provided  to  them.  A  potential  source  of  such  information  problems  may  be  that 
consumers are not sufficiently experienced and engaged with markets, for example in 
terms  of  comparing  deals,  being  familiar  with  contract  terms,  or    reading 
communications from providers. 

Results of G-S regression analysis of the survey data, presented in Table 41, show that 
few  of  the  market-related  factors  examined  in the  survey  have  statistically  significant 
relationships with most indicators in this dimension (this can be seen from the matrix 
in Figure 29 as well as from table 45). However, some individual indicators are linked 
with a number of market-related variables. 

In  particular,  the  extent  that  consumers  feel  uninformed  about  prices,  quality  and 
conditions  when  choosing  and  buying  goods  and  services  (Indicator  4)  is  linked  with 
most  market-related  factors  examined  in  the  analysis.  In  nearly  all  cases,  the  results 
imply  that  the  characteristics  in  Table  41  are  linked  with  a  higher  likelihood  of 
vulnerability  on  this  indicator,  which  makes  intuitive  sense  since,  as  noted  in  section 
6.4  above,  the  bases  used  for  comparison  are  the  highest  levels  of  experience  and 
engagement  with  markets.  For  example,  consumers  who  do  not  know  their  energy, 
internet  or  bank  contract  terms  and  conditions  “at  all”  are  more  likely  to  feel  highly 
uninformed (with odds ratios of 1.38, 1.24 and 1.32). 

In  addition,  the  results  show  that  finding  it  difficult  to  read  bills  and  communications 
from  providers  is,  in  general,  associated  with  a  higher  likelihood  of  having  problems 
comparing deals due to information-related factors (Indicator 6). 

For  example,  those  who  found  it  difficult  or  very  difficult  to  read  the  most  recent  bill 
from  their  energy  supplier  are  more  likely  to  have  problems  comparing  deals  in  all 
three key sectors due to information related factors. With a marginal effect of 29.3pp, 
the  impact  of  finding  it  very  difficult  to  read  their  latest  energy  bill  on  the  likelihood 
that  consumers  have  problems  comparing  deals  in  the  energy  sector  due  to 
information  related  factors  is  the  largest  effect  of  any  market-related  factor  in  this 
dimension. Similarly, those who found it difficult to read the latest communication from 
their internet provider or bank are also more likely to have problems comparing deals 
in the three sectors as a result of information related factors. 

226 

 
 
Table 41: 

Impact of market-related characteristics on indicators in Dimension 3; Having difficulty obtaining and 

assimilating information 

6 │ Drivers of and factors linked to consumer vulnerability 

Indicator 
4: 
Feels 
uninforme
d about 
prices 

Unable to read Ts&Cs due to small print 
Compare energy deals: When need to renew 
Compare energy deals: Sporadically 
Compare energy deals: Only the first time 
Compare energy deals: Never 
Know energy contract: Not at all 
Know energy contract: Not very 
Know energy contract: Fair amount 
Know energy contract/Read energy bill: No 
response 
Read energy bill: Glanced or skim read 
Read energy bill: Looked at total price 
Read energy bill: Not at all 
Read energy bill: Don't know 
Read energy bill: No response 
Read energy bill: Easy 
Read energy bill: Difficult 
Read energy bill: Very difficult 
Did not read energy bill 
Compare internet deals: When need to renew 
Compare internet deals: Sporadically 
Compare internet deals: Only the first time 
Compare internet deals: Never 
Compare internet deals: No response 
Know internet contract conditions: Not at all 
Know internet contract conditions: Not very 
Know internet contr conditions: Fair amount 
Know internet contr/read comm: No response 
Read internet comm: Glanced or skim read 
Read internet communication: Saw what it was 
Read internet communication: Not read it at all 
Read internet communication: Don't know 
Read internet communication: No response 

1.192*** 
1.178*** 
1.226*** 
1.261*** 
1.391*** 
1.381*** 
1.264*** 
1.104** 

1.455*** 
- 
- 
- 
- 
- 
1.323*** 
1.390*** 
1.468*** 
1.339*** 
1.163*** 
1.084* 
1.137*** 
1.161*** 
1.396*** 
1.237*** 
1.278*** 
1.162*** 
1.056 
- 
- 
- 
- 
- 

227 

Indicator 
5: 
Gets info. 
from 
adverts 
only 

- 
-0.0130*** 
0.0286*** 
0.00613 
- 
- 
- 
- 

- 
0.00143 
0.00213 
0.00120 
-0.0207*** 
-0.00113 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
0.00500 
0.0199** 
0.00666 
0.00190 
0.0396* 

0.101*** 
0.0839*** 
0.0188 

0.0128 
- 
- 
- 
- 
- 
0.0602*** 
0.185*** 
0.293*** 
0.0929*** 
- 
- 
- 
- 
- 
-0.0187 
0.0103 
0.00184 
-0.0551*** 
- 
- 
- 
- 
- 

Energy sector 
Indicator 
6: 
Problems 
comparing 
due to 
info. 
factors 
0.0636*** 

Indicator 
7: 
Not 
switched 
due to 
info. 
factors 

- 
- 
- 
- 
- 
0.0165 
0.0138 
0.0028 

Indicator 
7: 
Not 
switched 
due to 
info. 
factors 

Indicator 
5: 
Gets info. 
from 
adverts 
only 

- 
-0.00688 
0.00860 
0.0118* 
0.0187*** 
- 
- 
- 

Online sector 
Indicator 
6: 
Problems 
comparing 
due to 
info. 
factors 
0.0329*** 
0.000196 
0.00496 
-0.00110 
-0.00642 
- 
- 
- 

Indicator 
5: 
Gets info. 
from 
adverts 
only 

0.0143*** 
-0.00134 
0.00979* 
-0.00154 
0.00370 
-0.0117*** 
-0.00302 
-0.00220 

Finance sector 
Indicator 
6: 
Problems 
comparing 
due to 
info. 
factors 
0.0387*** 
- 
- 
- 
- 
- 
- 
- 

Indicator 
7: 
Not 
switched 
due to 
info. 
factors 

0.00829* 
- 
- 
- 
- 
- 
- 
- 

- 
0.0039 
0.0127* 
0.0038 
0.0050 
- 
- 
- 
- 
- 
-0.0065** 
0.0019 
0.00020 
-0.0020 
-0.0049 
- 
- 
- 
- 
- 
- 
- 
- 
- 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
-0.0115*** 
0.0407*** 
0.00848* 
- 
- 
- 
- 
- 
- 
0.00783* 
0.0130** 
0.0164** 
0.00647 
0.0176 

- 
-0.00919** 
-0.0153*** 
0.00562 
0.00143 
0.00556 
0.0131* 
0.0401*** 
0.0917*** 
- 

0.0448*** 
0.0547*** 
0.0192*** 
0.0373** 
- 
- 
- 
- 
- 

-0.00361 
- 
- 
- 
- 
- 
- 
- 
- 
- 
-0.00307 
0.00983* 
0.00363 
0.00591 
-0.00979 
- 
- 
- 
- 
0.00482 
0.0101 
0.00182 
-0.00523 
0.0226 

- 
-0.0209*** 
-0.0217*** 
0.0351** 
0.0316* 
0.0158 
0.0314*** 
0.0785*** 
0.130*** 
- 
-0.0136** 
-0.000301 
-0.0205*** 
-0.0213*** 
-0.0313*** 
- 
- 
- 
- 
- 
- 
- 
- 
- 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

 
 
 
 
 
 
 
  
 
  
 
  
 
  
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
  
 
  
 
  
 
  
 
  
  
  
  
  
  
  
  
  
  
6 │ Drivers of and factors linked to consumer vulnerability 

Table 41: 

Impact of market-related characteristics on indicators in Dimension 3; Having difficulty obtaining and 

assimilating information 

Indicator 
4: 
Feels 
uninforme
d about 
prices 

Indicator 
5: 
Gets info. 
from 
adverts 
only 

Energy sector 
Indicator 
6: 
Problems 
comparing 
due to 
info. 
factors 

Indicator 
7: 
Not 
switched 
due to 
info. 
factors 

Indicator 
5: 
Gets info. 
from 
adverts 
only 

Indicator 
7: 
Not 
switched 
due to 
info. 
factors 

Indicator 
5: 
Gets info. 
from 
adverts 
only 

Finance sector 
Indicator 
6: 
Problems 
comparing 
due to 
info. 
factors 

Indicator 
7: 
Not 
switched 
due to 
info. 
factors 

0.00359 
0.0325** 
0.0523 
-0.00141 

1.189*** 
1.303*** 
0.822 
1.231*** 
1.140*** 
0.932* 
0.874*** 
0.935* 
1.322*** 
1.219*** 
1.142*** 
1.264*** 
- 
- 
- 
- 
- 
1.155*** 
1.323*** 
1.196 
1.342*** 
26,653 
0.0574 

- 
- 
- 
- 
- 
- 
- 
- 
0.0140 
-0.00135 
0.00924** 
-0.0121* 
- 
- 
- 
- 
- 
- 
- 
- 
- 
9,793  
0.0972 

Read internet communication: Easy 
Read internet communication: Difficult 
Read internet communication: Very difficult 
Did not read internet communication 
Compare deals from banks: When notified 
Compare deals from banks: Sporadically 
Compare deals from banks: Only the first time 
Compare deals from banks: Never 
Know bank contract conditions: Not at all 
Know bank contract conditions: Not very 
Know bank contract conditions: Fair amount 
Know bank contr/read comm: No response 
Read bank comm’n: Glanced or skim read 
Read bank communication: Saw what it was 
Read bank communication: Not read it at all 
Read bank communication: Don't know 
Read bank communication: No response 
Read bank communication: Easy 
Read bank communication: Difficult 
Read bank communication: Very Difficult 
Did not read bank communication 
N 
R2 
Marginal effect / Odds ratio 
Indicator code 
Note: Values in this table are results of general to specific (G-S) regression analysis. The G-S methodology is described in Annex 8. “-” implies that the explanatory variable 
was  not  included  in  the  final  regression  model  established  via  the  G-S  process.  */**/***  implies  that  a  result  is  statistically  significant  at  90%/95%/99%.  For  each  the 
indicator in this table, the R2 in the table is the Pseudo-R2 for the full regression model, which includes the explanatory variables shown in this table and other explanatory 
variables  (not  shown  in  this  table)  that  were  identified  for  the  regression  model  based  on  the  G-S  methodology.  The  indicators  are  defined  in  Table  5.  The  explanatory 
variables are defined in Table 7 and A8.1.2. There are no results for Indicator 7 for the online sector because the G-S process could not be run for this indicator due to low 
variation in the dependent variable. 
Source: London Economics analysis of the survey data 

0.00153 
0.0203* 
0.0225 
- 
-0.00789** 
0.0158*** 
0.00558 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
0.00569 
-0.00227 
- 
0.0142** 
10,228  
0.0925 

- 
- 
- 
- 
- 
- 
- 
- 
0.0101 
0.00935 
0.0000688 
- 
- 
- 
- 
- 
- 
-0.00306 
0.00326 
0.0106 
0.00242 
3,795  
0.1374 

0.00811 
0.0569*** 
0.173*** 
0.0209* 
-0.00472 
0.00664 
0.0278*** 
0.00557 
- 
- 
- 
- 
- 
- 
- 
- 
- 
0.0226** 
0.0632*** 
0.107** 
0.0406*** 
15,481  
0.0756 

0.0013 
0.0328 
- 
0.0038 
0.0053 
-0.0007 
0.0012 
-0.0063* 
- 
- 
- 
- 
0.0058 
0.0109 
0.0108 
0.0127 
-0.0029 
- 
- 
- 
- 
4,481  
0.1461 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
16,451  
0.0890 

0.164*** 
0.107*** 
0.0537*** 
0.242*** 
-0.0191*** 
0.0967*** 
0.0714*** 
0.0857*** 
- 
0.0661*** 
0.234*** 
0.277*** 
- 
15,481  
0.0859 

5_b_adv_ene 

5_b_adv_onl 

5_b_adv_fin 

Odds ratio 

Marginal 

Marginal 

Marginal 

Marginal 

Marginal 

Marginal 

Marginal 

Marginal 

Marginal 

7_ene 

6_ene 

7_onl 

6_onl 

6_fin 

7_fin 

4 

Online sector 
Indicator 
6: 
Problems 
comparing 
due to 
info. 
factors 
0.0566*** 
0.268*** 
0.285*** 
0.0785*** 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
0.00874* 
0.0410*** 
0.0276 
0.0166*** 
19,913  
0.1217 

228 

 
 
 
 
 
 
  
  
  
  
  
 
  
 
  
 
  
 
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

6.4.4.  Dimension 4: Inability or failure to buy, choose or access suitable 
products 

Market-related  issues  such  as  information  and  competition  problems  and  insufficient 
engagement  in  markets  by  consumers  may  be  related  to  the  fourth  dimension  of 
vulnerability  since  these  factors  may  cause  consumers  to  make  unsuitable  product 
choices. 

The  behavioural  experiment  results  are  especially  relevant  to  this  dimension  of 
vulnerability, since the experiments examined consumers’ ability to select the best deal 
in simulated purchasing situations in the three key markets of the study. Therefore, in 
this section we draw on the results of G-S regression analysis of respondents’ choices 
in the experiments.  

The experiment results presented in  Table 42 show that the extent that a respondent 
has  read  the  most  recent  bill  or  communication  from  their  energy  supplier,  internet 
provider or bank had an impact on their overall performance in the experiments.153 For 
example,  the  odds  ratio  for  having  not  read  their  most  recent  bill/communication  “at 
all” is 0.77, implying that these consumers were less likely to perform well overall than 
to those who read their bill/communication “completely”.154 

In  addition,  the  experiment  results  also  show  that  consumers  who  have  previously 
been  unable  to  read  the  terms  and  conditions  of  their  energy,  telecommunications  or 
banking contract due to overly small print performed worse in the experiments overall, 
with an odds ratio of 0.85. 

The  results  for  the  ‘Remedy  treatment’  variable  show  the  effect  of  the  remedy 
treatment relative to the marketing practice treatment on respondents’ ability to select 
the optimal deal in particular rounds of the experiments. These results imply that the 
remedy increased the likelihood that respondents selected the best offer in the second 
round of the energy sector experiment, the first round of the online sector experiment, 
and  both  rounds  of  the  finance  sector  experiment,  with  marginal  effects  of  15.6pp, 
19.8pp, 8.3pp and 5.8pp respectively.  

153  It  is  important  to  note  that,  as  explained  in  Annex  8,  in  the  regressions  of  experiment 
performance,  only  market-related  variables  corresponding  to  the  sector  of  the  experiment 
(or  in  the  case  of  the  cross-cutting  experiment,  the  sector  used  to  frame  the  experiment) 
were included as explanatory variables. 
That  is,  for  example,  in  regressions  of  performance  in  the  energy  sector  experiment,  only 
variables  representing  how  often  respondents  compare  deals  in  the  energy  sector,  to  what 
extent  they  know  their  energy  contract,  whether  they  have  read  their  most  recent  energy 
bill,  and  how  difficult  they  found  it  to  read  this bill were  included  as explanatory  variables, 
whereas variables corresponding to other sectors were not included. 
Similarly,  in  regressions  of  performance  in  the  online  (finance)  sector  experiment,  only 
variables representing how often respondents compare  deals in the online (finance)  sector, 
to  what  extent they  know  their internet  (bank)  contract,  whether  they  have  read  the  most 
recent communication from their internet provider (bank), and how difficult they found it to 
read  this  communication  were  included  as  explanatory  variables,  whereas  variables 
corresponding to other sectors were not included. 

154  The  variable  for  overall  experiment  performance  is  equal  to  the  total  number  of  correct 
choices  a  respondent  made  across  both  rounds  of  both  experiments  they  completed.  Since 
respondents  completed  two  experiments  each,  and  each  experiment  comprised  of  two 
rounds, this variable ranges from 0 (no correct choices) to 4 (four correct choices). 

229 

 
 
 
                                                 
6 │ Drivers of and factors linked to consumer vulnerability 

This suggests that marketing practices do drive this dimension of vulnerability, but also 
that  the  effect  of  marketing  practices  may  be  specific  to  the  sector  and  choice 
environment. The role of marketing practices is examined further in Chapter 8.  

230 

 
 
 
Table 42: 

Impact of market-related characteristics and consumers’ experience in markets on experiment performance 

6 │ Drivers of and factors linked to consumer vulnerability 

Energy Sector 

Online Sector 

Finance Sector 

Cross-cutting 

Round 1  Round 2  Round 1  Round 2  Round 1  Round 2  Round 1  Round 2 

Overall 
0.850** 

- 
- 
- 
- 
- 
- 

- 
- 
- 
- 
- 
- 

- 
- 
0.0317 
0.0201 
-0.0476 
0.00832 

- 
0.156*** 
- 
- 
- 
- 

- 
0.0831*** 
- 
- 
- 
- 

-0.0418** 
0.0577*** 
-0.0724** 
-0.00885 
0.0208 
0.00845 

Unable to read Ts&Cs due to small print 
Treatment 
Compare deals: When need to renew 
Compare deals: Sporadically 
Compare deals: Only the first time 
Compare deals: Never 
Compare deals: No response 
Know contract (and conditions): Not at all 
Know contract (and conditions): Not very 
Know contract (and conditions): Fair amount 
Know contract/read comm'n: No response 
Read bill/communication: Glanced or skim read 
Read bill/communication: Looked at price/what it was 
Read bill/communication: Not at all 
Read bill/communication: Don't know 
Read bill/communication: No response 
Read bill/communication: Easy 
Read bill/communication: Difficult 
Read bill/communication: Very difficult 
Did not read bill or communication 
N 
R2 
Marginal effect / Odds ratio 
Note: Values in this table are results of general to specific (G-S) regression analysis. The G-S methodology is described in Annex 8. “-” implies that the variable was not 
included in the final regression model established via the G-S process. */**/*** implies that a result is statistically significant at 90%/95%/99%. For each the indicator in 
this table, the R2 in the table is the Pseudo-R2 for the full regression model, which includes the explanatory variables shown in this table and other explanatory variables 
(not shown in this table) that were identified for the regression model based on the G-S methodology.  The explanatory variables are defined in Table 7 and A8.1.2. The 
dependent variables in the experiment analysis signify whether the respondent chose the optimal deal. For individual experiment rounds, the dependent variable is equal to 
one if the respondent chose the optimal deal, or zero otherwise. The ‘Overall’ performance measure is the total number of correct choices made by the respondent (ranging 
from 0 to 4). Thus, positive marginal effects imply a higher likelihood of choosing the optimal deal, and odds ratios greater than one imply better overall performance. 
Source: London Economics analysis of the survey data 

-0.0953*** 
0.198*** 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

-0.0689** 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

-0.000165 
-0.00532 
0.0198 
-0.0106 
- 
- 
- 
- 
- 
0.0326* 
-0.00993 
0.0948*** 
0.00704 
2,505 
0.1205 
Marginal 

- 
- 
- 
- 
0.860** 
0.767*** 
0.770*** 
0.909 
2.387*** 
- 
- 
- 
- 

5,051 
0.0421 
Odds ratio 

2,510 
0.1061 
Marginal 

2,522 
0.0710 
Marginal 

2,565 
0.0628 
Marginal 

2,510 
0.0823 
Marginal 

2,522 
0.0318 
Marginal 

2,565 
0.0237 
Marginal 

2,505 
0.1191 
Marginal 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

- 
- 
- 
- 

231 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

Results from G-S regression analysis of the survey data, presented in  Table 43, Table 
44 and Table 45, show that some of the market-related factors examined in the survey 
are linked with indicators of vulnerability in this dimension. 

For example, consumers who have previously been unable to read contract terms and 
conditions due to overly small print are more likely to be vulnerable in terms of having 
problems comparing deals due to market-related factors in all three sectors (Table 43); 
being prevented from switching due to termination costs and bundling (Table 44); and 
being excluded from e-commerce (Table 45). 

Furthermore, in general being less experienced and engaged with markets, in terms of 
comparing  deals,  knowing  contract  terms,  reading  communications,  and  the  difficulty 
of reading communications, is associated with a higher likelihood of vulnerability on a 
number  of  indicators,  especially  having  problems  comparing  deals  due  to  personal 
(Indicator 8a), market-related (Indicator 8b) and access-related (Indicator 8c) factors. 
This  can  be  seen  from  the  results  matrix  in  Figure  29  (on  page  221)  as  well  as  from 
the tables below.155 

For  example,  the  results  are  particularly  strong  for  variables  which  signify  that 
consumers do not know their contract conditions in the three key sectors. Specifically, 
relative  to  those  who  know  their  contract  conditions  ‘completely’  (marginal  effects 
given in parentheses): 

  Those who do not know the conditions of their energy contract ‘at all’ are more 
likely  to  have  problems  comparing  energy  deals  in  the  energy  sector  due  to 
personal  (23.4pp),  market-related  (10.0pp)  and  access-related  (11.4pp) 
factors (Table 43); and are more likely to have been prevented from switching 
their  energy  deal  due  to  personal  (8.9pp)  and  market-related  (5.6pp)  factors 
(Table 44). 

  Consumers who do not know the conditions of their internet contract ‘at all’ are 
more  likely  to  have  problems  comparing  internet  deals  due  to  personal 
(15.2pp),  market-related  (9.4pp)  and  access-related  (7.2pp)  factors  (Table 
43). 

  Those who do not know the contract conditions of their bank account ‘at all’ are 
more  likely  to  have  problems  comparing  bank  deals  due  to  personal  factors 
(22.3pp)  and  market-related  (21.7pp)  factors  (Table  43);  and  are  more  likely 
to have been prevented from switching their bank due to personal (6.2pp) and 
market-related (11.1pp) factors (Table 44). 

Likewise,  compared  to  those  who  found  it  very  easy  to  read  the  latest  bill  or 
communication (marginal effects in parentheses): 

  Consumers  who  found  it  very  difficult  to  read  the  most  recent  bill  from  their 
energy  supplier  are  more  likely  to  have  problems  comparing  deals  in  the 
energy  sector  due  to  personal  (13.9pp)  and  market-related  (39.9pp)  factors 
(Table 43). 

155  In  particular,  note  in  the  matrix  the  frequency  of  red  cells  for  indicators  8a,  8b  and  8c  in 

dimension 4. 

232 

 
 
                                                 
6 │ Drivers of and factors linked to consumer vulnerability 

  Those  who  found  it  very  difficult  to  read  the  latest  communication  from  their 
internet  provider  are  more  likely  to  have  problems  comparing  internet  deals 
due to personal (20.7pp) and market-related (57.9pp) factors (Table 43). 

  Those who found it very difficult to read the most recent communication from 
their  bank  are  more  likely  to  have  problems  comparing  bank  deals  due  to 
personal  (22.0pp)  and  market-related  (34.4pp)  factors  (Table  43);  and  are 
more  likely  to have  been  prevented  from  switching  their  bank due  to market-
related factors (14.2pp) (Table 44). 

233 

 
 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

Table 43: 

Impact of market-related characteristics on Indicator 8 in Dimension 4; Has problems comparing deals due to 

personal factors, market related factors, and access- related factors 

Energy sector 

Online sector 

Finance sector 

Unable to read Ts&Cs due to small print 
Compare  energy  deals:  When  need  to 
renew 
Compare energy deals: Sporadically 
Compare energy deals: Only the first time 
Compare energy deals: Never 

Indicator 
8a: 

Personal 
factors 
0.0376*** 

Indicator 
8b: 

Market 
factors 
0.0617*** 

Indicator 
8c: 

Access 
factors 
- 

Indicator 
8a: 

Personal 
factors 

- 

0.0329*** 
0.0396*** 
0.0914*** 
0.0631*** 

Indicator 
8b: 

Market 
factors 
0.0360*** 

0.00438 
0.0142 
0.0373*** 
-0.00266 

Indicator 
8c: 

Access 
factors 

- 

0.00700 
0.0185*** 
0.0365*** 
0.0293*** 

Indicator 
8a: 

Personal 
factors 
- 

0.0528*** 
0.0533*** 
0.119*** 
0.111*** 

Indicator 
8b: 

Market 
factors 
0.0718*** 

0.0560*** 
0.0376** 
0.0567*** 
0.0415*** 

Know energy contract: Not at all 
Know energy contract: Not very 
Know energy contract: Fair amount 
Know  energy 
response 

contract/Read  bill:  No 

0.234*** 
0.211*** 
0.0711*** 

0.100*** 
0.166*** 
0.0790*** 

0.114*** 
0.111*** 
0.0469*** 

0.296*** 

0.161*** 

0.378*** 

Read energy bill: Glanced or skim read 

0.0525*** 

0.00233 

0.00445 

Read energy bill: Looked at total price 
Read energy bill: Not at all 
Read energy bill: Don't know 
Read energy bill: No response 
Read energy bill: Easy 
Read energy bill: Difficult 
Read energy bill: Very difficult 
Did not read energy bill 
Compare  internet  deals:  When  need  to 
renew 
Compare internet deals: Sporadically 
Compare internet deals: Only the first time 
Compare internet deals: Never 
Compare internet deals: No response 
Know internet contract conditions: Not at all 
Know internet contract conditions: Not very 
Know 
internet  contract  conditions:  Fair 
amount 
Know 
response 

internet  contr./read  comm:  No 

0.0846*** 
0.142*** 
0.136*** 
- 
0.00766 
0.122*** 
0.139*** 
- 

0.0215 
0.0574*** 
0.111*** 
0.159*** 
0.102** 
-0.0218 
0.0549*** 

0.0189 
0.137*** 
0.0890*** 
- 
0.125*** 
0.331*** 
0.399*** 
- 

0.0120 
0.0574*** 
0.0849*** 
0.0341** 
-0.102*** 
- 
- 

0.0162 

0.0100 

- 

- 

0.0364*** 
0.0391*** 
0.0282* 
- 
- 
- 
- 
- 

0.0113 
0.00854 
0.0353*** 
0.0243** 
0.153*** 
-0.0183 
-0.00796 

- 
- 
- 

-0.0230*** 
-0.0169*** 
-0.00342 

0.0105 
0.0134 
-0.00628 

0.0400* 
0.0583*** 
0.0304** 

0.0224** 

0.0201 

-0.00155 

-0.00134 

- 

0.0332*** 

-0.0391*** 

- 
- 
- 

- 

- 

- 
- 
- 
- 
0.0216** 
0.0415*** 
0.0813*** 
0.0159 

- 
-
0.0278*** 
-
0.0406*** 
0.0219 
0.0155 
-0.0101 
0.0511*** 
0.133*** 
0.188*** 
- 

- 
- 
- 
- 
-0.0100** 
-0.00873* 
0.00357 
-0.0185*** 

0.0588*** 
0.0578*** 
0.0254 
- 
-0.0286** 
0.0180 
-0.00992 
- 

0.0141 
0.0357** 
0.0929*** 
0.109*** 
0.265*** 
-0.0473** 
-0.00898 

-0.0438*** 
-0.00208 
-0.00911 
- 
0.0427*** 
0.141*** 
0.118*** 
- 

-0.0130 
0.0191 
0.000979 
-0.0547*** 
-0.0228 
- 
- 

- 

- 

0.152*** 
0.141*** 

0.0942*** 
0.191*** 

0.0716*** 
0.0243*** 

-0.0204** 

0.0410*** 

0.0805*** 

0.00880** 

0.00166 

-0.0149 

0.296*** 

0.363*** 

0.0642** 

-0.0835** 

Indicator 
8c: 

Access 
factors 
- 

0.00102 
0.00689* 
0.00763* 
0.00455 
-
0.00858**
* 
-0.00372 
-0.000308 

- 

- 
- 
- 
- 
- 
- 
- 
- 

- 
- 
- 
- 
- 
-0.00189 
0.00466 

-0.00223 

0.00998* 

234 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 43: 

Impact of market-related characteristics on Indicator 8 in Dimension 4; Has problems comparing deals due to 

personal factors, market related factors, and access- related factors 

6 │ Drivers of and factors linked to consumer vulnerability 

Energy sector 

Indicator 
8b: 

Market 
factors 

Indicator 
8c: 

Access 
factors 

Indicator 
8a: 

Personal 
factors 

Online sector 

Indicator 
8b: 

Market 
factors 

Finance sector 

Indicator 
8c: 

Access 
factors 

Indicator 
8a: 

Personal 
factors 

Indicator 
8b: 

Market 
factors 

Indicator 
8c: 

Access 
factors 

Indicator 
8a: 

Personal 
factors 

-0.0102 

0.0358** 

0.0138 
0.0177 
- 
- 
- 
- 
- 
0.0223 
0.0481*** 

- 

- 

- 
- 
- 
0.00723 
0.0619** 
0.147* 
0.0286** 
0.0255 
0.0425*** 

0.0816*** 
0.110*** 

0.0735*** 
0.0550*** 

- 
- 
- 

- 
0.0201* 
0.0665*** 
0.0280 
0.0325* 
0.0438** 
0.0219* 
0.0790*** 
-0.0275 
- 

15,481 
0.1273 
Marginal 
8a_ene 

0.0786*** 
0.0612*** 
0.0481*** 

0.0342 
-0.0253** 
0.0488*** 
0.0137 
0.000390 
- 
0.0337*** 
0.107*** 
0.120** 
- 

15,481 
0.1084 
Marginal 
8b_ene 

Read  internet  comm'n:  Glanced  or  skim 
read 
Read  internet  communication:  Saw  what  it 
was 
Read internet communication: Not read it at 
all 
Read internet communication: Don't know 
Read internet communication: No response 
Read internet communication: Easy 
Read internet communication: Difficult 
Read internet communication: Very difficult 
Did not read internet communication 
Compare deals from banks: When notified 
Compare deals from banks: Sporadically 
Compare  deals  from  banks:  Only  the  first 
time 
Compare deals from banks: Never 

Know bank contract conditions: Not at all 
Know bank contract conditions: Not very 
Know bank contract conditions: Fair amount 
Know  bank  contract/read  comm'n:  No 
response 
Read bank comm'n: Glanced or skim read 
Read bank communication: Saw what it was 
Read bank communication: Not read it at all 
Read bank communication: Don't know 
Read bank communication: No response 
Read bank communication: Easy 
Read bank communication: Difficult 
Read bank communication: Very Difficult 
Did not read bank communication 
N 
R2 
Marginal effect / Odds ratio 
Indicator code 

235 

0.00942 

0.0145* 

0.0142 

0.0123** 

0.0259** 

0.116*** 

0.188*** 

0.0315*** 

-0.00905 
-0.0208** 
- 
- 
- 
- 
- 
0.00930 
-0.00286 

0.0207** 
-0.000173 
-
0.0390*** 
-0.00688 
0.00841 

0.0235 
0.00680 
0.00994 
0.0000581 
0.0287** 
- 
- 
- 
- 
- 

15,481 
0.1263 
Marginal 
8c_ene 

0.146*** 
0.118*** 
- 
0.0167* 
0.181*** 
0.207*** 
- 
0.0109 
0.0180* 

0.158*** 
0.183*** 
- 
0.0855*** 
0.413*** 
0.579*** 
- 
0.0236** 
0.0554*** 

0.0391*** 
0.0543*** 
- 
-0.00272 
0.0237** 
0.0407 
- 
- 
- 

0.0484*** 
0.0741*** 

0.0804*** 
0.0725*** 

-0.0112 
0.000968 
-0.0153** 

0.00235 
-0.00363 
0.0268** 
0.0158 
0.0130 
- 
0.0128* 
0.0385*** 
0.0212 
- 

19,913 
0.1442 
Marginal 
8a_onl 

0.0355* 
0.0350*** 
0.00889 

-0.00729 
- 
- 
- 
- 
- 
0.00891 
0.0624*** 
0.0497 
0.0231** 
19,913 
0.1461 
Marginal 
8b_onl 

- 
- 

- 
- 
- 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

19,913 
0.0714 
Marginal 
8c_onl 

- 

- 

- 
- 
- 
- 
- 
- 
- 

0.223*** 
0.219*** 
0.0699*** 

0.349*** 
0.0418*** 
0.156*** 
0.182*** 
0.119*** 
- 
0.0289** 
0.152*** 
0.220*** 
- 

15,481 
0.1210 
Marginal 
8a_fin 

- 

- 

- 
- 
- 
-0.00196 
0.112*** 
0.0834 
0.00123 

0.217*** 
0.262*** 
0.135*** 

0.265*** 
-0.00421 
0.187*** 
0.143*** 
0.132*** 
- 
0.107*** 
0.376*** 
0.344*** 
- 

15,481 
0.1049 
Marginal 
8b_fin 

- 

- 

- 
- 
- 
- 
- 
- 
- 

- 
- 
- 

- 
0.00179 
0.00154 
0.00178 
0.00742* 
0.00216 
- 
- 
- 
- 

15,481 
0.0933 
Marginal 
8c_fin 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

Note: Values in this table are results of general to specific (G-S) regression analysis. The G-S methodology is described in Annex 8. “-” implies that the explanatory variable 
was  not  included  in  the  final  regression  model  established  via  the  G-S  process.  */**/***  implies  that  a  result  is  statistically  significant  at  90%/95%/99%.  For  each  the 
indicator in this table, the R2 in the table is the Pseudo-R2 for the full regression model, which includes the explanatory variables shown in this table and other explanatory 
variables  (not  shown  in  this  table)  that  were  identified  for  the  regression  model  based  on  the  G-S  methodology.  The  indicators  are  defined  in  Table  5.  The  explanatory 
variables are defined in Table 7 and A8.1.2. 
Source: London Economics analysis of the survey data 

Table 44: 

Impact of market-related characteristics on Indicator 9 in Dimension 4; Has not switched due to personal 

factors, market related factors, access- related factors, termination costs, and bundling 

Indicato
r 9a: 
Persona
l factors 

Energy sector 
Indicato
r 9b: 
Market 
factors 

Indicato
r 9c: 
Access 
factors 

Indicato
r 9a: 
Persona
l factors 

Online sector 
Indicato
r 9b: 
Market 
factors 

Indicato
r 9c: 
Access 
factors 

Indicato
r 9a: 
Persona
l factors 

Finance sector 
Indicato
r 9b: 
Market 
factors 

Indicato
r 9c: 
Access 
factors 

Unable to read Ts&Cs due to small print 
Compare energy deals: When need to renew 
Compare energy deals: Sporadically 
Compare energy deals: Only the first time 
Compare energy deals: Never 
Know energy contract: Not at all 
Know energy contract: Not very 
Know energy contract: Fair amount 
Know energy contract/Read bill: No response 
Read energy bill: Glanced or skim read 
Read energy bill: Looked at total price 
Read energy bill: Not at all 
Read energy bill: Don't know 
Read energy bill: No response 
Read energy bill: Easy 
Read energy bill: Difficult 
Read energy bill: Very difficult 
Did not read energy bill 
Compare internet deals: When need to renew 
Compare internet deals: Sporadically 
Compare internet deals: Only the first time 
Compare internet deals: Never 
Compare internet deals: No response 
Know internet contract conditions: Not at all 
Know internet contract conditions: Not very 
Know internet contract conditions: Fair amount 
Know internet contr./read comm: No response 
Read internet comm’n: Glanced or skim read 
Read internet communication: Saw what it was 
Read internet communication: Not read it at all 

- 
0.0506* 
0.0331 
0.153*** 
0.135*** 
0.0887*** 
0.0706*** 
0.0312 
- 
0.0367** 
0.103*** 
0.123*** 
0.145*** 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
-0.00538 
0.0201 
-0.0209 

- 
-0.00602 
0.0713*** 
0.0573*** 
-0.00392 
0.0564* 
0.0524** 
0.0249 
- 
0.0118 
-0.0198* 
-0.00842 
-0.0254 
- 
0.00631 
0.0618*** 
0.0493 
- 
-0.0179 
0.00829 
0.00402 
0.0110 
-0.00551 
- 
- 
- 
- 
- 
- 
- 

0.0423*** 
0.0264 
0.0650* 
0.221*** 
0.318*** 
-0.0102 
0.0292 
0.0454** 
- 
- 
- 
- 
- 
- 
-0.0382** 
-0.0605*** 
-0.0372 
-0.0433*** 
-0.0208 
-0.00824 
-0.0470*** 
-0.0713*** 
0.0299 
- 
- 
- 
- 
- 
- 
- 

- 
-0.00268 
0.00833 
0.0101 
0.0160* 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
0.00272 
0.00932 
0.0280** 
0.0576*** 
- 
- 
- 
- 
- 
- 
- 
- 

- 
- 
- 
- 
- 
0.0393 
0.0111 
0.00432 
0.0343 
- 
- 
- 
- 
- 
- 
- 
- 
- 
0.000822 
0.0407*** 
0.0275* 
0.00427 
- 
- 
- 
- 
- 
- 
- 
- 

- 
-0.00768 
0.00466 
0.00408 
0.0209** 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
-0.00507 
0.00378 
0.00289 
0.0598*** 
- 
- 
- 
- 
- 
- 
- 
- 

- 
-0.0209** 
-0.0224** 
-0.00391 
-0.00272 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
-0.00231 
0.0398* 
0.0390 
0.00760 
- 
- 
- 
- 
- 
- 
- 
- 
- 
-0.00665 
-0.0462*** 
-0.00673 

0.00819* 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
-0.0135*** 
-0.00513 
0.0000794 
-0.0112*** 
0.000233 
0.00302 
-0.0096*** 
-0.000424 
0.00299 
- 
- 
- 
- 
- 
- 
- 

Indicato
r 9d: 
Termina
tion 
costs 
0.199*** 
-0.00119 
0.00737 
0.0102 
-0.0152** 
- 
- 
- 
- 
- 
- 
- 
- 
- 
0.0127* 
0.0171** 
0.0475*** 
-0.000327 
0.0165** 
-0.0117* 
-0.0182*** 
-0.0323*** 
-0.0558*** 
- 
- 
- 
- 
-0.0139** 
0.00691 
-0.0167** 

Indicato
r 9e: 
Bundlin
g 

0.209*** 
- 
- 
- 
- 
-0.0387*** 
-0.00224 
-0.00179 
-0.0142 
-0.00842 
0.00703 
-0.0204** 
-0.0609*** 
- 
- 
- 
- 
- 
0.00483 
-0.0142* 
-0.0246*** 
-0.0376*** 
-0.0797*** 
- 
- 
- 
- 
-0.0113 
0.0424*** 
-0.00606 

236 

 
 
 
 
 
 
 
 
Table 44: 

Impact of market-related characteristics on Indicator 9 in Dimension 4; Has not switched due to personal 

factors, market related factors, access- related factors, termination costs, and bundling 

6 │ Drivers of and factors linked to consumer vulnerability 

Indicato
r 9a: 
Persona
l factors 

Energy sector 
Indicato
r 9b: 
Market 
factors 

Indicato
r 9c: 
Access 
factors 

Indicato
r 9a: 
Persona
l factors 

Online sector 
Indicato
r 9b: 
Market 
factors 

Indicato
r 9c: 
Access 
factors 

Indicato
r 9a: 
Persona
l factors 

Finance sector 
Indicato
r 9b: 
Market 
factors 

Indicato
r 9c: 
Access 
factors 

Indicato
r 9e: 
Bundlin
g 

-0.00431 
-0.0417 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

Read internet communication: Don't know 
Read internet communication: No response 
Read internet communication: Easy 
Read internet communication: Difficult 
Read internet communication: Very difficult 
Did not read internet communication 
Compare deals from banks: When notified 
Compare deals from banks: Sporadically 
Compare deals from banks: Only the first time 
Compare deals from banks: Never 
Know bank contract conditions: Not at all 
Know bank contract conditions: Not very 
Know bank contract conditions: Fair amount 
Know bank contract/read comm'n: No response 
Read bank comm'n: Glanced or skim read 
Read bank communication: Saw what it was 
Read bank communication: Not read it at all 
Read bank communication: Don't know 
Read bank communication: No response 
Read bank communication: Easy 
Read bank communication: Difficult 
Read bank communication: Very Difficult 
Did not read bank communication 
N 
R2 
Marginal effect / Odds ratio 
Indicator code 
Note: Values in this table are results of general to specific (G-S) regression analysis. The G-S methodology is described in Annex 8. “-” implies that the explanatory variable was not included in the 
final  regression  model  established  via  the  G-S  process.  */**/***  implies  that  a  result  is  statistically  significant  at  90%/95%/99%.  For  each  the  indicator  in  this  table,  the  R2  in  the  table  is  the 
Pseudo-R2 for the full regression model, which includes the explanatory variables shown in this table and other explanatory variables (not shown in this table) that were identified for the regression 
model based on the G-S methodology. The indicators are defined in Table 5. The explanatory variables are defined in Table 7 and A8.1.2. 
Source: London Economics analysis of the survey data 

- 
- 
0.0232** 
0.114 
- 
0.0368** 
-0.000987 
-0.00469* 
-0.0075*** 
-0.00449* 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

-0.0198 
-0.0133 
0.0199** 
0.0459*** 
0.0561 
- 
0.0289*** 
-0.000724 
-0.0219*** 
-0.0589*** 
- 
- 
- 
- 
-0.0119* 
0.0193* 
0.0084 
-0.0129 
-0.0022 
0.000598 
0.0260** 
0.000789 
- 

- 
- 
- 
- 
- 
- 
0.00502 
-0.00499 
-0.0158 
-0.0587*** 
- 
- 
- 
- 
0.00775 
-0.00948 
0.0106 
0.0688*** 
0.0802*** 
- 
- 
- 
- 

- 
- 
0.0392*** 
0.147*** 
0.0705 
0.0303** 
- 
- 
- 
- 
- 
- 
- 
- 
0.00220 
-0.00860 
-0.0208** 
-0.0232*** 
-0.00545 
- 
- 
- 
- 

- 
- 
- 
- 
- 
- 
-0.0159** 
0.00934 
0.00683 
-0.00923 
- 
- 
- 
- 
-0.0118** 
0.000138 
-0.00484 
-0.0171*** 
-0.00947 
- 
- 
- 
- 

- 
- 
-0.0120 
-0.000300 
- 
0.00327 
0.0337 
-0.00245 
0.0445** 
0.0509*** 
0.0615** 
0.0434*** 
0.0261** 
- 
0.0266** 
0.0291* 
0.0504*** 
0.0759*** 
- 
- 
- 
- 
- 

-0.0264 
-0.0416 
- 
- 
- 
- 
0.0630** 
0.0679*** 
0.0145 
0.00422 
0.111*** 
0.0826*** 
0.0301** 
- 
0.0390*** 
0.0737*** 
0.0557** 
0.0898*** 
- 
0.00628 
0.0761** 
0.142* 
- 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
0.0507* 
0.0284* 
0.00753 
-0.000185 
- 
- 
- 
- 
- 
- 
- 
- 
- 

- 
- 
-0.00103 
0.0143 
- 
0.000128 
-0.00508 
0.00307 
0.0120* 
0.00872 
0.0180 
0.0165* 
0.00607 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

4,782  
0.0651 
Marginal 
7_9_mar_ene 

3,427  
0.0804 
Marginal 
7_9_mar_onl 

4,782  
0.1283 
Marginal 
7_9_per_ene 

4,782  
0.2145 
Marginal 
7_9_acc_ene 

4,368  
0.0597 
Marginal 
7_9_mar_fin 

3,483  
0.2094 
Marginal 
7_9_per_onl 

3,265  
0.1083 
Marginal 
7_9_acc_onl 

4,355  
0.1330 
Marginal 
7_9_per_fin 

4,095  
0.1538 
Marginal 
7_9_acc_fin 

26,653  
0.0852 
Marginal 
7_9_bun 

Indicato
r 9d: 
Termina
tion 
costs 
-0.00936 
-0.00124 
0.00577 
0.0358** 
0.0920** 
- 
0.00842 
-0.00258 
-0.0145** 
-0.0393*** 
0.0390*** 
0.00999 
0.00383 
0.00665 
- 
- 
- 
- 
- 
0.00522 
0.0370*** 
0.0136 
0.0225*** 
26,653  
0.1242 
Marginal 
7_9_ter 

237 

 
 
 
 
 
 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

Table 45: 

Impact of market-related dcharacteristics on Indicators 10 and 

11 in Dimension 4; Excluded from e-commerce and declined a loan 

Unable to read Ts&Cs due to small print 
Compare energy deals: When need to 
renew 
Compare energy deals: Sporadically 
Compare energy deals: Only the first time 
Compare energy deals: Never 
Know energy contract: Not at all 
Know energy contract: Not very 
Know energy contract: Fair amount 
Know energy contract/Read bill: No 
response 
Read energy bill: Glanced or skim read 
Read energy bill: Looked at total price 
Read energy bill: Not at all 
Read energy bill: Don't know 
Read energy bill: No response 
Read energy bill: Easy 
Read energy bill: Difficult 
Read energy bill: Very difficult 
Did not read energy bill 
Compare internet deals: When need to 
renew 
Compare internet deals: Sporadically 
Compare internet deals: Only the first 
time 
Compare internet deals: Never 
Compare internet deals: No response 
Know internet contract conditions: Not at 
all 
Know internet contract conditions: Not 
very 
Know internet contr conditions: Fair 
amount 
Know internet contr/read comm: No 
response 
Read internet comm'n: Glanced or skim 
read 
Read internet comm'n: Saw what it was 
Read internet comm'n: Not read it at all 
Read internet communication: Don't know 
Read internet communication: No 
response 
Read internet communication: Easy 
Read internet communication: Difficult 
Read internet communication: Very 
difficult 
Did not read internet communication 
Compare deals from banks: When notified 
Compare deals from banks: Sporadically 
Compare deals from banks: Only the first 
time 
Compare deals from banks: Never 
Know bank contract conditions: Not at all 
Know bank contract conditions: Not very 
Know bank contract conditions: Fair 
amount 

Excluded from e-commerce due 
to 

Indicator 10a: 
Difficulty of the 
process 

Indicator 10b: 
Not having 
payment card 

Indicator 11: 
Declined for a 
loan 

0.244*** 

0.185*** 

- 

0.0183* 
0.00102 
-0.00587 
-0.0168* 
- 
- 
- 

- 
- 
- 
- 
- 
- 
-0.00327 
0.0156 
0.0367* 
-0.00289 

- 
- 

- 
- 
- 

0.0115 

0.0191* 

0.00311 

0.0249 

-0.0181** 
0.0282** 
0.000493 
0.0177 

- 
0.0140 
0.0724*** 

0.145** 
- 
0.0231** 
0.0222** 

-0.00840 
-0.0372*** 
-0.0092 
0.0194* 

- 
- 
- 
- 
-0.0189* 
-0.00135 
-0.00586 

0.00594 
-0.00574 
0.00647 
-0.00327 
-0.0220* 
- 
- 
- 
- 
- 

0.00728 
-0.00275 

-0.00661 
-0.0252*** 
0.0501 

- 

- 

- 

- 

- 
- 
- 
- 

- 
- 
- 

0.0177* 
0.00133 
0.00239 
-0.00553 
- 
- 
- 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

0.0131* 
0.0329*** 

0.00949 
0.00358 
-0.0132 

-0.0183*** 

-0.0131*** 

-0.00759 

-0.00293 

- 
- 
- 
- 

- 
- 
- 

- 
- 
0.0114 
0.00472 

-0.0233*** 
-0.0202*** 
0.0376** 
0.0311*** 

- 
- 
0.00193 
-0.00504 

-0.00858* 
-0.0167*** 
0.0388 
0.0279** 

0.00340 

0.00465 

0.0105 

238 

 
 
 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

Table 45: 

Impact of market-related dcharacteristics on Indicators 10 and 

11 in Dimension 4; Excluded from e-commerce and declined a loan 

Excluded from e-commerce due 
to 

Indicator 10a: 
Difficulty of the 
process 

Indicator 10b: 
Not having 
payment card 

Indicator 11: 
Declined for a 
loan 

- 

-0.0125 

0.0164* 

0.00805 
- 

0.0336** 
-0.00208 

0.0639*** 
-0.0110* 

Know bank contr/read comm'n: No 
response 
Read bank comm'n: Glanced or skim read 
Read bank communication: Saw what it 
was 
Read bank communication: Not read it at 
- 
all 
- 
Read bank communication: Don't know 
- 
Read bank communication: No response 
0.0373*** 
Read bank communication: Easy 
0.0513*** 
Read bank communication: Difficult 
0.173*** 
Read bank communication: Very Difficult 
0.0387*** 
Did not read bank communication 
19,957  
N 
R2 
0.0903 
Marginal 
Marginal effect / Odds ratio 
Indicator code 
10_diff 
Note: Values in this table are results of general to specific (G-S) regression analysis. The G-S methodology is 
described in Annex 8. “-” implies that the explanatory variable was not included in the final regression model 
established via the G-S process. */**/*** implies that a result is statistically significant at 90%/95%/99%. 
For each the indicator in this table, the R2 in the table is the Pseudo-R2 for the full regression model, which 
includes  the  explanatory  variables  shown  in  this  table  and  other  explanatory  variables  (not  shown  in  this 
table)  that  were  identified  for  the  regression  model  based  on  the  G-S  methodology.  The  indicators  are 
defined in Table 5. The explanatory variables are defined in Table 7 and A8.1.2. 
Source: London Economics analysis of the survey data 

-0.00434 
-0.0108 
- 
-0.00648 
0.0128 
0.0692** 
- 
19,957  
0.1188 
Marginal 
10_card 

-0.00103 
-0.00818 
- 
- 
- 
- 
- 
4,971  
0.1230 
Marginal 
11_loan 

6.4.5.  Dimension 5: Higher susceptibility to marketing practices 

Results  of  the  survey  data  analysis  presented  in  Table  46  show  that  various  market-
related factors are linked with this dimension of vulnerability. For example, consumers 
who  have  previously  been  unable  to  read  the  terms  and  conditions  of  their  energy, 
telecommunications  or  banking  contract  due  to  overly  small  print  are  more  likely  to 
feel a high level of vulnerability due to the complexity of offers. 

Likewise, consumers who found it very difficult to read the most recent bill from their 
energy supplier, those who do not know their energy contract conditions ‘at all’, those 
who found it very difficult to read the latest communication from their bank, and those 
who do not know the contract  conditions of their internet subscription ‘at all’ are also 
more likely to feel a high level of vulnerability due to the complexity of offers. 

Table 46: 

Impact of market-related characteristics on Indicator 12 in 
Dimension 5; Feeling of vulnerability due to complexity of offers 

Indicator 12: Feels vulnerable due to complexity of 
offers 

Binary: “To a great 
extent” 

Scale: 1 (low) - 4 (high) 

Unable to read Ts&Cs due to small 
print 
Compare energy deals: When need 
to renew 
Compare energy deals: Sporadically 

0.0469*** 

- 
- 

1.658*** 

1.093** 
1.104** 

239 

 
 
 
 
 
 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

Table 46: 

Impact of market-related characteristics on Indicator 12 in 
Dimension 5; Feeling of vulnerability due to complexity of offers 

Indicator 12: Feels vulnerable due to complexity of 
offers 

Binary: “To a great 
extent” 

Scale: 1 (low) - 4 (high) 

Compare energy deals: Only the first time 
Compare energy deals: Never 
Know energy contract: Not at all 
Know energy contract: Not very 
Know energy contract: Fair amount 
Know energy contract/Read bill: No 
response 
Read energy bill: Glanced or skim read 
Read energy bill: Looked at total price 
Read energy bill: Not at all 
Read energy bill: Don't know 
Read energy bill: No response 
Read energy bill: Easy 
Read energy bill: Difficult 
Read energy bill: Very difficult 
Did not read energy bill 
Compare internet deals: When need to 
renew 
Compare internet deals: Sporadically 
Compare internet deals: Only the first 
time 
Compare internet deals: Never 
Compare internet deals: No response 
Know internet contract conditions: Not at 
all 
Know internet contract conditions: Not 
very 
Know internet contract conditions: Fair 
amount 
Know internet contr/read comm: No 
response 
Read internet communication: Glanced or 
skim read 
Read internet comm'n: Saw what it was 
Read internet comm'n: Not read it at all 
Read internet communication: Don't know 
Read internet communication: No 
response 
Read internet communication: Easy 
Read internet communication: Difficult 
Read internet communication: Very 
difficult 
Did not read internet communication 
Compare deals from banks: When notified 
Compare deals from banks: Sporadically 
Compare deals from banks: Only the first 
time 
Compare deals from banks: Never 
Know bank contract conditions: Not at all 
Know bank contract conditions: Not very 
Know bank contract conditions: Fair 
amount 
Know bank contr/read communication: No 
response 
Read bank comm'n: Glanced or skim read 
Read bank communication: Saw what it 
was 

- 
- 
0.0138** 
0.00478 
-0.00525 

0.0220** 
- 
- 
- 
- 
- 
-0.00270 
0.00996 
0.0217** 
0.000936 

- 
- 

- 
- 
- 

- 

- 

- 

- 

- 
- 
- 
- 

- 
- 
- 

- 
- 
- 
- 

- 
- 
0.0385*** 
0.0247*** 

0.00378 

0.0365*** 
-0.0161*** 

0.00979 

1.083* 
0.963 
1.166*** 
1.166*** 
1.050 

1.291*** 
- 
- 
- 
- 
- 
1.243*** 
1.424*** 
1.540*** 
1.183*** 

- 
- 

- 
- 
- 

0.931 

1.114** 

1.036 

0.859** 

- 
- 
- 
- 

- 
1.046 
1.225*** 

1.405 
1.064 
1.079* 
0.958 

0.967 
0.840*** 
1.372*** 
1.328*** 

1.115*** 

2.093*** 
0.982 

1.421*** 

240 

 
 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

Table 46: 

Impact of market-related characteristics on Indicator 12 in 
Dimension 5; Feeling of vulnerability due to complexity of offers 

Indicator 12: Feels vulnerable due to complexity of 
offers 

Binary: “To a great 
extent” 

Scale: 1 (low) - 4 (high) 

0.00187 
0.00162 
- 
0.00616 
0.0375*** 
0.0494*** 
- 
26,653 
0.1005 
Marginal 

Read bank communication: Not read it at 
all 
Read bank communication: Don't know 
Read bank communication: No response 
Read bank communication: Easy 
Read bank communication: Difficult 
Read bank communication: Very Difficult 
Did not read bank communication 
N 
R2 
Marginal effect / Odds ratio 
Indicator code 
Note: Values in this table are results of general to specific (G-S) regression analysis. The G-S methodology is 
described in Annex 8. “-” implies that the explanatory variable was not included in the final regression model 
established via the G-S process. */**/*** implies that a result is statistically significant at 90%/95%/99%. 
For each the indicator in this table, the R2 in the table is the Pseudo-R2 for the full regression model, which 
includes  the  explanatory  variables  shown  in  this  table  and  other  explanatory  variables  (not  shown  in  this 
table)  that  were  identified  for  the  regression  model  based  on  the  G-S  methodology.  The  indicators  are 
defined in Table 5. The explanatory variables are defined in Table 7 and A8.1.2. 
Source: London Economics analysis of the survey data 

1.250*** 
1.222*** 
- 
1.300*** 
1.640*** 
1.855*** 
- 
26,653 
0.0465 
Odds ratio 

_12_off_b2 

_12_off 

6.5.  Access drivers of vulnerability 

Another set of potential drivers of vulnerability relates to access to markets, products, 
or services. This can be linked to Cartwright’s “supply vulnerability” and can include: 

  Physical  access  drivers,  where  consumers  are  unable  to  access  retail  outlets 
either  due  to  restricted  mobility,  location  or  inaccessible  outlets  (i.e.  Clifton 
2013, Stearn 2012, Blocker et al. 2013); 

  Online  access  drivers,  where  consumers  cannot  access  online  services  either 
due to not having internet access, or due to inaccessible websites or apps; 

  Restricted access to information or advice, which could include information (i.e. 

bills or contracts) provided in inaccessible formats;  

  Restricted access to offers or services available to other consumer groups due 
to  not  meeting  certain  criteria  or  due  to  offers  only  being  available  through 
certain media (i.e. online); 

  Restricted access to procedures (i.e. delegation or redress); and 

 

Intentional exclusion from markets. 

Although access drivers relate primarily to the consumer environment in that they are, 
in the first instance, a result of the practices of economic actors, they are also linked to 
consumer  characteristics:  consumers  with  limited  mobility  or  limited  cognitive 
functions due to age, disability, or illness are, on the whole, more likely to be affected 
by access drivers. For instance, a UK report by the Payments Council (2012) presents a 
range of access challenges faced by consumers with disabilities or restricted mobility in 
the financial sector.  

The  survey  included  questions  on  internet  usage  which  asked  respondents  how  often 
they  use  the  internet  for  a  range  of  seven  different  activities,  namely  searching  for 

241 

 
 
 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

information, comparing prices, online banking, buying goods or services, selling goods 
or services, using social networks, and e-mail.156 

Based  on  respondents’  answers  to  these  questions,  several  variables  are  included  in 
the  data  analysis  in  order  to  proxy  for  online  access  drivers.  Specifically,  variables 
signifying  respondents’  overall  level  of  internet  usage,  in  terms  of  the  number  of 
different  activities  they  use  it  for  at  least  once  per  month,  as  well  as  variables 
representing whether respondents use the internet for particular activities at least once 
per month, are all examined as explanatory variables in the G-S regression analysis. 

The number of activities that respondents use the internet for at least once per month 
is  included  as  a  set  of  dummy  variables,  where  each  dummy  represents  a  different 
level  (‘Monthly internet  activities:  1’,  ‘Monthly  internet  activities:  2’,  etc.).  The  lowest 
overall level of internet use (i.e. ‘uses the internet for zero activities at least once per 
month’)  is  used  as  the  base,  meaning  that  the  results  presented  below  show  the 
impact of higher overall levels of internet usage. 

Figure  30  presents  a  matrix  summarising  the  links  between  respondents’  internet 
usage  and  the  various  indicators  of  vulnerability  examined  in  the  G-S  regression 
analysis  of  the  survey  data.  The  indicator  codes  across  the  top  of  the  matrix  match 
those in the list of indicators in section 3.6, as well as those in the table of indicators in 
A8.1.2.157 The internet use characteristics in the matrix (down the left hand side) are 
defined  (alongside  all  other  explanatory  variables  examined  in  the  data  analysis)  in 
Table 7 in section 3.7. 

In the matrix, red cells imply that consumers with a certain internet use characteristic 
are more likely to be vulnerable on the indicator in question, whereas green cells imply 
the  opposite  (i.e.  a  lower  likelihood  of  vulnerability  on  the  relevant  indicator).  Grey 
cells  show  that  a  characteristic  was  controlled  for  in  the  regression,  while  white  cells 
mean  that  a  characteristic  was  excluded  from  the  regression  (based  on  the  G-S 
approach). 

As  can  be  seen  from  the  matrix,  based  on  the  results  of  the  G-S  regression  analysis, 
the  variables  representing  overall  internet  use  only  have  statistically  significant 
relationships  with  six  indictors  of  vulnerability,  and  the  associations  between  the 
specific use dummies and the various indicators are quite mixed. In summary: 

  Those  who  use  the  internet  more  overall,  in  terms  of  the  number  of  different 
activities  they  use  it  for  at  least  once  per  month,  are  less  likely  to  have 
overpaid  for  services  due  to  being  unable  to  use  certain  payment  methods 
(Indicator  2),  less  likely  to  feel  vulnerable  due  to  personal  characteristics158 
(Indicator  3),  less  likely  to  have  been  prevented  from  switching  their  internet 

156  As  explained  in  section  3.7,  no  questions  were  asked  in  the  survey  on  hearing  or  visual 
impairment  for  practical  reasons;  individuals  with  visual  impairments  are  not  logged  in  to 
Ipsos’ panel, and individuals with hearing impairments cannot be contacted via CATI. 

157 No results could be produced for Indicator 7 for the online sector since the G-S process could 
not be run for this indicator due to the low total number of respondents who are vulnerable 
on this indicator. Hence, in the results tables in this section no results are presented for this 
indicator. 

158  Although  for  this  indicator  the  results  for  the  two  highest  levels  of  internet  use  are  not 

statistically significant. 

242 

 
 
                                                 
6 │ Drivers of and factors linked to consumer vulnerability 

provider or bank due to market-related factors (Indicator 9b), and less likely to 
have been declined for a loan (Indicator 11). 

  Controlling  for  characteristics  included  in  the  regressions  (e.g.  respondents’ 
overall  level  of  internet  use),  specific  internet  uses  are  typically  only  linked 
with a small number of indicators, and for any particular use the relationships 
usually differ between indicators; for example, the type of internet use that is 
linked  with  the  largest  number  of  indicators  is  comparing  prices,  which  is 
associated with a higher likelihood of vulnerability on six indicators, and lower 
likelihood of vulnerability on eight indicators. 

Based  on  the  survey  and  behavioural  experiment  results  and  insights  from  the 
literature, links between potential access drivers of vulnerability and each dimension of 
vulnerability  are  explored  in  more  detail  in  the  following  subsections.  The  full  set  of 
explanatory  variables  examined  in  the  G-S  analysis is  presented  in  Table  7 in  section 
3.7. This table also indicates which variables (i.e. which levels or categories) are used 
as bases in the analysis. 

243 

 
 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

Figure 30:  Matrix of access-related characteristics linked to survey-based indicators of vulnerability 

244 

Dimension:D1D2D3D4D5D4Feature:Sector:----Indicator:12345675675678a8b8c8a8b8c8a8b8c9a9b9c9a9b9c9a9b9c9d9e10a10b1112b12expCharacteristic / DriverUnassertiveOverpaidFeels vulnerableFeels uninformedUses adverts onlyProbl's comparingHasn't switchedUses adverts onlyProbl's comparingHasn't switchedUses adverts onlyProbl's comparingHasn't switchedPersonal factorsMarket factorsAccess factorsPersonal factorsMarket factorsAccess factorsPersonal factorsMarket factorsAccess factorsPersonal factorsMarket factorsAccess factorsPersonal factorsMarket factorsAccess factorsPersonal factorsMarket factorsAccess factorsTermination costBundled offersDifficult processPayment cardDeclined for loanVery vulnerableVulnerable scaleExperimentMonthly internet activities: 7-0.0538***0.920-0.0125-0.00542-0.00733-0.003970.007480.212***0.0241-0.0159-0.0619***-0.0567*-0.007740.07560.0152-0.0154*1.028Monthly internet activities: 6-0.0520***0.807-0.0118-0.00973-0.0145-0.005060.007180.211***0.05000.00793-0.0844**-0.0669**-0.02170.04870.0116-0.0247***0.930Monthly internet activities: 5-0.0390**0.719*-0.0134-0.00937-0.0122-0.0001430.02480.213***0.0530-0.0275-0.0639***-0.0563*-0.01210.04050.0240-0.0199***0.928Monthly internet activities: 4-0.02270.640**-0.00449-0.00304-0.004940.01030.04050.198***0.0241-0.0326-0.0486***-0.0537**-0.008850.05000.0438-0.0178***0.896Monthly internet activities: 3-0.01860.573***0.00855-0.00594-0.002760.006950.04680.148***0.0236-0.0247-0.0402***-0.0504**-0.01110.05300.0335-0.0179***0.921Monthly internet activities: 20.006430.715*0.0265-0.009280.02600.005700.116*0.04230.0502-0.0263-0.02570.09950.0484-0.006940.724***Monthly internet activities: 1-0.004850.567***-0.003190.008610.05670.0674-0.0198-0.0283-0.0487-0.0253-0.0197-0.111***0.006140.0003140.789Frequent internet use: Search-0.01801.20.0647**0.0616**-0.0519Frequent internet use: Compare prices-0.0538**0.0237***0.759***-0.0177-0.00514-0.0573***0.00987-0.0371***-0.0581***0.00986***-0.0581***0.0382**0.0354*0.0209**0.0243***0.0312***0.0217**Frequent internet use: Banking-0.0729***0.855***-0.014200.0156***-0.0341***-0.0673***Frequent internet use: Purchases0.812***0.92***-0.00708-0.0484**-0.0213**Frequent internet use: Selling0.0273*0.0558***-0.00227-0.00606***-0.0326***0.02330.01060.005550.0261**Frequent internet use: Social0.0205***0.00847-0.00342-0.0248**0.00942-0.0203***-0.0425***0.00928***-0.0328***-0.0333**Frequent internet use: Email-0.03360.0229***-0.0357***Information problemsProblems comparingNot switchedOnlineFinanceEnergyOnlineFinanceEnergy-EnergyOnlineFinance-e-com-OffersExcluded 
 
 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

6.5.1.  Dimension 1: Heightened risk of negative outcomes or impacts on well-
being 

What  access  drivers  have  in  common  is  the  fact  that  they  refer  to  consumers  being 
vulnerable  because  they  are  not  able  to  access  particular  goods  or  services  on  the 
same terms as other consumers, even if they are able to pay for the particular goods 
or services. This can in turn lead to reduced consumer welfare due to either: 

  Not obtaining the good or service; 

  Choosing a good or service that is more costly or less suited to their needs; or 

  Bearing additional costs to circumvent access barriers. 

Results of the G-S regression analysis of the survey data presented in  Table 47 below 
show  that  respondents’  overall  level  of  internet  usage,  in  terms  of  the  number  of 
activities they use the internet for at least once per month, is associated with a lower 
likelihood  of  overpaying  for  services  due  to  being  unable  to  use  certain  payment 
methods (the second indicator of vulnerability in this dimension).  

For example, those who use the internet at least once per month for all seven different 
activities mentioned in the survey are estimated to be 5.4 percentage points (pp) less 
likely  to  be  vulnerable  on  this  indicator  than  those  who  use  the  internet  for  zero 
activities at least once per month. 

Moreover, those who use the internet to compare prices and for online banking at least 
once per month are found to be 5.4pp and 7.3pp less likely to be unassertive (i.e. not 
take action) when they experience a problem when buying or using goods or services, 
whereas  those  who  sell  online  at  least  once  per  month  are  found  to  be  2.7pp  more 
likely to be vulnerable on this indicator. 

Table 47: 

Impact of access-related characteristics on indicators in 

Dimension 1 and Dimension 2 

Dimension 1 

Dimension 2 

Indicator 1: 
Unassertive if 
experiences 
problem 

Indicator 2: 
Overpaid for 
services 

Indicator 3: 
Maximum feeling of 
vulnerability 

- 
- 
- 
- 
- 
- 
- 
- 

-0.0538** 
-0.0729*** 
- 
0.027* 
- 
- 

7,826 

0.0929 
Marginal 

1 

-0.0538*** 
-0.0520*** 
-0.0390** 
-0.0227 
-0.0186 
0.0064 
-0.0049 
-0.018 

0.023*** 
- 
- 
0.055*** 
0.020*** 
- 
26,653 

0.1418 
Marginal 

2 

0.920 
0.807 
0.719* 
0.640** 
0.573*** 
0.715* 
0.567*** 
1.236 

- 
0.855*** 
0.812*** 
- 
- 
- 

26,653 

0.1227 
Odds ratio 

3 

Monthly internet activities: 7 
Monthly internet activities: 6 
Monthly internet activities: 5 
Monthly internet activities: 4 
Monthly internet activities: 3 
Monthly internet activities: 2 
Monthly internet activities: 1 
Frequent internet use: Search 
Frequent internet use: Compare 
prices 
Frequent internet use: Banking 
Frequent internet use: Purchases 
Frequent internet use: Selling 
Frequent internet use: Social 
Frequent internet use: Email 
N 
R2 

Marginal effect / Odds ratio 
Indicator code 

245 

 
 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

Note: Values in this table are results of general to specific (G-S) regression analysis. The G-S methodology is 
described in Annex 8. “-” implies that the explanatory variable was not included in the final regression model 
established via the G-S process. */**/*** implies that a result is statistically significant at 90%/95%/99%. 
For each the indicator in this table, the R2 in the table is the Pseudo-R2 for the full regression model, which 
includes  the  explanatory  variables  shown  in  this  table  and  other  explanatory  variables  (not  shown  in  this 
table)  that  were  identified  for  the  regression  model  based  on  the  G-S  methodology.  The  indicators  are 
defined in Table 5. The explanatory variables are defined in Table 7 and A8.1.2. 
Source: London Economics analysis of the survey data 

6.5.2.  Dimension 2: Having characteristics that limit ability to maximise well-
being 

One vulnerability indicator in the second dimension is analysed via the G-S regression 
approach,  namely  Indicator  3.  This  indicator  signifies  the  maximum  extent  that 
respondents feel vulnerable due to any of the personal characteristics mentioned in the 
relevant  survey  question  (i.e.  health  problems,  financial  circumstances,  employment 
situation, age, belonging to a minority group, personal issues, or ‘other’ reasons) on a 
scale from 1 (low) to 4 (high). 

The results of the G-S regression analysis presented in Table 47 show that those who 
use the internet more overall, in terms of the number of different activities they use it 
for  at  least  once  per  month,  are  less  likely  to  feel  a  high  level  of  vulnerability  due  to 
personal  characteristics  (with  odds  ratios  ranging  from  0.57  to  0.92),  although  the 
results for the two highest levels of internet use are not statistically significant.159 

Furthermore, those who use the internet for online banking and to make purchases at 
least once per month are also found to be less likely to be vulnerable on this indicator 
(with odds ratios of 0.86 and 0.81). 

6.5.3.  Dimension 3: Having difficulty in obtaining or assimilating information 

Access  drivers  of  vulnerability  can  include  limited  access  to  information  and  advice, 
which could in turn lead to problems for consumers in terms of being able to compare 
and make the right choices between goods or services. 

However,  the  survey  analysis  results  reveal  very  few  statistically  significant  links 
between the internet use variables and indicators of vulnerability in this dimension, as 
can  be  seen  from  Table  48  below.  Specifically,  the  only  statistically  significant 
relationships are: 

  Those who use the internet to compare prices and to make purchases at least 
once  per  month  are  less  likely  to  feel  uninformed  about  prices,  quality  and 
conditions  when  choosing  and  buying  goods  and  services  (with  odds  ratios  of 
0.76 and 0.92); 

  Those who use the internet for email at least once per month are slightly more 
likely  to  have  problems  comparing  internet  deals  in  the  online  sector  due  to 
information-related factors (with a marginal effect of 2.2pp); and  

159  A  possible explanation  for  this  might  be  that  these  consumers  may  be  more  aware  of  their 
own  vulnerability  due  to  the  extra  information  they  possess  as  a  result  of  their  higher 
internet activity. 

246 

 
 
                                                 
6 │ Drivers of and factors linked to consumer vulnerability 

  Those who use the internet for selling at least once per month are very slightly 
less likely to be prevented from switching due to information-related factors in 
the finance sector (with a marginal effect of 0.6pp). 

247 

 
 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

Table 48: 

Impact of access-related characteristics on indicators in Dimension 3; Having difficulty obtaining and 

assimilating information 

Energy sector 

Online sector 

Finance sector 

Indicator 
4: 

Indicator 
5: 

Indicator 
6: 

Indicator 
7: 

Indicator 
5: 

Indicator 
6: 

Indicator 
7: 

Indicator 
5: 

Indicator 
6: 

Indicator 
7: 

Feels 
uninform
ed about 
prices 

Gets info. 
from 
adverts 
only 

Problems 
comparin
g due to 
info. 
factors 

Monthly internet activities: 7 
Monthly internet activities: 6 

Monthly internet activities: 5 
Monthly internet activities: 4 
Monthly internet activities: 3 
Monthly internet activities: 2 
Monthly internet activities: 1 
Frequent internet use: Search 
Frequent  internet  use:  Compare 
prices 
Frequent internet use: Banking 
Frequent internet use: Purchases 

- 
- 

- 
- 
- 
- 
- 
- 

-0.0125 
-0.0118 

-0.0134 
-0.00449 
0.00855 
0.0265 
-0.00319 
- 

0.759*** 
- 
0.920*** 

- 
- 
- 

- 
- 

- 
- 
- 
- 
- 
- 

- 
- 
- 

Not 
switched 
due to 
info. 
factors 

-0.00542 
-0.00973 

-0.00937 
-0.00304 
-0.00594 
- 
- 
- 

- 
- 
- 

Gets info. 
from 
adverts 
only 

Problems 
comparin
g due to 
info. 
factors 

Not 
switched 
due to 
info. 
factors 

Gets info. 
from 
adverts 
only 

Problems 
comparin
g due to 
info. 
factors 

Not 
switched 
due to 
info. 
factors 

-0.00733 
-0.0145 

-0.0122 
-0.00494 
-0.00276 
-0.00928 
0.00861 
- 

-0.0177 
-0.0142 
-0.007 

- 
- 

- 
- 
- 
- 
- 
- 

- 
- 
- 

-0.00397 
-0.00506 
-
0.000143 
0.0103 
0.00695 
0.0260 
0.0567 
- 

-0.0051 
- 
- 

- 
- 

- 
- 
- 
- 
- 
- 

- 
- 
- 

- 
- 
- 
26,653 
0.0574 

- 
- 
- 
9,793  
0.0972 
Marginal 

Frequent internet use: Selling 
Frequent internet use: Social 
Frequent internet use: Email 
N 
R2 
Marginal effect / Odds ratio 
Indicator code 
Note: Values in this table are results of general to specific (G-S) regression analysis. The G-S methodology is described in Annex 8. “-” implies that the explanatory variable 
was  not  included  in  the  final  regression  model  established  via  the  G-S  process.  */**/***  implies  that  a  result  is  statistically  significant  at  90%/95%/99%.  For  each  the 
indicator in this table, the R2 in the table is the Pseudo-R2 for the full regression model, which includes the explanatory variables shown in this table and other explanatory 
variables  (not  shown  in  this  table)  that  were  identified  for  the  regression  model  based  on  the  G-S  methodology.  The  indicators  are  defined  in  Table  5.  The  explanatory 
variables are defined in Table 7 and A8.1.2.There are no results for Indicator 7 for the online sector because the G-S process could not be run for this indicator due to low 
variation in the dependent variable. 
Source: London Economics analysis of the survey data 

- 
- 
0.022*** 
19,913  
0.1217 
Marginal 

- 
- 
- 
15,481  
0.0756 
Marginal 

- 
0.008 
- 
4,481  
0.1461 
Marginal 

-0.0022 
-0.0034 
-0.0336 
16,451  
0.0890 
Marginal 

- 
- 
- 
15,481  
0.0859 
Marginal 

- 
- 
- 
10,228  
0.0925 
Marginal 

5_adv_ene 

5_adv_onl 

Odds ratio 

5_adv_fin 

Marginal 

7_ene 

6_ene 

7_onl 

6_onl 

7_fin 

6_fin 

4 

- 
- 

- 
- 
- 
- 
- 
- 

- 
- 
- 
-
0.006*** 
- 
- 
3,795  
0.1374 
Marginal 

248 

 
 
 
 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

6.5.4.  Dimension 4: Inability or failure to buy, choose or access suitable 
products 

Access  drivers  may  be  important  factors  contributing  to  the  fourth  dimension  of 
vulnerability,  since  physical  and  online  access  drivers  and  restricted  access  to  offers 
could prevent consumers from being able to choose suitable products, even if they are 
able to pay for the good or service. 

The results of the behavioural experiments are especially relevant to this dimension of 
vulnerability,  since  they  tested  consumers’  ability  to  choose  the  optimal  deal  in 
simulated  purchasing  scenarios.  However,  the  results  of  G-S  regression  analysis  of 
respondents’  choices  in  the  experiments,  presented  in  Table  49,  show  that  there  are 
few  strong  links  between  respondents’  internet  use  and  their  performance  in  the 
experiments. 

Those  who  use  the  internet  to  search  for  information  at  least  once  per  month 
performed  better  in  the  experiments  overall  and  in  particular  in  the  second  round  of 
the  cross-cutting  experiment,  whereas  those  who  sell  online  and  use  online  social 
networks at least once per month performed worse overall. 

The  results  for  individual  experiment  rounds  show  that  those  who  use  email  at  least 
once  per  month  performed  better  in  the  first  round  of  the  online  sector  experiment, 
those who compare prices at least once per month performed better in the first round 
of the finance sector  experiment, and those who bank online at least once per month 
performed better in both rounds of the cross-cutting experiment. 

249 

 
 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

Table 49: 

Impact of access-related characteristics on experiment performance 

Energy Sector 

Online Sector 

Finance Sector 

Cross-cutting 

Round 1 

Round 2 

Round 1 

Round 2 

Round 1 

Round 2 

Round 1 

Round 2 

Overall 

0.0508 
0.0421 
-0.0342 
0.0144 
-0.0403 
0.0462 
0.255 
- 
- 
- 
- 
- 
- 
- 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
-0.0592*** 
- 
- 

Monthly internet activities: 7 
Monthly internet activities: 6 
Monthly internet activities: 5 
Monthly internet activities: 4 
Monthly internet activities: 3 
Monthly internet activities: 2 
Monthly internet activities: 1 
Frequent internet use: Search 
Frequent internet use: Compare prices 
Frequent internet use: Banking 
Frequent internet use: Purchases 
Frequent internet use: Selling 
Frequent internet use: Social 
Frequent internet use: Email 
N 
R2 
Marginal effect / Odds ratio 
Note: Values in this table are results of general to specific (G-S) regression analysis. The G-S methodology is described in Annex 8. “-” implies that the variable was not 
included in the final regression model established via the G-S process. */**/*** implies that a result is statistically significant at 90%/95%/99%. For each the indicator in 
this table, the R2 in the table is the Pseudo-R2 for the full regression model, which includes the explanatory variables shown in this table and other explanatory variables 
(not  shown  in  this  table)  that  were  identified for  the  regression  model  based on  the  G-S  methodology.The  explanatory  variables  are  defined  in  Table  7  and  A8.1.2.  The 
dependent variables in the experiment analysis signify whether the respondent chose the optimal deal. For individual experiment rounds, the dependent variable is equal to 
one if the respondent chose the optimal deal, or zero otherwise. The ‘Overall’ performance measure is the total number of correct choices made by the respondent (ranging 
from 0 to 4). Thus, positive marginal effects imply a higher likelihood of choosing the optimal deal, and odds ratios greater than one imply better overall performance. 
Source: London Economics analysis of the survey data 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
0.0960*** 
2,510 
0.1061 
Marginal 

- 
- 
- 
- 
- 
- 
- 
- 
0.0764*** 
- 
- 
- 
- 
- 

-0.000766 
0.00299 
-0.0643 
0.00894 
-0.0115 
-0.00579 
0.00260 
0.0804 
- 
0.0782** 
- 
- 
- 
- 

1.459 
1.301 
1.056 
0.950 
0.965 
0.687 
0.943 
1.984* 
- 
- 
- 
0.716*** 
0.699*** 
- 

- 
- 
- 
- 
- 
- 
- 
0.111*** 
- 
0.0642** 
- 
- 
- 
- 

-0.00268 
-0.00520 
-0.0169 
-0.0987* 
-0.0348 
-0.0672 
0.0337 
- 
- 
- 
- 
- 
- 
- 

5,051 
0.0421 
Odds ratio 

2,505 
0.1205 
Marginal 

2,510 
0.0823 
Marginal 

2,522 
0.0710 
Marginal 

2,565 
0.0628 
Marginal 

2,522 
0.0318 
Marginal 

2,505 
0.1191 
Marginal 

2,565 
0.0237 
Marginal 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

250 

 
 
 
 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

Results from G-S regression analysis of the survey data, presented in  Table 50, Table 
51  and  Table  52  below,  show  that  some  internet  use  characteristics  are  linked  with 
some indicators of vulnerability in this dimension.  

Consumers  who  use  the  internet  more  overall,  in  terms  of  the  number  of  different 
activities they use it for at least once per month, are less likely to have been prevented 
from  switching  their  internet  provider  or  bank  due  to  market-related  factors.  For 
example,  using  the  internet  at  least  once  per  month  for  six  of  the  seven  activities 
mentioned  in the  survey  is  associated  with  an 8.4pp  decrease  in  the  likelihood  of  not 
switching in the online sector, and a 5.7pp decrease in the likelihood of not switching 
in  the  finance  sector  due  to  market  related  factors.  In  addition,  those  who  use  the 
internet more overall are also less likely to have been declined for a loan. 

However,  consumers  who  use  the  internet  more  overall  are  also  more  likely  to  have 
problems  comparing  deals  due  to  information-related  factors  in  the  online  sector.  For 
example, using the internet at least once per month for all seven activities mentioned 
in the survey is associated with a 21.2pp increase in the likelihood of being vulnerable 
on this indicator. A possible explanation for this is that these consumers may possess 
much  more  information  and  comparing  such  a  quantity  may  be  difficult.  Another 
possible explanation might be that these consumers may be more aware of their own 
vulnerability  due  to  the  extra  information  they  possess  as  a  result  of  their  higher 
internet activity. 

Several  specific  types  of  internet  use  have  mixed  impacts  on  the  indicators  in  this 
dimension. For example, comparing prices online at least once per month is linked with 
a  lower  likelihood  of  having  problems  comparing  deals  due  to  personal  factors  in  all 
three  key  sectors  and  of  having  problems  comparing  deals  due  to  market-related 
factors  in  the  online  sector;  but,  at  the  same  time,  comparing  prices  online  at  least 
once per month is linked with a higher likelihood of having problems comparing deals 
due to access-related factors in the online sector and not switching in the energy and 
finance  sectors  due  to  personal  and  market-related  factors  respectively.  A  possible 
interpretation  of  this  is  that  such  mixed  results  could  arise  because  consumers  with 
more information (due to using the internet) may be more aware of the problems they 
face  in  the  market  (and  so  more  likely  to  report  problems),  whilst  simultaneously 
benefiting from the access to the market that the internet provides. 

251 

 
 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

Table 50: 

Impact of access-related characteristics on Indicator 8 in Dimension 4; Has problems comparing deals due to 

personal factors, market related factors, and access- related factors 

Energy sector 

Online sector 

Finance sector 

Indicator 
8b: 
Market 
factors 
- 
- 
- 
- 
- 
- 
- 
0.064** 

Indicator 
8a: 
Personal 
factors 
- 
- 
- 
- 
- 
- 
- 
- 
-
0.0573*** 
- 
- 
- 
- 
- 
15,481  
0.1273 
Marginal 

Indicator 
8c: 
Access 
factors 
0.00748 
0.00718 
0.0248 
0.0405 
0.0468 
0.00570 
0.0674 
- 

Indicator 
8a: 
Personal 
factors 
- 
- 
- 
- 
- 
- 
- 
- 

Indicator 
8b: 
Market 
factors 
0.212*** 
0.211*** 
0.213*** 
0.198*** 
0.148*** 
0.116* 
-0.0198 
- 

Indicator 
8c: 
Access 
factors 
- 
- 
- 
- 
- 
- 
- 
- 

Indicator 
8a: 
Personal 
factors 
0.0241 
0.0500 
0.0530 
0.0241 
0.0236 
0.0423 
-0.0283 
- 

Indicator 
8b: 
Market 
factors 
- 
- 
- 
- 
- 
- 
- 
0.061** 

Indicator 
8c: 
Access 
factors 
- 
- 
- 
- 
- 
- 
- 
- 

internet  use:  Compare 

Monthly internet activities: 7 
Monthly internet activities: 6 
Monthly internet activities: 5 
Monthly internet activities: 4 
Monthly internet activities: 3 
Monthly internet activities: 2 
Monthly internet activities: 1 
Frequent internet use: Search 
Frequent 
prices 
Frequent internet use: Banking 
Frequent internet use: Purchases 
Frequent internet use: Selling 
Frequent internet use: Social 
Frequent internet use: Email 
N 
R2 
Marginal effect / Odds ratio 
Indicator code 
Note: Values in this table are results of general to specific (G-S) regression analysis. The G-S methodology is described in Annex 8. “-” implies that the explanatory variable 
was  not  included  in  the  final  regression  model  established  via  the  G-S  process.  */**/***  implies  that  a  result  is  statistically  significant  at  90%/95%/99%.  For  each  the 
indicator in this table, the R2 in the table is the Pseudo-R2 for the full regression model, which includes the explanatory variables shown in this table and other explanatory 
variables  (not  shown  in  this  table)  that  were  identified  for  the  regression  model  based  on  the  G-S  methodology.  The  indicators  are  defined  in  Table  5.  The  explanatory 
variables are defined in Table 7 and A8.1.2. 
Source: London Economics analysis of the survey data 

- 
- 
- 
-0.0326*** 
-0.0328*** 
- 
15,481  
0.1049 
Marginal 

-0.0581*** 
- 
- 
- 
- 
- 
15,481  
0.1210 
Marginal 

-0.0581*** 
- 
- 
- 
-0.0425*** 
- 
19,913  
0.1461 
Marginal 

- 
- 
- 
- 
-0.0248** 
- 
15,481  
0.1084 
Marginal 

0.009*** 
0.015*** 
- 
- 
0.009*** 
- 
19,913  
0.0714 
Marginal 

- 
- 
- 
- 
- 
- 
15,481  
0.0933 
Marginal 

0.009 
- 
- 
- 
0.009 
- 
15,481  
0.1263 
Marginal 

-0.0371*** 
- 
- 
- 
-0.0203*** 
- 

19,913  
0.1442 
Marginal 

8b_ene 

8a_ene 

8c_ene 

8b_onl 

8a_onl 

8c_onl 

8b_fin 

8a_fin 

8c_fin 

252 

 
 
 
 
 
 
Table 51: 

Impact of access-related characteristics on Indicator 9 in Dimension 4; Has not switched due to personal 

factors, market related factors, access- related factors, termination costs, and bundling 

6 │ Drivers of and factors linked to consumer vulnerability 

Indicato
r 9a: 
Personal 
factors 

Energy 

Indicato
r 9b: 
Market 
factors 

Indicato
r 9c: 
Access 
factors 

Indicato
r 9a: 
Personal 
factors 

Indicato
r 9c: 
Access 
factors 

Indicato
r 9a: 
Personal 
factors 

Monthly internet activities: 7 
Monthly internet activities: 6 

-0.0159 
0.00793 

Monthly internet activities: 5 

-0.0275 

Monthly internet activities: 4 

-0.0326 

-0.0247 
0.0502 
-0.0487 
-0.0519 

Monthly internet activities: 3 
Monthly internet activities: 2 
Monthly internet activities: 1 
Frequent internet use: Search 
Frequent 
prices 
Frequent internet use: Banking 
Frequent internet use: Purchases 
Frequent internet use: Selling 
Frequent internet use: Social 

internet  use:  Compare 

0.038** 
- 
-0.0484** 
0.023 
- 

- 
- 
- 
- 
-0.0333** 

- 
- 

- 

- 

- 
- 
- 
- 

- 
- 

- 

- 

- 
- 
- 
- 

- 
- 
- 
- 
- 

- 
- 

- 

- 

- 
- 
- 
- 

- 
- 
- 
- 
- 

Online 

Indicato
r 9b: 
Market 
factors 

-
0.0619*** 
-0.0844** 
-
0.0639*** 
-
0.0486*** 
-
0.0402*** 
- 
- 
- 

- 
- 
- 
- 
- 

- 
- 

- 

- 

- 
- 
- 
- 

- 
- 
- 
- 
- 

Finance 

Indicato
r 9b: 
Market 
factors 

-0.0567* 
-0.0669** 

-0.0563* 

-0.0537** 

-0.0504** 
-0.0263 
-0.0253 
- 

0.035* 
- 
- 
- 
- 

Indicato
r 9c: 
Access 
factors 

Indicato
r 9d: 
Terminat
ion costs 

Indicato
r 9e: 
Bundling 

- 
- 

- 

- 

- 
- 
- 
- 

- 
- 
- 
- 
- 

-0.00774 
-0.0217 

-0.0121 

-0.00885 

-0.0111 
-0.0257 
-0.0197 
- 

0.02** 
- 
- 
0.01 
- 

- 
- 

- 

- 

- 
- 
- 
- 

0.024*** 
- 
- 
- 
- 

- 
- 

- 

- 

- 
- 
- 
- 

- 
- 
-0.0213** 
- 
- 
-
0.0357*** 
4,355  
0.1330 

- 
4,782  
0.1283 

- 
4,782  
0.0651 

Frequent internet use: Email 
N 
R2 
Marginal effect / Odds ratio 
Indicator code 
Note: Values in this table are results of general to specific (G-S) regression analysis. The G-S methodology is described in Annex 8. “-” implies that the explanatory variable 
was  not  included  in  the  final  regression  model  established  via  the  G-S  process.  */**/***  implies  that  a  result  is  statistically  significant  at  90%/95%/99%.  For  each  the 
indicator in this table, the R2 in the table is the Pseudo-R2 for the full regression model, which includes the explanatory variables shown in this table and other explanatory 
variables  (not  shown  in  this  table)  that  were  identified  for  the  regression  model  based  on  the  G-S  methodology.  The  indicators  are  defined  in  Table  5.  The  explanatory 
variables are defined in Table 7 and A8.1.2. 
Source: London Economics analysis of the survey data 

- 
26,653  
0.0852 

- 
26,653  
0.1242 

- 
4,368  
0.0597 

- 
4,095  
0.1538 

- 
3,265  
0.1083 

- 
4,782  
0.2145 

- 
3,483  
0.2094 

- 
3,427  
0.0804 

Marginal 

Marginal 

Marginal 

Marginal 

Marginal 

Marginal 

Marginal 

Marginal 

Marginal 

Marginal 

Marginal 

9b_ene 

9a_ene 

9c_ene 

9b_onl 

9a_onl 

9c_onl 

9b_fin 

9_bun 

9a_fin 

9c_fin 

9_ter 

253 

 
 
 
 
 
 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

Table 52: 

Impact of access-related characteristics on Indicators 10 and 11 

in Dimension 4; Excluded from e-commerce and declined a loan 

Excluded from e-commerce due to: 

Indicator 11: 
Declined for a loan 

Indicator 10a: 
Difficulty of the 
process 
0.0756 
0.0487 
0.0405 
0.0500 
0.0530 
0.0995 
-0.111*** 
- 

Indicator 10b: Not 
having payment 
card 
0.0152 
0.0116 
0.0240 
0.0438 
0.0335 
0.0484 
0.00614 
- 

-0.0154* 
-0.0247*** 
-0.0199*** 
-0.0178*** 
-0.0179*** 
-0.00694 
0.000314 
- 

Monthly internet activities: 7 
Monthly internet activities: 6 
Monthly internet activities: 5 
Monthly internet activities: 4 
Monthly internet activities: 3 
Monthly internet activities: 2 
Monthly internet activities: 1 
Frequent internet use: Search 
Frequent  internet  use:  Compare 
prices 
Frequent internet use: Banking 
Frequent internet use: Purchases 
Frequent internet use: Selling 
Frequent internet use: Social 
Frequent internet use: Email 
N 
R2 
Marginal effect / Odds ratio 
Indicator code 
Note: Values in this table are results of general to specific (G-S) regression analysis. The G-S methodology is 
described in Annex 8. “-” implies that the explanatory variable was not included in the final regression model 
established via the G-S process. */**/*** implies that a result is statistically significant at 90%/95%/99%. 
For each the indicator in this table, the R2 in the table is the Pseudo-R2 for the full regression model, which 
includes  the  explanatory  variables  shown  in  this  table  and  other  explanatory  variables  (not  shown  in  this 
table)  that  were  identified  for  the  regression  model  based  on  the  G-S  methodology.  The  indicators  are 
defined in Table 5. The explanatory variables are defined in Table 7 and A8.1.2. 
Source: London Economics analysis of the survey data 

0.031*** 
-0.0341*** 
- 
0.005 
- 
- 
19,957  
0.0903 
Marginal 

0.021** 
-0.0673*** 
- 
0.026** 
- 
- 

19,957  
0.1188 
Marginal 

4,971  
0.1230 
Marginal 

- 
- 
- 
- 
- 
- 

11_loan 

10b 

10a 

6.5.5.  Dimension 5: Higher susceptibility to marketing practices 

Results of the survey data analysis, presented in Table 53, reveal only one statistically 
significant  link  between  respondents’  internet  usage  and  indicators  of  vulnerability  in 
this dimension. This result implies that those who use the internet for two of the seven 
activities  mentioned  in  the  survey  at  least  once  per  month  are  less  likely  to  feel 
vulnerable due to the complexity of offers than those who use the internet for none of 
these activities. 

Table 53: 

Impact of access-related characteristics on Indicator 12 in 

Dimension 5; Feeling of vulnerability due to complexity of offers 

Indicator 12: Feels vulnerable due to complexity of 
offers;  

Binary: “To a great extent” 

Scale: 1 (low) - 4 (high) 

Monthly internet activities: 7 
Monthly internet activities: 6 
Monthly internet activities: 5 
Monthly internet activities: 4 
Monthly internet activities: 3 
Monthly internet activities: 2 
Monthly internet activities: 1 
Frequent internet use: Search 
Frequent  internet  use:  Compare 
prices 
Frequent internet use: Banking 
Frequent internet use: Purchases 

- 
- 
- 
- 
- 
- 
- 
- 

- 
- 
- 

1.028 
0.930 
0.928 
0.896 
0.921 
0.724*** 
0.789 
- 

- 
- 
- 

254 

 
 
 
 
 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

Table 53: 

Impact of access-related characteristics on Indicator 12 in 

Dimension 5; Feeling of vulnerability due to complexity of offers 

Indicator 12: Feels vulnerable due to complexity of 
offers;  

Binary: “To a great extent” 

Scale: 1 (low) - 4 (high) 

- 
- 
- 

- 
- 
- 
26,653  
0.1005 
Marginal 

Frequent internet use: Selling 
Frequent internet use: Social 
Frequent internet use: Email 
N 
R2 
Marginal effect / Odds ratio 
Indicator code 
12_off_b2 
Note: Values in this table are results of general to specific (G-S) regression analysis. The G-S methodology is 
described in Annex 8. “-” implies that the explanatory variable was not included in the final regression model 
established  via  the  G-S  process.  */**/*** 
is  statistically  significant  at 
90%/95%/99%.For each the indicator in this table, the R2 in the table is the Pseudo-R2 for the full regression 
model,  which  includes  the  explanatory  variables  shown  in  this  table  and  other  explanatory  variables  (not 
shown  in  this  table)  that  were  identified  for  the  regression  model  based  on  the  G-S  methodology.  The 
indicators are defined in Table 5. The explanatory variables are defined in Table 7 and A8.1.2. 
Source: London Economics analysis of the survey data 

26,653  
0.0465 
Odds ratio 

implies  that  a  result 

12_off 

6.6.  Situational drivers of vulnerability 

The  final  set  of  drivers  relating  to  vulnerability  is  ‘situational’  drivers.  These  refer  to 
drivers  where  a  situation,  in  which  an  individual  finds  himself  or  herself,  either 
permanently  or  temporarily,  makes  them  vulnerable.  Situational  drivers  include 
employment  status,  marital  status  and  perceived  “budget  constraints”  (that  is,  the 
ease/difficulty of making ends meet).160 

Relevant results from the survey and experiment data analysis are summarised in the 
matrix presented in Figure 31. The indicator codes across the top of the matrix match 
those in the list of indicators in section 6.1.2, as well as those in the table of indicators 
in section 3.6.161 The consumer characteristics in the matrix (down the left hand  side) 
are defined (alongside all other explanatory variables examined in the data analysis) in 
Table 7 in section 3.7. 

In this matrix, red cells show that consumers with a particular characteristic are found 
to  be  more  likely  to  be  vulnerable  on  the  indicator  in  question,  whereas  green  cells 
mean  that  consumers  with  a  certain  characteristic  are  less  likely  to  be  vulnerable  on 
the  relevant  indicator.  Grey  cells  show  that  a  characteristic  was  controlled  for  in  the 
regression,  while  white  cells  mean  that  a  characteristic  was  not  included  in  the 
regression (based on the G-S approach). 

The findings of the survey data analysis highlight most clearly the relevance of budget 
constraints162  to  consumer  vulnerability,  since  finding  it  difficult  or  very  difficult  to 
makes  ends  is  associated  with  a  higher  chance  of  vulnerability  on  a  high  number  of 

160 Although situational drivers are closely linked to consumer characteristics, it can still be seen 
as a driver primarily related to the environment,  since  consumers can only be vulnerable if 
marketing practices specifically exploiting their situations are used.  

161 There are no results for Indicator 7 for the online sector since the G-S process could not be 
run for this indicator due to the low total number of respondents who are vulnerable on this 
indicator.  Hence,  in  the  results  tables  in  this  section  no  results  are  presented  for  this 
indicator. 

162 As measured by the ease/difficulty of making ends meet. 

255 

 
 
 
 
 
                                                 
6 │ Drivers of and factors linked to consumer vulnerability 

different  indicators,  including indicators  in  all  dimensions  (as  shown  by  the  consistent 
set of red cells across dimensions of vulnerability for the ‘making ends meet’ variables 
in Figure 31). 

Individuals that do not find making ends meet easy: 

  Are  less  likely  to  take  action  when  they  experience  a  problem  and  are  more 
likely  to  overpay  for  services  due  to  being  unable  to  use  certain  payment 
methods (Dimension 1) 

  Feel  a  higher  level  of  vulnerability  due  to  their  personal  characteristics 

(Dimension 2) 

  Are  more  likely  to  feel  uninformed  about  prices,  quality  and  conditions  when 
choosing  goods  and  services,  and  to  have  problems  comparing  deals  due  to 
information-related factors (Dimension 3) 

  Find  that  access-related  factors  are  barriers  to  making  comparisons  between 
energy deals; market-related factors are barriers to making comparisons in the 
online  sector;  that  personal,  market-related  and  access-related  factors  are 
barriers  to  making  comparisons  in  the  finance  sector  (Dimension  4,  Indicator 
8a/b/c);  and  that  access  factors  can  prevent  these  consumers  from  switching 
in all three key markets (Dimension 4, Indicator 9c)  

  Are  more  likely  to  find  that  termination  costs  and  bundling  are  barriers  to 

switching in general (Dimension 4, Indicator 9d/e) 

  Are more likely to be excluded from e-commerce (Dimension 4, Indicator 10), 

or to be declined for loans (Dimension 4, Indicator 11) 

  Are  more  likely  to  feel  vulnerable  due  to  the  complexity  of  offers,  terms  or 

conditions (Dimension 5) 

Budget  constraints,  as  captured  by  how  people  perceive  the  ease/difficulty  of  making 
ends meet, may therefore provide a relatively strong indication of how likely people are 
to suffer from consumer vulnerability.163 

There are a number of mechanisms that may relate budget constraints and consumer 
vulnerability,  as  individuals  facing  budget  constraints  may  also  be  in  lower  income 
groups.164  

Existing  literature  finds  for  instance  that  income  can  be  associated  with  behavioural 
vulnerability drivers. Lunn and Lyons (2010) note that people in lower socio-economic 
groups are more inclined towards some behavioural biases and tend to score lower on 
tests of general decision-making competence. One possible explanation is provided by 
Shah  (2012),  who  notes  that  low-income  consumers  face  cognitive  loads  due  to  their 

163 However, it is important to note that the base category for the ease/difficulty of making ends 
meet  measured  on  a  four  point  scale  –  “easy”,  “fairly  easy”,  “fairly  difficult”  and  “very 
difficult”  –  is  "easy".  The  statistical  significance  of  the  variables  for  the  ease/difficulty  of 
making  ends  meet  is  therefore  due  to  the  comparisons  against  a  group  that  finds  making 
ends meet easy. 

164 It should be borne in mind that the variables capturing individuals facing budget constraints 
may  reflect  lower,  as  well  as  higher,  income  groups  because  the  ease/difficulty  of  making 
ends meet reflects income as well as spending. 

256 

 
 
                                                 
6 │ Drivers of and factors linked to consumer vulnerability 

engagement  with  scarcity  and  trying  to  solve  scarcity  problems,  both  in  terms  of 
resource scarcity and time scarcity, which can in turn lead to suboptimal decisions.  

Income  is  also  related  to  access  drivers.  Low-income  consumers  may  have  limited 
access to bank accounts, which can result in them facing difficulties in obtaining other 
services  and  additional  costs  (which  is  discussed  in  more  detail  in  the  next  section). 
Secondly,  low  income  consumers  may  not  have  access  to  mainstream  financial 
products, such as loans, leading them to seek out alternative ways of obtaining credit 
(i.e. payday loans), which can contribute to over-indebtedness (i.e. Stearn 2012). 

Furthermore, income  can also be linked to access to  ICT tools, which are increasingly 
important in current marketplaces (Chang et al. 2004). This is partly due to costs, but 
partly due to other concerns (e.g. usability, safety and security) and inability to benefit 
from  some  of  the  opportunities  provided  by  internet  access,  for  instance  due  to  not 
having access to bank accounts or credit cards (Stearn 2012). 

In addition, a recent consumer empowerment survey commissioned by the Department 
of  Business  Innovation  and  Skills  in  the  UK  discovered  a  link  between  income  and 
consumer  empowerment,  noting  that  the  least  empowered  group  of  “constrained 
strugglers” tends to be the group facing considerable financial constraints. The authors 
of  the  survey  report,  however,  also  noted  that  financially  comfortable  consumers  can 
also display a low level of consumer empowerment due to limited interest in shopping 
or shopping around, calling this group the “consciously unengaged” (GfK 2015). 

Finally,  one  factor  closely  linked  to  income  is  over-indebtedness.  Although  household 
debt is prevalent across all income levels, studies show it to be predominantly affecting 
lower income consumers. Barba and Pivetti (2009) conclude for instance that although 
median  debt  rises  with  income,  overall  household  debt  to  finance  consumption 
concerns all consumers except the top 20% of the income distribution. 

The remainder of this section considers in greater detail the findings of the analysis of 
situation  drivers  of  consumer  vulnerability.  The  full  set  of  explanatory  variables 
examined  in  the  G-S  analysis  is  presented  in  Table  7  in  section  3.7.  This  table  also 
indicates  which  variables  (i.e.  which  levels  or  categories)  are  used  as  bases  in  the 
analysis. 

257 

 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

Figure 31:  Matrix of situational characteristics linked to survey-based indicators of vulnerability 

258 

Dimension:D1D2D3D4D5D4Feature:Sector:----Indicator:12345675675678a8b8c8a8b8c8a8b8c9a9b9c9a9b9c9a9b9c9d9e10a10b1112b12expCharacteristic / DriverUnassertiveOverpaidFeels vulnerableFeels uninformedUses adverts onlyProbl's comparingHasn't switchedUses adverts onlyProbl's comparingHasn't switchedUses adverts onlyProbl's comparingHasn't switchedPersonal factorsMarket factorsAccess factorsPersonal factorsMarket factorsAccess factorsPersonal factorsMarket factorsAccess factorsPersonal factorsMarket factorsAccess factorsPersonal factorsMarket factorsAccess factorsPersonal factorsMarket factorsAccess factorsTermination costBundled offersDifficult processPayment cardDeclined for loanVery vulnerableVulnerable scaleExperimentEmployed part-time0.009740.01101.193***1.002-0.00108-0.00360-2.86e-05-0.002650.000422-0.00511**-0.0151-0.0150*-0.0003560.00507-0.005980.003070.00584-0.00538-0.0211-0.009590.006010.008480.000911-0.00310-0.006981.044Self-employed full-time-0.0497*0.0184**0.9160.9690.002140.00402-0.007020.0209**0.01680.01320.0249-0.0220*-0.006960.01150.00426-0.003420.003970.0287-0.03570.0430**-0.006910.0213**0.0006630.001790.01490.999Self-employed part-time0.01450.01991.341***0.801**0.01320.00611-0.006640.004470.02130.001990.00107-0.0447***0.0193-0.01900.009830.0105-0.03070.00973-0.0449-4.59e-050.02370.03030.005310.02541.096Unemployed & looking-0.02210.0237***2.266***0.797***-0.0238**-0.00220-0.0112**0.0148**0.005710.00615-0.0240-0.00614-0.0225***0.000204-0.005220.0108**-0.0364***-0.0137-0.0118-0.00943-0.0006040.003180.0131-0.001730.0256***1.205***Unemployed & not looking-0.0727*0.003301.720***0.809*-0.0568**0.01520.00995-0.00775-0.0523***-0.05240.006080.00954-0.0393-0.006980.0101-0.0240-0.0207-0.00170-0.01720.02280.01970.005410.0957**0.0562*0.938Long-term sick or disabled-0.0265-0.008723.230***0.9340.000980-0.009720.01510.004050.007620.0298-0.0135-0.0004620.0668***-0.001240.0113*0.0193-0.0216-0.0145-0.00377-0.0224***-0.0153-0.02150.00949-0.01061.332***Housewife/Homemaker-0.0260-0.01011.268***0.950-0.01910.004570.0005830.00314-0.01660.00217-0.02180.00867-0.0332***-0.0253**0.005250.00520-0.00313-0.02000.0321-0.0285***0.0106-0.0145*-0.0206*-0.0120-0.004881.074Retired-0.0506**-0.004791.447***1.016-0.01690.008380.004010.002620.00740-0.000536-0.001510.00642-0.00537-0.002860.001710.00900-0.0164-0.0261*-0.02330.0138-0.009240.005230.003180.001960.01451.032In full time education-0.0007790.005721.540***0.794***0.00146-0.00640*-0.0180***0.0229**0.0167-0.00355-0.0511***0.0172-0.0282***0.005700.0162**0.004780.00116-0.0440***0.106***-1.19e-050.009490.00747-0.004460.0299**0.0289**0.948Studying with part-time job-0.0851**0.01611.515***0.852*-0.000137-0.01230.02090.0330-0.01300.003750.0183-0.02260.01650.000581-0.00473-0.02030.0171-0.01050.03130.01760.0002840.007070.02421.082Unemployed & looking 5+ years-0.134***-0.0331***-0.00880***-0.0518**Unemployed & not looking 5+ years-0.115***Long-term sick or disabled 5+ years-0.0632***0.07010.0392Housewife/Homemaker 5+ years0.861-0.0340Retired 5+ years0.0270**-0.00802***-0.01850.0242*0.0571**Remarried-0.04641.172*0.007660.01440.00113-0.01080.01620.0519**-0.00958-0.00772**0.0255-0.0274**-0.0350***0.009110.0351*0.03100.01340.753***Living with a partner-0.001031.0080.001460.002050.00731*0.0240**0.00646-0.002120.00945**0.0006070.009660.0182*0.002120.00579-0.001460.0110*0.0258***0.0143*0.962Single-0.01050.957-0.00142-0.002510.001190.0462***-0.009530.006180.0009630.002420.0267**0.00594-0.0009300.000713-0.00749**-0.005650.0004780.0008000.943*Divorced or separated-0.0469**1.136***0.0004330.0143**0.01680.0306***0.0228**-0.0006070.003500.004730.0294*0.000919-0.0166*-0.00551-0.0148*-0.001630.0263**0.918*Widowed0.009701.149**-0.007250.0165-0.00393-0.0448*0.0482***0.01850.005180.00216-0.00949-0.02030.01830.00471-0.00582-0.02590.02811.015Other-0.04390.842**0.002260.005730.02150.03580.006450.0337**0.005990.007450.0624-0.02100.00124-0.01440.007810.0367**0.0632***0.912Single parent-0.0431***1 dependent child0.975-0.005490.003810.0355*2 dependent children0.912**-0.01490.0365**0.0514*3+ dependent children1.088-0.0489***-0.01280.0353Single person household1.061*0.00210-0.01020.00541Friends buy online-0.0544***0.843***-0.0150***-0.0135***-0.0331***0.0104*Friends buy on credit0.0251***1.127***0.00864***0.00773**-0.0337***0.0117**-0.0110**-0.0226***0.00526*0.0207***0.0323***0.0145***1.121***Friends can't make ends meet0.0507***0.0250***1.678***-0.00730***0.00494***0.0372***0.0271***0.0353***0.0262***0.0238***1.406***Making ends meet: Fairly easy0.0734***0.008531.786***1.326***0.0126-0.005630.004780.01250.0222**-0.0009700.0292***0.0479***0.0461***0.003180.0729***0.0264*0.01620.0208**0.008530.0282***0.02010.0165**1.398***Making ends meet: Fairly difficult0.125***0.0233***3.683***1.317***0.0335***-0.0001140.001580.0181*0.0474***-0.0179**0.008300.0643***0.0602***0.0135***0.122***0.0391**0.0375*0.0518***0.0366***0.0540***0.0276*0.0227***1.567***Making ends meet: Very difficult0.169***0.0477***9.981***1.445***0.0386**0.001680.0139*0.0374***0.0649***-0.01400.0325**0.0980***0.0733***0.0220**0.137***0.04050.08110.0674***0.0290**0.0851***0.0725**0.0620***1.928***Making ends meet: Prefer not to say0.03760.007892.803***1.512**0.0756-0.005000.03050.0318-0.0137-0.0409-0.07250.0301-0.002240.0001190.0767-0.00412-0.0158-0.02460.1450.0403*1.464**-FinanceEnergyOnlineFinance-e-com-EnergyOnlineFinanceEnergyOnlineInformation problemsProblems comparingNot switchedExcludedOffers 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

6.6.1.  Dimension 1: Heightened risk of negative outcomes or impacts on well-
being 

Survey-based  evidence,  in  the  form  of  G-S  regression  analysis  results,  on  the 
relationship  between  situational  drivers  and  the  first  dimension  of  vulnerability,  a 
heightened risk of negative outcomes or impacts on well-being165, is presented in Table 
54. 

Importantly,  consumers  who  struggle  to  make  ends  meet  are  more  likely  to  be 
vulnerable due to being unassertive if they experience problems with goods or services 
(Indicator  1).  For  example,  those  who  find it  very  difficult  to  make  ends  meet  are  17 
percentage points (pp) more likely not to take action if they experience a problem than 
people  who  find  it  easy  to  make  ends  meet.  Moreover,  individuals  with  friends  that 
can’t make ends meet easily are 5pp more likely to be unassertive if they experience a 
problem. 

The relationship between difficulties in making ends meet and risk of vulnerability may 
be  due  to  the  income-related  mechanisms  (behavioural  vulnerability  drivers,  access 
drivers, access to ICT tools and over-indebtedness) discussed above. 

It is also of interest to note that individuals with friends who buy online (compared to 
those  with  friends  who  do  not)  are  around  5pp  less  likely  to  be  unassertive  if  they 
experience a problem when buying or using goods or services. Friends who shop online 
may  share  information  on  their  experiences  of  such  problems,  which  may  help 
individuals to be more assertive.  

Unemployed people who have been looking for employment for 5+  years are another 
group  that  is  particularly  likely  to  take  action  when  they  experience  a  problem  when 
buying  or  using  a  good  or  service  (as  captured  by  Indicator  1)166,  as  are  individuals 
studying with a part-time job.167 A possible explanation for these results is that these 
consumers may have more time available to devote to taking action to solve problems.  

165  Dimension  1  of  vulnerability  is  captured  through  indicators  of  whether  a  consumer  did  not 
take action when they experienced a problem when buying or using goods or services in the 
last 12 months (Indicator 1) and whether a consumer paid more for services in the last 12 
months due to being unable to use a certain payment method (Indicator 2). 

166 Unemployed people looking for employment for 5+ years are 13.4pp less likely than the full-
time  employed  to  be  unassertive  if  they  experience  a  problem  than  people  in  full-time 
employment. 

167 Individuals studying with a part-time job are 8.5pp less likely than the full-time employed to 

be unassertive if they experience a problem than people in full-time employment. 

259 

 
 
 
 
                                                 
6 │ Drivers of and factors linked to consumer vulnerability 

Table 54: 

Impact of situational drivers on indicators in Dimension 1 and 

Dimension 2 

Dimension 1 

Dimension 2 

Indicator 1: 
Unassertive if 
experiences 
problem 

Indicator 2: 
Overpaid for 
services 

Indicator 3: 
Maximum feeling 
of vulnerability 

Employed part-time 
Self-employed full-time 
Self-employed part-time 
Unemployed & looking 
Unemployed & not looking 
Long-term sick or disabled 
Housewife/Homemaker 
Retired 
In full time education 
Studying with part-time job 
Unemployed & looking 5+ years 
Unemployed & not looking 5+ 
years 
Long-term sick or disabled 5+ 
years 
Housewife/Homemaker 5+ years 
Retired 5+ years 
Remarried 
Living with a partner 
Single 
Divorced or separated 
Widowed 
Other 
Single parent 
1 dependent child 
2 dependent children 
3+ dependent children 
Dependent children: No response 
Friends buy online 
Friends buy on credit 
Friends can’t make ends meet 
Making ends meet: Fairly easy 
Making ends meet: Fairly difficult 
Making ends meet: Very difficult 
Making ends meet: Prefer not to 
say 
N 
R2 

0.00974 
-0.0497* 
0.0145 
-0.0221 
-0.0727* 
-0.0265 
-0.0260 
-0.0506** 
-0.000779 
-0.0851** 
-0.134*** 

0.0110 
0.0184** 
0.0199 
0.0237*** 
0.00330 
-0.00872 
-0.0101 
-0.00479 
0.00572 
0.0161 
-0.0331*** 

- 

- 

- 
- 
- 
-0.0464 
-0.00103 
-0.0105 
-0.0469** 
0.00970 
-0.0439 
- 
- 
- 
- 
- 
-0.0544*** 
- 
0.0507*** 
0.0734*** 
0.125*** 
0.169*** 

0.0376 
7,826 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
0.0251*** 
0.0250*** 
0.00853 
0.0233*** 
0.0477*** 

0.00789 
26,653 

1.193*** 
0.916 
1.341*** 
2.266*** 
1.720*** 
3.230*** 
1.268*** 
1.447*** 
1.540*** 
1.515*** 
- 

- 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
0.975 
0.912** 
1.088 
1.061* 
- 
1.127*** 
1.678*** 
1.786*** 
3.683*** 
9.981*** 

2.803*** 
26,653 

0.0929 
Marginal 

0.1418 
Marginal 

0.1227 
Odds ratio 

1 

Marginal effect / Odds ratio 
Indicator code 
Note: Values in this table are results of general to specific (G-S) regression analysis. The G-S methodology is 
described in Annex 8. “-” implies that the explanatory variable was not included in the final regression model 
established via the G-S process. */**/*** implies that a result is statistically significant at 90%/95%/99%. 
For each the indicator in this table, the R2 in the table is the Pseudo-R2 for the full regression model, which 
includes  the  explanatory  variables  shown  in  this  table  and  other  explanatory  variables  (not  shown  in  this 
table) that were identified for the regression model based on the G-S methodology The indicators are defined 
in Table 5. The explanatory variables are defined in Table 7 and A8.1.2. 
Source: London Economics analysis of the survey data 

3_max 

2 

260 

 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

6.6.2.  Dimension 2: Having characteristics that limit ability to maximise well-
being 

Results  from  G-S  regression  analysis  of  the  survey  data  on  the  relationship  between 
situational  drivers  and  second  dimension  of  vulnerability,  having  characteristics  that 
limit  ability  to  maximise  well-being168,  are  presented  in  Table  54  in  the  previous 
section. 

According to these results, several situational drivers are linked with a higher chance of 
feeling more vulnerable due to personal characteristics (Indicator 3). 

In particular, a consumer’s view regarding whether they can make ends meet more or 
less  easily  has  a  highly  significant  impact  on  feeling  more  vulnerable  due  to  personal 
characteristics. For example, the odds ratio for those who find it very difficult to make 
ends  meet  is  9.98,  implying  that  the  odds  that  this  group  will  feel  a  high  level  of 
vulnerability  are  around  ten-times  those  for  the  group  who  find it  easy  to  make  ends 
meet.  

All  those  with  an  employment  status  other  than  full-time169  are  more  likely  to  report 
that  they  feel  a  higher  level  of  vulnerability  due  to  personal  characteristics.  For 
example, for the long-term sick and disabled the odds ratio for Indicator 3 is 3.23. 

Furthermore,  consumers  living  in  a  household  with  two  dependent  children  are  less 
likely  to  feel  highly  vulnerable  due  to  personal  characteristics  (with  an  odds  ratio  of 
0.91), whereas those who have friends who buy on credit are more likely to feel high 
level of vulnerability due to personal characteristics (with an odds ratio of 1.13). 

6.6.3.  Dimension 3: Having difficulty in obtaining or assimilating information 

Table  55  below  provides  survey-based  evidence,  again  in  the  form  of  G-S  regression 
analysis results, on the relationship between situational drivers and the third dimension 
of vulnerability, having difficulty obtaining and assimilating information. 

The  most  consistent  results,  across  the  three  key  sectors,  relate  to  how  difficult 
consumers  find  it  to  make  ends  meet.  Compared  to  those  who  find  it  easy  to  make 
ends meet, consumers who find it very hard to make ends meet are more likely to feel 
uninformed  about  prices,  quality  and  conditions  when  choosing  goods  and  services 
(Indicator 4) and to have problems comparing deals due to information-related factors 
in all three key sectors (Indicator 6). 

Other  situational  characteristics  found  to  be  associated  with  a  higher  likelihood  of 
feeling  uninformed  about  prices,  quality  and  conditions  are  being  remarried,  divorced 
or separated, or widowed (as opposed to married), whereas characteristics linked with 
feeling  uninformed  are  being  self-employed  part-time, 
a 

likelihood  of 

lower 

168  Dimension  2  of  vulnerability  is  captured  by  perceptions  of  one’s  own  vulnerability  due  to 

personal characteristics. 

169  I.e.  all  independent  variables  for  which  full-time  employment  is  the  base  case:  employed 
part-time,  self-employed  full-time,  self-employed  part-time,  unemployed  &  looking, 
unemployed & not looking, long-term sick or disabled, housewife/homemaker, retired, in full 
time education and studying with part-time job. 

261 

 
 
 
                                                 
6 │ Drivers of and factors linked to consumer vulnerability 

unemployed, in full time education, or studying with a part-time job (as opposed to full 
time employed). 

However, with the exceptions of being in full-time education in the case of the energy 
sector  and  being  employed  part-time  and  living  with  a  partner  in  the  case  of  the 
finance  sector,  situational  drivers  are  rarely  associated  with  not  switching  due  to 
information-related factors (Indicator 7). 

262 

 
 
6 │ Drivers of and factors linked to consumer vulnerability 

Table 55: 

Impact of situational drivers on indicators in Dimension 3; Having difficulty obtaining and assimilating 

information 

Energy sector 

Online sector 

Finance sector 

Indicator 
4: 

Indicator 
5: 

Indicator 
6: 

Indicator 
7: 

Indicator 
5: 

Indicator 
6: 

Indicator 
7: 

Indicator 
5: 

Indicator 
6: 

Indicator 
7: 

Feels 
uninformed 
about 
prices 

Gets info. 
from 
adverts 
only 

Problems 
comparing 
due to info. 
factors 

Not 
switched 
due to info. 
factors 

Gets info. 
from 
adverts 
only 

Problems 
comparing 
due to info. 
factors 

Not 
switched 
due to info. 
factors 

Gets info. 
from 
adverts 
only 

Problems 
comparing 
due to info. 
factors 

Not 
switched 
due to info. 
factors 

-0.00108 
0.00214 
0.0132 
-0.0238** 
-0.0568** 
0.000980 
-0.0191 
-0.0169 
0.00146 
-0.000137 

-0.00360 
0.00402 
0.00611 
-0.00220 
0.0152 
- 
0.00457 
0.00838 
-0.00640* 
- 

-0.00003 
-0.00702 
-0.00664 
-0.0112** 
0.00995 
-0.00972 
0.000583 
0.00401 
-0.018*** 
-0.0123 

-0.00265 
0.0209** 
0.00447 
0.0148** 
-0.00775 
0.0151 
0.00314 
0.00262 
0.0229** 
0.0209 

- 

- 

- 

- 

- 

- 

0.861 
- 
1.172* 
1.008 
0.957 
1.136*** 
1.149** 
0.842** 
- 
- 

- 
0.0270** 
- 
- 
- 
- 
- 
- 
- 
- 

- 
- 
- 
- 
- 
- 
- 
- 
- 
-0.00549 

- 
- 
0.00766 
0.00146 
-0.00142 
0.000433 
-0.00725 
0.00226 
- 
- 

- 

- 

- 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

- 

- 

- 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

1.002 
0.969 
0.801** 
0.797*** 
0.809* 
0.934 
0.950 
1.016 
0.794*** 
0.852* 

- 

- 

- 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

- 

- 

- 

Employed part-time 
Self-employed full-time 
Self-employed part-time 
Unemployed & looking 
Unemployed & not looking 
Long-term sick or disabled 
Housewife/Homemaker 
Retired 
In full time education 
Studying with part-time job 
Unemployed  &  looking  5+ 
years 
Unemployed  &  not  looking 
5+ years 
Long-term  sick  or  disabled 
5+ years 
Housewife/Homemaker 
years 
Retired 5+ years 
Remarried 
Living with a partner 
Single 
Divorced or separated 
Widowed 
Other 
Single parent 
1 dependent child 

5+ 

263 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

- 

- 

- 

- 
- 
0.0144 
0.00205 
-0.00251 
0.0143** 
0.0165 
0.00573 
- 
- 

0.000422 
0.0168 
0.0213 
0.00571 
-0.052*** 
0.00405 
-0.0166 
0.00740 
0.0167 
0.0330 

-0.0051** 
0.0132 
0.00199 
0.00615 
- 
0.00762 
0.00217 
-0.000536 
-0.00355 
- 

- 

- 

- 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

- 

- 

- 

- 
- 
0.00113 
0.00731* 
0.00119 
- 
-0.00393 
0.0215 
- 
- 

 
 
 
 
 
 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
6 │ Drivers of and factors linked to consumer vulnerability 

Table 55: 

Impact of situational drivers on indicators in Dimension 3; Having difficulty obtaining and assimilating 

information 

Energy sector 

Online sector 

Finance sector 

Indicator 
4: 

Indicator 
5: 

Indicator 
6: 

Indicator 
7: 

Indicator 
5: 

Indicator 
6: 

Indicator 
7: 

Indicator 
5: 

Indicator 
6: 

Indicator 
7: 

Feels 
uninformed 
about 
prices 

Gets info. 
from 
adverts 
only 

Problems 
comparing 
due to info. 
factors 

Not 
switched 
due to info. 
factors 

Gets info. 
from 
adverts 
only 

Problems 
comparing 
due to info. 
factors 

Not 
switched 
due to info. 
factors 

Gets info. 
from 
adverts 
only 

Problems 
comparing 
due to info. 
factors 

Not 
switched 
due to info. 
factors 

- 

- 

- 

- 

- 

No 

- 
- 

- 
- 

- 
- 

- 
- 

- 
- 

- 
- 

- 
- 

- 
- 
- 

- 
- 
- 

- 
- 
- 

1.326*** 

0.00210 
- 
- 

-0.0149 
- 
-0.0489***  - 

- 
0.843*** 
- 

- 
- 
0.0086*** 

2 dependent children 
3+ dependent children 
Dependent 
children: 
response 
Friends buy online 
Friends buy on credit 
Friends  can’t  make  ends 
meet 
Making  ends  meet:  Fairly 
easy 
Making  ends  meet:  Fairly 
difficult 
Making  ends  meet:  Very 
difficult 
Making ends meet: Prefer not 
to say 
N 
R2 
Marginal effect / Odds ratio 
Indicator code 
Note: Values in this table are results of general to specific (G-S) regression analysis. The G-S methodology is described in Annex 8. “-” implies that the explanatory variable 
was  not  included  in  the  final  regression  model  established  via  the  G-S  process.  */**/***  implies  that  a  result  is  statistically  significant  at  90%/95%/99%.  For  each  the 
indicator in this table, the R2 in the table is the Pseudo-R2 for the full regression model, which includes the explanatory variables shown in this table and other explanatory 
variables  (not  shown  in  this  table)  that  were  identified  for  the  regression  model  based  on  the  G-S  methodology.  The  indicators  are  defined  in  Table  5.  The  explanatory 
variables are defined in Table 7 and A8.1.2. 
Source: London Economics analysis of the survey data 

- 
- 
0.00773** 

-0.00500 
4,481  
0.1461 

1.512** 
26,653 
0.0574 

0.0305 
19,913  
0.1217 

- 
10,228  
0.0925 

0.0318 
15,481  
0.0859 

- 
3,795  
0.1374 

16,451  
0.0890 

15,481  
0.0756 

9,793  
0.0972 

0.0335*** 

0.0374*** 

-0.000114 

1.317*** 

1.445*** 

0.0386** 

-0.00563 

0.00478 

0.00158 

0.0181* 

0.00168 

0.0139* 

5_b_adv_ene 

0.0126 

0.0125 

0.0756 

5_b_adv_onl 

5_b_adv_fin 

Odds ratio 

- 
- 
- 

- 
- 
- 

Marginal 

Marginal 

Marginal 

Marginal 

Marginal 

Marginal 

Marginal 

Marginal 

Marginal 

7_ene 

6_ene 

7_onl 

6_onl 

6_fin 

7_fin 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

4 

264 

 
 
 
 
 
 
  
  
  
  
  
  
  
  
  
  
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

6.6.4.  Dimension 4: Inability or failure to buy, choose or access suitable 
products 

Since  the  behavioural  experiments  tested  consumers’  ability  to  choose  the  optimal 
offer  in  simulated  purchasing  scenarios,  the  experiment  results  are  particularly 
relevant  to  this  dimension  of  vulnerability.  Results  of  G-S  regression  analysis  relating 
situational characteristics to respondents’ choices in the experiments  are presented in 
Table 56. 

The  results  in  Table  56  show  that  a  number  of  different  situational  drivers  are  linked 
with consumers’ ability to select the optimal deals in the experiments. 

Interestingly, the experiment results presented in Table 56 contrast with the results for 
the survey-based indicators of vulnerability in this dimension (see the discussion below 
relating  to  Table  57,  Table  58  and  Table  59)  and  other  dimensions  of  vulnerability  in 
terms of the effect of finding it had to make ends meet. 

The  experiment  results  imply  that  consumers  who  find  it  more  difficult  to  make  ends 
meet  are  more  likely  to  make  better  product  choices.  Specifically,  the  odds  ratio  for 
overall performance for those who find it very difficult to make ends meet is 1.326.170 

The experiment results imply that, given the same information and in the same choice 
environment, the choices of those who find it more difficult to make ends meet are at 
least  as  good  as  the  choices  of  those  who  find  making  ends  meet  easy.  Hence,  the 
contrast  between  the  experiment  results  and  the  results  for  the  survey-based 
indicators  would  suggest  that  it  is  external  drivers  (rather  than  internal,  personal 
drivers) that cause these consumers to be more vulnerable than their peers. 

In  addition,  being  self-employed  (part-time  or  full-time)  is  linked  with  better  overall 
performance  in  the  experiments,  whereas  being  in  full  time  education,  having  friends 
who buy goods and services on credit, and having friends who can’t make ends meet is 
associated with worse overall performance in the experiments. 

170 An odds ratio above one implies better overall experiment performance. 

265 

 
 
 
 
 
                                                 
6 │ Drivers of and factors linked to consumer vulnerability 

Table 56: 

Impact of situational characteristics on experiment performance 

Energy Sector 

Online Sector 

Finance Sector 

Cross-cutting 

Round 1 

Round 2 

Round 1 

Round 2 

Round 1 

Round 2 

Round 1 

Round 2 

Overall 

Employed part-time 
Self-employed full-time 
Self-employed part-time 
Unemployed & looking 
Unemployed & not looking 
Long-term sick or disabled 
Housewife/Homemaker 
Retired 
In full time education 
Studying with part-time job 
Unemployed & looking 5+ years 
Unemployed & not looking 5+ years 
Long-term sick or disabled 5+ years 
Housewife/Homemaker 5+ years 
Retired 5+ years 
Remarried 
Living with a partner 
Single 
Divorced or separated 
Widowed 
Other 
Single parent 
1 dependent child 
2 dependent children 
3+ dependent children 
Friends buy online 
Friends buy on credit 
Friends can't make ends meet 
Making ends meet: Fairly easy 
Making ends meet: Fairly difficult 
Making ends meet: Very difficult 
Making ends meet: Prefer not to say 
N 
R2 
Marginal effect / Odds ratio 

0.0771*** 
0.0355 
0.123*** 
0.0388 
0.0791 
0.0720* 
0.0226 
0.0498** 
-0.0509 
-0.0325 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
-0.0315 
-0.0375 
0.0273 
0.120** 
2,522 
0.0318 
Marginal 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
0.0503 
-0.0255 
-0.00310 
-0.0272 
0.0244 
0.203** 
- 
- 
- 
- 
0.0322 
- 
- 
- 
- 
- 
- 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

2,522 
0.0710 

Marginal 

2,510 
0.1061 

Marginal 

0.0369 
0.0121 
-0.0391 
0.0575 
0.0699 
0.135** 
-0.0429 
-0.0138 
0.0272 
0.0618 
- 
- 
- 
- 
- 
-0.0127 
0.0426 
0.0241 
-0.0840** 
0.0436 
-0.0408 
- 
-0.0807*** 
-0.0547 
0.0927 
- 
- 
-0.0323 
- 
- 
- 
- 

2,510 
0.0823 

Marginal 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

2,505 
0.1191 
Marginal 

2,505 
0.1205 

Marginal 

2,565 
0.0237 

Marginal 

-0.0587 
0.139*** 
0.0148 
0.0377 
0.0614 
0.108* 
-0.00852 
0.0110 
0.0595 
0.105 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
-0.0665*** 
- 
- 
- 
- 

2,565 
0.0628 
Marginal 

0.886 
1.313** 
1.469* 
0.998 
0.755 
1.129 
1.010 
0.956 
1.048 
1.309 
0.455*** 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
0.870** 
0.824*** 
1.035 
1.069 
1.326** 
1.525* 
5,051 
0.0421 
Odds ratio 

266 

 
 
 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

Note:  Values  in  this  table  are  results  of  general  to  specific  (G-S)  regression  analysis.  The  G-S  methodology  is  described  in  Annex  8.  “-”  implies  that  the  variable  was  not  included  in  the  final 
regression model established via the G-S process. */**/*** implies that a result is statistically significant at 90%/95%/99%. For each the indicator in this table, the R2 in the table is the Pseudo-R2 
for the full regression  model,  which includes the explanatory variables shown  in this table and  other explanatory variables (not shown in this table) that  were  identified for the regression  model 
based on the G-S methodology. The explanatory variables are defined in Table 7 and A8.1.2. The dependent variables in the experiment analysis signify whether the respondent chose the optimal 
deal. For individual experiment rounds, the dependent variable is equal to one if the respondent chose the optimal deal, or zero otherwise. The ‘Overall’ performance measure is the total number of 
correct choices made by the respondent (ranging from 0 to 4). Thus, positive marginal effects imply a higher likelihood of choosing the optimal deal, and odds ratios greater than one imply better 
overall performance. 
Source: London Economics analysis of the survey data 

267 

 
 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

Table 57 below provides survey-based evidence (i.e. results of G-S regression analysis 
of  the  survey  data)  on  the  relationships  between  situational  drivers  and  several 
indicators  in  the  fourth  dimension  of  vulnerability,  namely  whether  individuals  have 
problems comparing deals due to personal, market-related or access-related factors in 
the three key sectors (Indicator 8). 

For most situational drivers, there is no clear pattern regarding the impact on whether 
individuals find it more difficult to compare deals. 

The exception to the above is individuals who find it difficult to make ends meet. These 
consumers are more likely to have problems comparing deals in the energy sector due 
to access-related factors, in the online sector due to market-related factors, and in the 
finance sector due to personal, market-related and access-related factors; for example, 
for  those  who  find  it  very  difficult  to  make  ends  meet,  the  marginal  effects  for  these 
indicators range from 2.2pp to 9.8pp. 

Table  58  goes  on  to  examine  whether  individuals  do  not  switch  due  to  personal, 
market  or  access  factors  (Indicator  9).  The  results  show  that  individuals  that  have 
difficulty  making  ends  meet,  in  particular  those  who  say  that  making  ends  meet  is 
‘fairly difficult’, are also the individuals that are less likely to have switched providers in 
the energy, online and finance sectors due to access-related factors.  

Furthermore,  termination  costs  and  bundling  are  also  more  likely  to  be  barriers  to 
switching  for  individuals  that  have  difficulties  making  ends  meet.  Hence,  consumer 
policies  targeted  at  overcoming  these  barriers  for  people  that  find  it  difficult  to  make 
ends meet may help to alleviate consumer vulnerability. 

A  number  of  other  statistically  significant  relationships  are  shown  in  Table  58. 
However,  few  other  situational  drivers  are  found  to  be  consistently  associated  with 
either  higher  or  lower  vulnerability  on  the  indicators  in  this  table.  Two  of  the  more 
consistent drivers are being a housewife/homemaker (which is associated with a lower 
likelihood of not switching internet providers for market reasons and a lower likelihood 
of  not  switching  in  general  due  to  either  termination  costs  or  bundling)  and  having 
friends who buy on credit (which is associated with a higher likelihood of not switching 
banks  for  access  reasons  and  a  higher  likelihood  of  not  switching  in  general  due  to 
either termination costs or bundling). 

Moreover,  Table  59  provides  results  on  whether  situational  factors  affect  the  chances 
of an individual being excluded from e-commerce (due to difficulties in the purchasing 
process  or  not  having  a  payment  card)  or  being  declined  for  a  loan.  The  key  result 
from  Table  59,  consistent  with  other  findings  in  this  section,  is  that  an  individual  is 
more likely to be excluded from e-commerce if their friends cannot make ends meet. 

268 

 
 
 
Table 57: 

Impact of situational characteristics on Indicator 8 in Dimension 4; Has problems comparing deals due to 

personal factors, market-related factors, and access- related factors 

6 │ Drivers of and factors linked to consumer vulnerability 

Online 
Indicator 8a:  Indicator 8b:  Indicator 8c:  Indicator 8a:  Indicator 8b:  Indicator 8c:  Indicator 8a:  Indicator 8b:  Indicator 8c: 

Finance 

Energy 

Employed part-time 
Self-employed full-time 
Self-employed part-time 
Unemployed & looking 
Unemployed & not looking 
Long-term sick or disabled 
Housewife/Homemaker 
Retired 
In full time education 
Studying with part-time job 
Unemployed & looking 5+ years 
Unemployed & not looking 5+ yrs 
Long-term sick or disabled 5+ years 
Housewife/Homemaker 5+ years 
Retired 5+ years 
Remarried 
Living with a partner 
Single 
Divorced or separated 
Widowed 
Other 
Single parent 
1 dependent child 
2 dependent children 
3+ dependent children 
Dependent children: No response 
Friends buy online 
Friends buy on credit 
Friends can’t make ends meet 
Making ends meet: Fairly easy 
Making ends meet: Fairly difficult 
Making ends meet: Very difficult 
Making ends meet: Prefer not to say 
N 
R2 

Marginal effect / Odds ratio 

Personal 
factors 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
-0.0632*** 
- 
- 
- 
- 
- 
- 
- 
- 
- 
0.00381 
0.0365** 
-0.0128 
-0.0102 
- 
-0.0337*** 
- 
- 
- 
- 
- 

15,481 
0.1273 
Marginal 

Market factors  Access factors 

-0.0151 
0.0249 
0.00107 
-0.0240 
-0.0524 
0.0298 
-0.0218 
-0.00151 
-0.0511*** 
-0.0130 
- 
- 
- 
- 
- 
-0.0108 
0.0240** 
0.0462*** 
0.0168 
-0.0448* 
0.0358 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

15,481 
0.1084 
Marginal 

-0.0150* 
-0.0220* 
-0.0447*** 
-0.00614 
0.00608 
-0.0135 
0.00867 
0.00642 
0.0172 
0.00375 
- 
- 
0.0701 
- 
- 
0.0162 
0.00646 
-0.00953 
0.0306*** 
0.0482*** 
0.00645 
- 
- 
- 
- 
- 
-0.0150*** 
0.0117** 
- 
0.0222** 
0.0474*** 
0.0649*** 
-0.0137 

15,481 
0.1263 
Marginal 

Personal 
factors 
-0.000356 
-0.00696 
0.0193 
-0.0225*** 
0.00954 
-0.000462 
-0.0332*** 
-0.00537 
-0.0282*** 
0.0183 
- 
- 
- 
- 
- 
0.0519** 
-0.00212 
0.00618 
0.0228** 
0.0185 
0.0337** 
- 
- 
- 
- 
- 
-0.0135*** 
-0.0110** 
- 
-0.000970 
-0.0179** 
-0.0140 
-0.0409 

19,913 
0.1442 
Marginal 

Market factors  Access factors 

0.00507 
0.0115 
-0.0190 
0.000204 
-0.0393 
0.0668*** 
-0.0253** 
-0.00286 
0.00570 
-0.0226 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
0.0292*** 
0.00830 
0.0325** 
-0.0725 

19,913 
0.1461 
Marginal 

-0.00598 
0.00426 
0.00983 
-0.00522 
-0.00698 
-0.00124 
0.00525 
0.00171 
0.0162** 
0.0165 
- 
- 
0.0392 
- 
- 
-0.00958 
0.00945** 
0.000963 
-0.000607 
0.00518 
0.00599 
- 
- 
- 
- 
- 
- 
- 
-0.00730*** 
- 
- 
- 
- 

19,913 
0.0714 
Marginal 

Personal 
factors 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
-0.0226*** 
- 
0.0479*** 
0.0643*** 
0.0980*** 
0.0301 

15,481 
0.1210 
Marginal 

Market factors  Access factors 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
0.0461*** 
0.0602*** 
0.0733*** 
-0.00224 

15,481 
0.1049 
Marginal 

0.00307 
-0.00342 
0.0105 
0.0108** 
0.0101 
0.0113* 
0.00520 
0.00900 
0.00478 
0.000581 
-0.00880*** 
- 
- 
- 
-0.00802*** 
-0.00772** 
0.000607 
0.00242 
0.00350 
0.00216 
0.00745 
- 
- 
- 
- 
- 
- 
- 
0.00494*** 
0.00318 
0.0135*** 
0.0220** 
0.000119 
15,481 
0.0933 
Marginal 

Indicator code 
6_8_per_onl_r 
Note: Values in this table are results of general to specific (G-S) regression analysis. The G-S methodology is described in Annex 8. “-” implies that the explanatory variable was not included in the final regression model established 
via  the  G-S  process.  */**/***  implies  that  a  result  is  statistically  significant  at  90%/95%/99%.  %.  For  each  the  indicator  in  this  tabl e,  the  R2  in  the  table  is  the  Pseudo-R2  for  the  full  regression  model,  which  includes  the 
explanatory variables shown in this table and other explanatory variables (not shown in this table) that were identified for the regression model based on the G-S methodology. The indicators are defined in Table 5. The explanatory 
variables are defined in Table 7 and A8.1.2. 
Source: London Economics analysis of the survey data 

6_8_mar_ene_r 

6_8_mar_onl_r 

6_8_acc_ene_r 

6_8_per_ene_r 

6_8_mar_fin_r 

6_8_acc_onl_r 

6_8_acc_fin_r 

6_8_per_fin_r 

269 

 
 
 
 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

Table 58: 

Impact of situational characteristics on Indicator 9 in Dimension 4; Has not switched due to personal factors, 

market related factors, access-related factors, termination costs, and bundling 

Indicator 
9a: 
Personal 
factors 
0.00584 
0.00397 
-0.0307 
-0.0364*** 
-0.0240 
0.0193 
-0.00313 
-0.0164 
0.00116 
-0.00473 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
-0.0431*** 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

4,782  
0.1283 
Marginal 

Energy 
Indicator 
9b: 
Market 
factors 

Indicator 
9c: 
Access 
factors 

Indicator 
9a: 
Personal 
factors 

-0.00538 
0.0287 
0.00973 
-0.0137 
-0.0207 
-0.0216 
-0.0200 
-0.0261* 
-0.0440*** 
-0.0203 
- 
- 
- 
- 
- 
0.0255 
0.00966 
0.0267** 
0.00473 
-0.00949 
0.0624 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

4,782  
0.0651 
Marginal 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

-0.0211 
-0.0357 
-0.0449 
-0.0118 
-0.00170 
-0.0145 
0.0321 
-0.0233 
0.106*** 
0.0171 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
0.0355* 
0.0514* 
0.0353 
0.00541 
-0.0331*** 
- 
- 
0.0729*** 
0.122*** 
0.137*** 
0.0767 

4,782  
0.2145 
Marginal 

3,483  
0.2094 
Marginal 

Online 
Indicator 
9b: 
Market 
factors 
-0.00959 
0.0430** 
-0.00005 
-0.00943 
-0.0172 
-0.00377 
-0.0285*** 
0.0138 
-0.00001 
-0.0105 
- 
- 
- 
- 
- 
-0.0274** 
0.0182* 
0.00594 
0.0294* 
-0.0203 
-0.0210 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

3,427  
0.0804 
Marginal 

Indicator 
9c: 
Access 
factors 

Indicator 
9a: 
Personal 
factors 

Finance 
Indicator 
9b: 
Market 
factors 

Indicator 
9c: 
Access 
factors 

Indicator 
9d: 
Terminatio
n costs 

Indicator 
9e: 
Bundling 

0.00601 
-0.00691 
- 
-0.000604 
0.0228 
-0.0224*** 
0.0106 
-0.00924 
0.00949 
0.0313 
- 
- 
- 
- 
-0.0185 
- 
0.00212 
-0.000930 
0.000919 
- 
0.00124 
- 
- 
- 
- 
- 
- 
- 
- 
0.0264* 
0.0391** 
0.0405 
- 

3,265  
0.1083 
Marginal 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
-0.0350*** 
0.00579 
0.000713 
-0.0166* 
0.0183 
-0.0144 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

4,355  
0.1330 
Marginal 

4,368  
0.0597 
Marginal 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
0.00911 
-0.00146 
-0.00749** 
-0.00551 
0.00471 
- 
- 
- 
- 
- 
- 
- 
0.00526* 
- 
0.0162 
0.0375* 
0.0811 
- 

4,095  
0.1538 
Marginal 

0.00848 
0.0213** 
0.0237 
0.00318 
0.0197 
-0.0153 
-0.0145* 
0.00523 
0.00747 
0.0176 
- 
- 
- 
- 
0.0242* 
0.0351* 
0.0110* 
-0.00565 
-0.0148* 
-0.00582 
0.00781 
- 
- 
- 
- 
- 
- 
0.0207*** 
0.0372*** 
0.0208** 
0.0518*** 
0.0674*** 
-0.00412 

26,653  
0.1242 
Marginal 

0.000911 
0.000663 
0.0303 
0.0131 
0.00541 
-0.0215 
-0.0206* 
0.00318 
-0.00446 
0.000284 
-0.0518** 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
0.0323*** 
0.0271*** 
0.00853 
0.0366*** 
0.0290** 
-0.0158 

26,653  
0.0852 
Marginal 

Employed part-time 
Self-employed full-time 
Self-employed part-time 
Unemployed & looking 
Unemployed & not looking 
Long-term sick or disabled 
Housewife/Homemaker 
Retired 
In full time education 
Studying with part-time job 
Unemployed & looking 5+ years 
Unemployed & not looking 5+ years 
Long-term sick or disabled 5+ years 
Housewife/Homemaker 5+ years 
Retired 5+ years 
Remarried 
Living with a partner 
Single 
Divorced or separated 
Widowed 
Other 
Single parent 
1 dependent child 
2 dependent children 
3+ dependent children 
Dependent children: No response 
Friends buy online 
Friends buy on credit 
Friends can’t make ends meet 
Making ends meet: Fairly easy 
Making ends meet: Fairly difficult 
Making ends meet: Very difficult 
Making ends meet: Prefer not to say 
N 
R2 

Marginal effect / Odds ratio 

Indicator code 
Note: Values in this table are results of general to specific (G-S) regression analysis. The G-S methodology is described in Annex 4. “-” implies that the explanatory variable was not included in the final regression model established 
via the G-S process. */**/*** implies that a result is statistically significant at 90%/95%/99%. For each the indicator in this table, the R2 in the table is the Pseudo-R2 for the full regression model, which includes the explanatory 
variables shown in this table and other explanatory variables (not shown in this table) that were identified for the regressi on model based on the G-S methodology. The indicators are defined in Table 5. The explanatory variables 
are defined in Table 7 and A8.1.2. 
Source: London Economics analysis of the survey data 

7_9_mar_ene 

7_9_mar_onl 

7_9_acc_ene 

7_9_per_ene 

7_9_mar_fin 

7_9_per_onl 

7_9_acc_onl 

7_9_per_fin 

7_9_acc_fin 

7_9_bun 

7_9_ter 

270 

 
 
 
 
 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

Table 59: 

Impact of situational characteristics on Indicators 10 and 11 in 

Dimension 4; Excluded from e-commerce and declined a loan 

Excluded from e-commerce due to 

Indicator 10a: 
Difficulty of the 
process 

Indicator 10b: 
Not having 
payment card 

Indicator 11: 
Declined a loan 

- 

- 

-0.115*** 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

-0.00310 
0.00179 
0.00531 
-0.00173 
0.0957** 
0.00949 
-0.0120 
0.00196 
0.0299** 
0.00707 
- 

-0.00698 
0.0149 
0.0254 
0.0256*** 
0.0562* 
-0.0106 
-0.00488 
0.0145 
0.0289** 
0.0242 
- 

Employed part-time 
Self-employed full-time 
Self-employed part-time 
Unemployed & looking 
Unemployed & not looking 
Long-term sick or disabled 
Housewife/Homemaker 
Retired 
In full time education 
Studying with part-time job 
Unemployed & looking 5+ years 
Unemployed & not looking 5+ 
years 
Long-term sick or disabled 5+ 
years 
Housewife/Homemaker 5+ years 
Retired 5+ years 
Remarried 
Living with a partner 
Single 
Divorced or separated 
Widowed 
Other 
Single parent 
1 dependent child 
2 dependent children 
3+ dependent children 
Dependent children: No response 
Friends buy online 
Friends buy on credit 
Friends can’t make ends meet 
Making ends meet: Fairly easy 
Making ends meet: Fairly difficult 
Making ends meet: Very difficult 
Making ends meet: Prefer not to 
say 
N 
R2 
Marginal effect / Odds ratio 
Indicator code 
Note: Values in this table are results of general to specific (G-S) regression analysis. The G-S methodology is described in Annex 4. “-” implies 
that  the  explanatory  variable  was  not included in  the  final  regression  model  established  via  the  G-S  process.  */**/*** implies  that  a  result is 
statistically significant at 90%/95%/99%. For each the indicator in this table, the R2 in the table is the Pseudo-R2 for the full regression model, 
which includes the explanatory variables shown in this table and other explanatory variables (not shown in this table) that were identified for the 
regression  model  based  on  the  G-S  methodology.  The  indicators  are  defined  in  Table  5.  The  explanatory  variables  are  defined  in  Table  7  and 
A8.1.2. 
Source: London Economics analysis of the survey data 

- 
-0.0340 
0.0571** 
0.0310 
0.0258*** 
0.000478 
-0.00163 
-0.0259 
0.0367** 
- 
- 
- 
- 
- 
0.0104* 
- 
0.0353*** 
- 
- 
- 

- 
- 
- 
0.0134 
0.0143* 
0.000800 
0.0263** 
0.0281 
0.0632*** 
- 
- 
- 
- 
- 
- 
0.0145*** 
0.0262*** 
0.0282*** 
0.0540*** 
0.0851*** 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
0.0201 
0.0276* 
0.0725** 

- 
19,957  
0.0903 
Marginal 

0.145 
4,971  
0.1230 
Marginal 

19,957  
0.1188 
Marginal 

-0.0246 

10_card 

11_loan 

10_diff 

6.6.5.  Dimension 5: Higher susceptibility to marketing practices 

Finally, Table 60 provides evidence, based on the results of G-S regression analysis of 
the survey data, on which groups have a higher susceptibility to marketing practices.  

Again,  the  key  finding,  consistent  with  other  results  reported  in  this  section,  is  that 
consumers who struggle to make ends meet tend to be more susceptible to marketing 
practices. For  example, those who find it ‘very difficult’ to make ends meet are 6.2pp 

271 

 
 
 
 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

more likely to report that they feel vulnerable ‘to a great extent’ due to the complexity 
of offers, terms or conditions. 

In addition, those who are unemployed and looking for employment, long- term sick or 
disabled and who have friends who can’t make ends meet are more likely to feel a high 
level  of  vulnerability  due  to  marketing  practices  (based  on  the  results  for  the  scale-
based indicator in Table 60).  

Conversely,  those  who  are  remarried,  single,  or  divorced  or  separated  (rather  than 
married) are found to be less likely to feel a high level of vulnerability due to marketing 
practices. 

Table 60: 

Impact of situational characteristics on Indicator 12 in 

Dimension 5 

Indicator 12: Feels vulnerable due to complexity of 
offers; 

Binary: “To a great 
extent” 

Scale: 1 (low) - 4 (high) 

Employed part-time 
Self-employed full-time 
Self-employed part-time 
Unemployed & looking 
Unemployed & not looking 
Long-term sick or disabled 
Housewife/Homemaker 
Retired 
In full time education 
Studying with part-time job 
Unemployed & looking 5+ years 
Unemployed & not looking 5+ years 
Long-term sick or disabled 5+ years 
Housewife/Homemaker 5+ years 
Retired 5+ years 
Remarried 
Living with a partner 
Single 
Divorced or separated 
Widowed 
Other 
Single parent 
1 dependent child 
2 dependent children 
3+ dependent children 
Dependent children: No response 
Friends buy online 
Friends buy on credit 
Friends can’t make ends meet 
Making ends meet: Fairly easy 
Making ends meet: Fairly difficult 
Making ends meet: Very difficult 
Making ends meet: Prefer not to say 
N 
R2 
Marginal effect / Odds ratio 
Indicator code 
Note: Values in this table are results of general to specific (G-S) regression analysis. The G-S methodology is described in Annex 4. “-” implies 
that  the  explanatory  variable  was  not included in  the  final  regression  model  established  via  the  G-S  process.  */**/*** implies  that  a  result is 
statistically significant at 90%/95%/99%. For each the indicator in this table, the R2 in the table is the Pseudo-R2 for the full regression model, 
which includes the explanatory variables shown in this table and other explanatory variables (not shown in this table) that were identified for the 
regression  model  based  on  the  G-S  methodology.  The  indicators  are  defined  in  Table  5.  The  explanatory  variables  are  defined  in  Table  7  and 
A8.1.2. 
Source: London Economics analysis of the survey data 

- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
0.0238*** 
0.0165** 
0.0227*** 
0.0620*** 
0.0403* 
26,653 
0.1005 
Marginal 

1.044 
0.999 
1.096 
1.205*** 
0.938 
1.332*** 
1.074 
1.032 
0.948 
1.082 
- 
- 
- 
- 
- 
0.753*** 
0.962 
0.943* 
0.918* 
1.015 
0.912 
- 
- 
- 
- 
- 
- 
1.121*** 
1.406*** 
1.398*** 
1.567*** 
1.928*** 
1.464** 
26,653 
0.0465 
Odds ratio 

_12_off_b2 

_12_off 

272 

 
 
 
 
6 │ Drivers of and factors linked to consumer vulnerability 

6.7.  Country and country group level analysis of drivers of vulnerability  

This  section  examines  the  drivers  of  vulnerability  at  country  and  country  group  level. 
The  objective  is  to  assess  whether  and  to  what  extent  the  results  for  the  full  sample 
(reported in sections 6.2 to 6.6 above) hold at country and country group level171. 

Three  approaches  have  been  followed  in  order  to  examine  the  drivers  of  vulnerability 
at  a  country  and  country  group  level.  These  are  discussed  in  turn  in  section  6.7.1 
below. The country groups examined in the analysis are set out in section 6.7.2. 

The  results  are  discussed  in  section  6.7.3.  This  discussion is  limited  to  the  results  for 
the country groups and the focus is on comparison with the results for the full sample. 
The reasons for this are that there are practical limitations to the analysis that can be 
done at country level and that the analysis generates a very large volume of results172 
which cannot feasibly be discussed in their entirety. 

However,  the  full  results  from  all  three  approaches,  including  results  at  country  and 
country group level, are provided in Excel files alongside this report. 

6.7.1.  Approaches used 

Three  different  approaches  are  implemented  in  order  to  examine  the  drivers  of 
vulnerability at country and country group level: 

1.  Re-estimate  the  regression  models  derived  from  the  full  sample  for 
each country individually. The first method used to examine the drivers of 
vulnerability  at  country  level  is  to  re-estimate  the  regression  models 
established  from  the  full  sample173  using  the  data  for  each  country 
individually.  This  approach  produces  country-level  results  that  are  directly 
comparable  with  the  results  for  the  full  sample,  since  precisely  the  same 
regression  models  are  used.  However,  a  major  drawback  is  that,  due  to  the 
small  sample  sizes  and  low  rates  of  vulnerability  at  country  level,  this 
approach is practically unfeasible for some indicators/countries. For example, 
a  low  incidence  rate  of,  say,  5%  for  a  particular  indicator  would  imply  that 
just 50 respondents are identified as vulnerable on that indicator in a country 
sample  of  1,000.  Given  that  the  average  regression  model  for  an  indicator 
includes  65  explanatory  variables  (and  often  more),  estimating  the  model 
based on just 50 vulnerable respondents would mean that the assumptions of 
the  regression  are  no  longer  satisfied.  In  such  cases,  the  explanatory 
variables often predict which respondents are vulnerable almost perfectly due 
to  the  sheer  number  of  explanatory  variables  that  are  fitted  on  so  few 
respondents. 

171  As  we  have  seen  from  the  results  in  chapter  4,  there  are  large  variations  of  consumer 
vulnerability  incidence  between  countries.  In  order  to  understand  whether  these  variations 
are due to different underlying drivers, or simply to different levels of the same drivers, we 
perform the analysis at the country group level based on the incidence of vulnerability. 

172  The  volume  of  results  is  very  large  since,  where  feasible,  the  analysis  of  each  indicator  is 

replicated 30 times, once of each country. 

173 The analysis undertaken on the full sample established a regression model for each indicator 

using the general-to-specific econometric approach. 

273 

 
 
 
                                                 
6 │ Drivers of and factors linked to consumer vulnerability 

The ratio of the number of vulnerable respondents on a particular indicator – 
or, in general, the number of positive observations for any binary dependent 
variable in a regression  – to the number of explanatory variables included in 
the regression of that indicator is referred to in the econometrics literature as 
the  number  of  events  per  variable  (EPV).  A  rule  of  thumb  of  a  target  of  10 
EPV is applied in many studies, although simulations have found that EPV as 
low  as  4  or  as  high  as  20  might  be  appropriate  (Vittinghoff  and  McCulloch 
2006).  A  consequence  of  very  low  EPV,  which  is  often  seen  in  the  results  of 
our  country-level  regressions,  is  that  in  practice  the  confidence  intervals  of 
regression  coefficients  cannot  be  calculated  and  the  regressions  cannot  be 
estimated.  

Due  to  the  practical  limitations  described  above  and  also  the  very  large 
volume  of  results  produced174,  the  country-level  regression  results  are  not 
discussed  in  this  section.  However,  these  results  are  provided  in  Excel  files 
alongside this report. 

2.  Estimate  the  country-level  effect  for  each  driver  and  country  in  turn 
using  the  full  sample.  The  second  approach  is  to  estimate  an  augmented 
regression model for each indicator that is the same as the model established 
through  the  general  to  specific  procedure  on  the  full  sample  with  additional 
explanatory  variables  to  allow  the  effect  of  a  given  driver  to  vary  for  a 
particular country.175 This is done separately  – i.e. using a separate model  – 
for each driver and country combination in order to identify whether the effect 
of any driver differs for a particular country relative to the effect of that driver 
for the full sample. 

The key advantage of this approach, compared to the first approach described 
above,  is  that  only  a  subset  of  the  regression  coefficients  are  estimated  for 
the  individual  country,  making  this  approach  technically  feasible  even  for 
relatively  small  samples.176  In  addition,  a  further  advantage  is  that  this 
approach provides a direct comparison between a country and the rest of the 
sample;  it  reveals  whether  the  effect  of  a  given  characteristic  or  driver  is 
statistically different for a country relative to the rest of the sample. 

However,  the  key  disadvantage  of  this  approach  is  that  it  is  internally 
inconsistent.  Each  model  assumes  that  the  true  model  is  one  where  all 
coefficients  are  the  same  across  countries  except  for  the  coefficients  for  the 
country  specific  explanatory  variables.  Therefore,  since  in  each  model  the 

174 The volume of results produced is very large since, where it is feasible to do so, the analysis 
of  each  indicator  described  in  sections  6.2  to  6.6  is  replicated  30  times,  once  of  each 
country. 

175 The additional explanatory variables are interaction terms between a dummy representing the 
country  in  question  and  the  dummies  representing  the  relevant  driver  (e.g.  in  the  case  of 
age, the dummies representing each age band, or in the case of impulsiveness, the dummies 
representing each impulsiveness level). The country dummy itself is also included in order to 
control for any difference in the incidence of vulnerability in the country. 

176 Since the analysis is done using the whole sample, problems relating to low EPV (which affect 

the first methodology described above) do not arise. 

274 

 
 
                                                 
6 │ Drivers of and factors linked to consumer vulnerability 

effect of a different driver is allowed to vary by country, each model violates 
the assumptions of the other models.177  

The results from this approach are provided in full in Excel files alongside this 
report. 

3.  Re-estimate  the  regression  models  derived  from  the  full  sample  for 
country  groups.  The  final  approach  is  the  same  as  the  first  approach 
described above, except that instead of undertaking the analysis at individual 
country-level it is carried out for groups of countries together. Specifically, the 
analysis  is  conducted  for  the  five  country  groups  set  out  in  section  6.7.2 
below.  These  country  groups  are  established  via  cluster  analysis,  based  on 
the incidence of vulnerability on fifteen indicators. In cases where the sample 
size  is  still  insufficient  for  at  least  one  of  these  five  country  groups,  two  or 
more groups are further combined, as explained in section 6.7.2. 

Although of course this approach does  not provide country specific results, it 
does  show  whether  the  results  for  the  full  sample  hold  when  the  sample  is 
disaggregated  along  country  lines  based  on  similarities  between  countries  in 
terms  of  vulnerability.  In  addition,  most  importantly,  since  the  sample  sizes 
are larger when countries are grouped together the problems  relating to low 
EPV  (which  affect  the  first  methodology  described  above)  do  not  arise, 
meaning that the approach is technically feasible for all indicators. 

The  results  from  this  approach  are  discussed  in  section  6.7.3  below  and  can 
be seen in full in Excel files provided alongside this report. 

6.7.2.  Country groups 

Five  country  groups  are  constructed  based  on  the  survey  data,  using  cluster  analysis 
(see  Annex  7).  This  approach  assigns  countries  to  groups  based  on  the  incidence  of 
vulnerability on fifteen indicators.178 The five country groups are:179  

  Group  1.  A  group  consisting  mainly  of  Germanic  and  Scandinavian  countries. 
These  countries  exhibit  among  the  lowest  rates  of  vulnerability.  Countries 
included in this group are Germany, Denmark, Sweden, Norway, Luxembourg, 
the Netherlands, Austria, and Iceland. 

  Group 2. A group consisting mostly of countries that are  neighbours of group 
1. These countries have below average rates  of vulnerability. The  countries in 
this  group  are  the  Czech  Republic,  United  Kingdom,  Finland,  Belgium,  Malta 
and Slovakia. 

  Group  3.  A  group  including  the  Eastern  European  countries  with  above 
average  rates  of  vulnerability.  Countries  included  in  this  group  are  Hungary, 
Estonia, Lithuania, Latvia, Bulgaria, Poland and Spain. 

177 A further consideration, regarding interpretation of the results, is that the approach does not 

allow the calculation of (unbiased) country level marginal effects. 

178 Specifically, these indicators are (see Table 5 for definitions): 1, 2, 3, 4, 8_1_ene, 8_2_ene, 

8_1_fin, 8_2_fin, 8_1_onl, 8_2_onl, 9e_bun, 9d_ter, 10b, 10a, and 12_off. 

179 The rate of vulnerability referred to in these bullet points is the average rate of vulnerability 

based on the indicators included in the cluster analysis. 

275 

 
 
 
                                                 
6 │ Drivers of and factors linked to consumer vulnerability 

  Group  4.  A  group  consisting  of  countries  in  South  East  Europe.  These 
countries  have  among  the  highest  rates  of  vulnerability,  with  especially  high 
incidence  rates  on  several  indicators.  Countries  included  in  this  group  are 
Greece, Cyprus and Romania.  

  Group  5.  A  group  of  countries  located  either  on  the  Adriatic  Sea  or  Atlantic 
Ocean.  These  countries  exhibit  average  or  high  rates  of  vulnerability on  some 
indicators, but low incidence of vulnerability on other indicators. The countries 
in this group are Italy, Slovenia, France, Portugal, Ireland and Croatia. 

For  14  indicators180  groups  1,  2,  3  and  4  are  each  represented  by  a  single  country 
(Denmark, the United Kingdom, Lithuania and Romania, respectively), and a further 10 
indicators181  are  not  available  for  11  countries  (Bulgaria,  Croatia,  Cyprus,  Greece, 
Hungary, Italy, Latvia, Malta, Poland, Slovenia and Spain). 

to 

the 

low  rates  of  vulnerability  and  small  sample  sizes 

Due 
for  some 
countries/indicators,  combining  the  countries  into  the  groups  above  does  not  always 
provide  sufficient  sample  size  for  the  analysis  of  vulnerability  drivers  to  be  conducted 
at  country  group  level.  Therefore,  for  some  indicators  the  groups  above  are  further 
combined as follows: 

  Groups 1 and 2 are grouped together for 10 indicators;182 
  Groups 3 and 4 are grouped together for 5 indicators;183 
  Groups 3, 4 and 5 are grouped together for 8 indicators.184 

6.7.3.  Drivers of vulnerability at country group level 

The  following  subsections  compare  the  results  for  the  full  sample  with  the  results  for 
the country groups established via the cluster analysis. The focus is on comparing the 
high  level  results  for  the  full  sample  for  each  type  of  driver  with  the  corresponding 
results  at  country  group  level,  in  order  to  assess  whether,  overall,  the  country  group 
results align with the full sample results. The comparison is done by type of driver (i.e. 
by  type  of  explanatory  variable),  in  order  to  mirror  the  structure  of  the  relevant 
sections in Chapter 6. 

The  results  show  that,  in  general,  the  high-level  findings  for  the  full  sample  hold  at 
country  group  level.  Although  there  are  only  half  (51%)  as  many  statistically 
significant  results  at  country  group  level  as  there  are  for  the  full  sample185,  in  most 

180  Specifically  the  following  indicators  (see  Table  5  for  definitions):  11_loan,  7_ene,  9a_ene, 
9b_ene, 9c_ene, 7_onl, 9a_onl, 9b_onl, 9c_onl, 7_fin, 9a_fin, 9b_fin, 9c_fin, expt_overall. 
181  Specifically  the  following  indicators  (see  Table  5  for  definitions)5_adv_ene,  6_ene,  8a_ene, 

8b_ene, 8c_ene, 5_adv_fin, 6_fin, 8a_fin, 8b_fin, 8c_fin. 

182  Specifically  the  following  indicators  (see  Table  5  for  definitions):  7_ene,  5_adv_onl,  7_fin, 

8c_fin, 9c_ene, 9a_onl, 9c_onl, 9a_fin, 9c_fin, 11_loan. 

183  Specifically  the  following  indicators  (see  Table  5  for  definitions):  5_adv_ene,  7_ene,  8a_fin, 

9a_ene, 9b_fin. 

184 Specifically the following indicators (see Table 5 for definitions): 5_adv_onl, 5_adv_fin, 7_fin, 

8c_fin, 9c_ene, 9a_onl, 9c_fin, 11_loan. 

185 Fewer statistically significant results are found at country group level. This is expected due to 
the  considerably  smaller  sample  sizes  available  at  country  group  level,  and  thus,  ceteris 
paribus, larger standard errors and fewer statistically significant results. 

276 

 
 
                                                 
6 │ Drivers of and factors linked to consumer vulnerability 

cases  the  statistically  significant  country  group  level  results  mirror  those  for  the  full 
sample. In particular, depending on the country group in question:  

  Among  results  which  imply  that  variables  are  linked  with  higher  vulnerability, 
83%  to  87%  of  statistically  significant  results  at  country  group  level  have  a 
matching  result  (i.e.  a  statistically  significant  result  in  the  same  direction)  for 
the full sample. 

  Among  results  which  imply  that  variables  are  linked  with  lower  vulnerability, 
58%  to  75%  of  statistically  significant  results  at  country  group  level  have  a 
matching result for the full sample. 

  Only 1.7% to 3.9% of statistically significant results at country group level are 

in the opposite direction of the corresponding result for the full sample. 

Therefore, in summary it may be concluded that most of the results for the full sample 
can  be  generalised  to  the  country  groups,  or  at  least  are  not  contradicted  at  country 
group level, and relatively few drivers of vulnerability are found to be specific to certain 
country groups. 

The following subsections discuss the results for each type of driver in turn. 

Personal and demographic characteristics 

The results based on the full sample regarding the impact of personal and demographic 
characteristics  on  the  likelihood  of  vulnerability  (reported  in  section  6.2)  compare  to 
the corresponding country group level results as follows: 

  The analysis of the full sample found that younger consumers (aged 16-24 and 
25-34)  are  more  likely  to  be  vulnerable  on  indicators  in  dimension  1 
(heightened risk of negative outcomes  or impact on wellbeing) and dimension 
2 (having characteristics that limit ability to maximise wellbeing). These results 
hold in full for country groups 1, 2 and 3. Results in the same direction are also 
found  for  groups  4  and  5,  but  the  result  for  dimension  1  is  not  significant  for 
group 4 and the result for dimension 2 is not significant for group 5. 

  The  results  for  the  full  sample  show  that  consumers  above  the  age  of  55  are 
more  likely  to  be  vulnerable  on  some  indicators  in  dimensions  3  (having 
difficulty  obtaining  or  assimilating information)  and  4  (inability  to  buy,  choose 
or access suitable products), but are also less likely to be vulnerable on other 
indicators  in  these  dimensions.  While  the  results  are  mixed  at  country  group 
level,  as  they  are  for  the  full  sample,  group  1  has  predominantly  higher 
vulnerability  among  the  elderly  (ages  64-75  and  75+)  on  the  indicators  in 
dimensions 3 and 4;  the  elderly (ages 64-75 and 75+) in groups 3 and 4  are 
less  vulnerable  on  more  indicators  in  dimensions  3  and  4  than  the  number  of 
indicators on which they are more vulnerable. 

  Based  on  the  results  for  the  full  sample,  men  are  often  less  likely  to  be 
vulnerable  than  women  on  a  number  of  indicators  in  dimension  4  (inability  to 
buy,  choose  or  access  suitable  products).  This  holds  particularly  well  for 
country groups 2 and 5. However, less evidence of this relationship is found for 
groups 3 and 4.  

  The analysis of the full sample reveals that non-native speakers are more likely 
to  be  vulnerable  than  native  speakers  on  at  least  one  indicator  in  every 
dimension,  and  are  less  likely  to  be  vulnerable  on  relatively  few  indicators. 
Furthermore,  based  on  the  full  sample  results,  non-native  speakers  stand  out 
from  other  groups  because  in  every  dimension  they  are  more  likely  to  be 
vulnerable on more indicators than the number of indicators they are less likely 

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to  be  vulnerable  on.  These  results  hold  for  country  group  1  in  particular. 
However, in dimension 4 non-native speakers in country groups 3, 4, and 5 are 
less  vulnerable  more  often  (i.e.  on  more  indicators)  than  they  are  more 
vulnerable. 

In  summary,  with  a  small  number  of  exceptions186  the  high  level  results  for  the  full 
sample generally hold at country group level. Full regression results showing the links 
between  the  indicators  of  vulnerability  and  consumers’  personal  and  demographic 
characteristics at country group level are provided in Excel files alongside this report.  

Behavioural drivers of vulnerability 

The  results  for  the  full  sample  regarding  the  effects  of  behavioural  drivers  on  the 
likelihood  of  vulnerability  (reported  in  section  6.3)  compare  to  the  equivalent  country 
group level results as follows: 

  The  results  for  the  full  sample  show  that  those  with  high  or  very  high 
impulsiveness  based  on  the  first  and  fourth  ‘impulsiveness’  measures187  are 
more  likely  to  be  vulnerable  on  a  relatively  large  number  of  indicators, 
including  indicators  in  all  dimensions,  and  these  impulsiveness  measures  are 
the  behavioural  characteristics  which,  in  general,  are  most  strongly  linked  to 
higher  vulnerability.  Similarly,  impulsiveness  is  the  behavioural  characteristic 
that is most strongly linked with higher vulnerability at country group level; for 
groups 1 and 2 the first impulsiveness measure is most frequently linked with 
higher  consumer  vulnerability,  whereas  for  groups  3  and  5  it  is  the  fourth 
impulsiveness measure. 

  According to the results for the full sample, those who are more trusting based 
on the two ‘trust’ measures188 are less likely to feel a high level of vulnerability 
for  reasons  of  age,  health  problems,  employment  etc.189,  and  those  who  are 
more  trusting  on  the  first  trust  measure  (in  particular)  are  also  less  likely  to 
feel  a  high  level  of  vulnerability  due  to  the  complexity  of  offers.190  These 
findings  hold  in  general  across  the  country  groups,  with  the  caveat  that  for 
group  1  only  the  results  for  the  first  trust  measure  are  statistically  significant 
and  for  group  4  only  the  results  for  the  second  trust  measure  are  statistically 
significant. 

186  For  example  the  result  highlighted  above  regarding  the  low  vulnerability  of  non-native 

speakers in country groups 3, 4, and 5 on indicators in dimension 4. 

187 These measures are labelled ‘Impulsiveness 1’ and ‘Impulsiveness 4’. They are based on the 
extent that respondents agree with the statements “I have a hard time breaking bad habits” 
and “I’m impulsive in the purchase decisions I take” respectively. 

188  These  measures  are  labelled  ‘Trust  1’  and  ‘Trust  2’.  They  are  based  on  the  extent  that 
respondents agree with the statements “you need to be very careful in dealing with people” 
and “I believe most people can be trusted”. 

189  This  is  based  on  the  results  for  Indicator  3  in  dimension  2.  This  indicator  is  a  scale  from  1 
(low feeling of vulnerability) to 4 (high feeling of vulnerability), which is equal to the highest 
extent  that  respondents  feel  vulnerable  due  to  the  individual  reasons  of  health  problems, 
financial circumstances, employment situation, age, belonging to a minority group, personal 
issues, or other reasons. 

190 Based on the results for Indicator 12 in dimension 5. 

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6 │ Drivers of and factors linked to consumer vulnerability 

  The analysis of the full sample reveals that there are relationships between the 
‘credulity’  measure191  and  a  number  of  indicators  of  vulnerability,  although 
these are often in opposite directions across the various indicators. For the full 
sample  and  country  groups  1  and  2,  among  the  behavioural  characteristics 
high  credulity192  has  the  most  relationships,  overall,  with  the  indicators  of 
vulnerability,  and  similarly  for  groups  3  and  5  this  measure  has  the  second 
most  relationships  with  the  indicators  of  any  behavioural  characteristic. 
However,  for  country  group  4  the  high  credulity  measure  has  relatively  few 
results. 

  The  analysis  of  the  full  sample  and  at  country  group  level  for  every  group 
shows that those who identified the best interest rate for a savings account are 
less  likely  to  be  vulnerable  on  a  relatively  high  number  of  indicators. 
Furthermore, the results for the full sample and the country group level results 
both show that those who identified the best interest rate performed better in 
the experiments overall. 

In  summary,  the  high level  results  regarding  behavioural  drivers  of  vulnerability  from 
the full sample generally hold relatively well for the country groups. Regression results 
showing  the  links  between  the  indicators  of  vulnerability  and  behavioural  drivers  at 
country group level are available in Excel files. 

Market-related drivers of vulnerability and consumers’ experience in markets 

The  results  regarding  the  effects  of  market-related  drivers  on  the  likelihood  of 
vulnerability  based  on  the  full  sample  (reported  in  section  6.4)  compare  to  the 
corresponding country group level results as follows: 

  The  analysis  of  the  full  sample  found  that  having  been  unable  to  read  the 
terms and conditions of a contract due to overly small print is associated with a 
higher  likelihood  of  vulnerability  on  20  out  of  the  38  indicators,  including 
indicators  in  every  dimension.  Very  similar  results  are  found  at  country  group 
level, with this variable being connected with a higher likelihood of vulnerability 
on between 13 and 16 indicators dependent on the country group. 

  Based  on  the  full  sample  results,  being  less  experienced  and  engaged  with  a 
market193  is  associated  with  a  higher  likelihood  of  vulnerability  in  that  market 
on a number of indicators, especially having problems comparing deals due to 
information-related,  personal,  market-related  and  access-related  factors.  For 
the  most  part,  these  relationships  hold  for  the  various  country  groups  and 
sectors, although the association is absent for group 4 for the energy market. 

  The  results  for  the  full  sample  show  that  the  extent  that  consumers  feel 
uninformed  about  prices,  quality  and  conditions  when  choosing  and  buying 
goods  and  services  is  linked  to  almost  all  of  the  market-related  factors 
examined  in  the  analysis.194  These  results  imply  that  lower  experience  and 

191 This measure is labelled ‘Credulity’. It is based on the extent that respondents agree with the 
statement  “most  advertisements  report  objective  fact,  I  trust  most  of  the  information 
provided in advertisements”. 

192 This measure is “Credulity: High” and corresponds to “agree” to “most advertisements report 

objective fact, I trust most of the information provided in advertisements”. 

193  That  is,  not  knowing  contract  terms,  not  reading  communications,  and  finding  it  difficult  to 

read communications. 

194 Specifically, all except whether respondents have read the most recent bill or communication 

from their provider in the energy, online or finance sectors. 

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6 │ Drivers of and factors linked to consumer vulnerability 

engagement  with  markets  is  usually  associated  with  a  higher  likelihood  of 
vulnerability  on  this  indicator.  This  overall  finding  holds  for  most  country 
groups,  although  for  group  4  the  link  between  market-related  factors  and  the 
extent  that  consumers  feel  uninformed  about  prices,  quality  and  conditions  is 
only found for the energy market. 

Overall,  the  high  level  results  linking  market-related  factors  with  vulnerability  at 
country  group  level  are  similar  to  those  found  for  the  full  sample,  although  there  are 
some  notable  differences  for  country  group  4,  in  particular  relating  to  market 
experience.  Regression  results  showing  the  relationships  between  indicators  of 
vulnerability  and  market-related  factors  at  country  group  level  are  provided  in  Excel 
files. 

Access drivers of vulnerability 

The results for the full sample regarding the effects of access drivers on the likelihood 
of vulnerability (reported in section 6.5) compare to the equivalent country group level 
results as follows: 

  The  results  for  all  country  groups  show  that  those  who  use  the  internet  more 
overall,  in  terms  of  the  number  of  different  activities  they  use  it  for  at  least 
once  per  month,  are  less  likely  to  have  been  declined  for  a  loan.  For  country 
group 5, this is the only result relating to consumers’ level of internet use that 
is the same as the result for the full sample. 

  Mirroring  the  full  sample  result,  in  country  groups  2,  3,  and  4  those  who  use 
the internet more overall are less likely to have been prevented from switching 
bank  due  to  market-related  factors.  However,  the  result  that  higher  internet 
use  is  linked  with  a  lower  likelihood  of  having  been  prevented  from  switching 
internet provider due to market-related factors is only found for group 2.  

 

 

In line with the result for the full sample, consumers in country groups 1 and 3 
who use the internet more overall are  less likely to have overpaid for services 
due to being unable to use certain payment methods. 

In contrast with the result for the full sample, for the individual country groups 
no  association  is  found  between  level  of  internet  use  and  the  likelihood  that 
consumers feel vulnerable due to personal characteristics. 

In  summary,  relative  to  the  other  types  of  drivers,  the  access  drivers  have  the  most 
diverse results across the country groups. Regression results showing the associations 
between  access  drivers  and  the  indicators  of  vulnerability  at  country  group  level  are 
available in Excel files. 

Situational drivers of vulnerability 

The results based on the full sample regarding the impact of situational drivers on the 
likelihood  of  vulnerability  (reported  in  section  6.6)  compare  to  the  corresponding 
country group level results as follows: 

  A  number  of  results  for  the  full  sample  relating  to  consumers  who  find  it 
difficult  to  make  ends  meet  hold  for  all  country  groups.  In  particular,  in  all 
country  groups  these  consumers:  feel  a  higher  level  of  vulnerability  due  to 
their  personal  characteristics;  are  more  likely  to  feel  vulnerable  due  to  the 
complexity  of  offers,  terms  or  conditions;  are  more  likely  to  feel  uninformed 
about  prices,  quality  and  conditions;  and  are  more  likely  to  have  problems 
comparing deals due to information-related factors.  

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  Other results for the  full sample relating to those who find it difficult to make 
ends  meet  only  hold  for  certain  country  groups.  In  particular,  these 
consumers: are more likely to be excluded from e-commerce in country groups 
1  and  2;  are  more  likely  to  be  declined  a  loan  in  groups  3,  4  and  5;  are  less 
likely  to  take  action  when  they  experience  a  problem  and  more  likely  to 
overpay for services due to being unable to use certain payment methods in all 
groups except group 4; and are more likely to find that termination costs and 
bundling are barriers to switching in groups 1 and 3. 

  Moreover, consumers who find it difficult to make ends meet find that access-
related  factors  are  barriers  to  making  comparisons  between  energy  deals; 
market-related factors are barriers to making comparisons in the online sector; 
that personal, market-related and access-related factors are barriers to making 
comparisons  in  the  finance  sector.  These  results  hold  for  the  full  sample  and 
country  groups  1  and  2  independently.  In  country  groups  3,  4,  and  5,  these 
consumers  find  a  mixture  of  the  barriers  to  making  comparisons  between 
deals.195  

  Related to the point above, consumers who find it difficult to make ends meet 
find  that  access-related  factors  can  prevent  them  from  switching.  For  the  full 
sample, this is found for all three markets, while in country groups 1 and 2 this 
is only found for the energy sector, and in groups 3, 4, and 5 it is found for the 
finance sector. 

 

In  addition,  the  result  for  the  full  sample  that  those  with  employment  status 
other than full-time employed or full-time self-employed are more likely to feel 
vulnerable  due  to  personal  characteristics  holds  independently  for  country 
group  2.  For  other  country  groups,  most  consumer  groups  who  are  not  full-
time  employed  feel  more  vulnerable  due  to  personal  characteristics,  although 
this is not true for all groups in all countries.196 

In  summary,  the  full  sample  results  hold  in  general  for  all  country  groups.  Although 
there  are  some  differences  between  the  results  for  the  full  sample  and  the  results  at 
country-level,  these  should  not  be  overstated.  Regression  results  showing  the 
relationships  between  situational  drivers  and  the  vulnerability  indicators  at  country 
group level are provided in Excel files. 

195 Namely, consumers who find it difficult to make ends meet find that: access-related 
factors  are  barriers  to  comparing  deals  in  the  energy  sector  in  country  group  4; 
market-related  factors  are  barriers  to  comparing  deals  in  the  online  sector  in 
country groups 4 and 5; and personal factors are barriers to comparing deals in the 
finance sector in country groups 3, 4 and 5.  

196 The exceptions that do not feel more vulnerable than those who are full-time employed are: 
housewife/homemaker  for  country  groups  1  and  5;  part-time  self-employed  for  country 
groups 2, 3 and 4; studying with part-time job for country groups 3 and 4; and retired and 
long-term sick or disabled only for country group 4. 

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7 │ Population of the theoretical framework of vulnerability 

7.  POPULATION OF THE THEORETICAL FRAMEWORK OF VULNERABILITY 

Box 5:  Summary of Chapter 7 

This  chapter  populates  the  theoretical  framework  developed  in  Chapter  3,  based  on 
the  results  of  the  data  analysis  reported  in  Chapter  6.  In  doing  so  the  chapter 
provides  a  concise  overall  picture  of  the  most  important  drivers  and  effects  of 
vulnerability. In order to populate the framework, selection criteria are set such that 
drivers are included or excluded from the framework in a systematic way, based on 
the  overall  strength  of  the  effects  of  the  drivers  on  the  various  dimensions  of 
vulnerability established in Chapter 3. The main conclusions that can be drawn from 
the populated framework are that: 

  Market-related  drivers  are  especially  influential.  These  drivers  have  more 
relationships with the various dimensions of vulnerability than any other type 
of driver. The impact of being unable to read contract terms and conditions is 
highlighted in particular, with this driver being associated with an increase in 
the  likelihood  of  vulnerability  across  all  dimensions.  Furthermore,  being 
disengaged  from  markets  (e.g.  in  terms  of  not  knowing  contract  conditions, 
or  not  reading  communications)  is  also  linked  with  higher  vulnerability  in 
several  dimensions.  These  results  suggest  that  measures  to  improve 
consumers’  engagement  with  markets  may  be  effective 
in  tackling 
vulnerability. 

  Behavioural drivers also have important effects. In particular, consumers who 
are  willing  to  take  risks  are,  generally,  less  likely  to  be  vulnerable,  as  are 
those  who  have  sector  knowledge  and  computational  ability.  In  addition, 
more impulsive consumer are, on the whole, more likely to be vulnerable on 
several  dimensions.  These  results  suggest  that  behavioural  biases  such  as 
risk aversion and cognitive limitations are relevant to vulnerability, and that 
measures that target these factors, for example interventions to improve the  
presentation of information, may be effective in reducing vulnerability.  

  Among  the  situational  drivers,  finding  it  difficult  to  make  ends  meet  and 
having  friends  who  cannot  make  ends  meet  are  found  to  be  especially 
important.  This  suggests  that  measures  to  reduce  financial  pressure  on 
consumers (e.g. social tariffs) may be effective in reducing vulnerability. 

The  theoretical  framework  developed  in  Chapter  3  is  populated  in  this  chapter. 
Whereas  the  analysis  in  Chapter  6  examined  the  drivers  of  vulnerability  in  detail  by 
indicator, this chapter aims to provide a concise overall picture of the most important 
drivers and effects of vulnerability. 

The  framework  is  populated  using  the  results  of  the  regression  analysis  reported  in 
Chapter 6, which relate the indicators of vulnerability in each dimension to consumers’ 
personal  characteristics  and  drivers  of  vulnerability  (including  behavioural,  market-
related, access and situational drivers).  

In section 7.1, criteria are set for selecting the drivers of vulnerability to be included in 
the framework based on the results of the regression analysis presented in chapter  6. 
Then,  in  section  7.2,  the  relationships  to  be  included  in  the  framework  for  each 
dimension  of  vulnerability  are  identified  based  on  the  selection  criteria.  Finally,  the 
theoretical framework is populated in section 1.3. 

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7 │ Population of the theoretical framework of vulnerability 

7.1.  Criteria for selecting drivers to be included in the framework 

The  first  step  in  order  to  populate  the  theoretical  framework  is  to  specify  criteria  for 
selecting  drivers  of  vulnerability  to  be included in  the  framework.  Detailed  analysis  of 
the  drivers  of  vulnerability  is  presented  by  indicator  in  Chapter  6.197  The  aim  of  the 
present chapter is to synthesise these results,  prioritising the most important drivers, 
so  that  a  concise  overall  picture  of  the  main  drivers  can  be  presented.  Hence,  the 
analysis in this chapter is done  at dimension level. The  selection criteria are specified 
for each dimension of vulnerability using the results (from Chapter 6) for indicators in 
that dimension.  

The aim of setting the selection criteria is to ensure that the most important drivers of 
each  vulnerability  dimension  are  included  in  the  framework.  A  driver  is  particularly 
important  to  a  dimension  if  it  is  related  to  a  substantial  number  of  indicators  in  that 
dimension, or it has a particularly strong relationship with at least one indicator in that 
dimension. Hence, the selection criteria take into account a) the number of statistically 
significant  results198  for  a  given  characteristic  or  driver  across  the  indicators  within  a 
dimension  and  b)  the  magnitudes  of  the  effects  of  a  given  characteristic  or  driver  on 
the indicators of each dimension. 

For the purpose of populating the framework, due to the large number of indicators in 
the  fourth  dimension,  this  dimension  is  divided  into  four  components.  These 
components  are  referred  to  as  ‘problems  comparing’199,  ‘problems  switching’200, 
‘problems  choosing’201  and  ‘exclusion  from  markets/  transactions’202.  The  results 
relating  to  respondents’  overall  performance  in  the  experiments  are  included  in  the 
framework for the fourth dimension, since the experiments tested consumers’ ability to 
select the optimal deal in simulated purchasing scenarios and so are especially relevant 
to this dimension. 

The  selection  criteria  for  each  dimension  of  vulnerability  are  set  out  in  Table  61. 
Sections  7.2.1  to  7.2.8  identify  the  variables  and  relationships  to  be  included  in  the 
framework for each dimension of vulnerability, based on the criteria in Table 61.203  

The  criteria  are  a  mechanism  for  specifying  inclusion/exclusion  thresholds  in  a 
systematic way and setting the criteria is necessarily a judgement decision. Adjusting 
the  criteria  would  change  the  number  of  drivers  in  the  framework,  although  it  is 
important  to  note  that  if  the  criteria  were  loosened  the  drivers  currently  in  the 
framework (based on the criteria in Table 61) would still be included. 

The  criteria  set  specific  limits  so  that  only  the  most  important  drivers  are  included  in 
the  framework  and  so  that  similar  numbers  of  drivers  are  included  for  each 

197 Detailed summaries of the results of that analysis are shown in the summary matrices in that 

chapter. 

198 At the 90% significance level. 
199 Based on indicators 8a, 8b and 8c. 
200 Based on indicators 9a, 9b, 9c, 9d and 9e. 
201 Based on respondents’ overall performance in the experiments. 
202 Based on indicators 10a, 10b and 11. 
203  The  selection  criteria  differ  between  dimensions,  since  the  dimensions  include  different 
numbers of indicators and because the indicators are a mixture of binary and scale variables. 
As explained in  Chapter  6, for binary indicators the regression results are  marginal effects, 
whereas for scale indicators the regression results are odds ratios. 

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7 │ Population of the theoretical framework of vulnerability 

dimension.204  However,  since  the  results  (in  Chapter  6)  are  stronger  for  some 
dimensions  than  others,  there  is  a  trade-off  between  the  aim  to  include  similar 
numbers  of  drivers  for  each  dimension  and  the  aim  to  treat  the  dimensions 
consistently by setting similar criteria for each dimension. 

204 The aim was to include between 10 and 50 drivers per dimension. In practice between 12 and 

46 drivers are included per dimension. 

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7 │ Population of the theoretical framework of vulnerability 

Table 61: 

Criteria for selecting drivers to be included in the theoretical 

framework 

Dimension  Selection criteria based on regression results in Chapter 6 

Dimension 1 

(2 indicators) 

  At least one statistically significant effect on an indicator of at least 5pp in 

magnitude, and no effects in the opposite direction. 

  Relationships selected: 33 

Dimension 2 

(1 indicator) 

  Odds ratio more extreme than 1.4 or 0.7 
  Relationships selected: 19 

Dimension 3 

(9 indicators) 

  At least four statistically significant effects in one direction, with at least 

three times as many effects in one direction than in the other direction (i.e. 
the effects must be predominantly in one direction); OR 

  At least one effect of at least 10pp in magnitude or an odds ratio205 more 
extreme than 1.4 or 0.7, and statistically significant effects only in one 
direction.206 

  Relationships selected: 17 

Dimension 4, 
Problems 
comparing 

(9 indicators) 

  At least four statistically significant effects in one direction, with at least 

three times as many effects in one direction than in the other direction (i.e. 
the effects must be predominantly in one direction); OR 

  At least one effect of at least 10pp in magnitude, and statistically significant 

effects only in one direction. 

  Relationships selected: 46 

Dimension 4, 
Switching 
problems 

(11 
indicators) 

Dimension 4, 
Exclusion 

(3 indicators) 

  At least four statistically significant effects in one direction, with at least 

three times as many effects in one direction than in the other direction (i.e. 
the effects must be predominantly in one direction); OR 

  At least one effect of at least 10pp in magnitude, and statistically significant 

effects only in one direction. 

  Relationships selected: 12 

  At least two statistically significant effects (of any magnitude) in one 

direction, and none in the other direction; OR 

  At least one effect of at least 10pp in magnitude, and statistically significant 

effects only in one direction. 

  Relationships selected: 27 

205  Dimension  3  is  the  only  dimension  that  includes  both  binary  and  scale-based  indicators 
(Indicator 4 is a scale-based indicator, whereas all other indicators in dimension 3 are binary 
indicators).  Therefore,  the  criteria  for  this  dimension  must  take  into  account  both  marginal 
effects and odds ratios. 

206  To  ensure  consistency  in  the  way  that  dimensions  are  treated,  where  several  dimensions 
include similar numbers (and types) of indicators, an effort is made to ensure similar criteria 
are set for these dimensions. For example, since dimension 3 and the ‘problems comparing’ 
and ‘switching problems’ components of dimension 4 each include 9 or 11 indicators, similar 
criteria are set for these dimensions (n.b. the only difference between the criteria for these 
dimensions  is  that  the  criteria  for  dimension  3  also  accommodate  drivers  with  an  extreme 
odds ratio on Indicator 4, which is the only scale-based indicator in these dimensions). The 
second criterion for these dimensions sets a high benchmark with respect to the magnitude 
of  marginal  effects  (10pp)  to  ensure  that  the  number  of  drivers  included  for  ‘problems 
comparing’ is less than 50 (the aim was to include 10 to 50 drivers per dimension, and if this 
benchmark  was  lowered  to  5pp  a  further  nine  drivers  would  included  for  ‘problems 
comparing’, taking the total to 55). 

285 

 
 
 
                                                 
7 │ Population of the theoretical framework of vulnerability 

Table 61: 

Criteria for selecting drivers to be included in the theoretical 

framework 

Dimension  Selection criteria based on regression results in Chapter 6 

Dimension 4, 
Problems 
choosing 

(1 indicator) 

Dimension 5, 

(1 indicator) 

  Odds ratio more extreme than 1.2 or 0.85207 
  Relationships selected: 23 

  Odds ratio more extreme than 1.4 or 0.7 
  Relationships selected: 15 

Note:  ‘pp’  stands  for  percentage  points.  The  dimensions  are  1.  Heightened  risk  of  negative  outcomes  or 
impacts on well-being; 2. Having characteristics that limit ability to maximise well-being; 3. Having difficulty 
in obtaining or assimilating information; 4. Inability or failure to buy, choose or access suitable products; and 
5.  Higher  susceptibility  to  marketing  practices,  creating  imbalances  in  market  interactions.  As  explained  in 
Chapter 6, for binary indicators the regression results are marginal effects (measured in percentage points), 
whereas  for  scale-based  indicators  the  regression  results  are  odds  ratios.  Relationships  with  non-response 
and don’t know dummies are excluded. 

7.2.  Drivers and relationships to be included in the framework 

The second step  required in order to populate the theoretical framework is to identify 
the variables and relationships to be included in the framework for each dimension of 
vulnerability. This is done in sections 7.2.1 to 7.2.8 below. 

7.2.1.  Dimension 1: Heightened risk of negative outcomes or impacts on well-
being 

There  are  two  survey-based  indicators  of  vulnerability  included  in  this  dimension, 
namely:208 

  Unassertive (took no action) when experienced a problem; and 

  Overpaid for services due to being unable to use certain payment methods. 

Based  on  the  relevant  criterion  set  out  in  Table  61  above,  thirty-three  variables  that 
are  associated  with  this  dimension  are  identified  for  the  framework.209  These  thirty-
three  variables  correspond  to  nineteen  drivers  and  characteristics,  as  shown  in  Table 
62  (in  this  table  the  drivers  and  characteristics  are  specified  in  bold  text,  while 
corresponding variables are shown in parentheses).  

Table  62  also  shows  whether,  based  on  the  regression  analysis,  these  drivers  and 
characteristics increase or decrease the likelihood of vulnerability in this dimension. In 
addition,  the  table  also  gives  information  on  the  magnitudes  of  the  effects  of  the 
variables,  in  terms  of  both  the  number  of  statistically  significant  effects  across  the 

207 Setting the benchmarks for this component of dimension 4 at 1.4 and 0.7 (as for dimensions 
2 and 5) would mean  that only seven drivers are  included for this component. Therefore it 
was decided to loosen the selection criterion for this component. 

208 These indicators are defined in Table 5 in section 3.6, as well as in A8.1.2. 
209  It  is  assessed  whether  variables  meet  the  selection  criteria  based  on  the  results  of  the 
regression  analysis  presented  in  Chapter  6.  Non-response  and  don’t  know  dummies  are 
excluded. 

286 

 
 
                                                 
7 │ Population of the theoretical framework of vulnerability 

indicators in the dimension, and the largest individual effect on a single indicator in the 
dimension.210  

For  example,  as  can  be  seen  from  Table  62,  finding  it  very  difficult  to  read 
communications  from  internet  providers,  finding  it  very  difficult  to  make  ends  meet, 
and  being  unable  to  read  contract  terms  and  conditions  are  the  variables  with  the 
largest  effects  on  single  indicators  in  the  dimension,  with  marginal  effects  of  21pp, 
17pp and 15pp respectively.  

Table 62: 

Impacts of selected drivers and characteristics on the likelihood of 

vulnerability in Dimension 1 

Driver/characteristic (corresponding 
variables in parentheses) 

Personal and demographic characteristics 

Young age 

   (Age 16-24) 

Overall 
impact 

No. of effects 
Up-
ward 

Down-
ward 

Max. 
effect 

Increase 

- 

- 

- 

- 

- 

1 

- 

- 

- 

- 

- 

- 

- 

- 

2 

0.07 

2 

1 

2 

1 

- 

0.08 

0.07 

0.09 

0.06 

0.07 

1 

0.15 

1 

1 

2 

2 

2 

1 

1 

0.06 

0.13 

0.07 

0.12 

0.08 

0.11 

0.08 

Having a mother tongue different to the official 
language 

Increase 

   (Non-native tongue, no difficulty) 

   (Non-native tongue, difficulty) 

Behavioural characteristics 

Having high credulity 

   (Credulity: High) 

   (Credulity: Very high) 

Having computational ability 

   (Computation test 2 correct) 

Market-related drivers 

Increase 

Decrease 

Being unable to read contract Ts&Cs due to small 
print 

Increase 

   (Unable to read Ts&Cs due to small print) 

Rarely comparing energy and bank deals (i.e. less 
often than from time to time’) 
   (Compare energy deals: When need to renew) 

Increase 

   (Compare energy deals: Only the first time) 

   (Compare deals from banks: When notified) 

Not knowing their bank contract conditions 
‘completely’  
   (Know bank contract conditions: Not at all) 

   (Know bank contract conditions: Not very) 

Increase 

Not reading bills/communications from providers 
‘in detail’, in the three key sectors 
   (Read energy bill: Looked at total price) 

Increase 

   (Read energy bill: Not at all) 

210 These indicators are defined in Table 5 in section 3.6. 

287 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                                 
7 │ Population of the theoretical framework of vulnerability 

Table 62: 

Impacts of selected drivers and characteristics on the likelihood of 

Overall 
impact 

No. of effects 
Up-
ward 

Down-
ward 

Increase 

vulnerability in Dimension 1 

Driver/characteristic (corresponding 
variables in parentheses) 

   (Read internet communication: Not read it at all) 

   (Read internet communication: Saw what it was) 

   (Read bank communication: Saw what it was) 

Finding it difficult to read communications from 
internet providers and banks 
   (Read internet communication: Difficult) 

   (Read internet communication: Very difficult) 

   (Read bank communication: Very difficult) 

Access drivers 

Using the internet for many activities at least once 
per month 
   (Monthly internet activities: 7) 

Decrease 

   (Monthly internet activities: 6) 

Using the internet to bank online at least once per 
month 
   (Frequent internet use: Banking) 

Decrease 

Selling online at least once per month 

Increase 

   (Frequent internet use: Selling) 

Situational drivers 

Being unemployed and not seeking employment, 
rather than full time employed 

Decrease 

   (Unemployed & not looking) 

Being retired, rather than full time employed 

Decrease 

   (Retired) 

Being in education, rather than full time employed 

Decrease 

   (Studying with part-time job) 

Being unemployed and seeking employment for 5+ 
years 

Decrease 

   (Unemployed & looking 5+ years) 

Having friends who buy online 

Decrease 

   (Friends buy online) 

Having friends who can’t make ends meet 

Increase 

   (Friends can’t make ends meet) 

Finding it difficult to make ends meet 

Increase 

   (Making ends meet: Fairly easy) 

   (Making ends meet: Fairly difficult) 

   (Making ends meet: Very difficult) 

- 

- 

- 

- 

- 

- 

1 

1 

1 

- 

1 

1 

1 

2 

1 

- 

- 

- 

- 

Max. 
effect 
0.11 

0.12 

0.08 

0.09 

0.21 

0.06 

0.05 

0.05 

0.07 

1 

2 

2 

2 

2 

1 

- 

- 

- 

2 

0.06 

- 

- 

- 

- 

- 

2 

1 

2 

2 

0.07 

0.05 

0.09 

0.13 

0.05 

0.05 

0.07 

0.13 

0.17 

Note:  Effects  are  all  marginal  effects.  Drivers  and  characteristics  are  specified  in  bold  text.  Corresponding 
variables  are  shown  in  parentheses.  Omitted  non-response  variables:  ‘Read  internet  communication:  No 
response’; and ‘Know bank contract/read communication: No response’. 

288 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7 │ Population of the theoretical framework of vulnerability 

7.2.2.  Dimension 2: Having characteristics that limit ability to maximise well-
being 

One  survey-based  indicator  of  vulnerability  is  included  in  this  dimension,  namely 
Perception of own vulnerability due to personal characteristics.211  

Based  on  the  criterion  specified  in  Table  61  above,  nineteen  variables  that  are  linked 
with this dimension are identified for the framework.212 These variables correspond to 
thirteen drivers and characteristics, as shown in Table 63.213  

Table  63  also  shows  whether,  based  on  the  regression  analysis,  these  drivers  and 
characteristics increase or decrease the likelihood of vulnerability in this dimension. In 
addition,  Table  63  also  shows  the  magnitudes  of  the  effects  of  the  variables  selected 
for the framework in terms of effect sizes.  

Since the effects are odds ratios, the ‘largest’ effects are those that are furthest above 
or  furthest  below  a  value  of  1.  Therefore,  as  can  be  seen  from  Table  63,  the  largest 
magnitude effects in this dimension are the effects of finding it ‘very’ or ‘fairly’ difficult 
to make ends meet and the effect of being long-term sick or disabled, with odds ratios 
of 9.98, 3.68 and 3.23 respectively. 

Table 63: 

Impacts of selected drivers and characteristics on the likelihood 

of vulnerability in Dimension 2 

Driver/characteristic (corresponding variables in 
parentheses) 

Overall 
impact 

Effect 
size 

Personal and demographic characteristics 

Young age 

   (Age 16-24) 

Increase 

Having a mother tongue different to the official language 

Increase 

   (Non-native tongue, difficulty) 

Behavioural characteristics 

Having very high trust in people 

   (Trust 1: Very high) 

Having very high credulity 

   (Credulity: Very high) 

Decrease 

Increase 

Being very willing to take risks (rather than not at all willing 
to take risks) 

Decrease 

   (Risk taking: Very) 

Market-related drivers 

1.49 

2.29 

0.56 

1.59 

0.64 

211 This indicator is defined in Table 5 in section 3.6, as well as in A8.1.2. 
212  It  is  assessed  whether  variables  meet  the  selection  criteria  based  on  the  results  of  the 
regression  analysis  presented  in  chapter  6.  Non-response  and  don’t  know  dummies  are 
excluded. 

213  In  Table  63  the  drivers  and  characteristics  are  specified  in  bold  text,  while  corresponding 

variables are shown in parentheses. 

289 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                                 
7 │ Population of the theoretical framework of vulnerability 

Table 63: 

Impacts of selected drivers and characteristics on the likelihood 

of vulnerability in Dimension 2 

Driver/characteristic (corresponding variables in 
parentheses) 

Overall 
impact 

Effect 
size 

Being unable to read contract Ts&Cs due to small print 

Increase 

   (Unable to read Ts&Cs due to small print) 

Access drivers 

Using the internet for some activities at least once per 
month 
   (Monthly internet activities: 4) 

Decrease 

   (Monthly internet activities: 3) 

   (Monthly internet activities: 1) 

Situational drivers 

Being unemployed, rather than full time employed 

Increase 

   (Unemployed & looking) 

   (Unemployed & not looking) 

Being long-term sick or disabled, rather than full time 
employed 
   (Long-term sick or disabled) 

Increase 

Being retired, rather than full time employed 

Increase 

   (Retired) 

Being in education, rather than full time employed 

Increase 

   (In full time education) 

   (Studying with part-time job) 

Having friends who can’t make ends meet 

Increase 

   (Friends can’t make ends meet) 

Finding it difficult to make ends meet 

Increase 

   (Making ends meet: Fairly easy) 

   (Making ends meet: Fairly difficult) 

   (Making ends meet: Very difficult) 

1.60 

0.64 

0.57 

0.57 

2.27 

1.72 

3.23 

1.45 

1.54 

1.52 

1.68 

1.79 

3.68 

9.98 

Note:  Effects  are  odd-ratios.  Omitted  non-response  variables:  ‘Risk  taking:  No  response’;  ‘Know  bank 
contract/read comm'n: No response’; and ‘Making ends meet: Prefer not to say’. 

7.2.3.  Dimension 3: Having difficulty in obtaining or assimilating information 

Nine indicators of vulnerability are included in this dimension, namely:214 

  Does not feel informed about prices etc.; 

  Gets  information  from  advertisements  only  when  comparing  deals  in  the 

energy sector; 

  Gets information from advertisements only when comparing deals in the online 

sector; 

214 These indicators are defined in Table 5 in section 3.6, as well as in A8.1.2. 

290 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                                 
7 │ Population of the theoretical framework of vulnerability 

  Gets  information  from  advertisements  only  when  comparing  deals  in  the 

finance sector; 

  Has problems comparing deals due to information-related factors in the energy 

sector; 

  Has problems comparing deals due to information-related factors in the online 

sector; 

  Has problems comparing deals due to information-related factors in the finance 

sector; 

  Has not switched due to information-related factors in the energy sector; and 
  Has not switched due to information-related factors in the finance sector.215 
Based on the criteria set out in Table 61 above, seventeen variables are identified for 
the  framework  for  this  dimension,  which  in  turn  correspond  to  eight  drivers  and 
characteristics, as shown in Table 64.216  

Table  64  also  shows  whether,  based  on  the  regression  analysis,  these  drivers  and 
characteristics increase or decrease the likelihood of vulnerability in this dimension. In 
addition,  this  table  also  presents  information  on  the  magnitudes  of  the  effects  of  the 
variables,  in  terms  of  both  the  number  of  statistically  significant  effects  across  the 
indicators in the dimension, and the largest individual effect on a single indicator in the 
dimension.  Since  the  indicators  for  dimension  3  include  binary  and  scale-based 
indicators,  the  largest  marginal  effect  and  the  most  extreme  odds  ratio  are  both 
presented for each variable in Table 64.217 

As can be seen from Table 64, finding it ‘difficult’ to read communications from internet 
providers has the largest number of statistically significant effects in the same direction 
on indicators in dimension 3 (5 upward effects), while finding it ‘very difficult’ to read 
energy bills has the largest individual marginal effect (29pp). The most extreme odds 
ratios  are  those  for  very  high  impulsiveness  on  the  third  impulsiveness  measure 218 
(1.66) and, in the opposite direction, being very willing to take risks (0.42). 

Table 64: 

Impacts of selected drivers and characteristics on the likelihood of 

vulnerability in Dimension 3 

Driver/characteristic (corresponding 
variables in parentheses) 

Overall 
impact 

No. of effects 
Down
-ward 

Up-
ward 

Max. effect 

ME 

OR 

Behavioural characteristics 

Having very high trust in people 

Increase 

   (Trust 1: Very high) 

- 

1 

- 

1.47 

215  As  explained  in  Chapter  6,  there  are  no  results  for  the  indicator  ‘Has  not  switched  due  to 
information-related  factors  in  the  online  sector’,  because  the  general-to-specific  regression 
analysis procedure could not be run for this indicator. 

216  It  is  assessed  whether  variables  meet  the  selection  criteria  based  on  the  results  of  the 
regression  analysis  presented  in  Chapter  6.  Non-response  and  don’t  know  dummies  are 
excluded.  In  Table  64  the  drivers  and  characteristics  are  specified  in  bold  text,  while 
corresponding variables are shown in parentheses. 

217 Indicator 4 (Does not feel informed about prices etc) is a scale-based indicator, whereas all 

other indicators in dimension 3 are binary indicators. 

218  This  measure  is  based  on  the  extent  that  respondents  agree  with  the  statement  “People 

would say that I have very strong self-discipline”. 

291 

 
 
 
 
 
 
 
 
 
 
 
 
 
                                                 
7 │ Population of the theoretical framework of vulnerability 

Table 64: 

Impacts of selected drivers and characteristics on the likelihood of 

vulnerability in Dimension 3 

Driver/characteristic (corresponding 
variables in parentheses) 

Overall 
impact 

No. of effects 
Down
-ward 

Up-
ward 

Max. effect 

ME 

OR 

Having higher impulsiveness (i.e. high or very 
high rather than very low impulsiveness) 
   (Impulsiveness 3: High) 

Increase 

   (Impulsiveness 3: Very high) 

Being very willing to take risks (rather than not 
at all willing to take risks) 

Decrease 

- 

- 

1 

1 

- 

- 

1.57 

1.66 

   (Risk taking: Very) 

1 

- 

- 

0.42 

Knowledge of the online sector (proxied by 
knowing the meaning of Mbps) 

Decrease 

   (Knows meaning of Mbps) 

Market-related drivers 

Being unable to read contract T&Cs due to 
small print  

   (Unable to read Ts&Cs due to small print) 

Finding it difficult to read bills/communications 
from providers, in the three key sectors 
   (Read energy bill: Easy) 

Increase 

Increase 

   (Read energy bill: Difficult) 

   (Read energy bill: Very difficult) 

   (Read internet communication: Difficult) 

   (Read internet communication: Very Difficult) 

   (Read bank communication: Easy) 

   (Read bank communication: Difficult) 

   (Read bank communication: Very Difficult) 

Not knowing their bank contract conditions 
‘completely’ 
   (Know bank contract conditions: Not at all) 

   (Know bank contract conditions: Not very) 

Situational drivers 

Increase 

4 

- 

0.02 

0.94 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

6 

0.06 

1.19 

4 

4 

4 

5 

2 

4 

4 

2 

2 

2 

0.06 

1.32 

0.19 

1.39 

0.29 

1.47 

0.27 

1.30 

0.29 

- 

0.07 

1.16 

0.23 

1.32 

0.28 

- 

0.16 

1.32 

0.11 

1.22 

Finding it difficult to make ends meet 

Increase 

   (Making ends meet: Very difficult) 

- 

4 

0.04 

1.45 

Note:  Effects  include  marginal  effects  (ME)  and  odd-ratios  (OR).  Omitted  non-response  variables:  ‘Know 
energy  contract/Read  energy  bill:  No  response’;  ‘Making  ends  meet:  Prefer  not  to  say’;  and  ‘Did  not  read 
bank communication’. 

7.2.4.  Dimension 4: Inability or failure to buy, choose or access suitable 
products – Problems Comparing (Indicator 8) 

There are nine indicators of vulnerability included in this dimension, namely:219 

219 These indicators are defined in Table 5 in section 3.6, as well as in A8.1.2. 

292 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                                 
7 │ Population of the theoretical framework of vulnerability 

  Has problems comparing deals due to personal factors in the energy sector; 

  Has problems comparing deals due to personal factors in the online sector; 

  Has problems comparing deals due to personal factors in the finance sector; 

  Has  problems  comparing  deals  due  to  market-related  factors  in  the  energy 

sector; 

  Has  problems  comparing  deals  due  to  market-related  factors  in  the  online 

sector; 

  Has  problems  comparing  deals  due  to  market-related  factors  in  the  finance 

sector; 

  Has  problems  comparing  deals  due  to  access-related  factors  in  the  energy 

sector; 

  Has  problems  comparing  deals  due  to  access-related  factors  in  the  online 

sector; and 

  Has  problems  comparing  deals  due  to  access-related  factors  in  the  finance 

sector. 

Based on the criteria in Table 61 above, forty-six variables that are associated with this 
dimension  are  identified  for  the  framework.  These  variables  in  turn  correspond  to 
fourteen drivers and characteristics, as shown in Table 65.220  

Table  65  also  shows  whether,  based  on  the  regression  analysis,  these  drivers  and 
characteristics increase or decrease the likelihood of vulnerability in this dimension. In 
addition,  this  table  also  provides  information  on  the  magnitudes  of  the  effects  of  the 
variables, in terms of the number of statistically significant effects across the indicators 
in  the  dimension  and  the  largest  individual  effect  on  a  single  indicator  in  the 
dimension. 

For  example,  as  can  be  seen  from  Table  65,  being  male  has  the  largest  number  of 
statistically  significant  effects  in  the  same  direction  on  indicators  in  this  dimension  (7 
downward  effects),  although  these  are  relatively  small  magnitude  effects  (at  most 
3pp). Having very high impulsiveness on the fourth impulsiveness measure221 also has 
a  large  number  of  statistically  significant  effects  in  the  same  direction  (6  upward 
effects), and these effects are of a larger magnitude (at most 14pp). 

Table 65: 

Impacts of selected drivers and characteristics on the likelihood of 

vulnerability in Dimension 4 – Problems comparing 

Driver/characteristic (corresponding 
variables in parentheses) 

Personal and demographic characteristics 

Older age 

   (Age 55-64) 

Overall 
impact 

No. of effects 
Up-
ward 

Down-
ward 

Max. 
effect 

Increase 

- 

4 

0.11 

220  It  is  assessed  whether  variables  meet  the  selection  criteria  based  on  the  results  of  the 
regression  analysis  presented  in  chapter  6.  Non-response  and  don’t  know  dummies  are 
excluded.  In  Table  65  the  drivers  and  characteristics  are  specified  in  bold  text,  while 
corresponding variables are shown in parentheses. 

221  This  measure  is  based  on  the  extent  that  respondents  agree  with  the  statement  “I’m 

impulsive in the purchase decisions I take”. 

293 

 
 
 
 
 
 
 
 
 
 
 
                                                 
7 │ Population of the theoretical framework of vulnerability 

Table 65: 

Impacts of selected drivers and characteristics on the likelihood of 

vulnerability in Dimension 4 – Problems comparing 

Driver/characteristic (corresponding 
variables in parentheses) 

Overall 
impact 

No. of effects 
Up-
ward 

Down-
ward 

Max. 
effect 

   (Age 65-74) 

   (Age 75+) 

Gender (being male) 

   (Male) 

Decrease 

Having a mother tongue different to the official 
language 

Decrease 

   (Non-native tongue, difficulty) 

Behavioural characteristics 

Having higher credulity (i.e. low or high rather 
than very low credulity) 
   (Credulity: Low ) 

Decrease 

   (Credulity: High) 

Having higher impulsiveness (i.e. high or very high 
rather than very low impulsiveness) 
   (Impulsiveness 1: High) 

Increase 

   (Impulsiveness 1: Very high) 

Market-related drivers 

1 

- 

7 

4 

3 

- 

0.15 

0.25 

0.03 

1 

- 

0.18 

4 

5 

1 

- 

- 

- 

5 

6 

0.04 

0.08 

0.09 

0.14 

Being unable to read contract Ts&Cs due to small 
print  

Increase 

   (Unable to read Ts&Cs due to small print) 

- 

4 

0.07 

Rarely comparing deals (i.e. less often than ‘from 
time to time’), in the three key sectors 
   (Compare energy deals: Sporadically) 

Increase 

   (Compare energy deals: Only the first time) 

   (Compare energy deals: Never) 

   (Compare internet deals: Only the first time) 

   (Compare internet deals: Never) 

   (Compare deals from banks: Sporadically) 

   (Compare deals from banks: Only the first time) 

   (Compare deals from banks: Never) 

Not knowing their contract conditions 
‘completely’, in the three key sectors 
   (Know energy contract: Not very) 

   (Know energy contract: Fair amount) 

   (Know internet contract conditions: Not very) 

   (Know bank contract conditions: Not at all) 

   (Know bank contract conditions: Not very) 

Increase 

Not reading bills/communications from providers 
‘in detail’, in the three key sectors 
   (Read energy bill: Not at all) 

Increase 

   (Read internet communication: Saw what it was) 

   (Read internet communication: Not read it at all) 

- 

- 

- 

- 

1 

- 

- 

- 

1 

- 

- 

1 

- 

- 

- 

- 

5 

6 

4 

4 

4 

4 

5 

4 

4 

4 

4 

4 

4 

4 

5 

3 

0.05 

0.12 

0.11 

0.11 

0.16 

0.06 

0.08 

0.11 

0.21 

0.08 

0.19 

0.22 

0.26 

0.14 

0.19 

0.16 

294 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7 │ Population of the theoretical framework of vulnerability 

Table 65: 

Impacts of selected drivers and characteristics on the likelihood of 

vulnerability in Dimension 4 – Problems comparing 

Driver/characteristic (corresponding 
variables in parentheses) 

Overall 
impact 

No. of effects 
Up-
ward 

Down-
ward 

Max. 
effect 

   (Read bank communication: Saw what it was) 

   (Read bank communication: Not read it at all) 

Finding it difficult to read bills/communications 
from providers, in the three key sectors 
   (Read energy bill: Difficult) 

   (Read energy bill: Very difficult) 

   (Read internet communication: Difficult) 

   (Read internet communication: Very difficult) 

   (Read bank communication: Easy) 

   (Read bank communication: Difficult) 

   (Read bank communication: Very Difficult) 

Access drivers 

Increase 

Using the internet for many activities at least once 
per month 
   (Monthly internet activities: 7) 

Increase 

   (Monthly internet activities: 6) 

   (Monthly internet activities: 5) 

   (Monthly internet activities: 4) 

   (Monthly internet activities: 3) 

   (Monthly internet activities: 2) 

- 

- 

1 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

5 

2 

5 

5 

5 

3 

5 

6 

3 

1 

1 

1 

1 

1 

1 

0.19 

0.18 

0.33 

0.40 

0.41 

0.58 

0.11 

0.38 

0.34 

0.21 

0.21 

0.21 

0.20 

0.15 

0.12 

Using the internet to compare prices at least once 
per month 

Decrease 

   (Frequent internet use: Compare prices) 

4 

1 

0.06 

Using the internet for social networking at least 
once per month 

Decrease 

   (Frequent internet use: Social) 

Situational drivers 

Finding it difficult to make ends meet 

Increase 

   (Making ends meet: Fairly easy) 

   (Making ends meet: Fairly difficult) 

   (Making ends meet: Very difficult) 

4 

1 

0.04 

- 

1 

- 

4 

4 

5 

0.05 

0.06 

0.10 

Note: Effects are marginal effects. Omitted non-response variables: ‘Know energy contract/Read energy bill: 
No  response’;  ‘Know  internet  contract/read  communication:  No  response’;  and  ‘Know  bank  contract/read 
communication: No response’. Omitted ‘Don’t know’ answer variables:  ‘Read energy bill: Don't know’; and 
Read bank communication: Don't know’. 

295 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7 │ Population of the theoretical framework of vulnerability 

7.2.5.  Dimension 4: Inability or failure to buy, choose or access suitable 
products – Problems switching (Indicator 9) 

Eleven indicators of vulnerability are included in this dimension, namely:222 

  Has not switched due to personal factors in the energy sector; 

  Has not switched due to personal factors in the online sector; 

  Has not switched due to personal factors in the finance sector; 

  Has not switched due to market-related factors in the energy sector; 

  Has not switched due to market-related factors in the online sector; 

  Has not switched due to market-related factors in the finance sector; 

  Has not switched due to access-related factors in the energy sector; 

  Has not switched due to access-related factors in the online sector; 

  Has not switched due to access-related factors in the finance sector; 

  Has not switched due to termination costs; and 

  Has not switched due to bundling. 

Based  on  the  criteria  in  Table  61  above,  twelve  variables  are  identified  for  the 
framework  for  this  dimension.  These  in  turn  correspond  to  eight  drivers  and 
characteristics, as shown in Table 66.223  

Table  66  also  shows  whether,  based  on  the  regression  analysis,  these  drivers  and 
characteristics increase or decrease the likelihood of vulnerability in this dimension. In 
addition,  Table  66  also  gives  information  on  the  magnitudes  of  the  effects  of  the 
variables,  in  terms  of  both  the  number  of  statistically  significant  effects  across  the 
indicators in the dimension, and the largest individual effect on a single indicator in the 
dimension. 

For example, the variables signifying that consumers rarely compare energy deals (i.e. 
never  or  only  when  they  first  chose  a  provider)  have  the  largest  individual  marginal 
effects (32pp and 22pp respectively). 

Table 66: 

Impacts of selected drivers and characteristics on the likelihood of 

vulnerability in Dimension 4 – Problems switching 

Driver/characteristic (corresponding 
variables in parentheses) 

Overall 
impact 

No. of effects 
Up-
ward 

Down-
ward 

Max. 
effect 

Behavioural characteristics 

Knowledge of the finance sector (proxied by 
identifying the best interest rate for a savings 
account) 

Decrease 

   (Identified best interest rate) 

4 

1 

0.03 

222 These indicators are defined in Table 5 in section 3.6, as well as in A8.1.2. 
223  It  is  assessed  whether  variables  meet  the  selection  criteria  based  on  the  results  of  the 
regression  analysis  presented  in  Chapter  6.  Non-response  and  don’t  know  dummies  are 
excluded.  In  Table  66  the  drivers  and  characteristics  are  specified  in  bold  text,  while 
corresponding variables are shown in parentheses. 

296 

 
 
 
 
 
 
 
 
 
 
                                                 
7 │ Population of the theoretical framework of vulnerability 

Table 66: 

Impacts of selected drivers and characteristics on the likelihood of 

vulnerability in Dimension 4 – Problems switching 

Market-related drivers 

Being unable to read contract T&Cs due to small 
print  

Increase 

   (Unable to read Ts&Cs due to small print) 

- 

4 

0.21 

Rarely comparing energy deals (i.e. less often than 
‘from time to time’) 
   (Compare energy deals: Only the first time) 

Increase 

   (Compare energy deals: Never) 

Finding it difficult to read communications from 
internet providers and banks 
   (Read internet communication: Difficult) 

   (Read bank communication: Very Difficult) 

Increase 

- 

1 

- 

- 

3 

4 

3 

1 

0.22 

0.32 

0.15 

0.14 

Rarely comparing bank deals (i.e. less often than 
‘from time to time’) 

Decrease 

   (Compare deals from banks: Never) 

4 

1 

0.06 

Not knowing their bank contract conditions 
‘completely’ 
   (Know bank contract conditions: Not at all) 

   (Know bank contract conditions: Not very) 

Access drivers 

Increase 

- 

- 

4 

4 

0.11 

0.08 

Using the internet to compare prices at least once 
per month 

Increase 

   (Frequent internet use: Compare prices) 

- 

4 

0.04 

Situational drivers 

Finding it difficult to make ends meet 

Increase 

   (Making ends meet: Fairly difficult) 

   (Making ends meet: Very difficult) 

Note: Effects are marginal effects.  

- 

- 

5 

3 

0.12 

0.14 

7.2.6.  Dimension 4: Inability or failure to buy, choose or access suitable 
products – Exclusion (Indicator 10 & Indicator 11) 

There are three indicators of vulnerability included in this dimension, namely:224 

  Excluded from e-commerce due to difficulty of the process; 

  Excluded from e-commerce due to not having a payment card; and 

  Declined for a loan. 

Based  on  the  criteria  in  Table  61  above,  twenty-seven  variables  that  are  linked  with 
this dimension are identified for the framework. In turn, these variables correspond to 
nineteen drivers and characteristics, as shown in Table 67.225  

224 These indicators are defined in Table 5 in section 3.6, as well as in A8.1.2. 

297 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                                 
7 │ Population of the theoretical framework of vulnerability 

Table  67  also  shows  whether,  based  on  the  regression  analysis,  these  drivers  and 
characteristics  increase  or  decrease  the  likelihood  of  vulnerability  in  this  dimension. 
This table also provides information on the magnitudes of the effects of the variables, 
in  terms  of  the  number  of  statistically  significant  effects  across  the  indicators  in  the 
dimension and the largest individual effect on a single indicator in the dimension. 

As can be seen from Table 67, several variables have effects in the same direction on 
all  three  indicators  in  this  dimension  4,  including  being  aged  55-65,  having  high 
impulsiveness  on  the  fourth  impulsiveness  measure,  being  able  to  identify  the  best 
interest rate for a savings account, never comparing bank deals, and not knowing your 
bank contract conditions very much. 

Being  unable  to  read  contract  terms  and  conditions  due  to  overly  small  print  is  the 
variable with the largest individual marginal effect, at 24pp. 

Table 67: 

Impacts of selected drivers and characteristics on the likelihood of 

vulnerability in Dimension 4 – Exclusion 

Driver/characteristic (corresponding 
variables in parentheses) 

Personal and demographic characteristics 

Overall 
impact 

No. of effects 
Up-
ward 

Down-
ward 

Max. 
effect 

Older age 

   (Age 45-54) 

   (Age 55-64) 

   (Age 65-74) 

Decrease 

Living in a low density region (rather than a high 
density region) 

Increase 

   (Low density region) 

Behavioural characteristics 

Having higher impulsiveness (i.e. high or very 
high rather than very low impulsiveness)1 
   (Impulsiveness 1: High) 

Increase 

   (Impulsiveness 1: Very high) 

   (Impulsiveness 4: High) 

   (Impulsiveness 4: Very high) 

Knowledge of the finance sector (proxied by 
identifying the best interest rate for a savings 
account) 

Decrease 

   (Identified best interest rate) 

Market-related drivers 

Being unable to read contract T&Cs due to small 
print  

Increase 

2 

3 

2 

- 

- 

- 

- 

- 

- 

- 

- 

0.02 

0.04 

0.05 

2 

0.02 

2 

2 

3 

2 

0.04 

0.08 

0.03 

0.03 

3 

- 

0.02 

225  It  is  assessed  whether  variables  meet  the  selection  criteria  based  on  the  results  of  the 
regression  analysis  presented  in  chapter  6.  Non-response  and  don’t  know  dummies  are 
excluded.  In  Table  67  the  drivers  and  characteristics  are  specified  in  bold  text,  while 
corresponding variables are shown in parentheses. 

298 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                                                                                                                                     
7 │ Population of the theoretical framework of vulnerability 

Table 67: 

Impacts of selected drivers and characteristics on the likelihood of 

vulnerability in Dimension 4 – Exclusion 

Driver/characteristic (corresponding 
variables in parentheses) 

   (Unable to read Ts&Cs due to small print) 

Overall 
impact 

No. of effects 
Up-
ward 

Down-
ward 

Max. 
effect 

Rarely comparing energy deals (i.e. less often 
than ‘from time to time’) 

Increase 

   (Compare energy deals: When need to renew) 

Finding it difficult to read communications from 
internet providers and banks 
   (Read internet communication: Very difficult) 

   (Read bank communication: Very difficult) 

Rarely comparing bank deals (i.e. less often than 
‘from time to time’) 
   (Compare deals from banks: Only the first time) 

   (Compare deals from banks: Never) 

Not knowing their bank contract conditions 
‘completely’ 

   (Know bank contract conditions: Not very) 

Access drivers 

Using the internet for one activity at least once 
per month (rather than no monthly internet 
activities) 
   (Monthly internet activities: 1) 

Increase 

Decrease 

Increase 

Decrease 

Using the internet to compare prices at least once 
per month 

Increase 

   (Frequent internet use: Compare prices) 

Using the internet to bank online at least once per 
month 

Decrease 

   (Frequent internet use: Banking) 

Situational drivers 

Being unemployed and not seeking employment, 
rather than full time employed 

Increase 

   (Unemployed & not looking) 

Being in education, rather than full time employed 

Increase 

   (In full time education) 

Being unemployed and not seeking employment 
for 5+ years 

Decrease 

   (Unemployed & not looking 5+ years) 

Living with a partner, rather than being married 

Increase 

   (Living with a partner) 

‘Other’ marital status, rather than being married 

Increase 

   (Other) 

Having friends who can’t make ends meet 

Increase 

   (Friends can’t make ends meet) 

Finding it difficult to make ends meet 

Increase 

299 

- 

- 

- 

- 

2 

3 

- 

1 

- 

2 

- 

- 

1 

- 

- 

- 

2 

0.24 

2 

0.02 

1 

2 

- 

- 

0.15 

0.17 

0.02 

0.04 

3 

0.03 

- 

0.11 

2 

0.03 

- 

0.07 

2 

2 

- 

2 

2 

2 

0.10 

0.03 

0.12 

0.03 

0.06 

0.04 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7 │ Population of the theoretical framework of vulnerability 

Table 67: 

Impacts of selected drivers and characteristics on the likelihood of 

vulnerability in Dimension 4 – Exclusion 

Driver/characteristic (corresponding 
variables in parentheses) 

Overall 
impact 

No. of effects 
Up-
ward 

Down-
ward 

Max. 
effect 

   (Making ends meet: Fairly difficult)  

   (Making ends meet: Very difficult) 

- 

2 

0.09 

Note: Effects are marginal effects. The measures ‘Impulsiveness 1’ and ‘Impulsiveness 4’ are based on the 
extent  that  respondents  agree  with  the  statements  “I  have  a  hard  time  breaking  bad  habits”  and  “I’m 
impulsive  in  the  purchase  decisions  I  take”  respectively.  Omitted  non-response  variables:  ‘Know  bank 
contract/read communication: No response’. 

7.2.7.  Dimension 4: Inability or failure to buy, choose or access suitable 
products – Problems choosing (based on the experiment results) 

Results  relating  to  respondents’  overall  ability  to  choose  the  correct  offer  in  the 
experiments are also included in the framework for the fourth dimension.226 

Based  on  the  criterion  in  Table  61  above,  twenty-three  variables  that  are  associated 
with  respondents’  overall  performance  in  the  experiments  are  included  in  the 
framework for this dimension.227 These variables in turn correspond to sixteen drivers 
and characteristics, as shown in Table 68.228  

Table  68  also  shows  whether,  based  on  the  regression  analysis,  these  drivers  and 
characteristics increase or decrease the likelihood of vulnerability in this dimension. In 
addition, this table also shows the  magnitudes of the  effects of the  variables selected 
for the framework in terms of effect sizes. 

Since  the  effects  in  Table  68  are  odds  ratios,  the  ‘largest’  effects  are  those  furthest 
from  a  value  of  1.  Hence,  the  largest  magnitude  effects  are  the  effects  of  using  the 
internet to search for information at least once per month (with an odds ratio of 1.93) 
and  being  unemployed  and  seeking  employment  for  at  least  five  years  (with  an  odds 
ratio of 0.46). 

Table 68: 

Impacts of selected drivers and characteristics on the likelihood 

of vulnerability in Dimension 4 – Experiment 

Driver/characteristic (corresponding variables in 
parentheses) 

Overall 
impact* 

Effect 
size 

Personal and demographic characteristics 

Older age 

   (Age 55-64) 

   (Age 75+) 

Increase 

0.82 

0.51 

226 The experiments are especially relevant to this dimension since they tested consumers’ ability 

to select the optimal deal in simulated purchasing scenarios. 

227  It  is  assessed  whether  variables  meet  the  selection  criteria  based  on  the  results  of  the 
regression  analysis  presented  in  Chapter  6.  Non-response  and  don’t  know  dummies  are 
excluded. 

228  In  Table  68  the  drivers  and  characteristics  are  specified  in  bold  text,  while  corresponding 

variables are shown in parentheses 

300 

 
 
 
 
 
 
 
  
 
 
 
                                                 
7 │ Population of the theoretical framework of vulnerability 

Table 68: 

Impacts of selected drivers and characteristics on the likelihood 

of vulnerability in Dimension 4 – Experiment 

Driver/characteristic (corresponding variables in 
parentheses) 

Having a low education level 

   (Low education) 

Overall 
impact* 
Increase 

Effect 
size 

Having a mother tongue different to the official language 

Increase 

   (Non-native tongue, no difficulty) 

Behavioural characteristics 

Failing the credulity test 

   (Credulity test: Incorrect) 

Being fairly willing to take risks (rather than not at all 
willing to take risks) 

   (Risk taking: Fairly) 

Having computational ability 

   (Computation test 2 correct) 

Increase 

Increase 

Decrease 

Knowledge of the finance sector (proxied by identifying the 
best interest rate for a savings account) 

Decrease 

   (Identified best interest rate) 

Market-related drivers 

Being unable to read contract T&Cs due to small print  

Increase 

   (Unable to read Ts&Cs due to small print) 

Not reading bills/communications from providers ‘in detail’, 
in the three key sectors 
   (Read energy bill: Looked at total price) 

Increase 

   (Read energy bill: Not at all) 

   (Read internet communication: Saw what it was) 

   (Read internet communication: Not read it at all) 

   (Read bank communication: Saw what it was) 

   (Read bank communication: Not read it at all) 

Access drivers 

Using the internet to search for information at least once per 
month 

Decrease 

   (Frequent internet use: Search) 

Selling online at least once per month 

Increase 

   (Frequent internet use: Selling) 

Using the internet for social networking at least once per 
month 

Increase 

   (Frequent internet use: Social) 

Situational drivers 

Being self-employed, rather than full time employed 

Decrease 

   (Self-employed full-time) 

   (Self-employed part-time) 

Being unemployed and seeking employment for 5+ years 

Increase 

301 

0.75 

0.77 

0.82 

0.84 

1.41 

1.51 

0.85 

0.77 

0.77 

0.77 

0.77 

0.77 

0.77 

1.93 

0.72 

0.70 

1.31 

1.47 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7 │ Population of the theoretical framework of vulnerability 

Table 68: 

Impacts of selected drivers and characteristics on the likelihood 

of vulnerability in Dimension 4 – Experiment 

Driver/characteristic (corresponding variables in 
parentheses) 

Overall 
impact* 

   (Unemployed & looking 5+ years) 

Having friends who can’t make ends meet 

Increase 

   (Friends can’t make ends meet) 

Finding it difficult to make ends meet 

Decrease 

   (Making ends meet: Very difficult) 

Effect 
size 

0.46 

0.82 

1.33 

Note:  Effects  are  odds-ratios.  Omitted  non-response  variables:  ‘Read  energy  bill:  No  response’;  ‘Read 
internet  communication:  No  response’;  ‘Read  bank  communication:  No  response’;  and  ‘Making  ends  meet: 
Prefer  not  to  say’.  *In  this  table,  odds  ratios  greater  than  one  imply  better  overall  performance  in  the 
experiments (i.e. a decrease in the likelihood of vulnerability), since the overall performance measure in the 
experiments is the total number of correct choices made by the respondent (ranging from 0 to 4).  

7.2.8.  Dimension 5: Higher susceptibility to marketing practices 

One  indicator  of  vulnerability  is  included  in  this  dimension,  namely  Perception  of  own 
vulnerability due to complexity of offers.229  

Fifteen  variables  are  included  in  the  framework  for  this  dimension,  based  on  the 
criterion  given  in  Table  61  above.230  In  turn,  these  variables  correspond  to  eight 
drivers and characteristics, as shown in Table 69.231  

Table  69  also  shows  whether,  based  on  the  regression  analysis,  these  drivers  and 
characteristics increase or decrease the likelihood of vulnerability in this dimension. In 
addition,  Table  69  also  shows  the  magnitudes  of  the  effects  of  the  variables  selected 
for the framework in terms of effect sizes.  

Since the effects are odds ratios, the ‘largest’ effects are those that are furthest from a 
value  of  1.  Thus,  the  largest  magnitude  effects  in  this  dimension  are  the  effects  of 
being a non-native speaker (specifically when this causes difficulty) and finding it very 
difficult  to  make  ends  meet  (with  odds  ratios  of  2.00  and  1.98  respectively),  and,  in 
the opposite direction, having very high trust in people (with an odds ratio of 0.51). 

Table 69: 

Impacts of selected drivers and characteristics on the likelihood of 

vulnerability in Dimension 5 

Driver/characteristic (corresponding variables in 
parentheses) 

Overall 
impact 

Effect 
size 

Personal and demographic characteristics 

Having a mother tongue different to the official language 

Increase 

229 This indicator is defined in Table 5 in section 3.6, as well as in A8.1.2. 
230  It  is  assessed  whether  variables  meet  the  selection  criteria  based  on  the  results  of  the 
regression  analysis  presented  in  chapter  6.  Non-response  and  don’t  know  dummies  are 
excluded. 

231  In  Table  69  the  drivers  and  characteristics  are  specified  in  bold  text,  while  corresponding 

variables are shown in parentheses. 

302 

 
 
 
 
 
 
 
  
 
                                                 
7 │ Population of the theoretical framework of vulnerability 

Table 69: 

Impacts of selected drivers and characteristics on the likelihood of 

vulnerability in Dimension 5 

   (Non-native tongue, difficulty) 

Behavioural characteristics 

Having high or very high trust in people 

Decrease 

   (Trust 1: High) 

   (Trust 1: Very high) 

Being willing to take risks (i.e. not very, fairly or very rather 
than not at all willing to take risks) 
   (Risk taking: Not very) 

Decrease 

   (Risk taking: Fairly) 

   (Risk taking: Very) 

Market-related drivers 

Being unable to read contract Ts&Cs due to small print  

Increase 

   (Unable to read Ts&Cs due to small print) 

Not reading communications from banks ‘in detail’ (i.e. 
seeing but not reading communications) 

Increase 

   (Read bank communication: Saw what it was) 

Finding it very difficult to read bills/communications from 
energy suppliers and banks 
   (Read energy bill: Difficult) 

Increase 

   (Read energy bill: Very difficult) 

   (Read bank communication: Difficult) 

   (Read bank communication: Very Difficult) 

Situational drivers 

Having friends who can’t make ends meet 

Increase 

   (Friends can’t make ends meet) 

Finding it fairly difficult to make ends meet 

Increase 

   (Making ends meet: Fairly difficult) 

   (Making ends meet: Very difficult) 

Note: Omitted non-response variables: ‘Risk taking: No response’; ‘Know bank contract/read 
communication: No response’; and ‘Making ends meet: Prefer not to say’. Effects are odds-ratios. 

7.3.  Population of the theoretical framework 

2.00 

0.64 

0.51 

0.68 

0.62 

0.67 

1.66 

1.42 

1.42 

1.54 

1.64 

1.86 

1.41 

1.57 

1.93 

The  final  step  is  to  draw  together  all  the  relationships  identified  in  sections  7.2.1  to 
7.2.8 above into a single framework of the causes and effects of vulnerability.  

This is done in Figure 32 below, which shows, in the ‘matrix of causes’, the impacts of 
different  causes  of  vulnerability  –  including  demographic  characteristics,  behavioural 
drivers, market-related drivers, access drivers and situational drivers  – on the various 
dimensions  of  vulnerability,  and,  in  the  ‘matrix  of  effects’,  the  effects  of  each 
vulnerability dimension. 

In the framework, a red arrow in the matrix of causes signifies that a characteristic or 
driver  increases  the  likelihood  of  vulnerability  in  a  particular  dimension,  whereas  a 
green  arrow  indicates  that  a  characteristic  or  driver  reduces  the  likelihood  of 

303 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7 │ Population of the theoretical framework of vulnerability 

vulnerability  in  a  given  dimension.  In  the  matrix  of  effects,  a  cross  signifies  that  a 
certain vulnerability dimension has a particular effect.  

The  framework  shows  that  market-related  drivers  are  particularly  important,  with 
forty-eight  relationships  between  these  drivers  and  the  various  dimensions  of 
vulnerability based on the selection criteria set out in the sections above. Being unable 
to  read  contract  terms  and  conditions  due  to  small  print  is  linked  with  an  increased 
likelihood  of  vulnerability  in  all  dimensions.  Moreover,  rarely  comparing  deals,  not 
knowing  one’s  contract  conditions,  not  reading  bills/communications  from  providers, 
and  finding  it  difficult  to  read  bills/communications  from  providers,  which  are  all 
indications  of  being  disengaged  from  the  key  markets  of  the  study,  are  all  associated 
with  a  higher  likelihood  of  vulnerability  in  multiple  dimensions.  These  findings 
regarding  the  significance  of  market-related  drivers  have  an  important  bearing  on 
which  types  of  policy  measure  would  be  expected  to  be  effective  in  alleviating 
vulnerability, as discussed in Chapter 3. 

In  addition,  the  framework  illustrates  the  importance  of  behavioural  drivers  and 
characteristics,  with  twenty  links  between  these  factors  and  the  dimensions  of 
vulnerability  in  total.  For  example,  those  who  identify  themselves  as  being  willing  to 
take  risks  are  less  likely  to  be  vulnerable  in  several  dimensions,  suggesting  that  risk 
aversion  may  be  a  relevant  bias  affecting  vulnerability.  Likewise,  consumers  with 
sector  knowledge232  and  computational  ability  are  in  general  less  likely  to  be 
vulnerable. Conversely, those with higher impulsiveness are, overall, more likely to be 
vulnerable  on  several  dimensions.  In  terms  of  policy,  the  importance  of  behavioural 
drivers suggests that measures that target these factors, such as measures which aim 
to  ensure  the  presentation  of  information  is  appropriate,  may  be  effective  in  tackling 
vulnerability. 

Twenty-nine  links  are  identified  in  total  between  situational  factors  and  the  various 
dimensions  of  vulnerability  based  on  the  selection  criteria  set  out  in  the  sections 
above.  However,  these  are  dominated  by  the  impacts  of  finding  it  difficult  to  make 
ends  meet  and  having  friends  who  cannot  make  ends  meet,  which,  from  a  policy, 
suggests  that  financial  support  measures  (e.g.  social  tariffs)  may  be  effective  in 
alleviating vulnerability. 

232  Proxied by correctly identifying the  best interest  rate  for a savings account for the financial 

sector, and by knowing the meaning of the term “Mbps” for the online sector. 

304 

 
 
                                                 
7 │ Population of the theoretical framework of vulnerability 

Figure 32:  Framework of causes and effects of consumer vulnerability 

305 

Dimension 1Dimension 2Dimension 3Dimension 4 Problems comparingDimension 4 Problems switchingDimension 4 Problems choosingDimension 4 ExclusionDimension 5CAUSES OF VULNERABILITYYoung ageOlder ageGender (being male)Living in a low density regionHaving a low education levelHaving a mother tongue different to the official languageHaving higher trust in peopleHaving higher credulityFailing the credulity testHaving higher impulsivenessBeing willing to take risksHaving computational abilityKnowledge of the online sectorKnowledge of the finance sectorBeing unable to read contract Ts&Cs due to small printRarely comparing deals from energy suppliersRarely comparing deals from internet providersRarely comparing deals from banksNot knowing their energy supply contract conditionsNot knowing their internet provider contract conditionsNot knowing their bank contract conditionsNot reading bills/communications from energy suppliersNot reading bills/communications from internet providersNot reading bills/communications from banksFinding it difficult to read bills/communications from energy suppliersFinding it difficult to read bills/communications from internet providersFinding it difficult to read bills/communications from banksUsing the internet for some activities at least once per monthUsing the internet to search for information at least once per monthUsing the internet to compare prices at least once per monthUsing the internet to bank online at least once per monthUsing the internet to sell online at least once per monthUsing the internet for social networking at least once per monthBeing unemployedBeing in educationBeing self-employedBeing retiredBeing long-term sick or disabledBeing unemployed for 5+ yearsLiving with a partner, rather than being married'Other' marital status, rather than being marriedHaving friends who buy onlineFinding it difficult to make ends meetHaving friends who can't make ends meetEFFECTS OF VULNERABILITYExperiencing problems when buying or using goods or servicesFailing to solve problems that occur when buying or using goods or servicesInability to obtain redress due to cost or time required by the complaint procedureBeing faced with high cost of obtaining supportHigher relative impact of losses on financial products on low-income consumersFinancial impact of high APR rates on those unable to access mainstream financeOver-indebtedness resulting from sub-optimal financial decisionsHigher energy use and costs as a result of unemploymentDisconnection in the energy sectorBeing a victim of a fraud or scamFeeling vulnerable due to marketing practicesFeeling vulnerable due to personal characteristicsFeeling uninformed in markets (e.g. regarding price and quality etc)Basing consumer decisions on limited informationSigning contracts without fully understanding the implicationsBeing mislead by or misunderstanding information or advertisingHaving problems comparing goods and servicesNot comparing goods and servicesNot switching deals when it is in the consumer's interestBeing offered unsuitable or more costly goods or servicesPurchasing suboptimal goods or servicesMaking a purchase that is not in the consumer's interestNot making a purchase that is in the consumer's interestPaying more than necessary for goods or servicesBeing denied access to goods or servicesSocial exclusion as a result of exclusion from specific marketsHealth impact due to lack of access to affordable energy tariffsLoss of control over personal data due to online services' lack of transparencySituational driversMATRIX OF CAUSESMATRIX OF EFFECTSDemographic characteristicsBehavioural characteristicsMarket-related driversAccess drivers 
 
 
 
7 │ Population of the theoretical framework of vulnerability 

7.4.  Permanent versus transitory nature of consumer vulnerability 

This  section  considers  the  extent  that  consumer  vulnerability  is  likely  to  be  a 
permanent  (i.e.  structural)  or  transitory  condition.  The  only  direct  evidence  available 
regarding this issue comes from the in-depth consumer interviews. However, relevant 
insights can also be drawn from the data analysis presented in section 7.3 above, since 
the results of this analysis allow us to consider whether the most important drivers of 
vulnerability are likely to be permanent or transitory. 

The  framework  of  causes  and  effects  of  consumer  vulnerability,  presented  above, 
identified the key drivers of vulnerability across the 5 vulnerability dimensions used in 
this study. Almost all drivers of vulnerability can be considered transitory or dynamic, 
in  that  an  individual’s  situation  or  behaviours  can  change  such  that  the  likelihood  of 
vulnerability  is  reduced.  In  this  section  we  briefly  consider  some  of  these  and  draw 
upon  insights  from  the  qualitative  interviews  as  to  how  people  adopt  strategies  to 
mitigate these problems.    

The  in-depth  biographical  interviews  conducted  as  part  of  this  study  identified  a 
number  of  strategies  people  use  to  prevent  possible  difficulties  when  interacting  in 
markets.  One  of  the  main  strategies  reported  by  participants  is  to  thoroughly  and 
systematically study the different options before selecting a supplier, and the benefit of 
such strategies is evident from the results of the data analysis (summarised in section 
7.3  above)  since  market-related  drivers  are  found  to  be  consistently  linked  with  the 
likelihood  of  vulnerability.  In  particular,  consumers  who  do  not  compare  offers  in  the 
market, do not know their contract terms and conditions, and do not/find it difficult to 
read  communications  from  providers,  tend  to  be  more  vulnerable  across  several 
vulnerability dimensions. Hence, measures that aid comparison and encourage search 
and awareness of alternatives may aid consumers. 

interviews, 

the  qualitative 

Lack of knowledge of contracts and not reading or understanding communications from 
providers  is  also  associated  with  a  higher likelihood  of  vulnerability.  This is  supported 
by 
that  reading 
communications (e.g.  energy bills, communication from banks  etc.), and making sure 
that  they  are  well  understood  (e.g.  by  looking  up  the  meaning  of  the  different  terms 
online,  or  asking  the  supplier  to  provide  details),  is  used  by  consumers  to  avoid 
difficulties  with  providers.  Therefore, 
framing  of 
communications  and  information  tools  to  aid  understanding  may  help  mitigate  this 
form of transitory vulnerability.  

in  which  respondents  reported 

interventions  such  as 

the 

Access  drivers  associated  with  the  frequency  of  internet  use  are  also  associated  with 
some  dimensions  of  vulnerability.  The  participants  in  the  qualitative  focus  groups 
reported  they  undertook  actions  to  ensure  they  were  informed  about  security  issues, 
installed  anti-virus  software  and  added  blockers.  One  respondent  mentioned  avoiding 
posting  personal  information  on  social  media,  due  to  security  concerns.  Others 
reported  that  they  take  basic  preventive  measures,  such  as  not  opening  emails  from 
unknown  addresses  or  clicking  on  suspicious  links.  Therefore,  actions  that  improve 
these  consumers’  awareness  and  confidence  in  the  online  environment  (in  addition  to 
providing access to the internet) can help consumers in these situations. 

Behavioural  drivers  of  vulnerability  are  associated  with  low  computational  ability  and 
poor sector knowledge, particularly in regard to the financial sector. Participants in the 
interviews  reported  adopting  a  proactive  approach  to  avoid  problems  in  the  financial 
sector.  For  example,  ensuring  they  read  and  understand  communications  from  their 
bank,  keeping  in  contact  with  their  account  manager,  developing  confidence  in 

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handling  various  financial  products  and  developing  strategies  in  order  to  maximise 
their benefit  (e.g. receiving their salary on one account, transferring part of it towards 
another account with a better interest rate, and part onto the credit balance etc.).  

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8 │ Role of marketing practices 

8.  ROLE OF MARKETING PRACTICES 

Box 6:  Summary of Chapter 8 

This  chapter  focuses  on  the  interaction  between  marketing  practices  and  consumer 
vulnerability.  It  considers  a  range  of  problematic  marketing  practices  which  have 
been identified via the literature review and stakeholder consultation and it provides 
an analysis of the results from the behavioural experiments which tested the impact 
of marketing practices on consumer decision-making processes and on the likelihood 
of reaching sub-optimal choices. 

Relevant environments for the behavioural experiments are designed on the basis of 
lessons  from  the  behavioural  economics  literature  about  market  environments  that 
are likely to exploit consumer vulnerability.   

Practices  which  exploit behavioural  drivers  of  vulnerability include  framing,  dynamic 
pricing,  and  baiting. Framing  refers  to  the  way  in  which information is  presented  to 
consumers. It includes for example drip pricing, where consumers are only gradually 
shown  the  full  cost  of  the  transaction,  and  reference  pricing  where  the  price  of  a 
product is shown alongside a different price. Dynamic pricing and time-limited offers 
all relate to the way offers are framed, but they rely in particular on creating a sense 
of urgency by suggesting consumers may incur a future loss if they do not purchase 
the given product immediately. Baiting refers to advertising a very limited number of 
discounted products or services, with an expectation that a consumer will purchase a 
non-discounted product once the discounted one is no longer available. 

Practices  which  exploit  consumers’  limited  capacity  to  process  complex  information 
can  range  from  bundled  offers  which  make  it  difficult  for  consumers  to  identify  the 
cost  of  a  single  product  or  service  to  complexity  of  financial  products,  the  most 
commonly cited problem related to consumer vulnerability.  

Additional practices which also exploit different dimensions of consumer vulnerability 
include  those  which  focus  on  situational  vulnerability  such  as  pressure  selling  and 
those  which  create  or  exploit  access  and  accessibility  problems.  This  chapter 
discusses several forms which each of these can take. 

The  second  part  of  the  chapter  reports  on  the  results  from  the  four  behavioural 
experiments which were carried out in the context of this study. These comprised of 
three  sector-specific  experiments  and  one  cross-cutting  experiment.  The  finance 
sector  ‘framing’  experiment  tested  the  effect  of  the  prominence  of  important 
information. The online sector experiment, also a ‘framing’ experiment, examined the 
impact  of  dripping  of  information  and  prices.  The  energy  sector  behavioural 
experiment  tested  the  impact  of  complex  tariff  pricing.  Finally,  the  cross-cutting 
experiment examined the marketing practice of “teaser rates”. Each experiment had 
two versions: a marketing practice treatment and a remedy treatment with improved 
information being given to consumers. In addition, each experiment had two rounds, 
where  the  second  round  introduced  additional  complexity  to  the  decision  facing  the 
consumer.  

The  analysis  of  the  experimental  data  first  considers  the  impact  of  the  remedy 
treatment version of each experiment on the optimality of decision making. Overall, 
on  average  across  all  experiments,  a  statistically  significant  difference  of  7 
percentage  points  more  respondents  selected  the  optimal  offer  under  the  remedy 
treatment  compared  to  the  marketing  practice.  Remedy  treatment  gains  were  most 

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8 │ Role of marketing practices 

pronounced  in  the  finance  sector  experiment,  in  the  second  round  of  the  energy 
sector experiment and in the first round of the online sector experiment. At country-
level, they were stronger in Denmark and the UK. 

The experimental data is further explored to assess the impact of remedy treatments 
on the decision-making process of consumers. For three of the four experiments, the 
remedy  treatment  encourages  participants  to  rely  on  better  informational  tools, 
which in turn explain to a large extent the improvement in consumer outcomes.  

The  final  sub-section  examines  the  relationship  between  the  decisional  gains  under 
the  remedy  treatment  relative  to  the  marketing  practice  treatment  and  consumer 
vulnerability-associated  characteristics  such  as  socio-demographic,  behavioural  and 
market-related characteristics.  

While  it  is important  to  note  that  marketing  practices  are  an  issue  for  all  consumer 
groups  and  therefore  that  their  effects  are  not  confined  to  particular  types  of 
consumers,  it  is  evident  from  the  study’s  experimental  results  that  some  consumer 
groups are more susceptible to marketing practices than others. In particular, across 
all experiments, older respondents benefited more from the remedy treatments than 
those  in  the  base  age  group  of  35-44  year  olds;  respondents  with  a  lower  level  of 
education  relative  to  those  with  a  medium  or  high  level  of  education;  and  
respondents  who  are  relatively  less  experienced  and  engaged  with  the  key  markets 
of  the  study  (e.g.  do  not  know  their  contract  conditions  ‘at  all’,  ‘never’  compare 
deals, and did not read the last communication/bill from their provider ‘at all’).  

This  chapter  focuses  on  the  role  of  marketing  practices  in  causing  consumer 
vulnerability. It examines a number  of issues  raised in the research questions set out 
in  section  1.4  (in  particular  RQ3  and  RQ8),  including  the  extent  that  consumer 
vulnerability  is  linked  to  problematic  marketing  practices,  whether  vulnerability  is 
linked to market-related factors such as simplification and transparency of offers, and 
whether  particular  socio-demographic  characteristics  make  consumers  more 
susceptible to problematic marketing practices. 

This  chapter  draws  mainly  on  evidence  from  the  literature  review,  stakeholder 
consultation  and  behavioural  experiments,  with  supporting  evidence  coming  from  the 
consumer survey and biographical consumer interviews. 

8.1.  Problematic marketing practices identified via the literature review 
and stakeholder consultation 

As  a  first  step,  this  section  considers  a  range  of  problematic  marketing  practices 
identified  through  the  literature  review  and  stakeholder  consultation.  These  practices 
are grouped as follows: 

  Practices exploiting behavioural drivers; 

  Practices exploiting situational vulnerability drivers; 

  Double disadvantages; and 

  Access and accessibility problems. 

It is important to note that the problematic practices identified do not necessarily aim 
to exploit particular vulnerabilities. A particular marketing practice can be problematic 
if  it  has  unintended  consequences  that  disproportionately  affect  certain  potentially 

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8 │ Role of marketing practices 

vulnerable  consumer  groups.  In  addition,  in  some  cases,  potentially  problematic 
practices  may  have  their  justifications,  economic  or  otherwise,  for  instance  where 
payment methods used by lower-income consumers carry with them additional costs to 
suppliers. 

This  section  of  the  report  does  not  aim  to  determine  which  of  these  practices  are 
justified, but instead it takes the first step to identify them. The individual practices are 
outlined  in  more  detail  in  the  sections  below  and,  in  addition,  where  relevant  insight 
can  be  drawn  from  the  results  of  the  survey  data  analysis  presented  in  previous 
sections, these are highlighted in the discussion below. 

8.1.1.  Practices exploiting behavioural biases 

The  first  set  of  problematic  practices  identified  through  the  review  includes  practices 
that exploit behavioural drivers, including biases and cognitive limitations. As a result, 
these practices apply to a wide cross-section of consumers. 

Lunn  and  Lyons  (2010)  provide  an  overview  of  key  behavioural  biases  and  practices 
potentially  exploiting  these  biases.  One  of  the  identified  biases  relates  to  framing, 
where  consumers  will  make  different  choices  depending  on  how  information  is 
presented.  Specific  practices  exploiting  the  framing  biases  include  drip  pricing  or 
reference pricing:   

  Drip pricing relates to consumers only being shown part of the price up front, 
with additional costs and charges being shown at later stages. This practice is 
particularly  relevant  in  the  area  of  e-commerce  and  it  is  widely  used  in  the 
sales  of  airline  tickets,  where  taxes  and  fees  tend  to  be  displayed  at  a  later 
stage  in  the  booking  process.  The  practice  is  also  used  to  a  lesser  extent  in 
other  areas,  such  as  mobile  phone  contracts  and  hotel  bookings  (Civic 
Consulting 2010 in European Parliament 2011). 

  Reference  pricing  relates  to  showing  the  price  of  a  product  alongside  a 
different  price  (“older”  price  or  price  of  a  competing  product)  and  it  is  used 
across  a  wide  number  of  sectors  including  e-commerce  (European  Parliament 
2011).  In  addition,  according  to  one  interviewee  from  a  Belgian  consumer 
organisation, price comparisons can also be misleading by using incorrect and 
incomplete information. 

Two  of  the  behavioural  experiments  conducted  for  this  study  examined  the  impact  of 
framing  of  information,  in  particular  the  finance  sector  experiment,  which  tested  the 
effect  of  the  prominence  of  important  information,  and  the  online  sector  experiment, 
which examined the impact of dripping of information and prices. 

Dynamic  pricing  and  time-limited  offers  all  relate  to  the  way  offers  are  framed,  but 
they  rely  in  particular  on  creating  a  sense  of  urgency  by  suggesting  consumers  may 
incur  a  future  loss  if  they  do  not  purchase  the  given  product  immediately.  Time-
limited offers aim to achieve this by indicating that a given offer will only be valid for 
a  certain  time  (similar  practice  include  indicating  how  many  other  consumers  are 
currently  looking  at  the  same  offer  (European  Parliament  2011)),  while  dynamic 
pricing  refers  to  prices  of  goods  and  services  changing  depending  on  circumstances. 
Dynamic pricing can have a negative impact by exploiting behavioural biases (as well 
as segmenting and discriminating between consumers, as noted in the Report from the 
Multi-Stakeholder Dialogue on Comparison Tools. However, it is important to note that 
dynamic pricing can also be used to benefit consumers. For example, in the context of 
smart metering in the energy sector, dynamic tariffs could benefit consumers by better 

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reflecting their consumption patterns and allowing cost savings through use during off-
peak hours (see for instance BEUC 2013b).  

Next,  baiting  refers  to  advertising  a  very  limited  number  of  discounted  products  or 
services, with an expectation that a consumer will purchase a non-discounted product 
once the discounted  one is no longer available (European Parliament 2011). A similar 
practice used in the finance sector is known as “teaser rates” (Shui and Ausubel 2004 
and Ausubel, 1999 in Gathergood 2012), namely low introductory interest rates aiming 
to  entice  consumers  to  purchase  a  particular  financial  product.  The  cross-cutting 
behavioural experiment conducted for this study examined the practice of teaser rates, 
framed using the three key sectors of the study (see section 2.4). 

The  above  practices  are  in  place  across  a  number  of  sectors  and  product  types,  with 
the online sector making it particularly easy to employ them. While the practices may 
be used in the case of higher cost products or services (i.e. travel), and they can result 
in substantial one-off loss of consumer welfare, they generally do not carry with them 
longer-term recurring costs, as practices relating to financial or energy sector contracts 
would.  At  the  same  time,  they  appear  to  be  in  common  use,  making  them  high 
frequency/low  cost  practices,  where  low  cost  should  be  seen  in  relation  to  other 
practices outlined in this section.   

Another  set  of  practices  exploit  consumers’  limited  capacity  to  process  complex 
information.  These  practices  relate  to,  intentionally  or  unintentionally,  presenting 
information in ways that make it difficult for consumers to understand the true cost of 
products or services they are purchasing. This can range from  bundled offers where 
products  or services are bundled together making it difficult for  consumers to identify 
the cost of a single product or service (European Parliament 2011), to the complexity 
of retail financial products. Complexity of financial products is the most commonly 
cited  problem  related  to  consumer  vulnerability  (see  for  instance  Perry  and  Morris 
2005) and is discussed in more detail in the discussion of the financial sector in section 
9.1. 

Complexity is an issue that also applies to other products and sectors. An interviewee 
from  a  consumer  organisation  in  Belgium  noted,  for  instance,  that  insufficient  clarity 
concerning the difference between fixed and variable energy tariffs can be problematic 
and  can  put  consumers  in  situations  of  vulnerability,  while  a  Dutch  study  into 
automotive insurance sector has shown that complexity of language used by insurance 
providers  means  that  a  majority  of  policyholders  do  not  fully  understand  their 
policies.233 The complexity of offers in the energy and online sector is also discussed in 
more detail in sections 9.2 and 9.3. 

The  energy  sector  behavioural  experiment  conducted  for  this  study  tested  the  impact 
of  complex  tariff  pricing,  where  energy  tariffs  comprise  of  multiple  different 
components,  compared  to  a  situation  where  this  practice  is  remedied  via  new 
information tools. The results of this experiment are discussed in section 8.2. 

While  online  comparison  tools  can  address  some  of  the  complexity  faced  by 
consumers,  they  can  also  have  their  own  pitfalls.  The  report  from  the  Multi-

233 See http://amweb.nl/assurantiemagazine-1221720/gemiddelde-consument-snapt-niet-veel-

van-autopolis 

311 

 
 
 
                                                 
8 │ Role of marketing practices 

Stakeholder  Dialogue  on  Comparison  Tools  (2013)  notes  that  comparison  tools  can 
carry  with  them  a  range  of  risks,  in  particular  with  regard  to  transparency  and 
impartiality of comparisons, quality of information or comprehensiveness.   

Overall,  practices  related  to  consumers’  cognitive  limitations  in  processing  large 
amounts  of  complex  information  can  apply  to  a  wide  range  of  products  and  services, 
but  they  are  particularly  salient  in  the  case  of  complex  offers  and  contracts  in  the 
financial and energy sectors. Given that consumers face such situations less frequently 
than  other  retail  situations,  but  that  an  inappropriate  contract  choice  can  have  large 
cost implications, these practices may be seen as low frequency/high cost practices. 

The  discussion  above  suggests  it  is  relevant  to  examine  whether  the  impact  of 
marketing  practices  varies  depending  on  a  consumer’s  behavioural  characteristics, 
such  as  character  traits  including  whether  they  are  trusting  of  people,  their  credulity, 
their  impulsiveness,  and  the  extent  that  they  are  willing  to  take  risks  and  cognitive 
capacity, for example as measured by level of education. 

Therefore, the analysis of the behavioural experiment data discussed  below examines 
whether  the  marketing  practices  tested  in  the  various  experiments  had  greater  or 
lesser  impacts,  relative  to  the  remedies,  on  those  with  certain  behavioural 
characteristics. 

8.1.2.  Marketing practices exploiting situational vulnerability drivers 

Another  set  of  problematic  practices  relates  to  the  use  of  aggressive  marketing  that 
actively  exploits  situations  of  vulnerability  that  consumers  may  find  themselves  in.  In 
the  financial  sector  there  is  substantial  research  on  instances  where  low-income 
consumers are sold expensive products due to economic actors exploiting their current 
situation.  Similarly,  marketing  practices  in  the  online  sector  can  exploit  consumers’ 
lack of digital sophistication, as well as use profiling techniques to identify and exploit 
other  potential  sources  of  vulnerability.  Such  practices  in  both  these  sectors  are 
described in more detail in Chapter 9. 

The  Voice  Group  in  its  2010  study  on  motherhood,  marketization  and  consumer 
vulnerability,  noted  that  economic  operators  adopt  marketing  practices  which  try  to 
exploit  the  uncertainties  and  perceived  loss  of  control  associated  with  new  life 
situations  that  new  and  expectant  mothers  find  themselves  in.  This  includes  using 
notions  such  as  “good  mother”  and  “bad  mother”  in  marketing  of  products,  or 
distributing goods through midwives and hospitals to give them a seal of “institutional 
approval”. 

The  way  that  products  are  marketed  to  consumers  can  also  be  closely  linked  to 
vulnerability.  This  includes  practices  such  as  doorstep  selling  (still  identified  as  a 
problem  in  Belgium,  France,  Greece,  and  Portugal),  which  often  targets  specific 
consumer groups (e.g. older consumers) and relies on aggressive marketing to entice 
consumers  to  sign  potentially  disadvantageous  contracts.  Doorstep  selling  can  be 
particularly effective, as shown in a 2011 UK study commissioned by Ofgem234, which 
found that 29% of consumers learned about deals via a salesperson at their door. It is, 

234 Face‐to‐ face interviews conducted by Ipsos‐MORI across Great Britain, published January 

2011, http://www.ofgem.gov.uk/MARKETS/RETMKTS/RMR/Documents1/Ips 
osMori_switching_omnibus_201 1.pdf  

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8 │ Role of marketing practices 

however,  also  worth  noting  that  the  practice  is  increasingly  being  restricted  or 
prohibited across the EU. 

Telephone  selling  was  found  to  be  similarly  problematic  in  selected  Member  States 
(e.g. Finland), as well as selling in shopping malls (Sweden). Yet another problematic 
practice identified by a Slovakian stakeholder, is the practice of “presentations”, which 
gather  groups  of  people  in  order  to  demonstrate  and  sell  a  set  of  products  (such  as 
kitchen,  electronic,  health  devices).  These  are  usually  more  expensive  than 
comparable products on the market, but the presentations tend to target older, single 
consumers living in rural locations, who have limited access to information. 

8.1.3.  Double disadvantages 

Another  set  of  problematic  practices  is  referred  to  here  as  “double  disadvantages”. 
These  are  practices  that  may  not  target  particular  vulnerability  drivers  or  particular 
in  an  already 
consumer  groups,  but  result 
disadvantaged  position  incur  additional  costs  or  are  not  able  to  benefit  from  savings 
available to other consumers. Hence they face another set of disadvantages.  

in  situations  where  consumers 

Double disadvantages are particularly pertinent in the energy and financial sectors and 
are discussed in more detail in sections 9.2 and 9.1. They include, among others, more 
expensive  energy  tariffs  available  to  low-income  consumers  due  to  limited  payment 
methods  available  to  them  or  the  limited  access  to  mainstream  financial  services, 
which steers consumers towards more expensive sources of credit.  

Indeed, the survey data analysis reported in Chapter 6 found that consumers who find 
it  ‘very  difficult’  to  make  ends  meet  are  more  likely  to  have  overpaid  for  energy 
telecommunications or banking services due to not being able to use certain payment 
methods (Indicator 2,  Table 54), and are more likely to have been turned down for a 
loan (Indicator 11, Table 59). 

Other  forms  of  double  disadvantages  relate  to  costs  associated  with  accessing 
information or services in a particular way. This includes high costs of calling telephone 
helplines  from  prepaid  mobile  phones,  which  are  popular  among  low-income 
consumers (Stearn 2012) or the  costs  of  obtaining a bill in paper form as opposed to 
online.  The  latter  is  an  issue  highlighted  in  Finland,  where  a  Finnish  Consumer 
Ombudsman took a mobile phone company to the Market Court in June 2013 in order 
to  prohibit  the  company  from  applying  a  contract  term  under  which  it  charges 
consumers a separate fee for a paper invoice.   

8.1.4.  Access and accessibility problems 

A final set of problematic practices refers to certain vulnerable consumer groups being 
denied access to goods, services and markets. One of the main problems encountered 
in  particular  by  consumers  with  disabilities,  as  well  as  older  consumers,  relate  to 
lacking  accessibility  of  goods  and  services.  Stearn  (2012)  in  his  review  of  consumer 
vulnerability in the UK notes that: 

  Call  centres  and  helplines  are  unprepared  to  service  consumers  with 
disabilities, with, for instance, call centre workers assuming consumers can see 
and refusing to deviate from the script even if prompted to do so or hanging up 
if consumers use a text relay service; 

313 

 
 
 
8 │ Role of marketing practices 

  Use  of  menus  and  entering  numbers  through  a  telephone  is  difficult  for 
consumers with learning disabilities or those who have suffered a head injury; 
and 

 

Information  such  as  contract  terms  and  conditions  can  be  provided  to 
consumers in non-accessible formats, such as in overly small print. 

These  accessibility  issues  are  further  compounded  by  the  lack  of  access  to 
procedures.  The  Payments  Council  (2012)  notes  that  in  the  UK  it  is  not  possible  for 
consumers unable to access payment services to delegate them to carers or family in a 
way  that  is  secure,  respects  their  privacy,  and  can  be  limited  to  specific  services. 
Similar  problems  have  also  been  noted  by  disability  organisations,  where  ineffective 
delegation  and  legal  representation  mechanisms  can  carry  with  them  risks  for  some 
consumers  with  disabilities.  These  issues  in  turn  mean  that  individuals  may  be  more 
vulnerable to financial crime, ranging from small unauthorised purchases to transfer of 
property (SCIE 2011).  

Another  issue  related  to  access  and  accessibility  is  the  exclusion  of  certain 
consumer  groups from markets.  One of these practices is  retail redlining, where 
retailers  avoid  opening  franchises  in  disadvantaged  areas  for  reasons  other  than 
objective  economic  ones.  A  US  study  by  D’Rozario  and  Williams  (2005)  notes  that  in 
the US the practice is associated with retailers redlining areas with a larger proportion 
of ethnic minority, lower education, and lower income consumers. Redlining can range 
from charging higher fees to franchises in specific areas to refusing to open franchises 
and  serving  consumers  in  specific  retail  areas.  Although  existing  research  on  retail 
redlining  focuses  primarily  on  ethnic  minorities  in  the  US,  there  is  evidence  of  similar 
segmentation  taking  place  in  Europe  (see  for  instance  the  2013  URBACT  II  report 
‘Against divided cities in Europe’). 

D’Rozario  and  Williams  (2005)  also  identify  the  practice  of  “weblining”,  as  a  potential 
problem,  where  operators  can  restrict  offers  to  particular  consumer  groups,  which  is 
discussed  in  more  detail  in  section  9.3.  A  similar  practice  is  marketing  exclusion 
where  suppliers  do  not  target  certain  consumer  groups,  since  they  are  seen  as 
unprofitable  (Cartwright  2011).  Such  practices  have  also  been  raised  as  a  potential 
concern  in  the  context  of  consumer  profiling  in  the  report  from  the  Multi-Stakeholder 
Dialogue  on  Comparison  Tools  (2013),  where  the  stakeholders  noted  that  modern 
technology allow vendors to restrict access for certain “consumer profiles”. A different 
case of exclusion has been noted by an organisation in Finland, where consumers with 
a bad credit history are sometimes denied access to their online bank accounts.  

Finally, practices relating to access and accessibility also include restricting consumers’ 
access  to  offers  that  are  available  to  others.  For  instance,  if  an  operator  presents 
certain  offers  to  online  customers  only,  it  prevents  consumers  who  do  not  have  an 
internet  connection  from  accessing  these  deals.  Thus,  while  lack  of  internet  access  is 
not in itself a problematic marketing practice,  offering certain deals exclusively online 
could be considered as one. Since such practices usually imply additional cost or other 
detriment to the consumer, they are closely linked to “double disadvantages” and can 
be of particular relevance in the financial and energy sectors. 

8.2.  Evidence from the behavioural experiments 

The behavioural experiments are a key source of evidence to the study with respect to 
the  impact  of  marketing  practices.  As  explained  in  the  methodology  section,  the 
treatments  in  the  experiments  were  designed  to  reflect  both  ‘problematic’  marketing 
practices  in  the  key  markets  of  interest  to  the  study  (the  marketing  practice 

314 

 
 
8 │ Role of marketing practices 

treatments) and, conversely, ‘remedied’ marketing practices (the remedy treatments). 
The designs of the marketing practice and remedy treatments in each experiment are 
described in section 2.4. 

Therefore, the experiments allow us to examine whether and to what extent consumer 
vulnerability,  in  the  form  of  suboptimal  product  choices,  is  linked  to  problematic 
marketing  practices  or  simply  socio-demographic  characteristics,  and  whether  the 
impact  of  marketing  practices  is  dependent  on  the  consumer’s  own  characteristics. 
Hence, the experiments directly address the third research question set out in  section 
1.4. The following sections examine: 

  The impact of the marketing practice and remedy treatments on choices; 

  The  impact  of  the  marketing  practice  and  remedy  treatments  on  the  decision 

process; and 

  The  variation  in  the  impact  of  marketing  practices  depending  on  personal 

characteristics. 

8.2.1.  Impact of the marketing practice and remedy treatments 

The first part of the experimental data analysis reported in this chapter focuses on the 
proportions of respondents who selected the optimal offer in the different experiments, 
and  whether  these  proportions  differed  between  the  marketing  practice  and  remedy 
treatments. 

This  analysis  provides  evidence  regarding  which  of  the  marketing  practice  treatments 
tested  in  the  experiments  cause  consumer  vulnerability  in  the  form  of  suboptimal 
product choices. 

First,  we  test  whether  differences  between  the  treatment  groups  (in  terms  of  the 
shares who chose the optimal offer) are statistically significant. Then, the robustness of 
these  results  is  tested  in  a  multivariate  setting  by  including  variables  representing 
respondents’ personal characteristics as control variables in regressions. 

Differences in proportions 

Table 70 shows the share of respondents who selected the optimal offer in each round 
of  each  experiment235,  under  each  treatment.  The  treatment  effect  is  the  difference 
between  the  share  that  chose  the  optimal  offer  under  the  remedy  treatment  and  the 
share  that  chose  the  optimal  offer  under  the  marketing  practice  treatment.  Positive 
values for the treatment effect indicate that respondents were more likely to select the 
optimal offer under the remedy. For each round of each experiment, Table 70 indicates 
whether the treatment effect is statistically significant. 

The  results  show  that,  depending  on  the  sector  and  round  of  the  experiment, 
respondents  were  either  more  likely  to  choose  the  optimal  offer  under  the  remedy 
treatment or there was very little difference between treatments. 

235 In the first round of each experiment, respondents chose between two offers. In the  second 
round,  the  offer  that  they  selected  in  the  first  round  became  ‘their  current  deal’,  and  they 
chose between that deal and two ‘new’ offers. 

315 

 
 
 
                                                 
8 │ Role of marketing practices 

Specifically,  more  respondents  chose  the  optimal  offer  under  the  remedy  than  under 
the marketing practice  in the second round of  the energy sector experiment, the first 
round  of  the  online  sector  experiment,  and  both  rounds  of  the  finance  sector 
experiment, and these differences are statistically significant. 

Overall,  on  average  across  all  experiments  and  rounds,  69%  of  respondents  selected 
the optimal offer under the remedy treatment compared to 63% under the marketing 
practice (a difference that is statistically significant).236 

Table 70: 

Share of respondents choosing the optimal offer 

Experiment  sector  and 
round 

Marketing 
practice 

Proportion 

Remedy 

Base count 

Treatment 
effect 

Marketing 
practice 

Remedy 

Energy sector 

Online sector 

Round 1 

Round 2 

Round 1 

Round 2 

Finance sector 

Round 1 

Cross-cutting 

Round 2 

Round 1 

Round 2 

78% 

22% 

65% 

62% 

82% 

82% 

62% 

49% 

79% 

37% 

83% 

64% 

91% 

88% 

61% 

50% 

1pp 

15pp  *** 

19pp  *** 

2pp 

9pp  *** 

6pp  *** 

0pp 

1pp 

1263 

1263 

1254 

1254 

1252 

1252 

1282 

1282 

1259 

1259 

1256 

1256 

1253 

1253 

1283 

1283 

63% 

Average 
69% 
Note:  The  treatment  effect,  measured  in  percentage  points  (pp),  is  the  difference  between  the  share  that 
chose  the  optimal  offer  under  the  remedy  treatment  and  share  that  chose  the  optimal  offer  under  the 
marketing practice treatment. */ **/ *** implies that the treatment effect is statistically significant at 95%/ 
99%/  99.9%.  To  one  decimal  place  the  average  values  for  the  marketing  practice  and  remedy  treatments 
are 62.5% and 69.2% respectively, meaning that the difference rounds to 7pp. 
Source: London Economics analysis of the experiment data 

7pp  *** 

10,102 

10,102 

Country  level  results  presented  in  Table  71  below  show  that  the  treatment  effects 
varied across countries as well as experiments:  

 

In  the  second  round  of  the  energy  sector  experiment,  the  remedy  had  a 
positive  treatment  effect  in  all  countries  in  the  dataset,  and  this  was 
statistically significant in all countries except Romania. 

  The most consistent treatment effect is observed in the first round of the online 
sector  experiment,  in  which  respondents  in  all  countries  were  more  likely  to 
choose  the  optimal  offer  under  the  remedy  treatment,  with  statistically 
significant treatment effects for all countries. 

 

In  the  finance  sector  experiment,  the  treatment  effect  was  statistically 
significant in all countries in the first round and in all countries except Romania 
in the second round. 

  Overall,  on  average  across  all  experiments  and  rounds,  the  treatment  effect 

was statistically significant in all countries and largest in Denmark and the UK. 

236  To  one  decimal  place  the  values  are  62.5%  for  the  marketing  practice  and  69.2%  for  the 
remedy, meaning the difference is 6.7pp, which rounds to 7pp as reported elsewhere in this 
chapter. 

316 

 
 
 
 
 
  
  
 
  
 
  
 
  
                                                 
8 │ Role of marketing practices 

Table 71: 

Treatment effects by country (percentage points) 

Sector and round 

UK 

Denmark  Portugal  Lithuania  Romania 

Energy sector 

Online sector 

Round 1 

Round 2 

Round 1 

Round 2 

-2pp 

23pp 

22pp 

6pp 

*** 

*** 

Finance sector 

Round 1 

10pp 

*** 

Cross-cutting 

Round 2 

Round 1 

8pp 

0pp 

** 

0pp 

18pp 

17pp 

7pp 

9pp 

6pp 

7pp 

*** 

*** 

** 

* 

1pp 

19pp 

18pp 

1pp 

9pp 

8pp 

-6pp 

*** 

*** 

** 

* 

3pp 

10pp 

24pp 

-5pp 

9pp 

7pp 

-2pp 

** 

*** 

** 

* 

2pp 

7pp 

14pp 

*** 

** 

3pp 

8pp 

1pp 

-1pp 

2pp 

Round 2 

2pp 
Average 
9pp  ***  8pp  ***  6pp  ***  5pp  ***  5pp  *** 
Note:  The  treatment  effects  are  the  differences  in  the  shares  of  respondents  who  chose  the  optimal  offer 
between  the  remedy  and  marketing  practice  treatments.  Positive  values  indicate  that  more  respondents 
chose  the  optimal  offer  under  the  remedy  treatment  than  under  the  marketing  practice  treatment.  */  **/ 
*** implies that the treatment effect is statistically significant at 95%/ 99%/ 99.9%.   
Source: London Economics analysis of the experiment data 

-1pp 

-5pp 

8pp 

Regression analysis controlling for personal characteristics 

The  treatment  effects  are  examined  in  a  multivariate  setting  by  including  variables 
representing  respondents’  personal  characteristics  as  control  variables  in  regressions. 
Since  the  dependent  variables  in  the  regressions  are  binary,  representing  whether  or 
not  the  respondent  selected  the  optimal  offer  in  each  round  of  each  experiment, 
logistic regression models are estimated. 

The  key  explanatory  variable  in  each  regression  is  a  binary  variable  representing 
whether the respondent was in the marketing practice or remedy treatment. 

In  addition,  the  following  demographic  and  personal  characteristics  and  variables 
reflecting respondents’ experience in the key markets of the study are also included as 
control  variables  (relevant  question  numbers  from  the  survey,  which  is  presented  in 
Annex 13, are given in parentheses): 

  Demographic and personal characteristics: 

  Financial situation (Q10a) 

  Age (Q1) 

  Gender (Q2) 

  Mother tongue different from the official language (Q11) 

  Level of education (Q7a) 

  Personal situation (married, etc.) (Q8) 

  Children under 18 (Q10) 

 

Internet usage to search for information online (Q12.1) 

  Variables to control for respondents’ experience in the key markets examined 

in the experiments: 

  Whether respondents know the contract conditions of their provider/deal 

in the energy, finance and online sectors (A5, B4, C2) 

  Whether respondents have compared providers/deals in the energy, 

finance and online sectors (A6, B5, C3) 

  Whether respondents have read their most recent bill/communication in 

the energy, finance and online sectors (A10a/b, B9, C8) 

  Whether respondents find it easy or difficult to compare deals in the 

energy, finance and online sectors (A8, B7, C5) 

317 

 
 
 
 
 
 
 
 
  
 
  
 
 
 
 
 
  
 
 
 
 
 
 
  
 
 
 
 
 
  
8 │ Role of marketing practices 

The regression coefficients on the treatment variables are presented in Table 72 below 
(since  in  this  section  we  are  less  interested  in  the  effects  of  the  control  variables 
themselves and in order to keep the results concise, we do not report the coefficients 
on  the  control  variables  here;  the  effects  of  respondents’  characteristics  and 
experience in markets are examined in Chapter 6). 

These  regression  results  confirm  the  treatment  effects  identified  through  the 
differences  in  proportions  tests  reported  above;  the  marginal  effects  in  Table  72  are 
very similar to the treatment effects reported in Table 70 above. 

Table 72: 

Treatment effect regression coefficients and marginal effects 

Experiment sector and 
round 

Regression 
coefficient 

P-value 

Marginal effect 
(percentage 
points) 

Energy sector 

Online sector 

Finance sector 

Cross-cutting 

Round 1 
Round 2 
Round 1 
Round 2 
Round 1 
Round 2 
Round 1 
Round 2 

0.06 
0.75  *** 
1.11  *** 
0.14 
0.88  *** 
0.53  *** 
-0.01 
0.06 

0.55 
0.00 
0.00 
0.11 
0.00 
0.00 
0.92 
0.49 

1pp 
14pp 
19pp 
3pp 
9pp 
6pp 
0pp 
1pp 

Note: */ **/ *** implies that the treatment effect is statistically significant at 90%/ 95%/ 99%. The Pseudo-
R2 measures of explained variation for each regression (including control variables) are (from first to last row 
in the table): 0.0329; 0.0646; 0.0964; 0.0390; 0.101; 0.0956; 0.0261; 0.0488. 
Source: London Economics analysis of the experiment data 

8.2.2.  Impact of the marketing practice and remedy treatments on the decision 
process 

The second part of the experiment data analysis reported in this chapter examines the 
effects  of  the  treatments  on  the  underlying  thought  process  behind  respondents’ 
choices.  In  particular,  the  analysis  examines  whether  respondents  focussed  on 
different  elements  of  the  deals  shown  to  them  depending  on  which  treatment  they 
were  allocated  to.  This  in  turn  helps  to  shed  light  on  why  certain  marketing  practice 
and remedy treatments had greater impacts than other on respondents’ choices. 

This  part  of  the  analysis  is  based  on  the  follow-up  questions  which  were  asked  to 
respondents after each experiment. These questions asked respondents to think about 
the  choices  they  just  made  and  to  identify,  from  a  list  of  possible  options,  which 
feature was the most important when they made their decision. 

Energy sector experiment 

The  follow-up  question  to  the  energy  sector  experiment  asked  respondents  in  the 
marketing practice treatment which of six elements was the most important when they 
made their choices, namely the following:  

  The standing charge 

  The price per kWh for the first batch of electricity 

  The price per kWh for additional electricity 

  Any discounts that applied 

318 

 
 
 
  
  
 
  
 
  
 
8 │ Role of marketing practices 

  The total cost of the deal over 12 months 

  Other features 

Respondents in the remedy treatment were asked about the same six elements and, in 
addition, the Personal Projection and Tariff Comparison Rate (i.e. the new information 
tools  that  were  shown  under  the  remedy  treatment  to  help  consumers  choose  the 
optimal  deal,  as  explained  in  section  2.4).  The  follow-up  question  was  asked  once  to 
each  respondent who did the electricity sector  experiment referring to both rounds  of 
the  experiment.  The  shares  of  respondents  who  reported  that  each  feature  was  most 
important when they made their choices are shown in Table 73 below. 

A  larger  proportion  of  respondents  in  the  marketing  practice  treatment  than  those  in 
the  remedy  treatment  reported  that  the  standing  charge  was  the  most  important 
element  in  their  choices,  by  31%  to  22%.  This  may  be  because  under  the  marketing 
practice  treatment  the  standing  charge  was  the  piece  of  information  displayed  at  the 
top of each offer, making it more prominent than other features of the deals. 

Similarly, the prices per kWh for the first batch and additional electricity were the most 
important elements more often among those in the marketing practice treatment than 
among those in remedy treatment.  

Among  those  in  the  remedy  treatment,  in  total  only  15%  reported  that  the  new 
information tools were the most important feature in their choices (8% for the Personal 
Projection,  plus  7%  for  the  TCR).  This  suggests  that  if  these  information  tools  help 
consumers  make  better  choices,  then  increasing  awareness  and  use  of  these  tools 
would improve the  effectiveness  of the  remedy. Whether using these tools did indeed 
assist respondents to make better choices in the experiments can be seen from Figure 
33 and Figure 34 below. 

Table 73: 

Energy sector experiment follow-up question results 

Feature 

Marketing 
practice 

Remedy 

Treatment 
effect  

8% 

31% 

11% 

22% 

-3pp** 

-9pp*** 

The standing charge 
The  price  per  kWh  for  the  first  batch  of 
electricity 
The price per kWh for additional electricity 
Any discounts that applied 
The total cost of the deal over 12 months 
The Personal Projection 
The Tariff Comparison Rate (TCR) 
Other features 
Note: Based on 1,263 respondents in the marketing practice treatment and 1,259 respondents in the remedy 
treatment.  “:”  means  not  applicable.  The  survey  question  read:  “Thinking  about  the  choices  you  made 
between  the  electricity  deals  you  just  saw,  which  of  the  following  features  was  the  most  important  when 
making  your  decision?”.  The  treatment  effect  is  the  difference  between  the  share  who  stated  the  relevant 
feature  under  the  remedy  treatment  and  the  share  who  stated  the  relevant  feature  under  the  marketing 
practice treatment. */**/*** implies that the treatment effect is statistically significant at 95%/99%/99.9%.  
Source: London Economics analysis of the experiment data 

-4pp*** 
-1pp 
+2pp 
: 
: 
+1pp 

6% 
11% 
34% 
8% 
7% 
5% 

10% 
12% 
32% 
: 
: 
4% 

Figure 33 shows that, in fact, those who based their decision on the Personal Projection 
or Tariff Comparison Rate were not, in general, more likely to choose the best deal in 
the  first  round  of  the  energy  sector  experiment.  In  particular,  those  who  based  their 
choice on the standing charge were most likely to select the optimal deal in this round, 
irrespective of which treatment they were allocated to. 

319 

 
 
 
8 │ Role of marketing practices 

This  can  be  explained  by  the  fact  that  in  this  round  of  this  experiment  (in  which 
respondents choose between just two deals, as explained in section  2.4) the standing 
charge of the optimal deal was lower than the standing charge of the other deal.237  

Since respondents were more likely to base their choices on the standing charge in the 
marketing practice treatment (which, as  explained above, may be because under this 
treatment  the  standing  charge  was  the  piece  of  information  displayed  at  the  top  of 
each  offer),  this  may  explain  why,  overall,  there  was  no  treatment  effect  in  the  first 
round  of  the  energy  sector  experiment  (i.e.  the  benefit  of  the  remedy  was  cancelled 
out  by  those  in  the  marketing  practice  treatment  who  based  their  choices  on  the 
standing charge).238 

Figure 33:  Share of respondents who chose the correct offer among those 

who based their choices on each feature, first round of the electricity 
sector experiment 

Note: Based on 1,263 respondents in the marketing practice treatment and 1,259 respondents in the remedy 
treatment.  The  survey  question  read:  “Thinking  about  the  choices  you  made  between  the  electricity  deals 
you just saw, which of the following features was the most important when making your decision?”.  
Source: London Economics analysis of the experiment data 

Figure  34  shows  that  those  who  based  their  decision  on  the  Personal  Projection  or 
Tariff Comparison Rate were more likely to choose the best deal in the second round of 
the energy sector experiment, compared to those who based their decision on most of 
the other features (the only exception being those who based their choice on the total 
cost over twelve months).  

237 It should be noted that many deals are available in the market in reality, and in real world 

situations the standing charge of the optimal deal may be higher than the standing charge of 
other deals, meaning that in these situations consumers who focus on this feature would be 
unlikely to select the best deal. 

238 Thus, in some real world situations where the standing charge of the optimal deal may be 
higher than the standing charge of other deals the treatment effect could be greater than 
that seen in the experiments. 

320 

89%71%71%62%79%71%86%76%80%56%82%79%82%76%0%10%20%30%40%50%60%70%80%90%100%Standing chargePrice per kWh, first batchPrice per kWh, additional kWhDiscountsTotal cost over 12 monthsPersonal ProjectionTariff Comparison RateOther featuresMarketing Practice TreatmentRemedy Treatment 
 
 
                                                 
8 │ Role of marketing practices 

In  particular,  consumers  who  used  these  information  tools  were  considerably  more 
likely to select the correct deal than those  who based their  choice  on some individual 
feature of the deals (such as the standing charge, per unit prices, etc.). This suggests 
that  the  remedy  helped  to  prevent  consumers  from  suffering  from  particular 
behavioural  biases  in  their  decision  making,  such  as  using  ‘rules  of  thumb’  (e.g. 
assuming  that  the  advantageousness  of  a  deal  on  one  feature  reflects  how  good  it  is 
overall) or limited attention to information.  

The increased effect of the remedy in the second round might be explained by several 
factors: 

  Firstly,  in  the  second  round  respondents  chose  between  three  offers  (rather 
than two, as in the first round) and had to incur a short time delay (simulating 
search  costs)  before  they  could  see  the  new  offers  (see  section  2.4).  Hence, 
the  choice  between  the  alternative  offers  was  generally  more  challenging239, 
which  may  have  increased  the  effectiveness  of  the  remedy.  This  explanation 
would  suggest  that,  as  the  difficulty  of  the  purchasing  decision  increases,  the 
effectiveness of this remedy also increases. 

  Secondly,  unlike  in  the  first  round,  selecting  a  deal  based  on  the  standing 
charge240,  as  those  in  the  marketing  practice  treatment  tended  to  do,  did  not 
necessarily result in choosing the optimal deal in the second round. 

Despite  the  impact  of  the  remedy  shown  in  Figure  34,  as  noted  above  just  15%  of 
those  in  the  remedy  treatment  based  their  choices  on  the  new  information  tools, 
suggesting that increasing consumer awareness and understanding of such tools would 
improve their effectiveness. 

239 Consequently, the share of respondents who choose the correct offer was lower in the second 

round than in the first round, irrespective of which feature they based their choices on. 

240 I.e. the piece of information displayed at the top of each offer under  the marketing practice 

treatment. 

321 

 
 
 
                                                 
8 │ Role of marketing practices 

Figure 34:  Share of respondents who chose the correct offer among those 

who based their choices on each feature, second round of the 
electricity sector experiment 

Note: Based on 1,263 respondents in the marketing practice treatment and 1,259 respondents in the remedy 
treatment.  The  survey  question  read:  “Thinking  about  the  choices  you  made  between  the  electricity  deals 
you just saw, which of the following features was the most important when making your decision?”.  
Source: London Economics analysis of the experiment data 

Online sector experiment 

The follow-up question to the online sector experiment asked respondents which of the 
following six features was the most important when they made their choices: 

  The price per month 

  The discount 

  The set-up fee 

  The free calls 

  The total cost for 12 months 

  Other features 

This question was asked once to each respondent who did the experiment, referring to 
their  choices  in  both  rounds  of  the  experiment.  The  shares  of  respondents  who 
reported  that  each  feature  was  most  important  when  they  made  their  choices  are 
shown in Table 74 below. 

The key difference between the treatments shown in Table 74 is that under the remedy 
treatment  respondents  were  more  likely  to  base  their  choices  on  the  total  cost  of  the 
offers over 12 months, which is as expected since (as explained in section  2.4) under 

322 

8%16%25%32%33%17%17%30%28%30%53%52%44%16%0%10%20%30%40%50%60%Standing chargePrice per kWh, first batchPrice per kWh, additional kWhDiscountsTotal cost over 12 monthsPersonal ProjectionTariff Comparison RateOther featuresMarketing Practice TreatmentRemedy Treatment 
 
 
8 │ Role of marketing practices 

the  remedy  this  information  was  shown  up  front  at  the  start  of  the  purchasing 
process.241 

Table 74:  Online sector experiment follow-up question results 

Feature 

Marketing 
practice 

Remedy 

Treatment 
effect  

The price per month 
The discount 
The set-up fee 
The free calls 
The total cost for 12 months 
Other features 
Note: Based on 1,254 respondents in the marketing practice treatment and 1,256 respondents in the remedy 
treatment.  The  survey  question  read:  “Thinking  about  the  choices  you  made  between  the  broadband, 
telephone  and  TV  packages  you  just  saw,  which  of  the  following  features  was  the  most  important  when 
making  your  decision?”.  The  treatment  effect  is  the  difference  between  the  share  who  stated  the  relevant 
feature  under  the  remedy  treatment  and  the  share  who  stated  the  relevant  feature  under  the  marketing 
practice treatment. */**/*** implies that the treatment effect is statistically significant at 95%/99%/99.9%.  
Source: London Economics analysis of the experiment data 

-7pp*** 
-2pp* 
-0pp 
+0pp 
+9pp*** 
+0pp 

33% 
4% 
6% 
2% 
50% 
4% 

26% 
3% 
5% 
2% 
59% 
4% 

Figure  35  shows  that  those  who  made  their  choices  based  on  the  total  cost  over  12 
months  were  more  likely  to  choose  the  correct  offer  in  the  first  round  of  the  online 
sector experiment, relative to those who based their decisions on other features. 

This  was  particularly  the  case  among  respondents    in  the  remedy  treatment,  which, 
combined  with  the  fact  that  a  higher  share  of  those  in  the  remedy  treatment  based 
their choices on this feature in the first place (see  Table 74 above),  drives the strong 
overall treatment effect for this round of this experiment (see Table 70 above).242 

241 As explained in section 2.4, the marketing practice examined in the online sector experiment 
was ‘dripping’ of information, so the purchasing process comprised of several stages in this 
experiment. 

242  At  19pp,  the  treatment  effect  was  stronger  for  this  round  of  this  experiment  than  for  any 

other experiment round. 

323 

 
 
 
                                                 
8 │ Role of marketing practices 

Figure 35:  Share of respondents who chose the correct offer among those 

who based their choices on each feature, first round of the online 
sector experiment 

Note: Based on 1,254 respondents in the marketing practice treatment and 1,256 respondents in the remedy 
treatment.  The  survey  question  read:  “Thinking  about  the  choices  you  made  between  the  broadband, 
telephone  and  TV  packages  you  just  saw,  which  of  the  following  features  was  the  most  important  when 
making your decision?”.  
Source: London Economics analysis of the experiment data 

Figure 36 shows that those who based their decision on total cost over 12 months were 
also  more  likely  to  select  the  optimal  deal  in  the  second  round  of  the  online  sector 
experiment, compared to those who made their choices based on other features. 

However,  those  who  made  their  choices  based  on  the  price  per  month  were  also 
relatively  likely  to  choose  the  best  offer  (61%,  irrespective  of  treatment),  and  the 
share who based their decision on this feature was higher under the marketing practice 
treatment  than  under  the  remedy  treatment  (33%  to  26%,  see  Table  74).  This 
partially off-sets the benefit of the remedy in the second round, meaning that overall, 
the treatment effect is not statistically significant (as shown in Table 70 above). 

324 

42%43%76%57%82%43%70%50%88%54%92%78%0%10%20%30%40%50%60%70%80%90%100%Price per monthDiscountSet-up feeFree callsTotal cost for 12 monthsOther featuresMarketing Practice TreatmentRemedy Treatment 
 
 
8 │ Role of marketing practices 

Figure 36:  Share of respondents who chose the correct offer among those 
who based their choices on each feature, second round of the online 
sector experiment 

Note: Based on 1,254 respondents in the marketing practice treatment and 1,256 respondents in the remedy 
treatment.  The  survey  question  read:  “Thinking  about  the  choices  you  made  between  the  broadband, 
telephone  and  TV  packages  you  just  saw,  which  of  the  following  features  was  the  most  important  when 
making your decision?”.  
Source: London Economics analysis of the experiment data 

Finance sector experiment 

The follow-up question to the online sector experiment asked respondents which of five 
features was the most important when they made their choices, namely: 

  The interest rate 

  The APR 

  The monthly payments 

  The total amount repayable 

  Other features 

This follow-up question was asked once to every respondent who did the online sector 
experiment referring to the choices they made in both rounds of the  experiment. The 
shares of respondents who based their choices on each feature are shown in  Table 75 
below. 

The main result shown in Table 75 is that, compared to those in the marketing practice 
treatment, a higher share of respondents in the remedy treatment based their choices 
on  the  APR  (i.e.  the  feature  that  determined  which  loan  was  optimal  in  the 
experiment).  

This  illustrates  the  effectiveness  of  the  remedy  treatment,  which  tested  the  effect  of 
the  prominence  of  information  (as  explained  in  section  2.4),  since  under  the  remedy 
the APR was shown in large font at the top of the offer. 

However, under the remedy treatment 17% based their response on the APR whereas 
considerably  more  (56%)  based  their  choices  on  the  total  amount  repayable,  which 

325 

61%11%11%57%76%28%61%19%25%27%76%29%0%10%20%30%40%50%60%70%80%Price per monthDiscountSet-up feeFree callsTotal cost for 12 monthsOther featuresMarketing Practice TreatmentRemedy Treatment 
 
 
 
8 │ Role of marketing practices 

was  the  last item listed in  the  small text  at  the  bottom  of  each  offer  (see  Figure  3 in 
section 2.4). 

Table 75: 

Finance sector experiment follow-up question results 

Feature 

Marketing 
practice 

Remedy 

Treatment 
effect  

The interest rate 
The APR 
The monthly payments 
The total amount repayable 
Other features 
Note: Based on 1,252 respondents in the marketing practice treatment and 1,253 respondents in the remedy 
treatment.  The  survey  question  read:  “Thinking  of  the  choices  you  made  between  the  loans  you  just  saw, 
which of the following features was the most important when making your decision?”. The treatment effect is 
the difference between the share who stated the relevant feature under the remedy treatment and the share 
who  stated  the  relevant  feature  under  the  marketing  practice  treatment.  */**/***  implies  that  the 
treatment effect is statistically significant at 95%/99%/99.9%.  
Source: London Economics analysis of the experiment data 

-1pp 
+7pp*** 
+0pp 
-6pp*** 
-1pp 

10% 
17% 
13% 
56% 
3% 

11% 
10% 
13% 
62% 
4% 

Figure  37  shows  that  respondents  who  based  their  choices  in  the  first  round  of  the 
finance sector experiment on the interest rate were less likely to select the best loan, 
whereas  a  great  majority  of  those  who  based  their  choices  on  the  APR,  monthly 
payments and total amount repayable selected the correct loan (in the experiment the 
best  loan  was  always  the  one  with  the  lowest  APR,  and  also  the  one  with  the  lowest 
total amount repayable). 

Figure 37:  Share of respondents who chose the correct offer among those 

who based their choices on each feature, first round of the finance 
sector experiment 

Note: Based on 1,252 respondents in the marketing practice treatment and 1,253 respondents in the remedy 
treatment.  The  survey  question  read:  “Thinking  of  the  choices  you  made  between  the  loans  you  just  saw, 
which of the following features was the most important when making your decision?”.  
Source: London Economics analysis of the experiment data 

326 

43%78%78%93%43%70%89%86%97%79%0%20%40%60%80%100%120%The interest rateThe APRThe monthly paymentsThe total amount repayableOther featuresMarketing Practice TreatmentRemedy Treatment 
 
 
 
8 │ Role of marketing practices 

Figure  38  shows  a  similar  pattern  for  the  second  round  of  the  finance  sector 
experiment,  with  respondents  being  less  likely  to  choose  the  best  loan  if  they  based 
their  decision  on  the  interest  rate  and  much  more  likely  to  select  the  correct  loan  if 
they made their choices based on the APR or total amount repayable. 

It  is  important  to  note,  however,  that  while  the  APR  is  the  most  reliable  indicator  to 
select  a  loan  in  any  situation243,  the  total  amount  repayable  provides  a  similar  result 
only in particular cases where the time dimension of the credit plays a very limited role 
(such  as  the  simplified  example  taken  as  basis  for  the  experiment,  i.e.  a  loan  for  1 
year,  repaid  in  equal  monthly  instalments).  In  many  real-world  situations  the 
consumer  is  confronted  with  e.g.  choice  between  credits  of  different  duration  or  with 
different  schemes  of  repayment.  In  cases  such  as  these,  basing  one's  choice  on  the 
total  amount  payable  leads  to  sub-optimal  choices244  and  the  APR  remains  the  only 
reliable indicator to make the optimal choice.  

Figure 38:  Share of respondents who chose the correct offer among those 

who based their choices on each feature, second round of the finance 
sector experiment 

Note: Based on 1,252 respondents in the marketing practice treatment and 1,253 respondents in the remedy 
treatment.  The  survey  question  read:  “Thinking  of  the  choices  you  made  between  the  loans  you  just  saw, 
which of the following features was the most important when making your decision?”.  
Source: London Economics analysis of the experiment data 

 the 

Rate 

Annual 

Percentage 

243 The explanation about the calculation of the Annual Percentage Rate of charge (APR) and on 
its  relevance  for  measuring  the  cost  of  the  credit  may  be  found  in   Study  on  the  calculation 
credit 
of 
agreements  http://ec.europa.eu/consumers/documents/study_apr_2013_final.pdf 
244  For  example,  in  the  case  of two  credit  offers  of  the  same  value  of  1000  Euro,  the  same 
interest rate of 10% and  the same  initial fee  of 50 Euro, and repaid in 24 months, but for the 
first  credit  repaid  by  equal  monthly  instalments  of  capital  +  interests  due,  and  for  the  second 
credit by a balloon payment, the APR amounts to 16.5% for the first and 12.9% for the second, 
thus  clearly  indicating  that  the  second  offer  is  the  best.  However,  the  total  amount  payable  is 
1154.17 Euro for the first credit and 1260 Euro for the second credit, thus guiding the consumer 
to wrongly choosing the first option. 

o-consumer 

charge 

for 

of 

327 

34%79%77%94%39%60%86%79%97%71%0%20%40%60%80%100%120%The interest rateThe APRThe monthly paymentsThe total amount repayableOther featuresMarketing Practice TreatmentRemedy Treatment 
 
 
 
                                                 
 
8 │ Role of marketing practices 

Cross-cutting experiment 

The follow-up question to the cross-cutting experiment asked respondents which of the 
following seven elements was the most important when they made their choices: 

  The monthly cost/interest of the deals 

  The monthly cost/interest over the first 4 months 

  The monthly cost/interest after the fourth month 

  The total cost/total interest over 6 months 

  The total cost/total interest over 12 months 

  The total cost/total interest over 24 months 

  Other features 

This question was asked once to each respondent who did the experiment referring to 
their  choices  in  both  rounds.  The  shares  of  respondents  who  based  their  choices  on 
each feature are shown in Table 76 below. 

These results show that in this experiment the remedy treatment was not successful in 
steering  consumers  towards  using  the  correct  piece  of  information  (i.e.  the  total  cost 
over 12 months); in fact, those in the remedy treatment were less likely to make their 
choices based on this piece of information.  

This  is  likely  to  be  why  the  remedy  treatment  had  no  clear  effect  in  this  experiment 
(see Table 70 above). An explanation may be that the remedy did not help consumers 
in  this  instance  because  it  introduced  too  much  additional  information;  i.e.  the  total 
cost or interest paid over 6, 12 and 24 months (as explained in section 2.4). 

Table 76: 

Cross-cutting experiment follow-up question results 

Feature 

Marketing 
practice 

Remedy 

Treatment 
effect  

Monthly cost/interest of the deals 
Monthly cost/interest over the first 4 
months 
Monthly cost/interest after the fourth 
month 
Total cost/ total interest over 6 months 
Total cost/total interest over 12 months 

Total cost/total interest over 24 months 

25% 

22% 

4% 

3% 
1% 

3% 

2% 
3% 

55% 

37% 

9% 
3% 

29% 
3% 

Other features 
Note:  Based  on  880  respondents  in  the  marketing  practice  treatment  and  881  respondents  in  the  remedy 
treatment.  No  data  are  available  for  CAPI  respondents  since  this  question  was  not  asked  to  these 
respondents.  The  survey  question  read:  “Thinking  about  the  choices  you  made  between  the 
[deal/provider/account] you just saw, which of the following features was the most important when making 
your  decision?”.  The  treatment  effect  is  the  difference  between  the  share  who  stated  the  relevant  feature 
under  the  remedy  treatment  and  the  share  who  stated  the  relevant  feature  under  the  marketing  practice 
treatment. */**/*** implies that the treatment effect is statistically significant at 95%/99%/99.9%.  
Source: London Economics analysis of the experiment data 

-3pp 

-0pp 

-1pp 
+2pp 
-
18pp*** 
+20pp**
* 
+0pp 

328 

 
 
8 │ Role of marketing practices 

8.2.3.  Variation in the impact of marketing practices depending on consumers’ 
personal characteristics 

The  behavioural  experiments  data  allows  us  to  examine  whether  the  impact  of 
marketing  practices  is  dependent  on  consumers’  socio-demographic,  behavioural  and 
market-related  characteristics.  That  is,  whether  the  treatment  effect  –  i.e.  the 
difference  between  respondents’  answers  under 
treatment  and 
respondents’  answers  under  the  marketing  practice  treatment  –  varies  depending  on 
consumers’ characteristics, such as their age, gender, region, mother tongue, level of 
education,  risk  attitudes,  trust,  impulsiveness,  credulity,  whether  they  know  their 
contract conditions, whether they compare deals, etc. 

the  remedy 

In  order  to  examine  this  issue,  we  compare  treatment  effects  across  groups  and  test 
whether  the  differences  in  the  treatment  effects  are  statistically  significant  (i.e.  that 
they  are  statistically  different  from  zero,  via  a  t-test).  This  analysis  is  presented  in 
Table 77 below.  

The  consumer  groups  examined  in  this  analysis  include  groups  defined  based  on 
demographic  and  behavioural  characteristics.  These  groups  are  defined  in  the  same 
way  as  elsewhere  in  this  report  (see  Table  153  in  Annex  4  for  the  full  set  of  variable 
definitions). 

The  treatment  effects  are  presented  at  the  consumer  sub-group  level  in  the  un-
shaded  rows  in  Table  77.  For  individual  experiment  rounds,  the  treatment  effects  are 
difference-in-proportions  equal  to  the  share  of  respondents  who  selected  the  correct 
deal  under  the  remedy  treatment  minus  the  share  of  respondents  who  selected  the 
correct  deal  under  the  marketing  practice  treatment.  For  ‘overall’  experiment 
performance,  the  treatment  effects  are  equal  to  the  number  of  correct  choices  made 
under  a  remedy  treatment  minus  the  number  of  correct  choices  made  under  a 
marketing  practice  treatment,  on  average  across  respondents.245  Thus,  for  both 
individual  experiment  rounds  and 
‘overall’  experiment  performance,  positive 
treatments effects indicate that respondents performed better under the remedy than 
under the marketing practice. 

The differences in treatment effects are shown in the grey shaded rows in Table 77 
and end in the abbreviation ‘d-i-d’, which stands for difference-in-differences. For each 
characteristic (age, gender, region, etc), the treatment effect of the first sub-group in 
the  table  is  the  base  level  to  which  the  treatment  effects  of  other  sub-groups  are 
compared. So, for example, in the case of age, the treatment effects of the 16-24, 25-
34, 45-54, 55-64, 65-74 and 75+ age groups are compared to the base age group of 
35-44  year  olds.  The  differences  in  treatment  effects  in  bold  text  in  Table  77  are 
statistically  significant.  These  figures  show  that  a  particular  group  is  more  or  less 
affected  by  the  remedy  treatment  (or  conversely  the  marketing  practice  treatment) 
and that this difference is statistically significant. 

The results in Table 77 show firstly that, focussing on overall experiment performance, 
respondents  in  all  groups  performed  better  under  the  remedy  treatments  than  under 
the  marketing  practice  treatments.  This  can  be  seen  from  the  fact  that  the  values  in 

245  Recall  that  each  respondent  made  two  choices  under  a  remedy  treatment  and  two  choices 

under a marketing practice treatment. 

329 

 
 
 
                                                 
8 │ Role of marketing practices 

the un-shaded rows in the right-hand column of the table are all positive and in most 
cases  are  statistically  significant  (indicated  using  asterisks).  This  suggests  that 
marketing  practices  are  an  issue  for  all  consumer  groups,  and  it  should  not  be 
considered  that  the  effects  of  marketing  practices  are  confined  to  particular  types  of 
consumers. 

However,  it  is  also  evident  from  Table  77  that  some  consumer  groups  are  more 
susceptible  to  marketing  practices  than  others.  This  can  be  seen  from  the  differences 
in treatment effects in bold text in the grey shaded rows in the table. Specifically: 

  Overall across all experiments,  older respondents  in the 65-74  age group 
benefited more from the remedy treatments than those in the base age group 
of  35-44  year  olds.  In  particular,  this  older  group  benefited  more  from  the 
remedies  than  the  base  group  in  the  second  round  of  the  online  sector 
experiment and both rounds of the finance sector experiment. 

  Respondents in all  age groups over the age of 45 benefited more  from the 
remedy treatment relative to those aged 35-44 in the first round of the online 
sector experiment. 

  Respondents  in  low  density  regions  benefited  more  from  the  remedy 
treatment  than  those  in  high  density  regions  in  the  first  round  of  the  online 
sector  experiment  and  the  first  round  of  the  finance  sector  experiment, 
although in terms of the impact of the remedies on overall performance in the 
experiments  there  was  no  (statistically  significant)  difference  between  these 
two groups. 

  Overall across all experiments, respondents  with a  lower  level of education 
benefited more from the remedy treatments than those with a medium or high 
level  of  education.  In  particular,  those  with  lower  educational  attainment 
benefited  more  from  the  remedies  than  those  with  higher  educational 
attainment  in  the  first  round  of  the  online  sector  experiment,  both  rounds  of 
the  finance  sector  experiment,  and  the  second  round  of  the  cross-cutting 
experiment. 

  Finally, those who are more willing to take risks (i.e. those who are ‘not very’, 
‘fairly’  or  ‘very’  willing  to  take  risks)  benefited  less  from  the  remedies  than 
those who are least willing to take risks (i.e. those who are ‘not at all’ willing to 
take  risks)  in  several  experiment  rounds.  For  example,  those  who  are  ‘fairly’ 
willing to take  risks benefited less from the remedies than those who are ‘not 
at all’ willing to take risks in the first round of the online sector experiment and 
both rounds of the finance sector experiment. 

 

In  general,  respondents  who  are  less  experienced  and  engaged  with  the  key 
markets  of  the  study  benefited  more  from  the  remedies.  Overall  across  all 
experiments: 

  Those  who  do  not  know  their  contract  conditions  ‘at  all’  benefited  more 
from  the  remedies  than  those  who  know  their  contract  conditions 
completely; 

  Those who ‘never’ compare deals benefited more from the remedies than 

those who compare deals ‘from time to time’; and  

  Those  who  did  not  read  the  last  communication/bill  from  their  provider 
‘at  all’  benefited  more  from  the  remedies  than  those  who  read  it  ‘in 
detail’. 

330 

 
 
 
Table 77: 

Treatment effects (difference-in-proportions) and difference-in-difference effects by behavioural and socio-

demographic subgroups 

8 │ Role of marketing practices 

Energy 

Online sector 

Finance sector 

Cross-cutting 

Round 1  Round 2  Round 1  Round 2  Round 1  Round 2  Round 1  Round 2 

Overall 

+2pp 
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-6pp 
-1pp 
-4pp 
+2pp 
+17pp 
-1pp 
-0pp 
+0pp 
+2pp 
-3pp 
-3pp 
-5pp 
-5pp 
-2pp 
-0pp 
+1pp 
-1pp 
+1pp 
+2pp 
-0pp 
-11pp 
-11pp 

-0pp 
+3pp 
-4pp 
-3pp 
+5pp 
+14pp*** 
-2pp 
+3pp 
-4pp 
-3pp 
+5pp 
+14pp 
-1pp 
-1pp 
+3pp 
+4pp 
-3pp 
+4pp 
+5pp 
+7pp 
+8pp 
-3pp 
+1pp 
+5pp 
-2pp 
+11pp*** 
+13pp** 
+1pp 
-10pp 
-11pp 

+19pp*** 
+7pp*** 
+8pp*** 
+1pp 
-12pp*** 

+20pp*** 
+4pp** 
+2pp 
+7pp 
-16pp*** 

-5pp 
+1pp 
+1pp 
-19pp** 
+6pp 

+2pp 
-0pp 
+0pp 
+9pp 
-2pp 

+0.11*** 
+0.12** 
+0.03 
+0.14*** 
+0.16*** 
+0.32*** 
+0.2** 
+0.01 
-0.08 
+0.03 
+0.05 
+0.21*** 
+0.09 
+0.14*** 
+0.13*** 
-0.01 
+0.11*** 
+0.15*** 
+0.16*** 
+0.04 
+0.05 
+0.19*** 
+0.13*** 
-0.06 
+0.11*** 
+0.21*** 
+0.10** 
+0.13*** 
+0.14 
+0.01 

+0.21*** 
+0.09*** 
+0.14*** 
+0.1 
-0.12** 

Personal & Demographic 
Age 35-44 (base) 
Age 16-24 
Age 25-34 
Age 45-54 
Age 55-64 
Age 65-74 
Age 75+ 
Age 16-24 d-i-d 
Age 25-34 d-i-d 
Age 45-54 d-i-d 
Age 55-64 d-i-d 
Age 65-74 d-i-d 
Age 75+ d-i-d 
Female (base) 
Male 
Male-Female d-i-d 
High density region (base) 
Medium density region 
Low density region 
Medium density region d-i-d 
Low density region d-i-d 
Native mother tongue (base) 
Foreign mother tongue 
Foreign mother tongue d-i-d 
High/Medium Education (base) 
Low Education 
Education d-i-d 
Not long-term sick or disabled (base) 
Long-term sick or disabled 
Long-term sick or disabled d-i-d 
Behavioural 
Risk taking: Not at all (base) 
Risk taking: Not very 
Risk taking: Fairly 
Risk taking: Very 
Risk taking: Not very d-i-d 

331 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8 │ Role of marketing practices  

Table 77: 

Treatment effects (difference-in-proportions) and difference-in-difference effects by behavioural and socio-

demographic subgroups 

Energy 

Online sector 

Finance sector 

Cross-cutting 

Risk taking: Fairly d-i-d 
Risk taking: Very d-i-d 
Trust 2: Very low (base) 
Trust 2: Low 
Trust 2: High 
Trust 2: Very high 
Trust 2: Low d-i-d 
Trust 2: High d-i-d 
Trust 2: Very high d-i-d 
Impulsiveness: Very low (base) 
Impulsiveness: Low 
Impulsiveness: High 
Impulsiveness: Very high 
Impulsiveness: Low d-i-d 
Impulsiveness: High d-i-d 
Impulsiveness: Very high d-i-d 
Credulity (base) 
No credulity 
Credulity d-i-d 
Market-related and experience 
Know contract conditions: Completely (base) 
Know contract conditions: Fair amount 
Know contract conditions: Not very 
Know contract conditions: Not at all 
Know contract conditions: Fair amount d-i-d 
Know contract conditions: Not very d-i-d 
Know contract conditions: Not at all d-i-d 
Compare deals: From time to time (base) 
Compare deals: When notified/need to renew 
Compare deals: Sporadically 
Compare deals: Only the first time 
Compare deals: Never 
Compare deals: When notified/to renew d-i-d 
Compare deals: Sporadically d-i-d 
Compare deals: Only the first time d-i-d 
Compare deals: Never d-i-d 
Read communication: In detail (base) 
Read communication: Glanced or skim read 

Round 1  Round 2  Round 1  Round 2  Round 1  Round 2  Round 1  Round 2 
-4pp 
-21pp** 
+4pp 
+6pp** 
-4pp* 
-4pp 
+2pp 
-9pp 
-8pp 
+4pp 
+1pp 
-2pp 
-3pp 
-2pp 
-6pp 
-7pp 
+1pp 
-1pp 
-2pp 

-18pp*** 
-13pp 
+11pp*** 
+6pp*** 
+5pp** 
+6pp 
-4pp 
-6pp 
-5pp 
+10pp*** 
+3pp* 
+7pp*** 
+8pp 
-7pp 
-3pp 
-2pp 
+5pp*** 
+11pp*** 
-6pp 

-11pp* 
-1pp 
+20pp*** 
+18pp*** 
+19pp*** 
+23pp*** 
-2pp 
-1pp 
+3pp 
+20pp*** 
+19pp*** 
+21pp*** 
+7pp 
-1pp 
+1pp 
-13pp 
+19pp*** 
+21pp*** 
-2pp 

+5pp 
+5pp 
+14pp*** 
+15pp*** 
+17pp*** 
+12pp** 
+1pp 
+3pp 
-2pp 
+12pp*** 
+16pp*** 
+17pp*** 
+12pp** 
+4pp 
+5pp 
-0pp 
+14pp*** 
+14pp*** 
+0pp 

-11pp** 
-18pp* 
+11pp*** 
+9pp*** 
+8pp*** 
+9pp* 
-2pp 
-3pp 
-2pp 
+12pp*** 
+8pp*** 
+8pp*** 
+9pp 
-4pp 
-4pp 
-2pp 
+8pp*** 
+13pp*** 
-5pp 

+4pp 
+10pp 
-0pp 
+5pp* 
+0pp 
+6pp 
+6pp 
+1pp 
+7pp 
+3pp 
+2pp 
+2pp 
+8pp 
-1pp 
-1pp 
+4pp 
+2pp 
+5pp 
-3pp 

-1pp 
+7pp 
-4pp 
+2pp 
+2pp 
+4pp 
+6pp 
+6pp 
+7pp 
+4pp 
-1pp 
-1pp 
+10pp 
-5pp 
-5pp 
+6pp 
+3pp 
+4pp 
-1pp 

+6pp 
-13pp 
+1pp 
-0pp 
-2pp 
+3pp 
-2pp 
-3pp 
+2pp 
+0pp 
+1pp 
-3pp 
-2pp 
+1pp 
-3pp 
-2pp 
+1pp 
-1pp 
+1pp 

+3pp 
+2pp 
-3pp 
+7pp* 
-1pp 
-6pp 
+4pp 
+3pp 
-1pp 
-1pp 
-7pp* 
+3pp* 
-4pp 
-4pp 
-10pp 
+1pp 
+6pp** 
-3pp 

+9pp* 
+17pp*** 
+16pp*** 
+10pp** 
+8pp 
+7pp 
+1pp 
+17pp*** 
+14pp*** 
+22pp*** 
+13pp*** 
+13pp*** 
-3pp 
+4pp 
-5pp 
-4pp 
+16pp*** 
+16pp*** 

+12pp*** 
+12pp*** 
+18pp*** 
+15pp** 
+0pp 
+6pp 
+3pp 
+14pp*** 
+9pp** 
+5pp 
+24pp*** 
+18pp*** 
-5pp 
-9pp 
+10pp 
+5pp 
+16pp*** 
+12pp*** 

+2pp 
+2pp 
-1pp 
+9pp 
-0pp 
-3pp 
+7pp 
+4pp 
-2pp 
-7pp 
+6pp 
+8pp 
-5pp 
-11pp 
+3pp 
+4pp 
+3pp 
-0pp 

+9pp*** 
+6pp*** 
+12pp*** 
+23pp*** 
-4pp 
+3pp 
+14pp 
+4pp* 
+16pp*** 
+5pp* 
+4pp* 
+13pp*** 
+12pp** 
+1pp 
-0pp 
+9pp** 
+8pp*** 
+8pp*** 

+5pp* 
+4pp** 
+6pp** 
+21pp*** 
-1pp 
+1pp 
+16pp* 
-2pp 
+12pp** 
+6pp* 
+2pp 
+11pp*** 
+13pp** 
+8pp 
+4pp 
+12pp** 
+4pp** 
+8pp*** 

-4pp 
+1pp 
-3pp 
+2pp 
+4pp 
+1pp 
+6pp 
-1pp 
-6pp 
-5pp 
-0pp 
+2pp 
-4pp 
-3pp 
+1pp 
+3pp 
-2pp 
-0pp 

+2pp 
-0pp 
-2pp 
-1pp 
-2pp 
-4pp 
-4pp 
-2pp 
+1pp 
-4pp 
-2pp 
+1pp 
+3pp 
-1pp 
-0pp 
+3pp 
+1pp 
-3pp 

Overall 

-0.08 
-0.12 
+0.14*** 
+0.15*** 
+0.12*** 
+0.15** 
+0.01 
-0.02 
+0.01 
+0.18*** 
+0.13*** 
+0.1*** 
+0.12* 
-0.05 
-0.07 
-0.05 
+0.13*** 
+0.16*** 
-0.03 

+0.1*** 
+0.11*** 
+0.17*** 
+0.29*** 
+0.01 
+0.07 
+0.19** 
+0.1*** 
+0.09* 
+0.12** 
+0.09* 
+0.22*** 
-0.01 
+0.02 
-0.01 
+0.12*** 
+0.14*** 
+0.12*** 

332 

 
 
 
 
 
 
 
 
 
 
 
 
 
Table 77: 

Treatment effects (difference-in-proportions) and difference-in-difference effects by behavioural and socio-

demographic subgroups 

Energy 

Online sector 

Finance sector 

Cross-cutting 

8 │ Role of marketing practices 

Round 1  Round 2  Round 1  Round 2  Round 1  Round 2  Round 1  Round 2 
-2pp 
+2pp 
+3pp 
-9pp* 
-8pp 
-4pp 
-3pp 
+4pp 
-0pp 
-3pp 
- 
-4pp 
-7pp 
- 

Read comm’n: Saw total price/what it was 
Read communication: Not read it at all 
Read communication: Don't know 
Read communication: Glanced/skim read d-i-d 
Read comm’n: Saw total price/what it was d-i-d 
Read communication: Not read it at all d-i-d 
Read communication: Don't know d-i-d 
Read bill: Very easy (base) 
Read bill: Easy 
Read bill: Difficult 
Read bill: Very difficult 
Read bill: Easy d-i-d 
Read bill: Difficult d-i-d 
Read bill: Very difficult d-i-d 
Note: Treatment effects are presented in un-shaded rows. For individual experiment rounds, the treatment effects are difference-in-proportions equal to the share of respondents who selected the 
correct deal under the remedy treatment minus the share of respondents who selected the correct deal under the marketing practice treatment. For ‘overall’ experiment performance, the treatment 
effects  are  equal  to  the  number  of  correct  choices  made  under  a  remedy  treatment  minus  the  number  of  correct  choices  made  under  a  marketing  practice  treatment,  on  average  across 
respondents. Differences in treatment effects are shown in grey shaded rows. For each characteristic (age, gender, region, etc), the treatment effect of the first sub-group in the table is the base 
level  to  which  the  treatment  effects  of  other  sub-groups  are  compared.  The  differences  in  treatment  effects  in  bold  text  are  statistically  significant.  */  **/  ***  implies  that  the  coefficient  is 
statistically significant at 95%/  99%/ 99.9%. “-”  means there are  no results because the sample size is too  small (less than 10  individuals). The sample sizes  for each consu mer group for each 
experiment are provided in A7.2.3. 
Source: London Economics analysis of experiment data 

+6pp 
+20pp*** 
+9pp 
-4pp 
-10pp 
+5pp 
-7pp 
+10pp*** 
+14pp*** 
+16pp* 
- 
+4pp 
+5pp 
- 

+5pp 
+17pp*** 
+11pp*** 
-0pp 
-3pp 
+9pp 
+3pp 
+6pp** 
+8pp*** 
+14pp*** 
- 
+2pp 
+9pp 
- 

+12pp*** 
+15pp*** 
+14pp** 
-0pp 
-4pp 
-1pp 
-2pp 
+18pp*** 
+15pp*** 
+13pp*** 
- 
-3pp 
-5pp 
- 

+0.07 
+0.24*** 
+0.16* 
-0.02 
-0.07 
+0.10* 
+0.02 
+0.13*** 
+0.11*** 
+0.14** 
- 
-0.02 
+0.00 
- 

-1pp 
+8pp* 
+6pp 
+3pp 
-6pp 
+3pp 
+1pp 
+3pp 
+8pp*** 
+9pp* 
- 
+5pp 
+6pp 
- 

+2pp 
+8pp* 
-1pp 
-4pp 
-2pp 
+5pp 
-5pp 
+4pp 
+1pp 
-10pp 
- 
-3pp 
-13pp 
- 

-6pp 
+2pp 
+7pp 
+2pp 
-4pp 
+3pp 
+9pp 
+1pp 
-3pp 
+5pp 
- 
-4pp 
+4pp 
- 

+0pp 
+1pp 
+1pp 
-3pp 
-1pp 
+0pp 
+1pp 
-1pp 
-1pp 
+2pp 
- 
+1pp 
+3pp 
- 

Overall 

333 

 
 
 
 
 
 
 
9 │ Complexities of consumer vulnerability across different sectors 

9.  COMPLEXITIES OF CONSUMER VULNERABILITY ACROSS DIFFERENT 
SECTORS 

Box 7:  Summary of Chapter 9 

This  chapter  examines  the  incidence  of  vulnerability,  as  well  as  vulnerability  drivers 
and  practices  specific  to  the  three  sectors  examined  in  the  study:  the  financial 
sector, the energy sector, and the online environment and electronic communications 
sector. 

With regard to the incidence of vulnerability, the survey results have shown that: 

 

Incidence  of  vulnerability  tends  to  be  higher  in  the  energy  and  financial 
sectors than in the online environment and electronic communications sector 
for the majority of the vulnerability indicators. 

  The  main  exceptions  are  indicators  in  dimension  5  (Higher  susceptibility  to 
marketing  practices):  consumers  performed  worse  under  the  marketing 
practice  treatment  in  the  online  environment      experiment  than  in  the 
financial sector experiment.  

The  main  categories  of  vulnerability  drivers  (behavioural  drivers,  market-related 
drivers, access drivers, situational drivers) as well as socio-demographic drivers can 
all  be  identified  in  the  three  sectors,  along  with  marketing  practices  that  exploit 
them. Each of the sectors however has important characteristics: 

  Specific  drivers  relevant  to  the  financial  sector  are  often  linked  to 
complexity  of  financial products.  Such  complexities  present  a  challenge  to  a 
very wide range of consumers. This is reflected in survey results, which show 
that consumers are most likely to find it difficult to understand and compare 
offers  in  the  financial  sector.  In  addition,  the  effects  of  drivers  and 
corresponding  marketing  practices  in  the  financial  sector  are  particularly 
important, since sub-optimal decisions in the sector can result in long-lasting 
financial impacts.  

  The  energy  sector  also  displays  some  of  the  complexities  encountered  by 
consumers  in  the  financial  sector.  The  survey  results  show  that  consumers 
appear to face difficulties in the sector to a similar extent as in the financial 
sector.  The  effects  of  vulnerability  drivers  and  marketing  practices  can  also 
be long-lasting and can, in some cases, include direct health impacts (e.g. as 
a result of self-disconnection). 

  Consumers  appear  to  have  fewer  problems  comparing  offers  in  the  online 
and  electronic  communications  sector  than  in  the  other  two  sectors. 
Nevertheless,  a  number  of  vulnerability  drivers  were  still  identified.  In 
addition, any challenges encountered by consumers in the sector can have a 
wide-ranging impact given that many goods and services are provided online. 
The  online  and  electronic  communications  sector  also  faces  sector-specific 
challenges relating to consumers’ limited control over their personal data. 

The  present  chapter  elaborates  on  sector-specific  differences  based  on  the  literature 
review,  stakeholder  and  expert  consultation,  and  the  survey  and  experiment  data 
analysis.  

The  drivers,  demographic  characteristics  and  market  practices  linked  to  vulnerability 
are discussed in more detail for each of the three key sectors of the study in turn. Each 

334 

 
 
9 │ Complexities of consumer vulnerability across different sectors 

sector  is  covered  in  a  separate  section  (i.e.  sections  9.1,  9.2  and  9.3).  Each  section 
includes  a  sub-section  on  the  incidence  of  vulnerability  based  on  the  survey  and 
experiment data, and a sub-section on the main drivers of vulnerability at sector level, 
drawing on the literature review, the stakeholder and expert consultation, and the data 
analysis. 

This  chapter  helps  to  address  three  of  the  research  questions  set  out  in  section  1.4, 
namely  RQ5  (How  to  explore  the  complexities  of  consumer  vulnerability  across 
different  sectors?),  RQ6  (Are  socio-demographic  characteristics  found  to  indicate 
vulnerability  in  one  sector,  likely  to  indicate  vulnerability  also  in  other  sectors?)  and 
RQ7 (Is it possible to identify drivers of vulnerability per sector [and to] assess which 
drivers of vulnerability are likely to operate across sectors?). 

9.1. Consumer vulnerability in the financial sector  

The financial services  sector is a highly complex environment where  many consumers 
could  be  seen  as  vulnerable  including  those  that  would  be  labelled  “sophisticated 
consumers”  in  other  sectors  (see  Cartwright  2011  and  ECCG  2013).  In  this  vein, it is 
important to highlight that although consumers are a highly heterogeneous group, the 
vulnerability drivers specific to the financial sector are cross-cutting in that they apply 
to  a  very  wide  range  of  consumers.  However,  some  consumer  groups  have 
nevertheless been identified as being more at risk of becoming vulnerable than others.  

The  feature  setting  the  financial  services  sector  apart  from  the  other  key  sectors 
examined  in  this  study  relates  to  the  high  monetary  costs  and  significant  potential 
detriment  to  well-being  that  sub-optimal  financial  decisions  may  cause  to  individual 
consumers,  and  also  to  the  economy  as  a  whole.  The  financial  crisis  showed  the  risk 
and  potential  consequences  of  poor  choices,  exposing  unsustainable  credit  practices 
that  initially  attracted  consumers  through  easy  and  low-cost  lending  only  to  prove 
more costly in the longer-term (Which? 2013). This risk to both society and consumers 
is  believed  to  become  increasingly  pertinent  as  products  and  services  become 
increasingly complex. 

9.1.1.  Incidence of vulnerability in the financial services sector 

Evidence  regarding  the  incidence  of  vulnerability  in  the  financial  services  sector  is 
available  from  the  survey  and  experiment  data.  In  particular,  several  indicators  of 
vulnerability for Dimensions 3, 4 and 5 are specific to the finance sector, namely: 

Dimension 3 (Having difficulty in obtaining or assimilating information): 

  Compares deals in the finance sector using information from advertisements 

only 

  Has problems comparing deals in the finance sector due to information-related 

factors 

  Has not switched bank account due to information-related factors  

Dimension 4 (Inability or failure to buy, choose or access suitable 
products): 

  Does not compare deals in the finance sector 

  Finds it very difficult to compare deals in the finance sector 

  Has problems comparing deals in the finance sector due to personal factors 

  Has problems comparing deals in the finance sector due to market-related 

factors  

335 

 
 
 
9 │ Complexities of consumer vulnerability across different sectors 

  Has problems comparing deals in the finance sector due to access-related 

factors 

  Has not switched bank account due to personal factors 

  Has not switched bank account due to market-related factors 

  Has not switched bank account due to access-related factors   

  Declined for a loan 

Dimension 5 (Higher susceptibility to marketing practices): 

  Experiment choices under the marketing practice treatment in the finance 

sector experiment 

The incidence of vulnerability in the finance sector based on these indicators is shown 
in  Table  78,  both  for  the  EU28  and  by  country246.  Overall  across  the  EU28,  the 
indicator  on  which  consumers  exhibit  the  highest  rate  of  vulnerability  in  the  finance 
sector (57%) is ‘does not compare deals’. Other indicators of vulnerability in this sector 
with  a  high  incidence  rate  are  ‘has  problems  comparing  deals  due  to  market-related 
factors’  and  ‘has  problems  comparing  deals  due  to  personal  factors’.  The  indicator  on 
which  vulnerability  is  lowest  overall  is  ‘has  not  switched  bank  account  due  to 
information-related  factors’.  Other  finance  sector  vulnerability  indicators  with  a  low 
incidence  rate  are  ‘compares  deals  using  information  from  advertisements  only’,  ‘has 
problems  comparing  deals  due  to  access-related  factors’  and  ‘has  not  switched  bank 
account  due  to  access-related  factors’.  A  further  discussion  on  country  differences  in 
vulnerability according to each indicator is provided in section 4.1 of the report. 

246 Data is presented for those countries where data on the relevant indicators is available. The 
EU28  average  is  always  based  on  countries  where  data  for  the  respective  indicator  is 
available. 

336 

 
 
 
                                                 
Table 78: 

Incidence of vulnerability in the finance sector based on sector specific indicators 

9 │ Complexities of consumer vulnerability across different sectors 

e
u
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d
e
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t
i

w
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d
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-
n
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s
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1% 

1% 

0% 

2% 

2% 

e
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s
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57% 

46% 

62% 

61% 

62% 

65% 

43% 

55% 

56% 

55% 

67% 

50% 

61% 

54% 

54% 

60% 

63% 

66% 

62% 

57% 

49% 

63% 

66% 

45% 

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F

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13% 

8% 

16% 

18% 

23% 

21% 

7% 

15% 

7% 

9% 

15% 

7% 

15% 

16% 

13% 

22% 

10% 

11% 

8% 

19% 

8% 

20% 

18% 

10% 

e
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31% 

20% 

31% 

27% 

34% 

39% 

38% 

34% 

22% 

e
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g
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33% 

41% 

38% 

l

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s
l
a
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d

g
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d
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r
-
s
s
e
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c
a

o
t

2% 

1% 

2% 

s
r
o
t
c
a
f

33% 

46% 

36% 

36% 

46% 

31% 

2% 

2% 

2% 

3% 

1% 

1% 

28% 

35% 

3% 

45% 

29% 

30% 

38% 

39% 

44% 

27% 

35% 

29% 

36% 

6% 

0% 

3% 

3% 

1% 

e
u
d

d
e
h
c
t
i

w
s

t
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n

s
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s
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EU28 
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337 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9 │ Complexities of consumer vulnerability across different sectors 

Table 78: 

Incidence of vulnerability in the finance sector based on sector specific indicators 

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SI 
ES 
SE 
NL 
UK 
IS 
NO 
Obs. (N) 
Note: The numbers of respondents and sets of countries on which these figures are based vary by indicator and sector, since in some countries some survey questions were 
not asked to consumers who answered via particular methods (in particular CAPI or CATI). The countries and survey modes for which each indicator is available can be seen 
in  Table  5  in  section  3.6.  Results  weighted  using  cross-country  weights  for  the  EU28  measure  and country  weights  for  the  country  measures.  *Share  of  consumers  who 
answered incorrectly in round 1 OR round 2 (i.e. who answered incorrectly in one or both rounds). 
Source: London Economics analysis of survey data 

52% 
25,151 

2% 
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27% 
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31% 
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7% 
25,151 

13% 
14,074 

1% 
14,074 

4,368 

1,252 

4,971 

4,368 

4,368 

4,368 

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7% 

5% 

2% 

3% 

338 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9 │ Complexities of consumer vulnerability across different sectors 

9.1.2.  Vulnerability drivers in the financial services sector 

The  following  sub-sections  provide  an  overview  of  vulnerability  drivers  in  the  finance 
sector,  as  well  as  of  related  personal  and  demographic  characteristics  and  market 
practices. The sub-sections are structured according to the main categories of drivers: 

  Personal and demographic characteristics; 

  Behavioural drivers; 

  Market-related drivers; 

  Access drivers; and 

  Situational drivers. 

Personal and demographic characteristics 

A  number  of  links  between  consumers’  personal  and  demographic  characteristics  and 
the likelihood of vulnerability on various finance sector specific indicators are revealed 
by the analysis of the survey and experiment data reported in chapter 6. The results of 
the  data  analysis  for  these  indicators  are  discussed  in  sections  6.2.3  and  6.2.4. 
Specifically,  according  to  the  results  of  the  data  analysis  (when  other  characteristics 
included in the regressions are controlled for): 

  Older  consumers  are  less  likely  to  be  vulnerable  on  a  number  of  indicators  in 
the  finance  sector.  In  particular,  those  in  55+  age  groups  are  less  likely  than 
the  base  group  (35-44  year  olds)  to  have  not  switched  banks  due  to  access-
related  factors  and  are  less  likely  to  have  been  declined  a  loan.  Furthermore, 
older  consumers  (aged  64-74  or  75+)  are  less  likely  to  have  problems 
comparing  banks  due  to  information-related  factors.  At  the  other  end  of  the 
age  spectrum,  young  consumers  (aged  16-24  or  24-35)  are  also  less  likely 
than  the  base  group  to  have  problems  comparing  banks  for  information 
reasons and to have not switched banks for access reasons.  These young age 
groups  are  also  less  likely  to  have  not  switched  banks  due  to  market-related 
factors. However, the youngest age group were less likely to select the correct 
deal in the first round of the finance sector experiment. 

  Men  are  more  likely  than  women  to  have  problems  comparing  banks  for 
information reasons, but are less likely to have problems comparing banks due 
to  personal  and  market-related  factors,  are  less  likely  to  have  not  switched 
banks due to personal factors, and were more likely to select the best offer in 
the second round of the finance sector experiment. 

  Consumers in low density regions are slightly more likely to suffer from access 
issues in the finance sector. In particular, consumers in low density regions are 
slightly  more  likely  than  those  in  high  density  regions  to  have  problems 
comparing  banks  and  to  have  been  prevented  from  switching  banks  due  to 
access-related factors. 

  Those  with  low  or  medium  education  are  more  likely  than  those  with  high 
education  to  compare  banks  using  information  from  adverts  only,  and  those 
with  low  education  were  also  less  likely  to  select  the  best  deal  in  the  second 
round of the finance sector experiment. 

  Non-native  speaks  are  less  likely  to  be  vulnerable  on  some  indicators  relating 
to  the  finance  sector.  Specifically,  compared  to  native  speakers,  non-native 
speakers whose language does not cause them difficulty are less likely to have 
problems  comparing  banks  for  information  reasons  and  to  have  not  switched 
banks  for  access  reasons,  whereas  non-native  speakers  whose  language 

339 

 
 
 
9 │ Complexities of consumer vulnerability across different sectors 

causes them difficulty are less likely to have problems comparing banks due to 
market-related factors. 

The  literature  review  also  highlighted  instances  where  personal  and  demographic 
characteristics  drive  vulnerability.  While  inability  to  process  complex  information, 
discussed in more detail in the following section, is a vulnerability driver cutting across 
the  entire  population,  it  can  be  a  more  substantial  problem  for  specific  consumer 
groups: 

  Low-income consumers with limited access to alternative financial products are 
more  likely  to  sign  contracts  they  do  not  fully  understand  due  the  perceived 
lack of options (Wilson, Howell, and Sheehan 2009); 

  As noted by Clifton et al. (2013), certain consumer groups, such as the elderly, 
very young, lower educated and those not in employment may not have access 
to  “knowledge-sharing  networks”,  which  can  otherwise  help  consumers 
navigate complex marketplaces; 

  Agarwal  (2010)  notes  that  both  young  and  old  consumers  tend  to  make 
financial  mistakes,  with  the  age  when  consumers  are  least  likely  to  make 
mistakes  being  53,  which  could  be  explained  by  trade-offs  between 
“experiential  capital”  (older  consumers)  and  “analytical  capital”  (younger 
consumers); 

  Stearn  (2012)  notes  in  his  report  on  consumer  vulnerability  for  the  UK 
organisation  Consumer  Focus  (now  Consumer  Futures)  that  complexity  of 
financial  products  tends  to  increase  with  age  meaning  that  older  consumers 
face  a  more  complex  market,  while  at  the  same  time  being  more  likely  to 
suffer  from  ill  health,  loss  of  cognitive  capabilities,  and  potentially  having  a 
shrinking social network.  

In addition, interviewed stakeholder organisations and sector experts have argued that 
the financial crisis and recession is having a disproportionate impact on consumer sub-
groups who are in particular situations relating to their employment, income and debt 
circumstances.  Specifically,  unemployment  and  precarious  employment  conditions, 
unstable  income,  the  risk  of  repossession,  defaulting  and  over  indebtedness  have 
become increasingly coupled with the increasing need for fast access to credit, which in 
turn  is  believed  to  drive  vulnerability  in  the  financial  sector.  The  2015  report  on 
consumer vulnerability by the Financial Conduct Authority in the UK noted that “people 
may be tipped into a spiral of debt, feel the need to take out high-cost products (such 
as  payday  loans)  or  take  on  higher  risks  (e.g.  travel  without  insurance).  Some  may 
withdraw from the market altogether, preferring to maintain control by keeping cash at 
home” (Financial Conduct Authority 2015).  

Behavioural drivers 

The  financial  sector  can  be  viewed  as  a  highly  complex  environment  where  most 
consumers  and  consumer  groups  may  become  vulnerable.  One  of  the  most  cited 
examples of a behavioural driver in the stakeholder interviews relates to the difficulty 
of  consumers  to  process  complex  information  concerning  financial  products  and 
services  (see  for  instance  Cartwright  2011,  Braunsberger,  Lucas  and  Roach  2004, 
Brennan and Coppack 2008). This applies to all consumer groups, including consumers 
with  higher  educational  attainment  and  experience.  The  difficulty  of  processing 
information relates mainly to cognitive limitations (Lunn and Lyons 2010) as well as 
inexperience  with  financial  services  resulting  in  consumers  making  sub-optimal 
choices in the marketplace (Braunsberger 2004, Brennan, Zevallos and Binney 2011). 
This  is  in  turn  mainly  attributed  to  the  complexity  of  financial  products.  For  instance, 
the  UK  consumer  organisation  Which?  notes,  in  its  2013  review  of  the  British  credit 

340 

 
 
9 │ Complexities of consumer vulnerability across different sectors 

markets,  that  complex  charging  structures  for  bank  overdrafts  make  comparison 
between  different  offers  difficult.  Complexity  in  financial  products  can  also  relate  to 
bundling  of  financial  products.  One  example  provided  by  a  Greek  consumer 
organisation  is  that  of  bundling  life  insurance  with  bonds  or  other  types  of  financial 
products. Complexity is also a particularly salient issue for innovative products, such 
as financial products that constitute combinations of existing products (e.g. options or 
securities),  or  products  based  on  “mixtures  of  complex  derivatives”  (Lumpkin  2010). 
Although such products have a positive impact in that they increase access to finance, 
they  may  not  be  suitable  for  all  consumers,  which  in  turn  is  often  a  result  of  their 
complexity (Lumpkin 2010). 

The  fact  that  most  consumers  only  buy  new  financial  products  and  services  on  an 
infrequent  basis  and  have,  therefore,  even  less  experience  in  the  financial  services 
market  than  in  other  markets  (European  Parliament  2014)  further  reinforces  the 
vulnerability drivers. At the same time it is also important to note that consumers with 
more  financial  experience  will  not  necessarily  always  make  better  decisions.  As  noted 
previously,  Braunsberger,  Lucas  and  Roach  (2004)  in  their  study  of  US  college 
students’  approach  to  credit  cards  found  that  adult  (non-student)  consumers  are  not 
necessarily more knowledgeable about credit cards. Instead, given that they are more 
experienced, they see themselves as more knowledgeable but in fact make similar sub-
optimal choices as the students. 

There  is  a  consensus  in  the  literature  that  individuals  need  extensive  knowledge  to 
avoid  making  mistakes  when  purchasing  financial  services  products.  Financial 
literacy  is  therefore  an  important  determinant  for  economic  behaviour247,  as 
consumers with low levels of financial literacy are generally248:  

  More likely to unknowingly commit financial mistakes; 

  More likely to accumulate debt; 

  Less likely to be able to cope with sudden economic shocks; and 

  Less likely to accumulate wealth and manage wealth effectively. 

In addition, studies have shown that consumers’ preferences and decision-making are 
also  to  a  large  extent  influenced  and  motivated  by  emotional  and  psychological 
experiences  rather  than  based  on  objective  and  researched  information  (European 
Parliament 2014). 

In  addition  to  consumers  making  sub-optimal  choices,  one  consequence  of  cognitive 
limitations  relates  to  service  providers  effectively  lowering  their  service  levels. 
Interviews  have  indicated  that  because  of  the  complexity  of  financial  products  some 
providers are increasingly simplifying products by also lowering the level and quality of 
services.  This  includes,  for  example,  insurance  products  being  “watered  down”  to 
include  only  the  minimum  of  protection  leaving  consumers  vulnerable  to  sudden 
economic shocks.  

247 While measures of financial literacy vary, financial literacy is often assessed using survey 

questions aimed at identifying the understanding of concepts such as interest rates, inflation, 
or risk diversification among the respondents. See for instance Lusardi et al. (2010) 

248 For a summary on review of existing literature see Lusardi et al. (2010) 

341 

 
 
 
                                                 
9 │ Complexities of consumer vulnerability across different sectors 

A number of other behavioural biases are also exploited in the financial sector. One of 
them is the status quo bias. Research by the UK Financial Conduct Authority (2008) 
has shown that, even though financial service providers often raise premiums or lower 
interest  rates  after  consumers  sign  contracts,  consumers  do  not  necessarily  cancel 
contracts  or  react  to  the  changes  in  costs.  Status  quo  bias  can  be  reinforced  by  the 
complex  procedures  and  high  costs  of  switching.  For  example,  evidence  from  France 
collected  in  the  interviews  suggests  that  switching  banks  is  very  difficult  due  to  the 
burdensome  administrative  procedures  placed  on  the  account  holder,  thus  reinforcing 
the status quo bias.  

Time  discounting  constitutes  another  important  driver  that  is  highly  relevant  to  the 
financial service industry. This relates in particular to “payday loans” or other forms of 
high-cost credit, which are common practice across Europe and, as discussed in more 
detail  in  the  following  sections,  were  identified  by  stakeholders  in  several  Member 
States as highly detrimental to vulnerable consumers. A 2013 study conducted for the 
UK  Department  of  Business,  Innovation  and  Skills  found  that  most  common  use  of 
payday loans for participants was for urgent and unexpected household expenses such 
as  the  repair  and  replacement  of  cars  and  white  goods  (Department  for  Business 
Innovation  &  Skills  2013).  This  practice  is  strongly  related  to  time  discounting  where 
the consumer puts more value on the present than future and is also coupled with the 
behavioural  biases  of  overconfidence  and  projection,  as  consumers  may  be  overly 
confident  that  they  will  be  able  to  pay  back  the  loans.  Existing  studies  show  that 
consumers  will  display  over-optimism  and  overconfidence  when  entering  credit 
agreements  (Wilson  2013).  While  overconfidence  is  not  a  bias  exclusively  related  to 
financial  products  (Decision  Technology  Ltd  2010),  it  is  likely  to  have  more  severe 
economic consequences than in other markets, exacerbated by the financial crisis and 
the financial difficulties that many low-income consumers can face. 

It  is  important  to  point  out  that  behavioural  vulnerability  drivers  are  also  correlated 
with  the  type  of  product  being  sought  out  by  the  consumer.  For  example,  for  more 
mature  consumers,  the  types  of  product  that  may  increase  vulnerability  relate  to 
mortgage,  insurance  or  pension  products  where  vulnerability  and  risks  are  not 
immediately identifiable and take a long-time to materialise (see European Parliament 
2014, FSCP 2012).  

Market-related drivers 

Market-related drivers primarily relate to problems including insufficient, misleading 
or inappropriate information provided to consumers. Lumpkin (2010) identifies the 
following  information  problems  facing  consumers:  product  complexity,  long  duration, 
unknown  quality,  opaque  pricing,  complex  disclosures,  and  financial  illiteracy.  These 
limitations  are  however  further  compounded  when  complexity  of  financial  products  is 
combined with insufficient disclosure of information. Which? (2013) notes for instance 
that: 

  Lack  of  transparency  with  regard  to  charges  applied  to  loans  exploits 
consumers’ over-confidence that they are able to repay the loan on time; and 

  Consumers are not always clear about the cost of credit (relating to payment in 

instalments for items in mail-order catalogues). 

Other  examples  of  information  problems  include  financial  advisors  not  explaining  risk 
levels of different products (Synovate 2011).  This  was, for instance,  identified by one 
of  the  interviewed  consumer  organisations  in  Slovenia,  where  consumers  took  out 
loans in  Swiss  Francs  on  the  basis  of incomplete  and  misleading information  on likely 

342 

 
 
9 │ Complexities of consumer vulnerability across different sectors 

risks. As a consequence, recent exchange rate fluctuations have resulted in consumers 
effectively facing much higher payments than initially expected.  

Another  example  of  consumers  not  having  information  to  make  informed  choices 
relates to situations where the choices presented to consumers are suboptimal due to 
economic actors not collecting necessary information from consumers (see for instance 
Synovate  2011).  This  can  mean  that  consumers  are  sold  products  and  services  that 
can  put  them  in  positions  of  vulnerability  (e.g.  when  taking  out  loans  they  have  no 
ability  to  repay).  Therefore  intentionally  or  unintentionally  failing  to  obtain  necessary 
information could be seen as an information problem and, subsequently, a vulnerability 
driver. 

This,  however,  does  not  mean  that  disclosure  of  information  is  in  itself  a  sufficient 
solution  to  such  problems.  Brennan,  Zevallos  and  Binney  (2011)  note  that  consumer 
advocacy  groups  in  Australia  find  that  when  lenders  disclose  information,  this  is  also 
done  in  an  overly  complex  or  inaccessible  way.  Wilson,  Howell,  and  Sheehan  (2009) 
similarly note that disclosure of information on its own does not help consumers make 
better choices due to the complexity and volume of such information. They also point 
out  that  some  consumers  sign  contracts  without  fully  understanding  them  since  they 
realise  they  have  few  alternatives  (i.e.  access  to  alternative  sources  of  finance).  The 
UK Office of Fair Trading (1999) also noted that the main beneficiaries of UK regulation 
aiming to help consumers better understand financial products may be higher income 
consumers.  Although  some  of  the  above  example  practices  stem  from  outside  of  the 
EU, there is ample evidence of complexity of financial products within the EU (see for 
instance Stearn 2012).  

Overall,  information  problems  combined  with  cognitive  limitations  outlined  in  the 
previous sections mean that consumers may be overly reliant on financial advisors and 
other  staff  who,  as  noted  above,  may  not  always  facilitate  the  choice  of  the  most 
adequate product. Interviewed consumer organisations have indicated that consumers 
do  not  understand  the  exact  terms  and  conditions  associated  with  their  product 
purchases and rely heavily on the sales staff to explain the terms of their products thus 
increasing potential vulnerability. For example, one organisation in Slovakia stated that 
contracts  tend  to  be  provided  in  fine  print  without  anyone  reading  them.  Thus, 
consumers  are  later  surprised  about  what  has  actually  been  signed.  Similar 
experiences  have  also  been  relayed  in  an  interview  with  a  Greek  consumer 
organisation. 

In  the  survey  and  experiment  data  analysis  reported  in  chapter  6,  several  sector 
specific  measures  are  examined  as  potential  market-related  divers  of  vulnerability, 
namely:  

  Consumers’ knowledge of their contract  

  The frequency at which consumers compare offers 

  The extent that consumers read bills/communications from their provider 

  The  extent  that  consumers  find  reading  bills/communications  from  their 

provider easy or difficult 

These  measures  of  consumer  experience  and  engagement  with  markets  are  found  to 
be especially important drivers of consumer vulnerability in the finance sector (but also 
important in the other two key sectors of the study). 

343 

 
 
 
9 │ Complexities of consumer vulnerability across different sectors 

In  particular,  consumers  who  do  not  know  their  bank  contract  conditions  ‘at  all’  or 
‘very much’, as well as those who found it ‘difficult’ or ‘very difficult’ to read the most 
recent communication from their bank, are considerably more likely to have problems 
comparing banks due to personal, information-related and market-related factors, and 
are  more  likely  to  have  been  prevented  from  switching  banks  for  market-related 
reasons. Similar findings are also revealed for the energy and online sectors. 

Therefore,  it  is  interesting  to  compare  the  incidence  rates  of  these  market-related 
drivers across sectors. This comparison can be seen in Table 79 below. 

Table 79: 

Share of respondents who gave each response to questions  

on experience and engagement with markets 

Description 

Know contract: Completely 
Know contract: Fair amount 
Know contract: Not very 
Know contract: Not at all 
Know contract: No response (not relevant) 
Compare deals: Yes, from time to time 
Compare deals: Yes, but only when I need to renew 
Compare deals: Yes, but only sporadically 
Compare deals: No, I only compared the first time 
Compare deals: No, I have never compared 
Read bill/communication: In detail 
Read bill/communication: Glanced over or skim read 
Read bill/communication: Only saw what it was  
Read bill/communication: Did not read it at all 
Read bill/communication: Don't know/Don't remember 
Read bill/communication: No response (not relevant) 
Read bill/communication: Very easy 
Read bill/communication: Fairly easy 
Read bill/communication: Fairly difficult 
Read bill/communication: Very difficult 
Note: Base is 26,653 respondents in EU28, Norway and Iceland. For difficulty to read the bill/communication 
only  those  responded  who  read  their  bill  “in  detail”,  “glanced  over  or  skim  read”,  or  “only  saw  what  it 
was/looked at the total price” in the energy sector and read their communication “in detail”, “glanced over or 
skim  read”  in  the  finance  and  online  sectors  answered  the  questions.  Base  is  19,913  respondents  with 
internet contracts in the frequency to compare deals in the online sector, since the relevant questions were 
not asked to all survey respondents. 
Source: London Economics analysis of the survey data 

Finance 
sector 
23% 
46% 
18% 
4% 
9% 
19% 
10% 
14% 
20% 
36% 
31% 
33% 
10% 
10% 
8% 
9% 
32% 
55% 
11% 
2% 

Energy 
sector 
14% 
38% 
30% 
13% 
6% 
18% 
12% 
14% 
14% 
42% 
26% 
30% 
20% 
14% 
5% 
6% 
16% 
52% 
27% 
5% 

Online 
sector 
18% 
35% 
15% 
5% 
27% 
31% 
19% 
17% 
16% 
17% 
18% 
28% 
9% 
12% 
7% 
27% 
33% 
59% 
8% 
1% 

Table 79 shows that the finance sector is relatively similar to the online sector in terms 
of the shares of consumers who do not know their contract conditions and who found it 
difficult  to  read  the  most  recent  communication  from  their  provider.  Both  of  these 
sectors perform better than the energy sector in these respects, which may explain, to 
some  extent,  why  the  incidence  of  vulnerability  is  lower  in  these  sectors  than  in  the 
energy sector on some sector specific indicators. 

Conversely,  however,  relative  to  the  other  two  sectors,  the  share  of  consumers  who 
did not read the last communication from their internet provider ‘in detail’ is relatively 
high. 

Access drivers 

344 

 
 
 
9 │ Complexities of consumer vulnerability across different sectors 

Access  to  financial  services  has  also  been  identified  as  a  significant  challenge.  As 
highlighted  by  interviewed  stakeholder  organisations,  low-income  consumers  face  a 
number of limitations in accessing financial services and products. This can range from 
limited access to bank accounts to a more limited range of available financial products 
and services.  

According  to  the  European  Commission  Factsheet  on  access  to  payment  accounts, 
across  the  EU,  58  million  people  over  the  age  of  15  do  not  have  a  payment  account 
and  2.5  million  are  estimated  to  have  been  denied  access  to  one  (European 
Commission  2013b).  Lack  of  access  to  a  transactional  bank  account  results  in  other 
double disadvantages,  such as potential costs  associated with depositing pay cheques 
(Office of Fair Trading 1999) or the costs of making bank transactions (e.g. according 
to  an  interview  with  an  organisation  in  France  these  can  cost  up  to  EUR  6  per 
transaction).  This  lack  of  access  can  also  be  particularly  problematic  in  increasingly 
cashless  economies,  such  as  Sweden.  According  to  stakeholder  interviews,  in  the 
particular case of Sweden this problem is partially addressed by the consumers having 
a  right  to  open  a  bank  account  and  the  banks  being  responsible  for  providing  one. 
Nevertheless,  there  may  still  be  challenges  with  regard  to  access  to  credit  cards  or 
consumers’  ability  to  use  IT  tools,  which  are  increasingly  important  in  everyday 
financial transactions.    

In  addition  to  not  having  accounts,  consumers  can  also  have  restricted  access  to 
accounts  they  hold.  A  Finnish  consumer  organisation  identified  cases  of  consumers 
with poor credit history not being allowed to have online access to their bank accounts 
and not being given PIN numbers to access online banking services. 

Consumers with low incomes usually also have insufficient savings or resources in their 
current  accounts  that  can  lead  to  use  of  overdrafts  to  manage  monthly  outgoings. 
Interviewees  have  argued  that  these  fees  are  excessive  in  relation  to  the  amount  of 
the  overdraft  and  are  heavily  penalising  the  low-income  consumers.  In  addition,  the 
lack of access to financial services may spill over to  other markets and basic services 
relating  to  the  so-called  “poverty  premium”,  which  can  be  linked  to  the  concept  of 
“double  disadvantages”.  Evidence,  mainly  from  the  UK,  shows  that  consumers  on  low 
incomes  pay  more  for  basic  services  than  mainstream  consumers  due  to  their  lack  of 
access  to  credit  and  are  therefore  forced  to  seek  more  costly  payment  solutions. 
Evidence  suggests  that  when  people  are  excluded  from  main  banking  markets,  they 
move into the payday loans market, which creates a vicious cycle of over-indebtedness 
(see  for  instance  Which?  2013).  This  is  exacerbated  by  the  practice  of  “greenlining” 
which  refers  to  financial  service  providers  not  adequately  checking  the  consumers’ 
ability to pay back the loan.  

Even if services are available, many may not be designed with a potential low-income 
consumer  in  mind.  One  UK  interviewee  argues  that  financial  services  are,  in  general, 
designed  for  the  “mainstream”  consumer,  as  this  market  segment  tends  to  maximise 
profit  and  minimise  risk  for  industry  actors.  With  regard  to  the  lack  of  physical 
access, consumers that live in remote rural areas may also face difficulties due to the 
closure  of local branches. Another issue is lack of alternative options to manage bank 
account  access.  However,  not  only  consumers  living  in  remote  locations  can  be 
vulnerable  to  closure  of  local  bank  offices,  but  also  the  elderly  and  people  with 
disabilities  who  may  have  a  strong  preference  for  face-to-face  contact  or  cash 
transactions  and  are  vulnerable  due  to  the  developments  of  online  or  telephone 
banking (Financial Services Authority 2008).  

345 

 
 
 
9 │ Complexities of consumer vulnerability across different sectors 

This  is  closely  linked  to  the  accessibility  of  branches,  websites  for  online  banking, 
ATMs and other related banking tools. According to a test249 carried out in the UK, 11 
of  the  examined  high  street  banks  failed  to  meet  the  needs  of  customers  with 
disabilities  in  regards  to  online  banking.  This  is  becoming  increasingly  pertinent  as 
more  and  more  transactions  are  expected  to  be  carried  out  online.  The  Payments 
Council (2012) in the UK summarises accessibility barriers in the financial sector as: 

  Limited  physical  access  to  bank  branches  or  cash  machines  due  to  queuing, 

lack of wheelchair access, or height of bank terminals; 

  Difficulties  for  consumers  with  dexterity  problems  in  manipulating  bank 

terminal buttons; 

  Difficulties  in  reading  cash  machine  screens  for  consumers  with  visual 

difficulties; 

  Challenges  with  data  entry  on  internet  forms  for  consumers  with  dexterity, 

fatigue, concentration, memory or visual issues; and 

  Difficulties in memorising PINs for consumers with cognitive limitations.  

Situational drivers 

Situational  drivers  refer  to  situations  in  which  individuals  finds  themselves,  either 
permanently  or  temporarily,  vulnerable  to  particular  forms  of  marketing.  In  the 
financial sector, a substantial amount of research has been conducted on the way low-
income consumers are sold expensive products due to economic actors exploiting their 
current  situation.  Although  a  lot  of  the  academic  research  into  these  practices  stems 
from  the  US  (e.g.  Hill  and  Kozup  2007  and  Montgomerie  2011),  the  practices  appear 
not  to  differ  significantly  from  those  identified  in  the  EU,  or  from  the  experiences 
reported by the consumer organisations. In their study on predatory lending practices 
in  the  US,  Hill  and  Kozup  (2007)  point  to  a  1963  study  by  Caplovitz,  which  showed 
how  vendors  of  home  appliances  exploited  low-income  consumers  through  product 
finance with excessively high interest rates. There are a number of other examples of 
such practices, including: 

  Aforementioned  “greenlining”,  which  refers  to  financial  services  providers  not 
adequately  checking  the  consumers’  ability  to  pay  back  the  loan  (Hill  and 
Kozup 2007); 

  Marketing loans inappropriate to the  circumstances of low-income consumers: 
Which? (2013) found in its research that payday loan providers would advertise 
loans for “nights out” or “to put in the bank for emergencies”; 

  Enticing  additional  borrowing  by  over-indebted  consumers  through  unsolicited 
credit limit increases (Which? 2013) or actively encouraging consumers to take 
out further loans (Hill and Kozup 2007); 

  Victimising consumers seeking credit by highlighting that they have no financial 

options other than expensive subprime loans (Hill and Kozup 2007); and 

  Targeting  vulnerable  groups,  such  as  older  consumers  experiencing  major  life 

changes (Montgomerie 2011). 

249 See http://www.theguardian.com/media-network/2012/oct/10/banks-failing-technology-

access-disabled 

346 

 
 
                                                 
9 │ Complexities of consumer vulnerability across different sectors 

In addition to the above practices, which could be considered as aggressive commercial 
practices,  high  annual  percentage  rates  (APR)  and  other  penalties  on  late  repayment 
that  are  in  excess  of  risk  levels  and  costs  to  lenders  could  be  seen  as  actively 
exploiting  the  financial  vulnerability  of  particular  consumer  groups  who  may  perceive 
or  experience  a  lack  of  banking  alternatives.  In  general,  “payday  loans”  have  been 
widely identified as problematic with regard to consumer vulnerability: they have been 
singled out as such in interviews conducted with organisations in Estonia, UK, Finland, 
Iceland, Portugal, Sweden, and Slovakia. 

Similar  practices,  identified  in  particular  by  organisations  in  Belgium  and  France,  are 
“revolving credit” services offered by shops to help consumers finance their purchases. 
Given  that  these  offers  are  advertised  in  retail  outlets  (usually  supermarkets),  they 
exploit consumers’ inability to afford their purchases by offering them expensive credit. 

9.2. Consumer vulnerability in the energy sector  

Light,  power  and  heating  continuously  affect  the  day-to-day  life  of  any  European 
consumer. As a consequence, the energy sector is key  to the functioning of European 
society.  Energy  prices  have  had  the  tendency  to  increase  over  the  past  years,  while 
incomes for the low-income group have remained stable or decreased (Ifo 2010). Due 
to  the  economic  crisis,  the  economic  climate  has  further  affected  the  ability  of 
consumers  to  pay  for  their  bills,  with  existing  research  showing  that  a  substantial 
number  of  households  are  at  risk  of  fuel  poverty.250  However,  vulnerability  in  the 
energy market is not only related to affordability, but can also be linked to the ability 
to access and choose the best tariff, or to the quality of housing.  

9.2.1.  Incidence of vulnerability in the energy sector 

The  survey  and  experiment  data  provide  evidence  regarding  the  incidence  of 
vulnerability  in  the  energy  sector  based  on  a  number  of  sector  specific  indicators, 
namely:  

Dimension 3 (Having difficulty in obtaining or assimilating information): 

  Compares deals in the energy sector using information from advertisements 

only 

  Has problems comparing deals in the energy sector due to information-related 

factors 

  Has not switched electricity supplier due to information-related factors  

Dimension 4 (Inability or failure to buy, choose or access suitable 
products): 

  Does not compare deals in the energy sector  

  Finds it very difficult to compare deals in the energy sector 

  Has problems comparing deals in the energy sector due to personal factors 

  Has problems comparing deals in the energy sector due to market-related 

factors  

250  In  the  UK,  between  3.1  million  and  9.2  million  households  are  estimated  to  be  facing  fuel 

poverty by 2016 (Hills 2012). 

347 

 
 
 
                                                 
9 │ Complexities of consumer vulnerability across different sectors 

  Has problems comparing deals in the energy sector due to access-related 

factors 

  Has not switched electricity supplier due to personal factors 

  Has not switched electricity supplier due to market-related factors 

  Has not switched electricity supplier due to access-related factors   

Dimension 5 (Higher susceptibility to marketing practices): 

  Experiment choices under the marketing practice treatment in the energy 

sector experiment 

The incidence of vulnerability in the energy sector based on these indicators is shown 
in  Table  80,  both  for  the  EU28  and  by  country251.  Overall  across  the  EU28,  the 
indicator  on  which  consumers  exhibit  the  highest  rate  of  vulnerability  in  the  energy 
sector (85%) is ‘incorrect experiment choices under the marketing practice’, based on 
the energy  experiment. Other indicators in this sector with a high incidence are ‘does 
not compare deals’ and ‘has problems comparing deals due to market-related factors’. 
The  indicators  on  which  vulnerability is,  overall,  the  lowest  are  ‘compares  deals  using 
information from advertisements only’ and ‘has not switched electricity supplier due to 
information-related factors’. A further discussion on country differences in vulnerability 
according to each indicator is provided in section 4.1 of the report. 

251 Data is presented for those countries where data on the relevant indicators is available. The 
EU28  average  is  always  based  on  countries  where  data  for  the  respective  indicator  is 
available. 

348 

 
 
 
                                                 
Table 80: 

Incidence of vulnerability in the energy sector based on sector specific indicators 

9 │ Complexities of consumer vulnerability across different sectors 

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9 │ Complexities of consumer vulnerability across different sectors 

Table 80: 

Incidence of vulnerability in the energy sector based on sector specific indicators 

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19% 

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11% 

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SI 
ES 
SE 
NL 
UK 
IS 
NO 
Obs. (N) 
Note: The numbers of respondents and sets of countries on which these figures are based vary by indicator and sector, since in some countries some survey questions were 
not asked to consumers who answered via particular methods (in particular CAPI or CATI). The countries and survey modes for which each indicator is available can be seen 
in  Table  5  in  section  3.6.  Results  weighted  using  cross-country  weights  for  the  EU28  measure  and country  weights  for  the  country  measures.  *Share  of  consumers  who 
answered incorrectly in round 1 OR round 2 (i.e. who answered incorrectly in one or both rounds). 
Source: London Economics analysis of survey data 

35% 
14,074 

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14,074 

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25,151 

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25,136 

16% 
14,074 

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14,074 

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10% 

46% 

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38% 

10% 

4% 

5% 

3% 

1% 

1% 

3% 

350 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9 │ Complexities of consumer vulnerability across different sectors 

9.2.2.  Vulnerability drivers in the energy sector 

The  following  sub-sections  provide  an  overview  of  vulnerability  drivers  in  the  energy 
sector,  as  well  as  of  related  personal  and  demographic  characteristics  and  market 
practices. The sub-sections are structured according to the main categories of drivers: 

  Personal and demographic characteristics; 

  Behavioural drivers; 

  Market-related drivers; 

  Access drivers; and 

  Situational drivers. 

Personal and demographic characteristics 

The analysis of the survey and experiment data reported in chapter 6 found a number 
of relationships between consumers’ personal and demographic characteristics and the 
likelihood  of  vulnerability  on  various  indicators  that  are  specific  to  the  energy  sector. 
The relevant results are discussed in sections 6.2.3 and 6.2.4. In particular, according 
to  the  results  of  the  data  analysis  (when  other  characteristics  included  in  the 
regressions are controlled for): 

  Older  age  is  linked  with  a  higher  likelihood  of  vulnerability  on  some  energy 
sector specific indicators. Specifically, those in 65+ age groups are more likely 
(than the base group of 35-44 year olds) to have problems comparing deals in 
the  energy  sector  due  to  access-related  factors;  those  aged  45-54,  55-64  or 
65-74 are more likely to have problems comparing energy deals due to market 
related factors; and those aged 75+ are more likely have been prevented from 
switching  for  access  reasons.  The  links  between  being  in  the  oldest  group 
(75+)  and  having  access  problems  in  the  energy  sector252  are  among  the 
strongest253  relationships  found  between  any  demographic  characteristic  and 
the  indicators  throughout  the  whole  analysis.  Furthermore,  older  consumers 
were considerably less likely to select the best deal in the second round of the 
energy sector experiment. However, older consumers (in 55+ age groups) are 
less likely to have not switched energy deals for information-related reasons.  

  Men  are  more  likely  than  women  to  have  problems  comparing  deals  in  the 
energy  sector  for  information-related  reasons,  but  are  less  likely  to  have 
problems comparing energy deals due to personal, market-related and access-
related factors and are less likely to have not switched energy deals for access 
reasons. 

  Consumers  with  low  education  are  less  likely  to  have  problems  comparing 
energy deals and to have been prevented from switching energy deals due to 
access-related factors, but were notably less likely to select the correct offer in 
the second round of the energy sector experiment. 

  Non-native  speakers  are  more  likely  to  suffer  from  access  problems  in  the 
energy sector. In particular, compared to native speakers, non-native speakers 
whose  language  does  not  cause  them  difficulty  are  more  likely  to  have 
problems  comparing  energy  deals  and  to  have  not  switched  energy  deals  due 

252 That is problems comparing and not switching due to access-related factors. 
253 With marginal effects above 24pp, as can be seen from Table 29 in section 6.2.3. 

351 

 
 
 
                                                 
9 │ Complexities of consumer vulnerability across different sectors 

to access-related factors.254 Furthermore, these consumers are also more likely 
to compare energy deals only using information from adverts. 

Behavioural drivers 

A  number  of  behavioural  drivers  and  problematic  practices  exploiting  these  drivers 
have  been  identified in the  energy  sector  throughout  the  data  collection  phase  of  the 
study.  Reference  pricing  is  one  practice  that  exploits  framing  biases  and  in  the 
energy sector  relates primarily to the provision of price  comparisons  to consumers.  It 
has been reported that these comparisons are often presenting incorrect or incomplete 
data (see for instance European Commission 2013a). This in turn makes it possible to 
steer consumers towards specific offers.  

Status  quo  bias  is  also  of  relevance  to  the  energy  sector,  since  consumers  who  are 
not  actively  selecting  an  energy  provider  and  better  offers  can  be  using  a  tariff  that 
might not be the most advantageous. Previous studies carried out in the UK by Ofgem 
(2014)  indicate  that  consumers  often  have  limited  ability  and  willingness  to  exercise 
effective choice in the energy market. This might be due to a number of obstacles such 
as complex information and complexity of choice. This makes it possible for economic 
actors  to  make  a  more  expensive  offer  the  default  one,  hence  steering  consumers 
towards  it.  According  to  the  Ofgem  study,  in  2013  only  seven  in  ten  consumers  felt 
confident  that  they  fully  understood  all  the  features  of  their  new  deal  and  that 
switching  contract  would  be  the  right  choice  (Ofgem  2014).  Similarly,  a  survey  of 
consumers conducted as part of an on-going investigation of energy markets by the UK 
Competition and Markets Authority found that: 

  36%  of  respondents  either  did  not  think  it  was  possible  or  did  not  know  if  it 
was possible to change one (or more) of the following: tariff, payment method 
and supplier; 

  34% of respondents said they had never considered switching supplier; 

  56% of respondents said they had never switched supplier, did not know it was 

possible or did not know if they had done so; and 

  72% said they had never switched tariff with an existing supplier, did not know 
it  was  possible,  or  did  not  know  if  they  had  done  so  (The  Competition  and 
Markets Authority 2015). 

Finally,  practices  exploiting  cognitive  limitations  have  also  been  highlighted  by  a 
number  of  stakeholder  interviewees.  While  this  relates  to  all  consumer  groups,  some 
groups  such  as  elderly  people,  people  with  disabilities,  or  migrants,  might  be 
disproportionately  affected  by  complexities  in  the  energy  sectors.  Examples  of 
potentially problematic practices include bundled offers, overly complex offers, and the 
use of complex language. This could take the form of bills with too many numbers and 
footnotes in small print, very long contracts with technical terms, as well as offers with 
unclear  clauses  (EC  Working  Group  on  Transparency  in  EU  Retail  Energy  Markets 
2012). According to a position paper published by BEUC in 2013, bundled offers in the 
energy sector can be packages including other services in addition to energy, such as 
insurance  policies  or  telecom  services.  In  these  cases  the  actual  energy  services 
provided are often unfavourable, not providing the best deals for the consumer (BEUC 

254  The  results  for  these  indicators  for  non-native  speakers  whose  language  causes  them 
difficulty  are  not  statistically  significant.  However,  this  may  be  due  to  the  fact  that  the 
sample size for such consumers is very small. 

352 

 
 
                                                 
9 │ Complexities of consumer vulnerability across different sectors 

2013a).  Furthermore,  insufficient  clarity  concerning  the  difference  between  fixed  and 
variable  energy  tariffs  has  been  highlighted  by  a  Belgian  consumer  organisation  as  a 
problematic practice which might put the consumer in a situation of vulnerability.  

Market-related drivers 

Issues  related  to  unclear  or  complex  information  could  also  be  seen  as  a  form  of 
information  asymmetry,  with  consumers  not  being  presented  with  sufficient 
information  to  make  an  informed  choice.  As  in  the  above  section,  this  tends  to  be 
exacerbated  by  socio-demographic  and/or  situational  drivers  such  as  age,  migrant 
background/lack of knowledge of a relevant language, or disability, as discussed in the 
guidance  document  of  the  EC  Vulnerable  Consumer  Working  Group  (2013).  Practices 
that  have  been  identified  include  overly  complex  offers,  use  of  complex  language, 
unclear  information  in  regards  to  terms  and  conditions  or  features  of  the  energy 
product and lack of transparency regarding billing information (see for instance Ofgem 
2014). The provision of vague or unclear billing information may be further problematic 
for consumers with low education or digital literacy as discussed by the EC Vulnerable 
Consumer Working Group (2013). In addition, these consumers tend to be less aware 
of their rights to redress which makes them even more vulnerable.  

Other  market-related  drivers  that  have  been  identified  include  high  energy  prices, 
the  level  of  competition,  and  changes  to  the  competitive  situation.  Generally, 
increasing  energy  prices  and  stable  or  decreasing  salary  levels  have  resulted  in  a 
higher  number  of  consumers  facing  energy  poverty  (see  for  instance  Ofgem  2013). 
This  situation  creates  further  problems  for  some  groups  of  consumers,  which  might 
face higher energy expenses due to illness or disability that requires an uninterruptible 
energy supply (EC Vulnerable Consumer Working Group 2013).  

A  number  of  interviewed  consumer  organisations  also  noted  that  the  recent  energy 
market  liberalisation  in  numerous  Member  States  (i.e.  moving  from  a  single 
monopoly  provider  to  multiple  providers)  could  be  seen  as  a  driver  of  vulnerability. 
Firstly,  consumers  who  are  not  fully  aware  of  the  new  market  conditions  could  fail  to 
benefit  from  better  offers.  Secondly,  aggressive  marketing  of  energy  services  to 
consumers who are not aware of the market liberalisation (often older consumers) can 
also be particularly problematic. These issues have been noted in Croatia, Estonia, and 
in Portugal. In Portugal this has also been associated with marketing practices relying 
on  extra  incentives  for  consumers  to  switch  suppliers,  such  as  discounts  on 
supermarket grocery purchases. 

At the same time, where the energy market is not liberalised, some of the interviewed 
stakeholders linked the situation with disproportionately high cost and subsequent fuel 
poverty faced by a number of consumers (noted in Greece and Bulgaria). 

Several  sector-level  measures  are  examined  as  potential  market-related  drivers  of 
vulnerability  in  the  survey  and  experiment  data  analysis  reported  in  Chapter  6, 
specifically:  

  Consumers’ knowledge of their contract  

  The frequency at which consumers compare offers 

  The extent that consumers read bills/communications from their provider 

  The  extent  that  consumers  find  reading  bills/communications  from  their 

provider easy or difficult 

353 

 
 
 
9 │ Complexities of consumer vulnerability across different sectors 

The  results  demonstrate  that  these  measures  of  consumers’  experience  and 
engagement with markets are particularly important drivers of  consumer vulnerability 
in the energy sector (as well as in the other two key sectors). 

Specifically,  consumers  who  do  not  know  their  energy  contract  conditions  ‘at  all’  or 
‘very  much’  are  more  likely  to  have  problems  comparing  energy  deals  due  to 
information-related, personal, market-related and access-related factors, and are more 
likely  to  have  been  prevented  from  switching  energy  suppliers  for  personal  and 
market-related reasons. Similarly, those who found it ‘difficult’ to read the most recent 
bill  from  their  energy  supplier  are  more  likely  to  have  problems  comparing  energy 
deals  due  to  information-related,  personal  and  market-related  factors,  and  are  more 
likely  to  have  not  switched  energy  suppliers  for  market  and  access-related  reasons. 
Similar findings are also found for the other two key sectors of the study. 

Thus,  it  is  useful  to  examine  how  the  incidence  rates  of  these  market-related  drivers 
compare across sectors. This comparison is made in Table 79 in section 9.1.2 above. 

As Table 79 shows, the energy sector stands out in that many consumers do not know 
their energy contract conditions ‘very much’ or ‘at all’, and also many consumers found 
it ‘difficult’ or ‘very difficult’ to  read their last energy bill. Given that these factors are 
linked  with  a  higher  likelihood  of  vulnerability,  these  characteristics  of  the  energy 
sector  can  explain  to  some  extent  the  higher  level  of  vulnerability  in  the  sector  on 
some  indicators255,  which  suggests  that  policies  to  improve  consumers’  engagement 
with the market and understanding of market information may be particularly relevant 
to the energy sector.  

Access drivers 

Access drivers generally relate to  lack  of  access to markets, product or  services. 
Specific problems identified in the energy sector include:  

 

Inaccessible websites, apps, or appliance displays (in particular for consumers 
with  disabilities  and  elderly  people)  (EC  Vulnerable  Consumer  Working  Group 
2013; BEUC 2013b); 

  Rural  consumers  facing  a  higher  likelihood  of  not  being  connected  to  the 
electricity  grid  or  having  access  to  gas  supply  (EC  Vulnerable  Consumer 
Working Group 2013); 

  Users of prepaid meters living in rural areas having difficulties in accessing top 
up  points.  However,  currently  this  concerns  only  a  small  group  of  consumers. 
In the UK, estimates indicate that 98.5% of people in rural areas using prepaid 
meters are not further away than five miles from the closest top-up point256;  
  Users  without  internet  access  may  face  additional  costs  and  difficulties  in 
topping up where meters rely on online features (O’Sullivan et. al 2012); and 

255  As  noted in  section  9.2.1,  the  rate  of  vulnerability  in  the  energy  sector exceeds  the  rate of 
vulnerability in  the  finance  sector on  several indicator,  and  the  incidence  of  vulnerability in 
the energy sector is higher than in online sector on all indicators that are available for both 
sectors,  except  for  the  share  of  consumers  who  compare  offers  using  information  from 
adverts only. 

256 See http://www.paypoint.co.uk/clients/energy-water 

354 

 
 
                                                 
9 │ Complexities of consumer vulnerability across different sectors 

  Access to advice and customer services being problematic for some  consumer 
groups  e.g.  those  who  might  not  afford  to  hold  the  line  for  expensive  phone 
calls to customer service,  or those unable to access  certain services (e.g. due 
to sight and hearing difficulties). 

Finally,  certain  consumer  groups  will  not  be  able  to  access  particular  offers, 
which  can  result  in  additional  costs.  This  can  be  linked  to  aforementioned  “double 
disadvantages”.  In  several  Member  States  (in  the  Netherlands  and  the  UK  for 
instance)  cheaper  energy  offers  tend  to  be  available  to  consumers  paying  via  direct 
debit,  which  are  not  available  to  consumers  without  a  transactional  bank 
account,  who  in  turn  are  more  likely  to  be  consumers  on  low  incomes,  consumers 
without employment, and consumers who have experienced life crises (e.g. divorce or 
severe  debt)  (Stearn  2012).  Similarly,  certain  energy  discounts  are  only  available 
online,  disadvantaging  consumers  without  internet  access  (more  often  elderly  or  low-
income consumers, or those living in a rural area with no internet connection). 

Another  double  disadvantage  in  the  energy  sector  is  the  requirement  for  low-income 
consumers  to  provide  an  up-front  security  deposit  in  order  to  sign  an  energy 
contract, sometimes equivalent to six months of energy bills, which is a high up-front 
cost for lower income consumers. O’Sullivan et al (2012) note this to be a problematic 
practice  in  New  Zealand,  where  lower  income  consumers  using  prepayment  meters 
would  be  required  to  pay  a  “cash  bond”  when  switching  providers,  but  there  is  also 
evidence  of  such  practices  in  the  EU.  Stakeholder  interviews  have  identified  this  as  a 
problem for consumers in the Netherlands.  

In addition to access challenges with regard to prepaid energy meters, O’Sullivan et 
al.  (2012)  note  that  prepaid  electricity  meters  which  are  suggested  to  lower-income 
consumers  or  provided  to  them  as  the  only  option  (to  a  third  of  prepaid  meter 
consumers surveyed in New Zealand the meter was suggested by the energy company 
or was the only option) are effectively more expensive than other means of payment. 
While this could be at least partially attributed to higher servicing cost, it does tend to 
disproportionately  affect  low  income  consumers  (Brophy  Haney  et  al.  2009).  At  the 
same  time,  prepaid  meters  are  viewed  positively  by  consumers,  partly  due  to 
mistakenly believing them to be less costly, and partly due to lack of monthly bills and 
more control of costs. A consequence of using a prepaid meter, however, could be that 
of self-discrimination or self-disconnection, where to save money consumers endanger 
their health and well-being and that of their families by choosing to use less energy or 
switching off the energy supply. Another identified problem is that of  meters shutting 
off if credit is close to zero, potentially having a disproportionate impact on consumers 
only able to top-up the meter by small amounts.  

Prepaid meters are increasingly in use in the EU.257 Prepaid meters are widely used in 
the  UK,  with  some  of  the  above  problems  also  reported258  and  where  there  are 
estimated  4.5  million  electricity  and  3.4  million  gas  prepayment  meter  accounts 
(Ofgem 2015). Problems related to higher effective costs  of prepaid meters have also 
been  identified  in  the  Netherlands,  where  one  consumer  association  noted  that  lower 

257 See: http://www.navigantresearch.com/newsroom/prepaid-electric-meters-are-expected-to-

have-an-installed-base-of-more-than-85-million-from-2014-to-2024 

258 See for instance:  http://www.theguardian.com/money/2013/apr/20/energy-bills-prepay-

meters-cost-poorer-households 

355 

 
 
 
                                                 
9 │ Complexities of consumer vulnerability across different sectors 

income  consumers  usually  face  higher  cost  tariffs,  either  due  to  having  to  provide 
security deposits, or due to the use of a prepaid meter.  

Situational drivers 

These  drivers  include  the  increased  susceptibility  to  particular  forms  of 
marketing due to personal (temporary or permanent) situation. 

One example of such practices in the energy sector is the aggressive telephone and 
doorstep  marketing,  which  often  targets  elderly  consumers.  This  kind  of  marketing 
often  results  in  unsolicited  contracts,  with  customers  changing  providers  following 
doorstep  sales  or  opting  for  a  contract  that  is  not  advantageous.  In  the  case  of 
doorstep selling, consumers rarely have the possibility to actually read the contractual 
conditions  and  the  relevant  information.  In  addition,  printed  materials  are  often 
withheld  until  the  consumer  has  signed  the  new  contract  (BEUC  2013a).  As  noted 
above, a UK study commissioned by Ofgem in 2011, found that 29% of consumers in 
the  UK  obtained  information  about  offers  via  a  salesperson  at  their  door,  a  higher 
proportion  than  for  other  information  sources,  highlighting  the  effectiveness  of  such 
marketing.  A  previous  Ofgem  investigation  from  2008  found  that  48%  of  electricity 
customers switched to suboptimal contracts after changing supplier following doorstep 
sales.259 Doorstep  sales have been identified as  one  of the main sources of  consumer 
complaints  in  Italy,  the  UK,  Belgium,  the  Czech  Republic,  and  Spain  (EC  Working 
Group on Transparency in EU Retail Energy Markets 2012).  

There  are  also  “double  disadvantages”  going  beyond  access  to  low-priced  offers.  A 
particular challenge relates to consumers in vulnerable situations due to age, illness or 
unemployment,  and  who  therefore  spend  more  time  at  home.  As  a  consequence, 
their expenses will be higher as they would need to maintain a higher/lower maximum 
temperature for longer periods of time, especially in more extreme climates.   

A last type of double disadvantage in the energy sector relates to quality of housing. 
Inefficient  housing  and  limited  capacity  to  move  or  upgrade  housing  might  result  in 
higher energy expenses. While an investment in efficiency measures can reduce future 
costs,  a  low-income  consumer  might  not  be  able  to  afford  this  investment  and  will 
therefore  need  to  face  higher  regular  expenses.  The  same  issue  is  valid  for  tenants, 
especially as low-income consumers will be more likely to rent their home rather than 
buying  one.  The  incentive  for  a  landlord  to  invest  in  energy  efficiency  measures  such 
as  isolation  or  the  installation  of  new  appliances  (boiler  etc.)  is  very  low,  and  in  turn 
the tenant will face higher energy expenses.  

When  combined  with  the  other  “double  disadvantages”  outlined  above,  these 
situational  factors  can  results  in  certain  consumer  groups  facing  relatively  high  costs. 
In  its  Consumer  Vulnerability  Strategy,  the  UK  utilities  regulator  Ofgem  notes  that 
while the wealthiest 10% of households spend 2.8% of their total spending on energy, 
for the poorest 10% this figure is 8.1% (Ofgem 2013).  

259 See for instance http://www.theguardian.com/business/2010/sep/02/energy-industry-ofgem-

misselling 

356 

 
 
                                                 
9 │ Complexities of consumer vulnerability across different sectors 

9.3. Consumer vulnerability in the online environment and electronic 

communications sector 

While  online  environment  and  electronic  communications  are  treated  here  as  a 
separate sector, a discussion of the online environment and electronic communications 
is  relevant  to  other  sectors,  as  increasing  number  of  products  and  services,  including 
those  in  the  energy  and  financial  sectors,  are  provided  electronically.  Therefore,  in 
addition to examining the online purchase of digital goods and services and electronic 
communication  services,  this  section  also  focuses  on  drivers  and  prices  linked  to  the 
online environment and electronic communications that apply across all sectors. 

9.3.1.  Incidence of vulnerability in the online sector 

Before  examining the drivers of  vulnerability in the  online environment and electronic 
communications sector, the incidence of vulnerability in the online sector is considered 
based on several sector specific indicators from the survey and experiment data. These 
indicators, which relate to vulnerability Dimensions 3, 4 and 5, are: 

Dimension 3 (Having difficulty in obtaining or assimilating information): 

  Compares deals in the online sector using information from advertisements 

only 

  Has problems comparing deals in the online sector due to information-related 

factors 

  Has not switched internet provider due to information-related factors  

Dimension 4 (Inability or failure to buy, choose or access suitable 
products): 

  Does not compare deals in the online sector  

  Finds it very difficult to compare deals in the online sector 

  Has problems comparing deals in the online sector due to personal factors 

  Has problems comparing deals in the online sector due to market-related 

factors  

  Has problems comparing deals in the online sector due to access-related 

factors 

  Has not switched internet provider due to personal factors 

  Has not switched internet provider due to market-related factors 

  Has not switched internet provider due to access-related factors   

Dimension 5 (Higher susceptibility to marketing practices): 

  Experiment choices under the marketing practice treatment in the online sector 

experiment 

The incidence of vulnerability in the online sector based on these indicators is shown in 
Table 81, both for the EU28 and by country260. Overall across the EU28, the indicator 
on which consumers exhibit the highest rate of vulnerability in the online sector (53%) 
is  ‘incorrect  experiment  choices  under  the  marketing  practice’,  based  on  the  online 

260 Data is presented for those countries where data on the relevant indicators is available. The 
EU28  average  is  always  based  on  countries  where  data  for  the  respective  indicator  is 
available. 

357 

 
 
 
                                                 
9 │ Complexities of consumer vulnerability across different sectors 

experiment.  Another  indicator  of  in  this  sector  with  a  high  incidence  rate  is  ‘does  not 
compare  deals’.  The  indicator  on  which  vulnerability  is  lowest  overall  is  ‘has  not 
switched  internet  provider  due  to  information-related  factors’,  followed  by  ‘has  not 
switched  internet  provider  due  to  personal  factors’  and  ‘has  not  switched  internet 
provider  due  to  access-related  factors’.  A  further  discussion  on  country  differences  in 
vulnerability according to each indicator is provided in section 4.1 of the report. 

358 

 
 
 
Table 81: 

Incidence of vulnerability in the online sector based on sector specific indicators 

9 │ Complexities of consumer vulnerability across different sectors 

e
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EU28 
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BE 
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CY 
CZ 
DK 
EE 
FI 
FR 
DE 
EL 
HU 
IE 
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LV 
LT 
LU 
MT 
PL 
PT 
RO 
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359 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
9 │ Complexities of consumer vulnerability across different sectors 

Table 81: 

Incidence of vulnerability in the online sector based on sector specific indicators 

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SI 
ES 
SE 
NL 
UK 
IS 
NO 
Obs. (N) 
Note: The numbers of respondents and sets of countries on which these figures are based vary by indicator and sector, since in some countries some survey questions were 
not asked to consumers who answered via particular methods (in particular CAPI or CATI). The countries and survey modes for which each indicator is available can be seen 
in  Table  5  in  section  3.6.  Results  weighted  using  cross-country  weights  for  the  EU28  measure  and country  weights  for  the  country  measures.  *Share  of  consumers  who 
answered incorrectly in round 1 OR round 2 (i.e. who answered incorrectly in one or both rounds). 
Source: London Economics analysis of survey data 

18% 
18,506 

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7% 
18,506 

21% 
18,506 

58% 
18,506 

6% 
18,550 

1,254 

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3,483 

3,483 

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360 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
9 │ Complexities of consumer vulnerability across different sectors 

9.3.2.  Vulnerability drivers in the online environment and electronic 
communications sector 

The  following  sub-sections  provide  an  overview  of  vulnerability  drivers  in  the  online 
environment and electronic communications sector, as well as of related personal and 
demographic  characteristics  and  market  practices.  The  sub-sections  are  structured 
according to the main categories of drivers: 

  Personal and demographic characteristics; 

  Behavioural drivers; 

  Market-related drivers; 

  Access drivers; and 

  Situational drivers. 

Personal and demographic characteristics 

The survey and experiment data analysis reported in  chapter 6 identifies a number of 
links between consumers’ personal and demographic characteristics and the likelihood 
of vulnerability on several indicators that are  specific to the  online sector.  The  results 
for  these  indicators  are  discussed  in  sections  6.2.3  and  6.2.4.  Specifically,  the  data 
analysis  results  show  that  (when  other  characteristics  included  in  the  regressions  are 
controlled for): 

  Older age is associated with having problems comparing deals due to personal, 
information-related  and  market-related  factors  in  the  online  sector,  and  with 
using few sources of information when comparing deals in the online sector. In 
particular, those in 45+ age groups are more likely than the base group (35-44 
year  olds)  to  have  problems  comparing  internet  deals  for  information  and 
market-related  reasons  whereas  those  in  age  groups  between  45  and  74  are 
more likely to have problems comparing internet deals due to personal factors 
and  to  compare 
from  adverts  only. 
Furthermore,  consumers  in  the  oldest  group  (75+)  were  considerably  less 
likely  to  select  the  best  offer  in  the  second  round  of  the  online  sector 
experiment.  These  relationships  are  stronger  for  older  groups,  and  the  links 
between  being  in  the  oldest  group  and  having  problems  comparing  deals  due 
to  market-related  factors  and  selecting  an  incorrect  offer  in  the  second  round 
of  the  experiment  are  among  the  strongest  relationships261  found  for  any 
demographic characteristic throughout the analysis. 

internet  deals  using  information 

  Men are less likely than women to have problems comparing deals in the online 

sector due to personal and market-related factors. 

  Consumers  in  low  density  regions  are  slightly  more  likely  than  those  in  high 
density  regions  to  have  problems  comparing  deals  in  the  online  sector  due  to 
personal and access-related factors. 

  Consumers  with  a  medium  level  of  education  are  more  likely  than  those  with 
high  education  to  compare  deals  in  the  online  sector  using  only  information 
from  adverts,  and  were  less  likely  to  select  the  correct  offer  in  both  the  first 
and second rounds of the online sector experiment. 

261 With marginal effects of 25pp and 28pp respectively, as can be seen from Table 29 and Table 

28 in section 6.2. 

361 

 
 
 
                                                 
9 │ Complexities of consumer vulnerability across different sectors 

  Being  a  non-native  speaker  is  linked  with  a  few  indicators  of  vulnerability 
relating to the online sector. Specifically, non-native speakers whose language 
does  not  cause  them  difficulty  are  more  likely  to  only  use  information  from 
adverts  when  comparing  internet  deals,  are  more  likely  to  have  problems 
comparing  internet  deals  for  access-related  reasons,  and  were  less  likely  to 
choose the best deal in the second round of the online sector experiment. Non-
native speakers whose  language causes them  difficulty were considerably less 
likely  to  choose  the  correct  offer  in  the  first  round  of  the  online  sector 
experiment. 

Behavioural drivers 

The  online environment makes it possible for  economic actors to  exploit, intentionally 
or  unintentionally,  consumers’  behavioural  biases,  since  it  allows  for  presenting  of 
information in ways not possible at brick-and-mortar retail outlets. Some examples of 
ways  that  the  biases  outlined  in  the  previous  sections  can  be  exploited  in  the  online 
environment include: 

  Status  quo  bias:  While  in  brick-and-mortar  retail  outlets  it  is  possible  to 
display certain offers as default, for instance by placing them first on a list and 
adding  the  term  “or”  before  other  offers,  online  environment  allows  for 
considerable additional flexibility in doing so (European Parliament 2011).  

  Bandwagon  effect:  Displaying  information  such  as  “this  offer  has  been 
viewed by X users” or “X users are currently viewing this offer” alongside offers 
provided  online  (European  Parliament)  allows  to  capitalise  on  the  bandwagon 
effect  (and  also  create  a  sense  of  urgency,  as  discussed  below).  Integration 
with  social  media  services,  for  instance  by  showing  the  number  of  “likes”  a 
particular page,  offer  or vendor  receives, also  expands these possibilities (see 
for instance Sundar, Oeldorf-Hirsch, and Xu 2008).  

  Framing: The online environment is particularly effective in allowing to exploit 
potential framing biases given the flexibility of providing information (European 
Parliament 2011).  

Articles in business press and on marketing portals focusing on exploiting such biases 
in online environment262 show that the above techniques are indeed being adopted by 
vendors in the sector. 

A number of specific practices are particularly facilitated in the online environment: 

  Drip  pricing,  where  consumers  are  not  shown  a  full  price  up  front,  but  are 
instead guided through a set of web pages with each page providing additional 
information, as well as additional costs (European Parliament 2011). 

  Reference pricing, where prices are compared to a “full price” of the product, 
price of a competing product or price of the same product sold by a competing 
vendor (European Parliament 2011).  

  Time-limited  offers,  where  an  offer  is  presented  in  a  way  that  creates  a 
sense  of  urgency  or  suggests  the  offer  will  not  be  available.  Although  “offer 
valid only until...” statements are used in brick-and-mortar  outlets, the online 

262 See for instance https://sellorelse.ogilvy.com/2012/05/17/behavioral-economics-sell-digital/ 
or http://www.powerretail.com.au/marketing/behavioural-economics-part-five-the-framing-
effect/ 

362 

 
 
                                                 
9 │ Complexities of consumer vulnerability across different sectors 

environment allows for a number of alternative ways of creating urgency. This 
includes  statements  such  as  “only  two  seats  left  at  this  price”,  “X  people  are 
currently  looking  at  this  offer”  (also  a  way  of  exploiting  a  bandwagon  effect), 
“only three rooms left” (even if more rooms at the same price may be available 
from other sites) (European Parliament 2011).  

  Baiting,  where  a  low  price  very  limited  offer  is  used  to  attract  consumers, 
who, after realising the offer is no longer available, may choose a higher-priced 
alternative.  Specifically  in  the  online  environment  this  practice  can  involve 
displaying “out of stock” items on an e-commerce site alongside higher priced 
alternatives  (European  Parliament  2011).  Beyond  e-commerce,  a  form  of 
baiting used in online environment involves so-called “click bait”, where online 
content  is  used  purely  for  the  purpose  of  attracting  “clicks”  which  in  turn 
generate  advertising  revenue,  or  in  some  cases  cause  users  to  download 
malware  onto  their  computers.263  While  this  is  often  done  through  banner 
advertising  sometimes  disguised  as  legitimate  content,  in  some  cases  it 
involves  content  such  as  news  reports  on  online  portals,  which  can  often 
constitute a link to a previous report or another website or a very short article 
with  little  content,  but  which  use  headlines  to  attract  “clicks”  from  web 
users.264  

A second set of behavioural drivers and associated practices, which are of relevance to 
the  online  environment  and  electronic  communications  refers  to  web  consumers’ 
inability to process complex  information.  While this is particularly relevant to the 
financial  sector,  outlined  above,  it  is  also  an  issue  with  regard  to  electronic 
communication contracts, as well as digital products and services.  

With regard to contracts for electronic communications, this can include practices such 
as: 

  Bundling  of  offers,  such  as  broadband  internet  contracts  including  also 
telephone  or  television  packages  making  it  difficult  to  determine  the  costs  of 
individual  components  and  compare  it  to  competing  offers  (European 
Commission 2013a). 

  Overly complex offers or complex language. Technical terminology used in 
the  electronic  communications  can  render  information  provided  to  consumers 
overly  complex.  For  instance  a  2012  study  for  Consumer  Focus  found  that 
consumers  had  limited  understanding  of  terminology  concerning  broadband 
traffic management, such as indications of speeds, data caps, or allowances, or 
meaning  of  terms  such  as  peer-to-peer  (P2P)  and  Voice  over  Internet  (VoIP). 
Similarly,  the  Special  Eurobarometer  381  has  shown  that  a  majority  of 
consumers  did  not  know  the  maximum  download  speeds  advertised  in  their 
contracts (Kisielowska-Lipman 2012).  

Complexity is a challenge in the domain of digital goods and services. Users of services 
primarily  provided  online,  such  as  social  media  services  or  file  hosting  and  sharing 
services,  enter  into  agreement  with  service  providers  by  agreeing  to  terms  and 
conditions  and  privacy  policies.  These  can  in  turn  contain  clauses  that  may  be  overly 

263 See for instance http://www.bbb.org/blog/2014/10/scammers-use-most-dangerous-

celebrities-as-click-bait-to-fool-consumers/ 

264  Facebook,  which  has  recently  taken  steps  to  address  click-baiting,  provides  an  overview  of 

this practice: http://newsroom.fb.com/news/2014/08/news-feed-fyi-click-baiting/   

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9 │ Complexities of consumer vulnerability across different sectors 

complex  for  an  average  user  to  fully  understand,  but  which  can  put  them  at  a 
disadvantage.  In  a  2012  US  legal  study  examining  terms  and  conditions  of  social 
networking websites, Rustad et al. (2012) argue that many social networking services 
include “arbitration clauses” in their terms of service, allowing the service providers to 
set  the  terms  of  potential  dispute  resolutions  often  to  the  disadvantage  to  the 
consumer.  These  clauses  are  in  turn  often  insufficiently  explained  and  difficult  for 
consumers  to  find,  being  placed  towards  the  end  of  the  agreements,  with  the 
agreements reviewed by the authors averaging 6,078 words (approx. 10 single-spaced 
pages in 10pt font). 

The  2011  study  on  digital  content  services  carried  out  for  the  European  Commission 
(Europe  Economics  2011)  identified  information  and  transparency,  including  lack  of 
information and unclear or complex information, as the second most common problem 
identified  by  users  of  digital  content,  behind  access,  suggesting  that  complexity  and 
availability of information about digital content is likely to be a significant vulnerability 
driver. 

Market-related drivers 

Market-related  drivers  refer  in  this  case  primarily  to  information  problems,  in 
particular information asymmetry between consumers and service providers. This can, 
on one hand, arise from complexity of offers or language used, as described above, or 
can  refer  to  information  about  products  or  services  being  obscured  from  the  user.  An 
issue  mentioned  by  a  number  of  regulators  in  the  online  environment  and  electronic 
communication sector is that of subscription or “premium” mobile services, highlighted 
by regulators in Austria, Poland and Portugal. This includes consumers being asked to 
send  costly  text  messages  to  obtain  a  certain  service  or  enter  a  prize  draw,  with  the 
costs of these and any follow-on messages not always being disclosed in a transparent 
way.  Similarly,  consumers  may  be  offered  mobile  services  (e.g.  weather  service  or  a 
map  of  speed  cameras),  which  in  fact  involve  a  paid  subscription  service.  While  the 
information  about  costs  is  usually  provided,  it  is  often  not  presented  in  an  easily 
accessible way.  

It  is  important  to  note,  however,  that  while  these  problems  are  highlighted  by 
interviewed  consumer  organisations  as  particularly  serious  in  some  Member  States 
(e.g.  Poland)  the  interviewees  also  indicated  that  some  Member  States  have  taken 
specific legislative steps to limit such practices (e.g. Portugal) and, more generally, the 
increasingly  widespread  use  of  smartphones  and  apps  means  that  such  services  are 
likely  to  become 
the  Austrian 
telecommunications regulator provides detailed statistics on enquiries, complaints and 
disputes  in  the  sector,  noting  that  569  enquiries  were  made  concerning  value-added 
text and voice services in 2011 compared to a total of 153 in 2013. It is however worth 
noting  that  this  pattern  reflects  the  broader  trend  in  the  sector,  with  the  number  of 
enquiries about mobile data services, which was the most  enquired about category in 
2011 with 1,490 enquiries, falling to 241 in 2013 (RTR 2013). 

less  widely  used  over 

time.  For 

instance, 

Another  key  information  problem  refers  to  the  actual  costs  of  using  “free”  online 
services, which are often not clear to users. A most common example is the “pay with 
data”  business  model,  where  consumers  allow  for  some  of  their  personal  data  to  be 
used  for  advertising  purposes  in  exchange  for  free  access  to  the  service  in  question. 
This could also be seen as an information problem in the sense that service providers 
gather  data  on  consumers  that  consumers  are  not  aware  of,  causing  another  form  of 
information asymmetry when faced with advertising (i.e. advertisers know more about 
the consumers than the consumers think they do). Warner and Sloan (2012) describe 
the usual online ecosystem engaged in the “pay with data” transactions, identifying: 

364 

 
 
9 │ Complexities of consumer vulnerability across different sectors 

  Profilers,  which  use  information  gathered  by  online  services  to  create 

consumer profiles and segment consumers into groups; 

  Advertising agencies developing the display advertising;  

  Advertising  exchanges  and  networks  (e.g.  Google  AdSense)  acting  as  an 
intermediary between advertisers and websites displaying the advertising; 

  Businesses buying the advertising; and 

  Websites displaying the advertising.  

Warner and Sloan (2012) also note that profiles created by profilers do in some cases 
identify specific individuals, despite assurance to the contrary. They conclude that “pay 
with data” relationships are one-sided, favouring the service provider and not allowing 
for consumers’ free and informed consent.  

Similar  situations  include  instances  where  products  or  services  allow  providers  to 
monitor  usage  of  a  product  or  extract  value  out  of  a  “free  product”  in  another  way 
without  this  being  clearly  communicated  to  the  consumer.  Such  practices  could  for 
instance include contract terms allowing vendors to monitor product usage, restricting 
product  use,  or  allowing  vendors  to  change  and  update  the  product  without  warning, 
as well as including “premium” features requiring payments. Where such practices are 
not sufficiently explained to users with lower level of digital sophistication, they could 
in  fact  be  seen  as  exploiting  consumer  vulnerability.  In  the  case  of  children,  such 
practices  can  include  allowing  in-game  purchases  by  children  using  their  parents’ 
accounts without additional verification (University of Amsterdam 2010). 

A  number  of  measures  are  investigated  as  possible  market-related  divers  of 
vulnerability  at  sector  level  in  the  survey  and  experiment  data  analysis  reported  in 
chapter 6, in particular: 

  Consumers’ knowledge of their contract  

  The frequency at which consumers compare offers 

  The extent that consumers read bills/communications from their provider 

  The  extent  that  consumers  find  reading  bills/communications  from  their 

provider easy or difficult 

According to the results of the data analysis, these measures of consumers’ experience 
and engagement with markets are particularly important drivers of vulnerability in the 
online sector (as well as in the other two key sectors). 

For  example,  consumers  who  do  not  know  the  conditions  of  their  contract  with  their 
internet provider ‘at all’ or ‘very much’, as well as those who found it difficult to read 
the  most  recent  communication  from  their  internet  provider,  are  more  likely  to  have 
problems  comparing  internet  providers  due  to  information-related,  personal,  market-
related  and  access-related  factors.  The  data  analysis  also  reveals  similar  findings  for 
the energy and finance sectors. 

Therefore,  it  is  useful  to  examine  how  the  incidence  rates  of  these  market-related 
drivers vary across sectors, as shown in Table 79 in section 9.1.2 above. 

As  Table  79  shows,  in  terms  of  the  shares  of  consumers  who  do  not  know  the 
conditions  of  their  contract  and  who  found  it  hard  to  read  the  most  recent 
communication from their provider, the online sector is relatively similar to the finance 
sector.  In  these  respects  these  two  sectors  perform  better  than  the  energy  sector, 

365 

 
 
 
 
9 │ Complexities of consumer vulnerability across different sectors 

which may, to some extent, explain why on some sector specific indicators the rate of 
vulnerability is lower in these sectors than in the energy sector. 

Furthermore, consumers are more likely to compare deals in the online sector, which is 
related  to  a  lower  likelihood  of  vulnerability  and  so  may  help  to  explain the  relatively 
low incidence of vulnerability on a number of indicators in the online sector.265 

Access drivers 

Access drivers refer primarily to: 

 

Inaccessible websites or apps 

  Access to internet, in particular broadband internet 

Despite a  considerable  number of initiatives in the area, such as the  recent  European 
Standard EN 301 549 on accessibility requirements suitable for public procurement of 
ICT products and services in Europe, there are a number of challenges with regard to 
e-accessibility.  Authors  of  the  2012  “Study  on  Economic  Assessment  for  Improving 
eAccessibility Services and Products” commissioned by the European Commission note 
that: 

 

“The  level  of  eAccessibility  is  clearly  lagging  behind  the  target  set  out  in  the 
Riga  declaration  in  2006  and  at  its  current  pace  the  target  of  the  Digital 
Agenda for 2015 will not be reached; 

  As many as 110 million Europeans, persons with disabilities and older persons 
(people  aged  65  and  above),  are  at  risks  of  being  digitally  excluded” 
(Technosite, Tech4i2, AbilityNet and NOVA 2012).  

With  regard  to  internet  access,  the  2015  Digital  Agenda  Scoreboard266  tracks  the 
development  of  broadband  coverage  in  the  EU  and  shows  that  while,  taking  into 
account  all  technologies,  broadband  access  is  available  to  all  households  in  the  EU, 
fixed-line  coverage  is  lower  in  rural  areas  (90%),  although  it  has  risen  significantly 
since  2011  (80%).  Nevertheless,  only  70%  of  homes  had  fixed-line  broadband 
subscription,  suggesting  that  there  are  a  substantial  number  of  consumers  without 
access  to  fixed-line  broadband  despite  relatively  broad  coverage.  Geographic 
dimension also plays a role, with some Member States (Poland, Slovakia, Estonia, and 
Romania)  lagging  behind  in  terms  of  take-up.  Although  in  some  cases  mobile 
broadband  can  be  seen  as  a  substitute  to  fixed-line  connections,  the  research 
conducted  for  the  Digital  Agenda  Scoreboard  suggests  that  such  connections  tend  to 
complement rather than substitute fixed line broadband.  

Another important aspect of internet access is affordability, with the scoreboard data 
showing  that  broadband  prices  are  dispersed  and  take-up  tends  to  be  lower  where 
prices  constitute  a  higher  share  of  average  income  (e.g.  in  Hungary,  Latvia,  Poland, 
Portugal, Romania). 

265 As noted in section 9.3.1, the rate of vulnerability in the online sector is lower than the rate 

of vulnerability in the finance sector on several indicators, and the rate of vulnerability in the 
online sector is lower than the rate of vulnerability in the energy sector on every indicator 
that is available for both sectors, except for the share of consumers who compare offers only 
using information from advertisements. 

266 See http://ec.europa.eu/digital-agenda/en/digital-agenda-scoreboard 

366 

 
 
                                                 
9 │ Complexities of consumer vulnerability across different sectors 

Both physical access and affordability relate to the concept of “digital divide”, namely 
the “distinction between those  who have internet access and are able to make use  of 
new services offered on the World Wide Web, and those who are excluded from these 
services”.267  The  two  main  digital  divide  indicators  used  by  the  Digital  Agenda 
Scoreboard  are  the  frequency  of  internet  use  and  availability  of  30Mbps  broadband. 
Hence, the key challenges for the EU relate to the 20% of Europeans who have never 
used  the  internet  and  the  fact  that  only  18%  of  rural  areas  have  30Mbpas 
broadband.268  The  two  dimensions  relating  to  access  and  use  of  internet  reflect  the 
typology used by Dewan and Riggins (2005) who note that the digital divide entails in 
addition  to  physical  access,  or  “first  order  effects”,  also  “second  order  effects”, 
referring  to  the  ability  to  use  the  technology.  Digital  literacy  therefore  remains  a 
challenge and will be a determinant of access.  

Beyond  lack  of  access  to  Internet  infrastructure,  due  to  cost  or  location,  practices 
based  on  access  involve  also  the  concept  of  “weblining”.  Based  on  the  practice  of 
“redlining”,  weblining  can  be  understood  as  “the  practice  of  denying  people 
opportunities based on their digital selves” (Andrews 2012). Exploiting consumers’ lack 
of  digital  sophistication  or  inability  to  understand  complex  terms  of  use  to  collect 
information  about  consumers  without  their  full  knowledge  is  a  problematic  practice  in 
itself,  and  denying  access  to  certain  products  or  services  on  that  basis  further 
compounds  the  problem.  While  weblining  has  not  yet  been  extensively  researched, 
Andrews  (2012)  identifies  practices  such  as  refusing  specific  insurance  packages  or 
showing  different  credit  card  limits  to  consumers  based  on  their  online  profiles  or 
consumers  with  particular  profiles  being  shown  different  type  of  advertising  (e.g. 
advertising  for  trade/vocational  schools  rather  than  universities  or  colleges  being 
shown to web users identified to be living in poor neighbourhoods). 

The latter case is one where targeted advertising, present for a long time in the offline 
world  and  not  in  itself  problematic  (e.g.  advertising  in  specific  magazines  or  during 
specific  television  programmes  generally  tends  to  focus  on  products  or  services  that 
may  appeal  to  the  magazine’s  readers  or  the  programme’s  audience),  can  lead  to 
excluding  certain  consumer  groups  not  only  from  accessing  specific  products  or 
services, but also from certain opportunities and aspects of society. 

the  sector  relatinfores 

final  aspect  of  access  drivers 

The 
to  “double 
in 
disadvantages”, described in previous sections, which refer to situations where lower 
income  consumers  are  faced  with  restricted  access  to  lower  cost  offers.  In  the  online 
environment  and  electronic  communications  sector  a  practice  highlighted  by 
interviewees in Germany relates to costs and choice of payment methods available on 
e-commerce  websites.  Where  card  payment  methods  used  predominantly  by  lower 
income consumers are not accepted or carry with them disproportionate additional fees 
or are not available, such that consumers would be facing a double disadvantage. 

Situational drivers 

Situational  drivers  in  the  case  of  online  environment  and  electronic  communications 
sector  relate  primarily  to  consumers’  increased  susceptibility  to  particular  forms  of 
marketing  due  to  personal  situation.  Specific  problematic  practices  identified  in  the 
research include primarily: 

267 See http://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Digital_divide 
268 See https://ec.europa.eu/digital-agenda/en/news/eu-digital-divide-infographic 

367 

 
 
 
                                                 
9 │ Complexities of consumer vulnerability across different sectors 

  Aggressive marketing, both offline and online; and 

  Practices  aiming  at  tying  lower-income  consumers  to  telecommunication 
contracts through high termination fees or offering lower monthly payments in 
exchange for contract extension. 

As noted above, aggressive marketing in the sector  can be mainly divided into offline 
and  online  marketing.  Offline,  there  are  indications  of  aggressive  marketing  of 
telecommunication offers, including doorstep selling.269 Online, aggressive marketing 
relates  primarily  to  using  the  aforementioned  profiling  techniques  to  identify  specific 
consumer  groups  and  target  marketing  accordingly.  While,  as  noted  above,  targeted 
marketing  is  in  itself  not  always  problematic,  it  can  potentially  be  seen  as  exploiting 
consumer  vulnerabilities  where,  for  instance,  online  profiling  is  used  to  sell 
inappropriate  or  illegal  medicines  or  supplements  to  consumers  identified  as  seeking 
such  products  or  being  particularly  susceptible  to  marketing  of  pharmaceuticals  and 
supplements  (see  for  example  Newman  2014).  In  addition,  targeted  marketing in  the 
online  environment  differs  from  offline  practices  in  that  it  relies  on  the  collection  of 
data  about  the  consumers.  In  that  sense  it  can  face  similar  challenges  to  “pay  with 
data”  services,  where  consumers  are  not  aware  of  the  way  their  information  is  used. 
The  review  of  the  data  broker  industry  in  the US,  which  supplies  the  advertisers,  has 
shown,  for  instance,  that  consumers  have  few  means  of  verifying  or  addressing  the 
way  their  information  is  used  by  such  companies  (US  Senate  Committee  on 
Commerce, Science, and Transportation 2013). 

9.4. Comparison of vulnerability across the three sectors 

This section compares the incidence of vulnerability across the key three sectors of the 
study  based  on  the  sector  specific  indicators.  The  rates  of  vulnerability  in  the  three 
sectors,  based  on  the  full  sample,  are  shown  in  Table  82  (for  each  sector  incidence 
rates are shown by country in Table 78, Table 80 and Table 81). 

Table 82: 

Indicator 

Incidence of vulnerability in three sectors based on sector 
specific indicators – All EU28 countries for which data are 
available 

Energy  Online  Finance 

Dimension 3 
Compares deals using information from advertisements only 
Has problems comparing deals due to information factors 
Has not switched due to information-related factors  
Dimension 4 
Does not compare deals 
Finds it very difficult to compare deals 
Has problems comparing deals due to personal factors 
Has problems comparing deals due to market-related factors  
Has problems comparing deals due to access-related factors 
Has not switched due to personal factors 

2% 
15% 
2% 

52% 
14% 
28% 
33% 
10% 
10% 

4% 
8% 
0% 

31% 
4% 
16% 
23% 
4% 
3% 

2% 
10% 
1% 

57% 
13% 
31% 
33% 
2% 
7% 

269  See  for  example  results  of  a  survey  by  a  German  consumer  organisation  which  found  that 
approximately a fifth of doorstep sales in late 2013 concerned telecommunication contracts: 
https://www.verbraucherzentrale-niedersachsen.de/ergebnis-abzocke-haustuer-telefon 

368 

 
 
 
 
 
 
 
 
 
 
                                                 
9 │ Complexities of consumer vulnerability across different sectors 

Has not switched due to market-related factors 
Has not switched due to access-related factors   
Excluded from e-commerce due to difficulty of the process 
Excluded from e-commerce due to not having payment card 
Declined for a loan 
Dimension 5 
Incorrect experiment choices under the marketing practice* 
Note: The numbers of respondents and sets of countries on which these figures are based vary by indicator 
and sector, since in some countries some survey questions were not asked to consumers who answered via 
particular methods (in particular CAPI or CATI). The countries and survey modes for which each indicator is 
available can be seen in Table 5 in section 3.6. *Share of consumers who answered incorrectly in round 1 OR 
round 2 (i.e. who answered incorrectly in one or both rounds). 
Source: London Economics analysis of survey data 

6% 
3% 
19% 
15% 
n.a. 

11% 
12% 
n.a. 
n.a. 
n.a. 

13% 
2% 
n.a. 
n.a. 
4% 

53% 

85% 

27% 

Comparison between the finance and energy sectors 

Table 82 shows that, on several indicators in Dimensions 3 and 4, the finance sector is 
relatively  similar  to  the  energy  sector  at  the  EU28  level  in  terms  of  the  incidence  of 
vulnerability  in  these  sectors.  In  particular,  these  two  sectors  are  similar  in  terms  of 
the  shares  of  consumers  who  compare  deals  using  information  from  adverts  only,  do 
not  compare  deals,  have  problems  comparing  deals  due  to  personal  and  market-
related factors, and have been prevented from switching due to market-related factors.  

However, the incidence of vulnerability in the finance sector is lower than in the energy 
sector on a number of other indicators, in particular the shares of consumers who have 
problems comparing deals due to access-related factors and who have been prevented 
from  switching  due  to  access-related  factors,  as  well  as  the  share  of  consumers  who 
failed to select the correct offer in both rounds under the marketing practice treatment 
in the experiments.  

Here,  though,  it  should  be  taken  into  account  that  in  some  countries  (e.g.  Bulgaria, 
Cyprus  and  Malta)  the  retail  energy  market  is  not  liberalised  or  has  only  been 
liberalised  recently.  This  can  explain  the  high  incidence  rates  in  these  countries  on 
some  indicators  (i.e.  indicators  relating  to  comparing  and  switching)  in  the  energy 
sector,  since  it  means  that  de  facto  there  are  few  or  no  options  for  consumers  to 
compare.270 

At country level, in terms of the shares of consumers who compare deals, the finance 
and energy sectors are similar in most EU28 countries, and in particular in Austria, the 
Czech  Republic,  Denmark,  Latvia,  Norway  and  Spain.  Conversely,  in  Ireland,  the 
Netherlands and the United Kingdom consumers are less likely to compare bank deals 
than  energy  deals,  whereas  in  Bulgaria  and  Malta  the  rate  of  vulnerability  on  this 
indicator is higher in the energy sector than in the finance sector.271 

The  incidence  rate  of  problems  comparing  deals  due  to  personal  factors  is  also  very 
similar for the finance and energy sectors in most countries, and especially in Sweden, 

270  For  information  on  the  number  of  years  that  the  energy  market  has  liberalised  and  the 
concentration  of  the  market  (i.e.  the  combined  market  share  of  the  three  or  four  largest 
suppliers) in different EU countries, see the First Energy Retail Market Study (available here: 
http://ec.europa.eu/consumers/archive/strategy/docs/retail_electricity_full_study_en.pdf), 
the  ACER  Market  Monitoring  Report  (2015),  and  the  forthcoming  Second  Energy  Retail 
Market Study. 

271 Here the caveat given in the paragraph above should be noted. 

369 

 
 
 
 
 
 
                                                 
9 │ Complexities of consumer vulnerability across different sectors 

Denmark,  Belgium  and  Estonia.  However,  the  rate  of  vulnerability  on  this  indicator  is 
much  higher  in  the  energy  sector  than  in  the  finance  sector  in  several  countries, 
namely  Luxembourg,  Slovakia,  Austria  and  Iceland,  whereas  vulnerability  on  this 
indicator  is  much  lower  in  the  energy  sector  than  in  the  finance  sector  in  only  one 
country, namely Ireland.  

The  incidence  rate  of  vulnerability  in  terms  of  having  problems  comparing  deals  for 
market-related  reasons  is  also  very  similar  in  the  finance  and  energy  sectors.  The 
similar incidence rates in these two sectors in the EU28 is due to relatively similar rates 
in most countries, and in particular similar rates between these two sectors in Estonia, 
Finland and France, and to some extent in Denmark, Luxembourg and Slovakia.  

Conversely,  in  Lithuania,  Portugal  and  Iceland,  the  incidence  of  vulnerability  in  terms 
of having problems comparing deals for market-related reasons is higher in the finance 
sector than in the energy sector, in contrast to the results for the ‘problems comparing 
deals due to personal factors’ indicator. 

The  most  systematic  difference  between  the  finance  and  energy  sectors  in  terms  of 
problems comparing deals, however, stems from having problems comparing deals due 
to access-related factors. Overall across the Member States272, the share of consumers 
who  do  not  compare  deals  due  to  access-related  factors  is  much  lower  in  the  finance 
sector (2%) than in the energy sector (10%).273 This result is found in most countries, 
and  the  gap  is  particularly  large  in  Luxembourg,  Lithuania  and  Romania.  Countries 
where, conversely, the gap is much smaller are the Netherlands, Belgium and Ireland 
and to some extent in the UK, due to a much lower than average share of consumers 
who do not compare energy deals for access-related reasons in these countries.  

The  differences  and  similarities  between  the  finance  and  energy  sectors  in  terms  of 
consumers having problems comparing deals due to market-related and access-related 
factors  also  translate  to  the  switching  indicators.  The  shares  of  consumers  being 
prevented  from  switching  for  market-related  factors  are  similar  in  all  five  of  the 
experiment  countries274,  with  the  exception  of  Lithuania,  where  this  share  is  much 
higher in the finance sector than in the energy sector. In contrast, not switching due to 
access-related factors is, overall, much lower in the finance sector than in the energy 
sector.  This  is  less  so  in  the  UK  and  Portugal,  where  a  lower  share  of  consumers  are 
prevented from switching for access-related reasons in the energy sector. 

Comparison between the finance and online sectors 

The incidence of vulnerability in the finance sector is higher than in the online sector 
on  several  indicators  in  Dimension  4,  namely  the  shares  of  consumers  who  do  not 
compare deals, find it very difficult to compare deals, have problems comparing deals 

272  The  17  Member  States  for  which  the  survey  provides  information  on  the  `problems 

comparing deals’ indicators. 

273 However, as noted above, the high incidence rates for some indicators (i.e. indicators relating 
to  comparing  and  switching)  in  the  energy  sector  are  likely  to  be  due  to  the  fact  that  the 
market  is  not  liberalised  in  some  countries  (meaning  de  facto  there  are  no  options  for 
consumers to compare). 

274  The  survey  questions  related  to  the  switching  of  providers  were  only  asked  in  the  five 
countries  where  the  experiments  were  performed:  Denmark,  Lithuania,  Portugal,  Romania 
and the United Kingdom. 

370 

 
 
                                                 
9 │ Complexities of consumer vulnerability across different sectors 

due  to  personal  and  market-related  factors,  and  have  been  prevented  from  switching 
due to market-related factors. 

The share of consumers who do not compare deals in the finance sector is consistently 
higher than in the online sector in most countries. The differences between these two 
sectors  on  this  indicator  are  particularly  large  in  Bulgaria,  Croatia,  Greece  and 
Portugal,  but  much  lower  in  the  Czech  Republic  Malta,  Norway  and  Sweden  (in  fact, 
Norway is the only country in which the incidence rate on  these indicators is higher in 
the online sector than in the finance sector). 

In  terms  of  consumers  who  find  it  very  difficult  to  compare  deals,  the  proportion  of 
vulnerable consumers in the finance sector is higher than in the online sector in most 
countries,  the  only  exceptions  being  Luxembourg  and  Norway.  Countries  with  a 
particularly  large  difference  between  the  two  sectors  where  the  incidence  rate  of 
vulnerability on these indicators in the finance sector is higher than the EU average are 
Portugal, Bulgaria, Romania, Hungary and Croatia. 

In terms of having problems comparing deals due to personal factors, the higher rate 
of vulnerability in the finance sector (31%) than in the online sector (16%) across the 
countries  as  a  whole  is  driven  by  particularly  large  differences  in  Lithuania,  Portugal 
and Romania, as well as smaller differences in most other countries. 

However,  conversely,  fewer  consumers  compare  deals  only  using  information  from 
adverts  in  the  finance  sector  than  in  the  online  sector,  and  in  the  experiments  fewer 
consumers  failed  to  select  the  best  offer  in  both  rounds  under  the  finance  sector 
marketing practice than under the online sector marketing practice. 

Comparison between the energy and online sectors 

Table  82 also  shows  that  the incidence  of  vulnerability in  the  energy  sector  is  higher 
than in the online sector on all indicators that are available for both sectors, except for 
the share  of consumers who compare  offers using information from adverts only. The 
largest differences between these two sectors are in terms of the shares of consumers 
who  do  not  compare  deals  (Dimension  4)  and,  in  the  experiments,  the  shares  of 
consumers  who  failed  to  select  the  best  deal  in  both  rounds  under  the  marketing 
practices examined for the respective sectors (Dimension 5). 

At country level, the difference between the energy and the online sectors in terms of 
the  share  of  consumers  who  do  not  compare  deals  is  zero  or  very  small  only  in  the 
Netherlands, Belgium, Sweden and Norway, since in these countries the propensity to 
not compare deals in the online sector is much higher than the EU rate.  

The difference in terms of the share of consumers who find it very difficult to compare 
deals  is  also  relatively  large  across  the  countries  as  a  whole,  with  the  shares  in  the 
energy  sector  in  Bulgaria  and  Lithuania  being much  larger  than  the  respective  shares 
in the online sector.275 

275  Again,  however,  it  is  important  to  note  that  the  energy  market  is  not  liberalised  in  some 

countries, meaning that de facto there are no options for consumers to compare.  

371 

 
 
 
                                                 
10 │ Best practice policy measures in Member States and at EU level 

10.  BEST PRACTICE POLICY MEASURES IN MEMBER STATES AND AT EU 

LEVEL 

Box 8:  Summary of Chapter 10 

This  chapter  examines  the  measures  addressing  consumer  vulnerability  in  place  at 
the  EU  and  Member  State  level  and  aims  to  identify  potential  good  practices.  The 
types of measures identified include: 

  Support measures, including both financial and non-financial support; 

  Protection  measures  targeting  economic  actors  and  aiming  to  eliminate 

problematic practices; and 

  Awareness-raising measures, such as information campaigns or provision 

of specific advice.  

Given  the  complexity  of  consumer  vulnerability,  what  constitutes  a  good  practice 
depends on the specific driver or marketing practice to be addressed, as well as the 
broader  market  environment.  Nevertheless,  it  is  possible  to  identify  a  set  of  broad 
measures likely to have a positive impact. These include:  

  Well  targeted  support  measures  in  the  energy-sector  that  address  fuel 

poverty among vulnerable consumers; 

  Measures  addressing  marketing  practices  that  exploit  consumers’  vulnerable 

situations; and 

  Measures improving the quality and transparency of information.  

Examining  national  approaches,  we  found  that  measures  addressing  consumer 
vulnerability are relatively common across the EU. At the same time, a considerably 
smaller  number  of  Member  States  appears  to  have  developed  a  broader  strategic 
approach to consumer vulnerability.  

When  combining  a  typology  of  Member  States  based  on  identified  measures  and 
strategic approach with data on indicators of vulnerability, no clear pattern could be 
identified. This suggests that there is no clear link between incidence of vulnerability 
and the national approach to vulnerability. It is however important to note that our 
research does not allow for  examining potential causal links (i.e. whether measures 
addressing  vulnerability  result  in  lower  incidence,  or  whether  high  incidence  of 
vulnerability spurs the introduction of relevant measures at national level). 

This section outlines the specific measures addressing consumer vulnerability identified 
in the EU28, Iceland and Norway. It also outlines the institutional structure and role of 
civil  society  in  individual  Member  States  with  regard  to  consumer  vulnerability.  In 
doing so, the chapter aims to address research questions RQ9, RQ11 and RQ12 set out 
in section 1.4.  

10.1.  Measures addressing consumer vulnerability 

This  subsection  outlines  measures  targeting  market  operators  across  the  investigated 
countries. A key finding of the research conducted to date is the fact that even despite 
limited overall engagement with the concept of consumer vulnerability across the EU, a 
number  of  relevant  measures  have  been  identified.  At  the  same  time,  developing  a 
comprehensive  list  of  measures  addressing  all  aspects  of  consumer  vulnerability  is 
challenging  given  the  breadth  of  the  concept.  For  instance,  when  investigating 
measures  aiming  to  address  potential  vulnerability  experienced  by  low-income 

372 

 
 
10 │ Best practice policy measures in Member States and at EU level 

consumers,  one  could  identify  a  broad  range  of  social  policy  measures.  This  is 
something  recognised  on  national  level,  with  one  interview  noting  that  consumer 
vulnerability in the Netherlands is considered a domain to be addressed by the welfare 
state system as opposed to consumer protection legislation.  

Another challenge is the fact that most measures aiming to improve the position of all 
consumers  (or  an  average  consumer)  in  the  market  are  likely  to  also  have  a  positive 
impact  on  vulnerable  consumers.  In  order  to  limit  the  scope  of  the  review,  only 
horizontal  measures  explicitly  targeting  consumer  vulnerability  and  measures 
addressing specific vulnerable consumer groups are considered here. 

The  first  category  of  measures  identified  includes  support  measures.  These  can 
include  financial  support  measures  that  help  cover  or  reduce  costs  faced  by 
vulnerable consumer. Some examples are: 

  Lower energy tariffs for selected consumer groups (e.g. pensioners in Spain).  

  Lower telecommunication tariffs for people with disabilities (e.g. in Cyprus). 

  Schemes  to  help  lower-income  consumers  cover  some  of  their  energy  costs 

(Italy). 

  Support  to  improve  energy  efficiency  of  housing  in  low-income  areas  (the 

affordable warmth obligation in the UK Energy Company Obligation). 

  Fund to support loans to young people in precarious situations (Italy). 

There  is  also  a  range  of  non-financial  support  measures.  These  include  a  broad 
range of measures aimed at assisting vulnerable consumers in the marketplace. Some 
examples are: 

  Allowing  longer  debt  repayment  periods  for  people  in  unexpected  life 

circumstances (Finland). 

 

Insolvency procedures for individuals (Greece). 

  Measures  to  promote  universal  access  to  low-cost  bank  accounts  (Italy)  or 
provisions  calling  for  operators  to  provide  baking  services  suitable  to 
vulnerable consumers (France). 

A  second  category  of  measures  includes  protection  measures  that  target  suppliers 
and aim to eliminate problematic practices. These include: 

  Protection  from  energy  disconnection  (e.g.  Belgium,  Cyprus,  Hungary,  Malta, 

Netherlands, Sweden). 

  Prohibition  of  marketing  practices  particularly  problematic  for  some  consumer 
groups (e.g. “cold calling”276 in Austria, doorstep sales in Denmark, use of text 
messaging in the “instant credit” industry in Finland). 

  Upper limits on APR rates (Estonia). 

 

Information  requirements  (e.g.  requirement  to  inform  consumers  in  writing  of 
unexpected bank fees, such as overdraft fees, in France). 

276 This refers to making unsolicited phone calls to consumers for sales and marketing purposes. 
It is a practice that has been prohibited in Austria, according to an interviewed stakeholder 
organisation. 

373 

 
 
 
                                                 
10 │ Best practice policy measures in Member States and at EU level 

  Regulating selling methods (e.g. requirement for salesmen of financial products 
in  France  to  present  two  alternative  offers  if  a  loan  is  to  exceed  a  certain 
amount).  

  Guidelines  and  codes  of  conduct  for  suppliers  (e.g.  code  of  conduct  for 

marketing to young people in Belgium). 

  The  final  set  of  measures  includes  awareness-raising  measures,  such  as 
information  campaigns  aiming  to  develop  skills  of  specific  consumer  groups 
(e.g.  both  older  consumers  and  children  in  Poland)  or  websites  with  advice 
content  for  specific  consumer  groups  (e.g.  a  French  website  about  banking 
services with specific sections devoted to consumers in difficult life situations).  

When  examining  potential  differences  across  the  three  sectors  investigated  in 
detail  in  the  study  (financial  sector,  energy  sector,  and  online  environment  and 
electronic communications sector) the following broad patterns can be identified: 

  Financial  support  measures  are  primarily  present  in  the  energy  sector, 
although, as noted above, financial support is in some cases also available for 
covering costs of telecommunication services. 

  Non-financial  support  measures  have  been  primarily  identified  in  the 
financial  sector  and  primarily  focus  on  assisting  low-income  consumers  in 
difficult financial situations. 

  Protection  and  awareness-raising  measures  can  be  identified  across  all 

three sectors. 

  Overall, 

fewer  specific  measures  have  been 

identified  in  the  online 
environment  and  electronic  communications  sector  than  in  the  financial 
and energy sectors.  

Given  the  complexity  of  vulnerability  as  a  concept,  identifying  good  practices  is 
challenging.  Firstly,  it  is  likely  to  involve  an  element  of  subjectivity  and,  secondly, 
certain  measures  will  be  able  to  address  certain  types  of  vulnerabilities  in  certain 
situations,  but  might  not  be  suitable  in  other  contexts.  There  are,  however,  selected 
measures that are likely to have a more universal impact: 

  Well-targeted  support  measures  in  the  energy  sector  are  likely  to  have  a 

positive impact on addressing fuel poverty across the EU. 

  Any measures aiming at improving the quality and transparency of information 
are likely to have a positive impact not only for a specific consumer groups, but 
also  for  a  broader  range  of  consumers.  This  is  due  to  the  fact  that  many 
consumers  can  experience  cognitive  limitations,  especially  when  faced  with 
complex  offers.  This  should  not  be  equated  with  providing  more  information, 
since,  as  discussed  in  the  previous  sections,  increased  disclosure  does  not 
always benefit consumers. 

  Measures restricting marketing practices which allow economic actors to exploit 
vulnerable situations consumers may find themselves in (i.e. doorstep  selling) 
are likely to have an overall positive impact on vulnerable consumers.  

10.2. 

Institutional structure and role of civil society 

There  is  a  wide  range  of  institutions  involved  in  the  consumer  protection  across  the 
individual countries. These can be broadly divided into two categories: 

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10 │ Best practice policy measures in Member States and at EU level 

  National  authorities:  These  include  relevant  ministries,  as  well  as  specific 
government agencies responsible for enforcing consumer policy or undertaking 
related  tasks  (e.g.  surveillance  or  testing).  In  some  cases  the  agencies  also 
play  a  role  in  formulating  national  consumer  policy  alongside  relevant 
ministries.  

  Civil society organisations: These include primarily consumer organisations. 
The structure of the consumer organisation systems varies significantly across 
Member States. Potential models include a network of regional centres under a 
single  national  organisation  (e.g.  Germany),  a  network  of  organisations 
represented  by  a  single  umbrella  organisation  (e.g.  Denmark,  Estonia),  or  a 
range  of  national  and  regional-level  organisations  (e.g.  France,  Greece).  In 
addition to consumer  organisations, relevant civil society bodies  with a role in 
consumer  policy  can  also  include  family  organisations  (e.g.  in  Belgium)  or 
worker organisations (e.g. in Austria).  

Overall, there are no dedicated authorities or civil society bodies with an explicit focus 
on  consumer  vulnerability.  In  some  cases  organisations  focusing  on  specific  social 
groups, such as disability organisations or organisations representing older people, will 
engage in consumer protection issues (e.g. disability organisation in Romania) but this 
is, by definition, limited to a specific population group at risk.  

Consumer  vulnerability  is  present  in  the  work  of  national  authorities  and  civil  society 
bodies,  primarily  in  the  form  of  individual  activities  targeting  a  specific  group  of 
consumers  (e.g.  seminars  for  older  consumers  in  Germany)  or  activities  targeting 
specific drivers (e.g. accessibility or work relating to complex offers). Overall, however, 
no  vulnerability-specific  work-streams  have  been  identified  throughout  the  review, 
suggesting  that  both  public  authorities  and  civil  society  organisations  might  not  be 
well-positioned  to  implement  more  integrated  strategies  for  addressing  consumer 
vulnerability.  

10.3. 

Typology of Member States 

For  the  purpose  of  this  study,  a  typology  of  Member  States  should  aim  to  effectively 
capture  the  main  elements  of  the  national  approach  to  consumer  vulnerability.  This 
would  therefore  need  to  include  the  following  aspects,  which  have  also  guided  the 
development of the country fiches presented in Annex 1: 

  The  overall  strategic  approach  to  consumer  vulnerability.  This  should 
distinguish  between  Member  States  where  the  concept  of  consumer 
vulnerability is present in legislation and the overall consumer protection policy 
framework from those where this is not the case. 

  Specific measures addressing consumer vulnerability. Including a notion 
of  vulnerability  in  legislation  or  the  consumer  policy  framework  does  not 
necessarily  mean  that  there  are  measures  in  place  aiming  to  address  it. 
Similarly,  there  could  be  situations  where  Member  States  have  measures  in 
place  addressing  specific  aspects  of  consumer  vulnerability  without  a  broader 
strategic framework in place.  

  The  institutional  structure.  This  should  aim  to  distinguish  Member  States 
with  public  bodies  or  civil  society  organisations  working  towards  addressing 
consumer vulnerability from those where such organisations do not exist.   

There are a number of challenges in implementing these aspects as part of a typology. 
Since  the  typology  needs  to,  in  as  far  as  possible,  be  based  on  observed  objective 

375 

 
 
 
10 │ Best practice policy measures in Member States and at EU level 

criteria, it is not possible to include a quality element in the assessment in a consistent 
manner (i.e. degree to which the approach to consumer vulnerability exists solely “on 
paper”  as  a  result  of  transposing  the  UCPD  or  is  part  of  a  longer-standing  consumer 
policy).  

Similarly, the information on measures collected though desk research and stakeholder 
consultation  does  not  allow  us  to  build  a  full  inventory  of  national  measures  due  to 
differences  in  quality  of  input  and  the  large  range  of  actions  that  could  be  seen  as 
being  relevant  to  consumer  vulnerability.  This  in  turn  means  that  it  is  difficult  to 
develop a clear-cut typology based on a number of measures, necessitating the use of 
a more subjective grouping.  

Finally, the investigation of the institutional structure and the role of civil society have 
shown that existing bodies play a role in addressing consumer vulnerability across the 
EU,  but  there  are  no  examples  of  structures  specifically  tailored  to  addressing 
vulnerability.  This  in  turn  means  that  it  is  not  feasible  to  develop  a  clear  typology  of 
institutional structures relating specifically to consumer vulnerability.  

Given the above challenges, we propose to focus the typology on the first two aspects, 
namely: 

  Strategic  approach  to  consumer  vulnerability,  distinguishing  between 
Member  States  where  a  broader  strategic  approach  to  consumer  vulnerability 
appears  to  be  in  place  (beyond  the  transposition  of  the  UCPD)  and  Member 
States where this appears not to be the case. 

  Measures  addressing  consumer  vulnerability,  distinguishing  between 
Member  States  with  a  large  range  of  sector-specific  measures  in  place  and 
Member States with a more limited number of measures.  

The following table shows the grouping of Member States based on the above aspects 
and  presented  as  a  2  by  2  matrix.  It  is  important  to  note  that  this  grouping  is 
indicative and can be subject to change on the basis of any additional research carried 
out in the study.  

376 

 
 
10 │ Best practice policy measures in Member States and at EU level 

Table 83: 
approach to consumer vulnerability 

Country grouping based on the main elements of the national 

Range of sector-specific 
measures 

Limited number of 
measures 

Broader strategic 
approach to 
vulnerability 

No strategic 
approach to 
vulnerability 

Finland 
Hungary 
Ireland 
Poland 
United Kingdom 
Belgium 
Bulgaria 
Cyprus 
Denmark 
Estonia 
France 
Greece 
Italy 
Malta 
Norway 
Portugal 
Slovenia 
Spain 
Sweden 

Czech Republic 
Romania 

Austria 
Croatia 
Germany 
Iceland 
Latvia 
Lithuania 
Luxembourg 
Netherlands 
Slovakia 

A typology of Member States is also established based on the incidence of vulnerability 
on  the  vulnerability  indicators  constructed  from  the  survey  data.  This  typology  is 
established through cluster analysis, as explained in Annex 7. 

Table  84  compares  the  typology  of  countries  based  on  the  main  elements  of  the 
national  approach  to  consumer  vulnerability  (from  Table  83  above)  with  the  country 
groups established via the cluster analysis (reported in  Annex 7), and also shows, for 
each country, the average level of vulnerability on average across the dimensions and 
by dimension.277 

Table  84  shows  that,  in  general,  the  typology  based  on  country’s  approaches  to 
consumer  vulnerability  does  not  match  the  typology  based  on  the  incidence  of 
vulnerability  established  via  the  cluster  analysis.  The  only  group  from  the  approach-
based typology that corresponds to some extent to a particular country group from the 
cluster  analysis  is  the  group  with  no  strategic  approach  to  vulnerability  and  a  limited 
number of measures, which is primarily composed of countries from group 1 from the 
cluster analysis.  

277  The  average  rates  of  vulnerability  shown  in  Table  84  are  calculated  based  on  the  fifteen 
indicators  used  in  the  cluster  analysis.  These  indicators  are  listed  in  Annex  7.  These 
indicators  are  used  in  the  cluster  analysis  since  they  are  balanced  across  the  three  key 
sectors  and  are  available  for  all  30  countries.  The  average  level  of  vulnerability  across  the 
dimensions  (the  Total  column  in  Table  84)  is  calculated  by  first  taking  the  average  of  the 
indicators for each dimension, then calculating the average of the dimension averages. 

377 

 
 
 
 
 
 
                                                 
10 │ Best practice policy measures in Member States and at EU level 

Group  1  from  the  cluster  analysis  is  a  group  consisting  mainly  of  Germanic  and 
Scandinavian countries278, which have among the lowest rates of vulnerability. Five out 
of  nine  countries  with  no  strategic  approach  to  vulnerability  and  a  limited  number  of 
measures are in group 1 from the cluster analysis. 

Table 84: 

Comparison of country typologies based on approach to 

consumer vulnerability and incidence of vulnerability 

Broad 
strategic 
approach 

Measures 
addressing 
vulnerability 

Cluster 
analysis 
group 

Average vulnerability 

Total  D1 

D2 

D3 

D4 

D5 

2 
4 

Yes 
Yes 

2 
2 
5 
3 
3 

22% 
33% 

19% 
40% 

34% 
29% 

41% 
69% 

Limited 
Limited 

Yes 
Yes 
Yes 
Yes 
Yes 

20% 
24% 
22% 
28% 
24% 

18% 
22% 
29% 
36% 
29% 

30% 
28% 
28% 
37% 
44% 

44% 
52% 
54% 
61% 
57% 

Range 
Range 
Range 
Range 
Range 

No 
No 
No 
No 
No 
No 
No 
No 
No 
No 
No 
No 
No 
No 

Range 
Range 
Range 
Range 
Range 
Range 
Range 
Range 
Range 
Range 
Range 
Range 
Range 
Range 

UK 
FI 
IE 
HU 
PL 
Average 
CZ 
RO 
Average 
NO 
SE 
DK 
BE 
FR 
MT 
IT 
SI 
PT 
ES 
EE 
EL 
BG 
CY 
Average 
AT 
NL 
DE 
LU 
IS 
SK 
HR 
LT 
LV 
Average 
Note:  ‘Range’  means  ‘Range  of  sector-specific  measures’,  ‘Limited’  means  ‘Limited  number  of  measures’.  
Highlight  figures  identify  the  groups  with  the  lowest  average  vulnerability  for  the  dimension  (dimension  3 
does not vary across the groups). 

39% 
30% 
37% 
32% 
40% 
35% 
35% 
39% 
40% 
46% 
35%  27%  54%  33%  24%  39% 
37% 
31% 
44% 
51% 
38%  30%  55%  32%  27%  44% 
32% 
27% 
31% 
28% 
30% 
29% 
34% 
32% 
43% 
34% 
40% 
35% 
43% 
35% 
42% 
36% 
49% 
37% 
48% 
39% 
41% 
40% 
48% 
42% 
45% 
44% 
45% 
52% 
36%  24%  56%  32%  27%  41% 
27% 
26% 
26% 
26% 
29% 
28% 
36% 
31% 
27% 
31% 
40% 
34% 
35% 
37% 
44% 
41% 
42% 
47% 
33%  25%  46%  33%  26%  34% 

30% 
29% 
34% 
30% 
29% 
35% 
30% 
28% 
28% 
39% 
38% 
28% 
40% 
35% 

37% 
38% 
42% 
47% 
53% 
45% 
59% 
63% 
65% 
61% 
63% 
73% 
63% 
71% 

23% 
23% 
24% 
26% 
29% 
32% 
27% 
25% 
24% 
24% 
24% 
29% 
31% 
36% 

15% 
21% 
17% 
24% 
15% 
22% 
19% 
23% 
18% 
21% 
36% 
30% 
42% 
32% 

Limited 
Limited 
Limited 
Limited 
Limited 
Limited 
Limited 
Limited 
Limited 

28% 
30% 
32% 
27% 
37% 
35% 
27% 
43% 
37% 

35% 
34% 
40% 
39% 
34% 
50% 
60% 
59% 
64% 

22% 
22% 
19% 
31% 
33% 
23% 
31% 
28% 
27% 

19% 
19% 
21% 
20% 
24% 
23% 
32% 
31% 
35% 

No 
No 
No 
No 
No 
No 
No 
No 
No 

1 
1 
1 
2 
5 
2 
5 
5 
5 
4 
3 
4 
3 
4 

1 
1 
1 
1 
1 
2 
5 
3 
3 

Similarly, there is no link between the country groups based  on country’s approaches 
to consumer vulnerability and the average level of vulnerability on average across the 
dimensions (as can be seen from Figure 39 below). 

Looking at the average level of vulnerability on average across the dimensions and by 
dimension (from Table 84): 

278 Countries included in this group are Germany, Denmark, Sweden, Norway, Luxembourg, the 

Netherlands, Austria, and Iceland. 

378 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                                 
10 │ Best practice policy measures in Member States and at EU level 

  The  countries  with  no  strategic  approach  to  vulnerability  and  only  a  limited 
number of measures have, on average, the lowest incidence of vulnerability on 
average across the dimensions (see the Total column in  Table 84), as well as, 
on  average,  lower  vulnerability  on  the  perceived  vulnerability  indicators 279 
(dimensions 2 and 5). 

  The  countries  with  no  strategic  approach  but  with  a  range  of  sector-specific 
measures  have,  on  average,  lower  vulnerability  on  indicators  relating  to 
negative  outcomes  or  impacts  of  wellbeing280  (dimension  1),  although  this 
average  figure  obscures  a  large  variation  within  this  group  (e.g.  Bulgaria  has 
the highest vulnerability in this dimension). 

  Countries  with  both  a  broad  strategic  approach  and  a  range  of  sector-specific 
measures  have,  on  average,  the  lowest  level  of  vulnerability  on  dimension  on 
indicators  relating  to  inability  or  failure  to  buy,  choose,  or  access  suitable 
products281 (dimension 4). 

279 Dimension 2 is represented by indicator 3 ‘Perception of own vulnerability due to personal characteristics’ 
and dimension 5 is represented by indicator 12_off ‘Perception of own vulnerability due to complexity of 
offers’. 

280  Dimension  1  is  represented  by  indicators  1  ‘Unassertive  (took  no  action)  when  experienced  a  problem’ 

and 2 ‘Overpaid for services due to being unable to use certain payment methods’. 

281  Dimension  4  is  represented  by  indicators  8_1_ene,  8_2_ene,  8_1_onl,  8_2_onl,  8_1_fin,  8_2_fin, 

9e_bun, 9d_ter, 10a, 10b.  

379 

 
 
 
 
                                                 
10 │ Best practice policy measures in Member States and at EU level 

Figure 39:  Level of vulnerability on average across all dimensions, by 

country group from the approach-based typology 

380 

42%41%37%34%31%31%28%26%26%45%44%42%40%39%37%36%35%35%34%32%29%28%27%44%31%40%39%35%32%30%0%10%20%30%40%50%LVLTHRSKISLUDENLATCYBGELEEESPTSIITMTFRBEDKSENOROCZPLHUIEFIUKBroadstrategic approach, limited measuresBroadstrategic approach, range of measuresNo strategic approach, range of measuresNo strategic approach, limited of measures 
 
 
 
11 │ Conclusions, recommendations and policy options 

11. CONCLUSIONS, RECOMMENDATIONS AND POLICY OPTIONS  

This  chapter  presents  conclusions,  recommendations  and  policy  options  based  on  the 
evidence  collected  and  analysis  undertaken  for  the  present  study.  In  particular  the 
chapter sets out:  

  Overall conclusions for the study 

  Recommendations for the Consumer Scoreboards and market studies; 

  Recommendations and conclusions concerning the UCPD Guidance; and 

  Policy options for mitigating consumer vulnerability. 

11.1.  Conclusions 

This  study  contributes  substantially  to  the  evidence  base  in  regard  to  consumer 
vulnerability. In particular, the study: 

  Demonstrates how consumer vulnerability can be operationalised and explored, 

conceptually and empirically through data collection. 

  Provides  insights  to  assist  consumer  policy-making,  such  as  insights  into  the 
key factors linked to consumer vulnerability, which policy measures or types of 
interventions  are  most  effective  in  mitigating  consumer  vulnerability,  and  the 
role of problematic commercial practices. 

  Provides  useful  insights  to  help  the  development  of  the  EU’s  consumer 
evidence  base,  including  the  Consumer  Scoreboards,  Market  Studies,  and 
Behavioural Studies. 

  Provides insights relevant to the UCPD and updating of the UCPD guidance. 

The study employs five different research methodologies:  

  A  literature  review  of  general  and  sector  specific  concepts  and  definitions  of 

vulnerability 

  Stakeholder  consultations  including  national  consumer  associations,  country 

experts and EU-level stakeholder organisations 

  A consumer survey in all EU Member States plus Iceland and Norway 

  Behavioural experiments and in-depth biographical interviews  

Understanding of consumer vulnerability 
The literature review identified two broad vulnerability dimensions: 1) vulnerability that 
relates to personal characteristics and, 2) a broader concept which takes into account 
the  transactional  situations  in  which  a  consumer  finds  themselves.  Vulnerability  can 
also  be  a  temporal  condition in  which  a  consumer  may  move  in  and out  of  a  state  of 
vulnerability  and  they  may  be  vulnerable  in  respect  to  some  transactions  but  not 
others.  

Based on the findings from the literature review, five core vulnerability dimensions are 
used in the study:  

1.  Heightened risk of negative outcomes or impacts on well-being - interpreting 

vulnerability as an ex-ante assessment of the likelihood of a negative outcome, 

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as a loss of welfare due to choices in markets, and as higher susceptibility to 
harm and diminished well-being. 

2.  Having characteristics that limit ability to maximise well-being - interpreting 
vulnerability as characteristics that limit consumers’ ability to maximise their 
utility and well-being, as consumers of diminished capacity to understand 
advertising and product effects, and as consumers’ certain abiding 
characteristics. 

3.  Having difficulty in obtaining or assimilating information - interpreting 

vulnerability as limitations obtaining or assimilating consumer information and 
‘informational vulnerability’ which refers to scenarios where suppliers may have 
better information than consumers.  

4.  Inability or failure to buy, choose or access suitable products - interpreting 

vulnerability as consumer inability to choose or access products and services 
which are suitable for their needs or do so without disproportionate effort or as 
not having access to beneficial products or services, including a distinction 
between buying unsuitable goods or services and failing to buy suitable goods 
and services.  

5.  Higher susceptibility to marketing practices, creating imbalances in market 

interactions - interpreting vulnerability as the effect of marketing practices and 
consumers’ special susceptibility. 

Vulnerability incidence rates 
The  highest  incidence  rate  across  the  EU  is  observed  for  dimension  5.  The  second 
highest  incidence  rate  is  observed  for  Dimension  4.  Overall,  almost  75%  of  EU-28 
consumers exhibit at least one Dimension 4 vulnerability indicator. No single dimension 
2  indicators,  namely  feelings  of  vulnerability  individual  personal  characteristics,  have 
particularly  high  vulnerability  incidence  rates.  However,  overall,  about  23%  of 
consumers  are  vulnerable  on  the  basis  of  one  or  several  personal  characteristics. 
Among the personal characteristics, financial and employment circumstances show the 
highest  incidence  rate.  The  incidence  of  dimension  1  and  3  vulnerability  indicators  is 
relatively  low,  generally  10%  or  less  and  never  exceeds  15%.  Overall,  both  the 
analysis of the incidence of vulnerability across the EU as whole and of the variation of 
vulnerability across EU Member States highlights that marketing practices, lack of use 
of information or imperfect information and access issues are the most frequent causes 
of vulnerability.  

Among the three sectors of specific interest for this study (finance, energy and online), 
the  incidence  of  vulnerability  tends  to  be  higher  in  the  energy  and  financial  sectors 
than in the online sector for the majority of the vulnerability indicators. 

Vulnerability drivers 
Using  the  results  from  the  analysis  of  the  incidence  and  drivers  of  vulnerability,  a 
theoretical  framework  of  vulnerability  was  populated.  This  framework  was  developed 
on the basis of the findings from the literature review. It provides an overall picture of 
the  most  important  drivers  and  effects  of  vulnerability.  The  key  points  from  the 
framework are: 

  Market-related  drivers  are  especially  important,  in  particular  being  unable  to 
read contract terms and conditions and being disengaged from markets (e.g. in 
terms of not knowing contract conditions, or not reading communications);  

  Behavioural  drivers  also  have  important  effects,  in  particular  more  impulsive 
consumers  are,  on  the  whole,  more  likely  to  be  vulnerable  on  several 

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dimensions  while  consumers  who  are  willing  to  take  risks  are  generally  less 
vulnerable; 

  Among the situational drivers, finding it difficult to make ends meet and having 
friends who cannot make ends meet are found to be especially important.  

  Among the access-related drivers, using the internet less frequently than once 

a month is linked to consumer vulnerability on several dimensions. 

  Some demographic characteristics, such as both young and old age and having 
a  mother  tongue  different  to  the  official  language,  are  linked  to  consumer 
vulnerability. 

The  study  also  examines  the  interaction  between  marketing  practices  and  consumer 
vulnerability. Four behavioural experiments were designed and implemented to assess 
the  effect  of  common  marketing  practices  on  consumer  decision  making.  The 
marketing  practices  tested  are:  more  or  less  prominence  given  to  important 
information  (in  the  financial  sector),  the  impact  of  dripping  prices  and information  (in 
the online sector), the complexity of tariffs (in the energy sector) and the impact of the 
use  of  a  teaser  rate  (across  the  three  sectors).  On  average,  across  the  four 
experiments, 58% of consumers did not manage to select the best deal on offer in the 
experiments.  But,  after  being  provided  with  clearer  information,  a  statistically 
significant 7 percentage points more respondents selected on average the optimal offer 
across all the experiments. 

Each of the sectors, however, has important specificities. In particular, in the financial 
and  energy  sectors,  complexity  presents  a  challenge  for  a  very  wide  range  of 
consumers as they are likely to find it difficult to understand and compare offers. 

11.1.1.  Revised definition of vulnerability 

This  study  explores  the  definitions  of  vulnerability  used  in  literature,  highlighting  the 
shift from definitions focusing on personal characteristics of the consumer to definitions 
taking into account the overall situation in which the consumers  find themselves.  The 
consequent investigation of vulnerability drivers and marketing practices confirms that 
the  broader  market  environment  is  an  important  element  of  vulnerability,  but  that 
temporary or permanent characteristics of the consumer also play an important role.  

Based  on  the  identified  literature,  the  study  aimed  to  operationalise  consumer 
vulnerability in terms of a set of five dimensions. This operationalisation can in turn be 
used to update and enhance existing vulnerability definitions. A “vulnerable consumer” 
could therefore be defined as: 

“A  consumer,  who,  as  a  result  of  socio-demographic  characteristics,  behavioural 
characteristics, personal situation, or market environment: 

 

Is at higher risk of experiencing negative outcomes in the market; 

  Has limited ability to maximise their well-being;  

  Has difficulty in obtaining or assimilating information; 

 

 

Is less able to buy, choose or access suitable products; or  

Is more susceptible to certain marketing practices”  

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11.2.  Recommendations for the Consumer Scoreboards and market studies 

This section sets out lessons that can be learned from the present study regarding the 
EU’s Consumer Scoreboards and future market studies. Specifically the section focuses 
on two issues: 

  What  question  areas,  in  terms  of  consumers’  outcomes,  attitudes  and 

behaviour, should future consumer surveys aim to examine? 

  What survey methods should be employed, and in particular should the use of 

mixed-mode surveys be considered? 

11.2.1.  Areas that should be examined in consumer surveys 

The  EU  Consumer  Scoreboards  are  based  on  regular  surveys  of  consumers,  and 
consumer surveys are a key tool used for evidence collection during EU market studies. 
Therefore,  it  is  useful  to  consider  what  lessons  can  be  drawn  from  the  present  study 
regarding  which  question  areas  future  consumer  surveys  should  aim  to  examine  –  in 
terms  of  consumers’  outcomes,  attitudes  and  behaviour  –  especially  if  these  surveys 
are  intended  to  cover  the  issue  of  consumer  vulnerability.  This  includes  questions  to 
operationalise vulnerability and questions to operationalise the drivers of vulnerability. 

Questions to operationalise vulnerability 

This study examines the various definitions of consumer vulnerability that are found in 
literature  and,  based  on  the  literature,  operationalises  vulnerability  in  terms  of  five 
dimensions.  A  suggested  revised  definition  of  consumer  vulnerability  is  proposed  on 
this basis in section 11.1.1. 

In terms of questions to measure the incidence of consumer vulnerability, our analysis 
found  that  different  consumers  may  be  vulnerable  depending  on  which  indicator  or 
dimension of vulnerability is considered.  

Therefore,  if  a  survey  is  intended  to  measure  vulnerability,  it  must  first  consider  how 
tight  a  definition  of  vulnerability  is  appropriate.  A  tighter  definition  can  be  targeted 
using  a  relatively  small  number  of  indicators,  e.g.  focussing  on  just  one  dimension  of 
vulnerability);  otherwise  a  broader  definition  will  imply  that  the  survey  needs  to 
include questions which can be used to construct a wide-ranging set of indicators (as in 
the present study). 

Whether  it  is  appropriate  to  examine  particular  dimensions  or  all  dimensions  of 
vulnerability  should  be  decided  based  on  initial  scoping  work.  That  said,  two  possible 
scenarios  in  which  it  may  be  appropriate  to  focus  on  particular  dimensions  of 
vulnerability include: 

  Where  the  policy  context  and/or  objective  of  the  survey/study  is  relatively 
focused;  for  example,  if  a  study  is  focused  in  particular  on  the  effect  of 
marketing practices and regulatory measures to deal with these practices, then 
the  survey/study  could  focus  specifically  on  the  fifth  dimension  (high 
susceptibility to marketing practices). 

  Where  existing  evidence  suggests  a  particular  dimension  is  more  relevant  to 
the sector; for example in a sector widely recognised for information problems, 
it  may  be  appropriate  to  focus  on  the  third  dimension  (having  difficulty  in 
obtaining or assimilating information). 

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The  indicators  developed  from  the  survey  and  experiment  data  collected  during  this 
study, in order to populate the vulnerability dimensions established from the literature, 
provide a good basis for operationalising consumer vulnerability.  

In  addition  to  these  indicators,  some  questions  used  to  operationalise  the  drivers  of 
vulnerability in this study could be used as measures of vulnerability in future studies. 
A  particular  candidate  is  the  question  on  making  ends  meet  (which  in  this  study  is 
included  among  the  situational  drivers  in  order  to  proxy  for  a  difficult  income 
situation),  since  this  question  is  found  to  be  closely  linked  with  many  indicators  of 
vulnerability  and  conceptually  could  fit  within  the  first  dimension  of  vulnerability 
(negative outcomes or impacts on well-being). 

Furthermore,  additional  questions  that  could  be  used  to  operationalise  vulnerability 
(not  included  in  the  survey  for  this  study)  may  include,  for  example,  questions  on 
arrears, such as arrears on bills or loans. Such questions would also fit within the first 
dimension  of  vulnerability  established in  this  study  (negative  outcomes  or  impacts  on 
well-being). 

Questions to operationalise the drivers of vulnerability 

The  questions  that  should  be  included  in  a  survey  would  again  depend  on  the 
objectives  of  the  survey.  Researchers  designing  a  survey  to  examine  the  drivers  of 
vulnerability  should  consider  including  questions  to  measure  the  variables  that  are 
shown in this study to be strongly linked to the various dimensions of vulnerability. In 
particular,  based  on  the  results  of  the  survey  and  experiment  and  the  data  analysis 
undertaken  for  the  present  study,  a  number  of  survey  question  areas  can  be 
highlighted as potentially very informative: 

  Firstly,  questions  that  examine  market-related  drivers,  including  market-
related  behaviour  and  characteristics,  are  particularly  important  since  these 
factors  are  found  to  be  associated  with  various  dimensions  of  consumer 
vulnerability (see the vulnerability framework presented in section 7.3).  

For  example,  these  might  include  questions  (similar  to  those  asked  in  the 
survey conducted for the present study) on the frequency at which consumers 
compare  deals  in  markets,  whether  they  know  their  own  contract  terms  and 
conditions, and whether they read and are able to understand communications 
from providers.  

A useful feature of such questions is that they may be asked with respect to a 
particular sector. This is important since, as shown in Table 82 in section 9.1.1, 
the incidence of these drivers may vary considerably across sectors, which may 
explain differences in the rates of vulnerability across sectors. 

The  specific  approach  taken  to  asking  these  questions  in  the  present  study, 
with consistent wording of questions and answers across sectors, could be used 
as a broad template for future surveys (the survey questionnaire is presented 
in Annex 12). 

Further  examples  of  questions  in  this  area,  not  covered  in  the  survey 
undertaken  for  the  present  study,  might  include  questions  on  consumers’ 
understanding of different types of marketing material from providers. 

  Secondly,  the  analysis  undertaken  for  the  present  study  also  highlights  that 
behavioural  drivers  and  characteristics  are  important,  which  suggests  that 
questions  designed  to  determine  consumers’  behavioural  characteristics  may 
be  useful  additions  to  the  usual  mix  of  survey  questions.  These  may  include 

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questions  that  ask  respondents  to  self-assess  their  own  character  and/or 
questions which ‘test’ respondents’ character.  

For example, the survey conducted for the present study included questions to 
test respondents’ computational ability, which asked them to identify which of 
two  offers  was  cheapest  (see  question  Q23  in  Annex  12),  and  a  question  to 
test respondents’ credulity, which required them to interpret the meaning of an 
advert  (see  question  Q14  in  Annex  12).  The  answers  to  these  questions  are 
found to be strongly related to some indicators of vulnerability. 

Possible  questions  to  test  behaviour  and  ability  also  include  behavioural 
experiments,  which  ask  respondents  to  make  decisions  in  simulated  market 
situations.  The  results  of  the  experiments  undertaken  for  the  present  study 
show  that  experiments  can  usefully  be  employed  to  explore  the  impact  of 
marketing practices and the effectiveness of remedies. Furthermore, follow-up 
questions  can  be  included  after  a  behavioural  experiment  in  order  to  further 
explore why consumers make particular decisions in certain circumstances. 

Experiments  can  be  particularly  useful  in  market  studies,  since  they  can  be 
designed  to  investigate  particular  market  practices  or  features  (e.g.  price 
frames  such  as  drip  pricing,  time  limited  discounts,  etc.).  Furthermore, 
experiments  could  also  be  designed  to  examine  possible  ways  to  alleviate  the 
effects  of  market-related  drivers.  For  example,  being  unable  to  understand 
bills/communications from providers was found to be an important driver (see 
section 7.3), and an experiment could be developed to test ways of remedying 
this driver (e.g. improvements to the format of bills/communications). 

  Thirdly,  some  situational  drivers  are  found  to  be  important  causes  of 
vulnerability, in particular difficult financial circumstances, as measured by how 
easy  consumers  find  it to  make  ends  meet,  and  similarly  whether  consumers’ 
friends  find  it  difficult  to  make  ends  meet.  This  suggests  that  future  surveys 
that  are  intended  to  examine  the  issue  of  consumer  vulnerability  should  also 
include questions on financial circumstances or hardship. 

Questions for the Scoreboards 

The Consumer Markets Scoreboard (CMS) is based on data from the Consumer Market 
Monitoring  Survey  (MMS).  For  each  of  51  consumer  markets,  the  CMS  (and  MMS) 
monitors six components of market performance  – comparability, trust, problems and 
complaints, satisfaction, choice and switching – via the following survey questions:282 

  Comparability:  “On  a  scale  from  0  to  10,  how  difficult  or  easy  was  it  to 
compare  the  products/services  sold  by  different  retailers/offered  by  different 
service providers?” 

  Trust:  “On  a  scale 

from  0  to  10,  to  what  extent  do  you  trust 
retailers/providers to respect the rules and regulations protecting consumers?” 

  Problems and complaints: “Within the past <X> year(s), did you experience 
any problem with the product/services you purchased/paid for, either with the 
product  or  the  retailer/the  service  or  provider,  where  you  thought  you  had  a 
legitimate  cause  for  complaint?”;  and  “Have  you  complained  about  any  of 
these problems?” 

282 See pages 11 and 12 of ‘Monitoring Consumer Markets in the European Union’ 2012:  
http://ec.europa.eu/consumers/archive/consumer_research/editions/docs/monitoring_consumer_markets_e

u_2012_en.pdf. 

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11 │ Conclusions, recommendations and policy options 

  Satisfaction:  “On  a  scale 

from  0  to  10,  to  what  extent  did  the 
products/services on offer from different retailers/providers live up to what you 
wanted within the past year?” 

  Choice:  “On  a  scale  from  0  to  10,  would  you  say  there  are  enough  different 

retailers/providers you can choose from?” 

  Switching: “Have you switched service or provider in the past year?”, and “On 
a  scale  from  0  to  10,  how  difficult  or  easy  do  you  think  it  would  have  been/ 
was it to switch provider in the past year?” 

Some  of  these  questions  are  similar  to  those  asked  in  the  survey  undertaken  for  the 
present study and so could be used to operationalise vulnerability via indicators similar 
to  those  examined  in  this  study.  For  example,  the  question  on  comparability  in  the 
MMS could be used to provide an indicator very similar to indicators 24, 26 and 28 in 
Table 5. 

Furthermore,  some  questions  in  the  MMS  match  the  dimensions  of  vulnerability 
established  in  this  study  based  on  the  literature  and  so  could  be  used  to  provide 
further/alternative  indicators  (for  example,  the  question  on  satisfaction  in  the  MMS 
could  provide  an  indicator  within  Dimension  1,  High  risk  of  negative  outcomes  of 
impacts  on  wellbeing,  since  dissatisfaction  with  a  good/service  can  be  seen  as  a 
negative outcome for consumers). 

However,  a  shortcoming  of  the  MMS,  in  terms  of  measuring  vulnerability in  the  same 
way as the present study, is the depth of questioning in the MMS (although it needs to 
be  acknowledged  that  the  brevity  of  the  MMS  is  an  advantage  in  terms  of  cost  and 
implementation).  In  particular,  the  MMS  does  not  explore  why  it  is  hard  to  compare 
offers, why consumers do not compare offers, why consumers do not switch.  

Therefore,  the  MMS  cannot  be  used  to  produce  such  a  rich  set  of  indicators  as  that 
which  is  investigated  in  this  study,  and  some  of  the  dimensions  of  vulnerability 
identified  in  this  study  cannot  be  examined  via  MMS  (e.g.  the  Dimension  3,  Having 
difficulty in obtaining or assimilating information). 

Hence,  if  the  objective  to  use  the  CMS/MMS  to  provide  an  ongoing  assessment  of 
consumer vulnerability, then it may be worthwhile considering including questions that 
provide more in depth information on the reasons why consumers find it hard to/don’t 
compare/switch. Examples of such questions include A9a, A9b, B8a, B8b, C6 and C6b 
in the survey undertaken for the present study (see Annex 13). 

However,  it  is  important  to  recognise  that  there  would  be  a  trade-off  to  be  made  in 
terms  of  the  cost  and  feasibility  of  the  MMS  (which  unlike  the  survey  for  the  present 
study is entirely a telephone survey). 

Likewise, the Consumer Conditions Scoreboard (CCS) could also be expanded in order 
to cover further aspects of consumer vulnerability that are identified in this study.  

The CCS examines issues such as whether consumers have encountered misleading or 
fraudulent  offers,  whether  consumers  have  had  reason  to  complain  when  buying  or 
using  goods  or  services  and  whether  they  took  action  when  this  occurred,  whether 
consumers find it easy to resolve disputes, and whether consumers are aware of their 
rights. 

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However, some aspects of vulnerability covered in the present study are not examined 
in the CCS, such as whether consumers feel informed about prices, quality etc. and the 
types  of  problems  that  consumers  encounter  (e.g.  problems  relating  to  termination 
costs,  bundling,  e-commerce  and  obtaining  credit,  which  are  the  subject  of  indicators 
47 to 51 in Table 5). Thus, if the aim is to provide an ongoing assessment of consumer 
vulnerability, then CCS could be expanded to cover these issues. Examples of relevant 
questions  in  the  survey  undertaken  for  this  study  includes  Q17.1,  Q23.1,  Q23.3, 
Q23.4, Q23.5, Q23.6 and Q13b (see Annex 13). 

11.2.2.  Survey methods 

An  in-depth  assessment  of  the  strengths  and  weaknesses  of  the  survey  methods 
employed  in  the  present  study,  including  a  thorough  evaluation  of  the  use  of  mixed 
modes, 
in  Annex  2.  In  this  section,  three 
recommendations are drawn regarding implementation of surveys: 

in  section  A2.10 

is  presented 

Recommendation 1: A mixed-mode pilot 

Estimating  the  exact  length  of  a  survey  during  the  development  stage  is  challenging; 
for example because it is difficult to predict how long it will take for CATI interviewers 
to  read  out  certain  questions,  a  problem  acerbated  by  differences  in  languages. 
Moreover,  some  socio-economic  groups  of  respondents  need  more  time  to  complete 
surveys  than  others.  During  questionnaire  development  it  would  also  be  beneficial  to 
acquire  a  clear  picture  of  possible  mode  effects  on  survey  results,  so  that  the 
questionnaire  can  be  adapted  if  needed.  In  Ipsos’  experience,  the  impact  of  these 
kinds  of  factors  can  only  be  tested  in  a  pilot;  tests  by  researchers  tend  not  to  be 
representative of real world results. 

For  these  reasons,  we  would  recommend  using  a  mixed-mode  pilot  for  future 
comparable  studies.  Such  a  pilot  could,  for  example,  consist  of  hundred  interviews  in 
every  mode  (CAWI+CAPI+CATI).  An  additional  option  would  be  to  add  an  additional 
language  to  the  pilot  that  usually  differs  considerably  from  English  when  it  comes  to 
interview length (for example German, a language that is often more time consuming 
to  read  out).  The  pilot  could  be  improved  further  by  targeting  specific  demographic 
groups.  For  the  current  study  it  would  have  been  beneficial  if  the  length  of  the  CATI 
and CAPI questionnaires had been tested on the socio-demographic groups targeted by 
these modes in the main stage ‒ i.e. older, lower educated and inactive respondents.  

Carrying out hundred interviews in every mode (CAWI+CAPI+CATI), would amount to 
300 pilot interviews. The increased number of pilot interviews would have an impact on 
the  time  needed  for  fieldwork.  Pilot  interviewing  using  CATI  and  CAPI  methods  is 
slightly  more  time  consuming  than  online  interviewing  and  would  require  additional 
scripting.  Testing  the  questionnaire  in  an  additional  language  would  require  the 
translation of the questionnaire. Therefore, a mixed-mode multi-language pilot of 300 
interviews (50 per mode per country) would require an estimated four additional weeks 
compared to the time schedule for the current study. More additional time ‒ up to six 
weeks  compared  to  the  schedule  for  the  current  study  ‒  would  be  required  when  it 
would be decided that the pilot should play a more prominent role in the development 
of  the  questionnaire,  for  example  to  test  mode  effects.  This  as  more  time  would  be 
needed for the pilot report and to implement changes. 

Recommendation 2: Allowing for a longer questionnaire 

For future studies that include a survey to investigate consumer vulnerability,  such as 
market  studies  that  wish  to  include  an  assessment  of  vulnerability  in  the  market  in 

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question,  we  would  recommend  budgeting  for  a  longer  questionnaire.  For  the  current 
complex  study  into  three  sectors,  a  10-minute  mixed-mode  survey  was  short.  The 
preliminary  mapping  of  vulnerability  drivers  and  indicators  provided  material  for 
several surveys: it proved to be challenging to compress all possible questions into the 
questionnaire. Since the aim was to maintain as many survey questions as possible it 
was  decided  to  ask  certain  questions  only  in  certain  survey  modes  and/or  in  certain 
countries.  This  made  analysing  the  data  challenging.  However,  it  should  be  stressed 
that  with  the  current  10-minute  survey  length  the  only  other  feasible  option  would 
have  been  to  ask  fewer  questions  across  all  modes.  This  would  have  made  analysing 
the  data  easier,  but  would  most  likely  have  resulted  in  the  omission  of  some  survey 
questions  that  have  now  been  identified  as  important  for  the  operationalisation  of 
vulnerability.  For  this  reason  we  believe  that  the  current  survey  achieved  its  goals 
when considering the budget limitations.  

Recommendation 3: Allowing for more offline interviews 

In the survey for this study, people that did not use internet at home were contacted 
by  offline  interviewing  methods.  As  described  in  section  A2.10.1,  this  mixed  method 
interviewing  approach  allowed  reaching  parts  of  the  population  that  would  not  have 
been  reached  by  using an  online  only  approach.  Additionally,  this  approach  was  cost-
effective:  in  countries  with  a  high  internet  penetration  the  proportion  of  people  that 
was  contacted  by  CAPI/CATI  methods  was  low,  reducing  the  overall  need  for  costly 
(compared to online) offline interviews. 

11.3.  Recommendations and conclusions concerning the UCPD Guidance 

One  of  the  objectives  of  this  study  is  to  provide  advice  on  whether  and  how  the 
chapter  on  vulnerable  consumers  in  the  UCPD  Guidance  could  be  reviewed  and/or 
expanded.  The  UCPD  chapter  on  vulnerable  consumers  notes  that  vulnerability  “may 
arise  out  of  the  consumers’  mental  or  physical  infirmity,  age  or  credulity”,  and  that 
members of these groups may be particularly susceptible to marketing practices, which 
should be assessed in relation to the average member of that group.  

Vulnerable consumers in the UCPD and UCPD Guidance 

This study has identified five dimensions of vulnerability (see Chapter 3): 

1)  Heightened risk of negative outcome or impacts on well-being 
2)  Having characteristics that limit ability to maximise well-being 
3)  Having difficulty in obtaining or assimilating information 
4) 
5)  Higher susceptibility to marketing practices 

Inability or failure to buy, choose or access suitable products 

The UCPD, and therefore also the UCPD Guidance, focuses on one of these dimensions, 
namely a higher susceptibility to marketing practices (the fifth dimension), although as 
explained in Chapter 3 the various dimensions of vulnerability considered in this study 
are interlinked. 

Drawing  on  the  populated  theoretical  framework  (see  Chapter  7),  the  personal 
characteristics included in the UCPD definition are found to lead to higher likelihood of 
vulnerability in other dimensions, rather than just susceptibility to marketing practices, 
in particular:  

  Higher  credulity  is  associated  with  a  higher  likelihood  of  vulnerability  within 

Dimensions 1 and 2; 

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11 │ Conclusions, recommendations and policy options 

  Being  younger  is  associated  with  a  higher  likelihood  of  vulnerability  in 

Dimensions 1 and 2 (also); and 

  Older age is associated with vulnerability in Dimension 4 (particularly problems 

comparing offers in markets and problems choosing between offers). 

This suggests that, while the UCPD is focused on commercial practices, the concept of 
vulnerability  at  EU  level  could  be  broadened  to  cover  additional  dimensions.  For 
example,  when  developing  consumer  protection  rules,  policy  makers  may  take  into 
account  the  findings  that  some  consumers  find  it  especially  difficult  to  obtain  or 
assimilate  information  (Dimension  3),  or  are  particularly  likely  to  fail  to  choose  or 
access suitable products (Dimension 4).283 

The  results  relating  consumers’  characteristics  to  the  vulnerability  indicator  which 
measures  the  extent  that  consumers  feel  vulnerable  due  to  the  complexity  of  offers, 
terms  or  conditions  (indicator  53  in  Table  5  in  section  3.6  ),  can  be  used  to  assess 
which consumer groups may be more susceptible to marketing practices (these results 
are  presented  in  section  8.2.3  in  subsections  relating  to  Dimension  5).  Specifically, 
these results show that: 

  Being  a  non-native  speaker  increases  the  extent  to  which  consumers  feel 

vulnerable due to the complexity of offers, terms or conditions; 

  Having  higher  trust  in  people  reduces  the  extent  to  which  consumers  feel 

vulnerable due to the complexity of offers, terms or conditions; and 

  Being  willing  to  take  risks  reduces  the  extent  to  which  consumers  feel 

vulnerable due to the complexity of offers, terms or conditions. 

In  addition,  the  behavioural  experiments  implemented  as  part  of  this  study 
investigated  the  impact  on  consumer  decision-making  of  a  series  of  marketing 
practices and remedies (Chapter 8).  

The  UCPD  Guidance  makes  it  clear  that  the  factors  cited  in  Article  5  as  the  basis  for 
establishing  the  vulnerability  of  a  particular  class  of  consumers  are  listed  indicatively, 
and can cover a wide range of situations. The results of the experiment data analysis 
support  treating  this  list  of  factors  as  indicative,  since  the  analysis  found  that 
marketing practices can be an issue for all consumer groups (see section 8.2.3). 

That  being  said,  a  number  of  different  groups  are  found  to  be  more  susceptible  to 
marketing practices based on the results of the behavioural experiments. In particular:  

  Older  consumers  (aged  65-74)  benefited  more  from  the  remedy  treatments 

overall across all experiments than those aged 35-44. 

  Consumers  with  a  lower  level  of  education  benefited  more  from  the  remedy 
treatments  overall  across  all  experiments  than  those  with  a  medium  or  high 
level of education. 

283  For  example,  consumers  who  are  more  impulsive  are  found  to  be  more  likely  to  be  vulnerable  in  these 
dimensions.  Other  characteristics  that  are  associated  with  a  higher  likelihood  of  vulnerability  in  these 
dimensions  are  set out  in  the  summary  to  Chapter  6  on  the  drivers of  vulnerability,  and  Chapter  7  on 
the population of the theoretical framework. 

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11 │ Conclusions, recommendations and policy options 

  Consumers  over  the  age  of  45  benefited  more  from  the  remedy  treatment 
relative to those aged 35-44 specifically in the first round284 of the online sector 
experiment. 

  Consumers  in  low  density  regions  benefited  more  from  the  remedy  treatment 
than  those  in  high  density  regions  specifically  in  the  first  round  of  the  online 
sector experiment and the first round of the finance sector experiment. 

  Consumers who are unwilling to take risks (i.e. ‘not at all’ willing to take risks) 
benefited more from the remedy treatment than those who are more willing to 
take  risks  (i.e.  ‘not  very’,  ‘fairly’  or  ‘very’  willing  to  take  risks)  in  several 
experiment rounds. 

  Consumers who are less experienced and engaged with the key markets of the 

study benefited more from the remedies overall across all experiments.  

The average consumer in the UCPD and UCPD Guidance 

The definition of the average consumer in the UCPD refers to being “well informed”, 
“observant”  and  “circumspect”.  In  Chapter  5  the  indicators  of  vulnerability  have  been 
mapped  against  two  dimensions  ‘well  informed’  and  ‘observant  and  circumspect’. 285 
The average consumer is represented by the median consumer for each of the relevant 
vulnerability indicators.  

The  indicators  of  the  median  consumer  within  the  characteristics  of  “well  informed” 
relate  to  the  self-reported  behavioural  characteristics  associated  with  “reading  the 
latest communication from providers in great, moderate or minimal detail, feeling quite 
informed,  consults  information  from  one  or  more  sources  and  does  not  rely  on 
advertisements.  

In  regard  to  the  characteristics  of  “observant  and  circumspect”  the  median  consumer 
tends  to  be  careful  when  dealing  with  people,  disagrees  that  advertisement  report 
objective  fact  (self-reported  measure  of  credulity),  is  “quite  careful  in  making 
decisions”,  “is  not  very  willing  to  take  risks,  tends  to  disagree  that  people  can  be 
trusted and can correctly identify the terminology for kWh, Mbps and interest rate”.  

11.4.  Policy options for mitigating consumer vulnerability 

The  populated  theoretical  framework  of  the  causes  and  effects  of  vulnerability, 
presented in Chapter 7, details how the individual causes of vulnerability (also referred 
to as drivers) map to the five key vulnerability dimensions.  

This  section  addresses  possible  policy  options  for  mitigating  vulnerability  using  this 
framework and other evidence from the study. The sub-sections below consider policy 
options  for  each  type  of  vulnerability  driver  identified  and  examined  in  the  study  in 
turn.  Examples  of  specific  policies  are  given  based  on  insights  from  the  literature 
review and stakeholder consultation. 

Potential policy options to mitigate personal and demographic drivers 

284  Note that each experiment comprised of two rounds, as explained in Chapter 2. 
285 As discussed in chapter 5, it is appropriate to define the average consumer  based on variables that are 
strongly related to the operationalisations of vulnerability, since the definition of the average consumer 
on this basis means it can be linked to vulnerability. 

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11 │ Conclusions, recommendations and policy options 

Being  a  non-native  speaker  is  linked  to  a  higher  likelihood  of  vulnerability  in 
Dimensions  1  and  2,  a  higher  likelihood  of  vulnerability  in  terms  of  having  problems 
choosing  between  deals  (in  Dimension  4),  and  a  higher  likelihood  of  vulnerability  in 
Dimension  5.  Regarding  Dimension  5,  this  driver  has  one  of  the  strongest  effects  on 
feeling  vulnerable  due  to  the  complexity  of  offers  (the  effect  being  to  increase  the 
feeling  of  vulnerability).  A  possible  policy  remedy  to  mitigate  the  effect  of  this 
vulnerability  driver  would  be  to  ensure  that  information  is  accessible  to  non-native 
speakers  through  community  groups,  public  bodies  or  other  sources.  An  example  of 
such a policy is a project of German consumer centres in Berlin, Hamburg and Bremen, 
which provides information for Turkish and Russian immigrants on consumer  rights in 
digital markets.286 

Young age is associated with a higher likelihood of vulnerability in Dimensions 1 and 2. 
Young  age  can,  for  example,  be  associated  with  feeling  unassertive  when  problems 
arise  and  the  young  may  have  problems  comparing  deals  due  to  information  factors. 
This  suggests  that  policies  that  target  age  should  also  consider  the  younger  age 
groups.  There  are  a  number  of  relevant  measures  in  this  area  identified  through 
interviews  and  desk  research.  Examples  of  information  campaigns  and  education 
measures  targeting  children  and  youth  have  been  identified  in  Austria,  Germany, 
Poland, Slovakia, and Sweden, where financial literacy is becoming integrated in high-
school  curricula.  Other  relevant  measures  include  codes  of  conduct,  such  as  the 
Belgian  code  of  conduct  on  advertising  and  marketing  of  financial  products  and 
services to young people or the work by Consumer Ombudsman in Norway on a Model 
Agreement for bankcards used by children. 

Older  age  is  linked  with  an  increased  likelihood  of  vulnerability  in  terms  of  certain 
aspects  of  Dimension  4,  in  particular  having  problems  comparing  and  choosing 
between  deals  (although  older  age  is  also  associated  with  a  lower  likelihood  of 
vulnerability  in  terms  of  other  aspects  of  Dimension  4,  namely  exclusion  from  e-
commerce and being declined a loan). Potential policy remedies to alleviate the effect 
of  older  age  as  a  vulnerability  driver  include  measures  that  address  accessibility 
challenges (online and offline) facing older citizens, such as in Poland, where the Office 
of Competition and Consumer Protection has taken direct action against suppliers using 
small  and  illegible  text  in  advertising  aimed  at  older  consumers.  Other  relevant 
measures include the targeting of advice and counselling services to the needs of older 
consumers, with such actions being identified in Germany, Poland, and Slovakia. 

Potential policy options to mitigate behavioural drivers 
Having  higher  credulity287  is  linked  with  a  higher  likelihood  of  vulnerability  in 
Dimensions 1 and 2, but a lower likelihood of vulnerability in terms of having problems 
comparing  deals  (in  Dimension  4).  Furthermore,  failing  the  credulity  test288  is 
associated  with  a  higher  likelihood  of  having  problems  choosing  between  deals  (in 
Dimension 4). Consumers who are more subject to credulity may be more susceptible 
to  framing  of  information.  Therefore,  policies  that  target  the  presentation  of 

286 See http://www.verbraucherzentrale-berlin.de/-20175. 
287  This  measure  is  based  on  the  extent  that  respondents  agree  with  the  statement  “most  advertisements 

report objective fact, I trust most of the information provided in advertisements”. 

288  The  credulity  test  asked  respondents  whether  the  radio  advertisement  “Sign-up  to  BEACH  BREAKS  and 
receive a free pair of sunglasses. Sunglasses delivered when you purchase one of our beach holidays” 1) 
offers free sunglasses to all who sign-up to beach breaks, 2) offers sunglasses only to those who buy a 
holiday  from  beach  breaks,  or  3)  they  do  not  know  which  answer  is  correct.  Those  who  identified  the 
second answer as the correct response answered the credulity test question correctly. 

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11 │ Conclusions, recommendations and policy options 

information may be important for these groups of consumers. For example, this would 
include  legislation  and  subsequent  actions  addressing  misleading  and  comparative 
advertising. While the UCPD already goes some way to address this issue, the review 
of  case  law  suggests  that  additional  steps  could  be  taken  in  terms  of  effectively 
enforcing such provisions in the context of consumer vulnerability. 

Being more impulsive289 is associated with a higher likelihood of vulnerability in terms 
of  feeling  uniformed  about  prices,  quality,  etc.  (in  Dimension  3),  having  problems 
comparing deals (in Dimension 4), and exclusion from e-commerce and being declined 
a loan (in Dimension 4). Similarly, being more willing to take risks is associated with a 
higher  likelihood  of  vulnerability  in  terms  of  having  problems  choosing  between  deals 
(in  Dimension  4).  Impulsiveness  may  be  linked  to  the  behavioural  biases  of 
overconfidence  and  projection  bias,  implying  that  possible  remedies  to  mitigate  this 
driver  would  include  those  targeting  these  behavioural  biases.  This  would include,  for 
example, information remedies that help consumers to better judge their future use of 
products and services (overconfidence), or improve predictions of future outcomes and 
situations  (prediction  bias).  Existing  examples  include  information  and  tools  assisting 
consumers in managing personal finances provided by a consumer authority in Ireland, 
and  a  website  and  comparison  tool  set  up  by  the  Financial  Supervision  Authority  in 
Estonia,  which  aims  to  help  consumers  to  better  manage  their  finances  and  debt 
repayments. 

Having better computational ability is associated with a lower likelihood of vulnerability 
in  terms  of  being  unassertive  when  problems  occur  (in  Dimension  1)  and  in  terms  of 
having  problems  choosing  between  deals  (in  Dimension  4).  Consumers  with  lower 
computational  ability  may  be  more  prone  to  the  use  of  heuristics  when  comparing 
offers  and  making  choices,  and  they  may  also  excessively  discount  future  relative  to 
present  outcomes.  Possible  remedies  to  mitigate  this  driver  would  help  consumers  to 
be aware of and understand future costs and benefits and not to over-value the costs 
and benefits in the short-term, such as presentation of information to improve ease of 
computation  and  comparison.  Relevant  examples  here  include  the  aforementioned 
websites and comparison tools in Ireland and Estonia. 

Potential policy options to mitigate market related drivers 

Being  unable  to  read  contract  terms  and  conditions  due  to  overly  small  print  is 
associated  with  a  higher  likelihood  of  vulnerability  in  all  dimensions.  Together  with 
finding  it  difficult  to  make  ends  meet,  this  driver  is  one  of  two  that  are  consistently 
linked  with  vulnerability  across  all  dimensions.  It  also  has  the  strongest  (i.e.  largest 
magnitude)  relationship  with  being  excluded  from  e-commerce  (in  Dimension  4). 
Similarly, not reading, or finding it difficult to read communications from suppliers and 
providers  in  all  three  sectors  is  related  to  an  increased  likelihood  of  vulnerability  in 
most  dimensions  (all  except  Dimension  2),  and  limited  knowledge  of  contract  terms 
and  conditions  in  all  three  sectors  is  linked  with  a  higher  likelihood  of  vulnerability  in 
several  dimensions  (all  except  Dimensions  2  and  5).  Potential  policy  remedies  to 
alleviate the effect of these drivers include remedies to ensure information is presented 
in  a  salient  and  accessible  way.  An  example  of  such  a  policy  is  the  regulation 
concerning  information  to  be  provided  on  investment  product  leaflets  in  Sweden. 

289 These findings are based on the results for 3 impulsiveness measures. These measures are based on the 
extent that respondents agree with the statements: “I have a hard time breaking bad habits”, “people 
would say that I have very strong self-discipline” and “I’m impulsive in the purchase decisions I take”. 

393 

 
 
 
 
                                                 
11 │ Conclusions, recommendations and policy options 

Similarly, actions taken by the Polish authority to address suppliers that use small and 
illegible text could also be seen as a relevant remedy. 

Not comparing deals in the market, or only comparing deals infrequently, is also linked 
to an increased likelihood of vulnerability in several dimensions. This is particularly the 
case  in  the  energy  sector,  where  limited  comparison  is  associated  with  a  higher 
likelihood  of  vulnerability  in  terms  of  being  unassertive  when  problems  occur  (in 
Dimension  1)  and  in  terms  of  having  problems  comparing  deals  and  not  switching 
deals (in Dimension 4). Rarely comparing deals from energy suppliers is the driver with 
the strongest effect on any single indicator relating to not switching (in Dimension 4). 
Possible remedies to mitigate the effect of this vulnerability driver include methods to 
increase awareness of alternatives and tools that ease comparison, for example online 
comparison tools for financial products provided by authorities in Portugal and Estonia. 
The aforementioned requirements on investment product leaflets in Sweden are also a 
measure that facilitates the comparison between complex products.  

A  further  market  related  driver  identified  in  the  literature  is  limited  knowledge  of  the 
competitive  situation  in  the  market  (such  as  energy  market  liberalisation).  Limited 
knowledge  of  markets  can  lead  to  issues  associated  with  the  effects  of  vulnerability, 
including  paying  higher  prices  for  goods  and  services,  purchasing  products  not  suited 
to  need,  feeling  vulnerable  due  to  marketing  practices  and  feeling  uninformed  in 
markets.  Possible  measures  to  mitigate  the  effect  of  this  driver  include  methods  to 
increase  awareness  of  existing  market  conditions.  Interviews  with  national-level 
stakeholders  have  shown  that  awareness  raising  campaigns,  organised  by  national 
authorities  and  civil  society  bodies,  are  almost  universal  across  the  EU,  although  it  is 
not clear what proportion of such campaigns directly addresses the changes in market 
conditions. 

Potential policy options to mitigate access drivers 

Low internet use is also associated with vulnerability.  In particular, using the internet 
less overall (in terms of the number of activities it is used for at least once per month) 
is linked with a higher likelihood of vulnerability in terms of overpaying for services due 
to  being  unable  to  use  certain  payment  methods  (in  Dimension  1),  feeling  vulnerable 
due  to  personal  characteristics  (in  Dimension  2),  and  being  declined  a  loan  (in 
Dimension  4).  Furthermore,  using  the  internet  to  search  for  information  at least  once 
per  month  is  linked  with  a  lower  likelihood  of  vulnerability  in  terms  of  being  able  to 
choose between offers290 (in Dimension 4). This result implies that policies to improve 
access to, knowledge of and confidence in the online environment may be effective in 
tackling vulnerability. An example of such a measure is the programme of ICT courses 
targeting older consumers in Malta. It is however worth noting that such remedies can 
also  be  relevant  to  consumers  much  more  familiar  with  the  online  environment.  For 
instance,  another  Maltese  project  aimed  to  educate  younger  consumers  about  issues 
such as privacy and cyber-bullying. 

The  literature  found  that  a  lack  of  physical  access,  for  example  due  to  geographic 
location  or  disability,  can  lead  to  an  increase  in  the  likelihood  of  vulnerability, 
particularly  in  terms  of  exclusion  from  markets.  Potential  policy  remedies  to  alleviate 
the  impact  of  this  driver  would include  measures  to  improve  the  accessibility  of  retail 
outlets  and  public  institution  offices.  Examples  include  a  range  of  accessibility 

290  That  is,  those  who  search  for  information  online  were  more  likely  to  choose  the  best  offer  in  the 

experiments. 

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11 │ Conclusions, recommendations and policy options 

measures in place across the EU, also outside of the consumer policy context, such as 
the Inclusive Service Provision standards in the UK. 

A further access-related driver identified in the literature is lack of access to offers due 
to  not  meeting  specific  criteria,  or  due  to  offers  only  being  available  through  certain 
media. This driver can arise due to situational drivers such as employment status and 
income,  or  confidence  in  online  transactions.  Possible  measures  to  remedy  this  driver 
could  aim  to  improve  the  definitions  of  what  constitutes  discriminatory  treatment.  An 
existing example is the role of the Discrimination Ombudsman in addressing questions 
concerning consumer vulnerability in Sweden. 

Potential policy options to mitigate situational drivers 

Finding  it  difficult  to  make  ends  meet  is  associated  with  an  increased  likelihood  of 
vulnerability across all dimensions. Together with being unable to read contract terms 
and conditions due to small print, this driver is one of two that are consistently linked 
with  vulnerability  in  all  dimensions.  It  also  has  the  strongest  (i.e.  largest  magnitude) 
relationship  with  feeling  vulnerable  due  to  personal  characteristics  (in  Dimension  2). 
Similarly,  having  friends  who  find  it  difficult  to  make  ends  meet  is  also  linked  with 
multiple  dimensions  of  vulnerability.  In  particular,  this  driver  is  associated  with  a 
higher  likelihood  of  vulnerability  in  terms  of  Dimensions  1  and  2,  having  problems 
choosing  between  deals  (in  Dimension  4),  being  excluded  from  e-commerce  (in 
Dimension  4  and  Dimension  5.  This  suggests  that  measures  to  reduce  financial 
pressure on consumers, as well as non-financial support measures, may be effective in 
reducing  vulnerability.  Examples  of  financial  support  measures  include  social  tariffs  in 
place  across  a  number  of  EU  Member  States,  whereas  examples  of  non-financial 
support measures include obligations to provide basic universal service, such as access 
to basic bank accounts in Italy, as well as other measures, such as upper APR limits in 
Estonia. 

Being  long-term  sick  or  disabled  has  one  of  the  strongest  (i.e.  largest  magnitude) 
relationships  with  feeling  vulnerable  due  to  personal  characteristics  (in  Dimension  2). 
Relevant  measures  include  protection  measures  aimed  at  consumers  largely  confined 
to  their  homes,  such  as  energy  network  disconnection  protection  in  a  number  of 
Member  States,  as  well  as  measures  accounting  for  potential  life  changes  due  to  a 
transition to long-term illness, such as extended debt repayment periods in Finland.  

395 

 
 
 
 
 
REFERENCES AND ANNEXES 

 
 
 
 
 
 
 
 
 
 
 
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407 

 
 
 
 
 
Annex 1│ Country fiches 

Annex 1  Country fiches 

The country fiches have been developed by the research team based on the following 
sources: 

Interviews with national consumer associations and other stakeholders; 

 
  The European Commission consumer policy and institutions country profiles291;  
  National consumer policy documents; and 
  Other sources, such as the UCPD legal database292, ANED national reports on 
consumer protection293, the CEER status review of implementation of the 3rd 
Energy Package294, as well as the ACER market monitoring reports295.  

The country fiches represent the understanding of the national situations at the time of 
writing. They have been updated throughout the study, taking into account comments 
from members of the European Commission’s Consumer Market Expert Group (CMEG). 
The structure of the country fiches is as follows: 

  Country fiche summary: This summarises the fiche findings with regard to 

the overall national approach to consumer vulnerability and specific measures. 
This clarifies the link between the country fiches the country typology presented 
in the body of the report.  

  National consumer protection framework: This brief section serves as an 

introduction to the consumer policy landscape in the Member State in question. 
It relies on secondary information (e.g. strategy documents or websites of 
relevant authorities) concerning national consumer protection strategies and 
key legislative instruments.   

  Approach to consumer vulnerability: This section examines the use of the 

concept of consumer vulnerability. The first subsection focuses on the 
transposition of the concept of consumer vulnerability in the UCPD and is based 
on the UCPD legal database. The second subsection examines the use of the 
concept in other legislation and relies on interviews and secondary research 
(e.g. strategy documents, websites of relevant authorities or consumer 
organisations, identified literature). The final subsection focuses on stakeholder 
assessment of the role of consumer vulnerability in national consumer 
protection framework, where such input was obtained in the interviews.  

291 See http://ec.europa.eu/consumers/empowerment/cons_networks_en.htm#national 
292 See https://webgate.ec.europa.eu/ucp/public/index.cfm?event=public.home.show 
293 See http://www.disability-europe.net/theme/consumer-protection 
294 CEER (2012). ‘Status Review of Customer and Retail Market Provisions from the 3rd Package 

as of 1 January 2012’. Available at: 
http://www.ceer.eu/portal/page/portal/EER_HOME/EER_PUBLICATIONS/CEER_PAPERS/Cust
omers/Tab3/C12-CEM-55-04_SR-3rd-Pack-customers_7-Nov-2012.pdf  

295 ACER (2013). Annual Report on the Results of Monitoring the Internal Electricity and Gas 

Markets in 2012. Available at: 
http://www.acer.europa.eu/Electricity/Market%20monitoring/Pages/Reports.aspx 

408 

 
 
 
                                                 
Annex 1│ Country fiches 

  Specific legislative measures: This section presents examples of legislative 
measures focusing on consumer vulnerability. It is based on interviews and is 
supplemented by desk research. It is not meant to serve as an exhaustive list of 
all relevant measures, but rather aims to highlight measures of particular 
relevance to consumer vulnerability that go beyond existing EU provisions.  
  Non-legislative measures: This section outlines non-legislative measures 

addressing consumer vulnerability.  

  Institutional structure and role of civil society: This final section outlines 

the consumer protection institutional structure in respective Member States, as 
well as relevant civil society organisations active in the field. Where relevant it 
identifies ways in which these civil society institutions help address consumer 
vulnerability.  

409 

 
 
 
 
 
 
Annex 1│ Country fiches 

Table 85:  Country fiche - Belgium 

Country fiche summary 

Approach  to  consumer  vulnerability:  While  the  concept  of  consumer  vulnerability 
appears  to  be  used  in  specific  sectors,  there  is  no  evidence  of  a  broader  strategic 
approach to tackling consumer vulnerability at national level.  

Measures addressing consumer vulnerability: There is evidence of a range of legislative 
and non-legislative measures addressing consumer vulnerability. 

National consumer protection framework 

The  Belgian  consumer  protection  policy  aims  to  protect  consumers  physically, 
economically,  and  legally.  It  aims  to  ensure  that  implemented  legislation  is  applied 
correctly  and  that  consumers  are  adequately  informed.  Furthermore,  it  has  the 
objective of establishing rules to ensure healthy competition between traders to benefit 
consumers,  and  to  promote  consumer  small  claims  procedures.  The  Federal  Public 
Service for the Economy, SMEs, Self-Employed and Energy is the responsible authority. 
From  the  31st  of  May  2014,  the  6th  book  of  “Market  practices  and  Consumer 
Protection”,  from  the  Economic  Law  Code,  is  replacing  the  law  from  April  6,  2010 
regulating market practices and consumer protection296. 

Approach to consumer vulnerability 

Consumer vulnerability within the context of the transposed UCPD  

The  term  “vulnerable”  is  present  in  the  book  of  “Market  Practices  and  Consumer 
Protection” from the Economic law Code (art VI. 93) as a result of the transposition of 
the UCPD. The concept of vulnerability has been transposed in line with the Directive’s 
wording,  using  the  terms  mental  or  physical  infirmity,  age  and  credulity.  The  term 
“average  consumer”  is  used  in  the  Belgian  legislation,  without  providing  a  specific 
definition.  

Consumer vulnerability in national consumer policy beyond the UCPD  

A  particular  application  of  the  term  is  in  the  energy  sector,  where  vulnerable 
consumers are seen as consumers in a precarious situation (consommateur en état de 
précarité  énergétique).  This  is  the  case  where  10%  or  more  of  a  family’s  income  is 
allocated to energy consumption. According to stakeholder interviews in Belgium, this 
group amounts to about 10-15% of households (715,000 households).  

Stakeholder assessment of the national approach to consumer vulnerability 

Interviews conducted with stakeholders noted that despite the legal term “vulnerable”, 
anyone could be vulnerable at some point, depending on a variety of different factors 
(situation, sector, time etc.). The term “vulnerable customer” is commonly known and 
used in Belgium297. 

Specific legislative measures  

There  are  a  number  of  sector  specific  measures  in  place.  For  instance  In  the  energy 
sector social tariffs for consumers with low-incomes, as well as for disabled consumers, 

296 See http://economie.fgov.be/fr/consommateurs/#.U57Op6ZwZMs 
297 CEER (2012). ‘Status Review of Customer and Retail Market Provisions from the 3rd Package 

as of 1 January 2012’ 

410 

 
 
 
                                                 
Annex 1│ Country fiches 

are applied for electricity and gas, based on primary law. Consumers are also protected 
from  disconnection298.  In  the  financial  sector,  this  includes  a  law  of  24  March  2003, 
establishing  the  right  to  basic  banking  services.  Existing  legislation  also  ensures  the 
right to a banking account for anyone, despite e.g. low income and/or debts299.  As of 
the 17th of April 2015 this law is replaced and integrated in book VII on Payment and 
Credit services of the Economic Law Code. 

Non-legislative measures 

Beyond  legislative  measures,  there  are  examples  of  support  measures  focusing  on 
vulnerability in the energy sector: The consumer association Test-Achats/Test-Aankoop 
has  organised  a  collective  purchase  of  electricity  and  gas  at  several  occasions.  This 
initiative  implies  that  consumers  sign  up  with  the  association  and,  depending  on  the 
number  of  consumers joining, the association negotiates with the service providers in 
order  to  reach  an  agreement  on  reduced  prices.  Service  providers  are  invited  to 
participate  in  an  auction  and  the  one  offering  the  lowest  price  will  be  chosen  as 
provider for the consumer that signed up. Similar initiatives were performed by  other 
associations or public bodies (towns, provinces) as for example the Ligue des Familles, 
representing  families  in  negotiations  with  energy  providers.  These  initiatives  are  of 
relevance to consumer vulnerability in that they effectively serve as a support measure 
for  low-income  consumers  (but  not  necessarily  only  low-income  consumers)  in  the 
energy sector by contributing to lowering the energy costs. 

In  the  financial  sector,  Financité,  the  Wallonia  network  of  financial  sector  NGOs, 
introduced a pilot project funded by the EC programme PROGRESS 2010, called “SIMS 
–  Social  Innovation  and  Mutual  Learning  in  Micro-Saving  in  Europe”.  This  project 
targeted low-income population groups and was designed to promote savings through 
financial education and/or financial incentive micro-savings programmes300.  
Finally, there are also a number of codes, agreements and recommendations in place 
in  Belgium,  which  address,  at  least  partially,  potentially  vulnerable  consumer  groups. 
These include for instance a code of conduct for advertising and marketing of banking 
and insurance products and services aimed at young people. 

Institutional structure and role of civil society 

Public bodies responsible for consumer policy 

In  Belgium,  the  federal  authorities  are  responsible  for  consumer  protection  with  the 
specific responsible authority being the Federal Public Service for the Economy, SMEs, 
Self-Employed  and  Energy.  This  area  of  responsibility  involves  the  following 
directorate-generals: 

  Directorate-General 

for  Economic  Regulation, 

responsible 

for  drafting, 

developing and coordinating consumer protection policy; 

298 CEER (2012). ‘Status Review of Customer and Retail Market Provisions from the 3rd Package 

as of 1 January 2012’ 

299 24 Mars 2003, Loi instaurant un service bancaire de base, 

http://www.ejustice.just.fgov.be/cgi_loi/loi_a.pl?language=fr&caller=list&cn=2003032442&l
a=f&fromtab=loi&sql=dt='loi'&tri=dd+as+rank&rech=1&numero=1 

300 SIMS – Social Innovation and Mutual Learning in Micro-Saving in Europe”, Assessment Report on the 

Programme Implemented in Belgium (2013). 

411 

 
 
 
 
 
                                                 
Annex 1│ Country fiches 

  Directorate-General  Economic  Inspection,  responsible 
application and implementation of economic legislation; 

for  monitoring  of 

  Directorate-General  Quality  and  Safety,  responsible  for  consumer  safety 
regarding  the  conformity  of  products  on  the  market  to  standards  and  CE 
marking conditions. 

In  addition  to  the  above,  The  Directorate-General  Economic  Inspection  is  the 
enforcement  authority  for  economic  legislation.  It  carries  out  e.g.  inspection  missions 
investigating compliance with economic legislation. 

Role of civil society 

The  Consumer  Council  is  the  central  advisory  body  for  consumer  issues  and  is 
composed  of  13  members  representing  consumer  associations,  and  13  members 
representing  production,  distribution  and  SMEs.  Consultation  between  public 
authorities  and  consumer  organisations  mainly  takes  place  within  the  context  of  the 
Consumer  Council.  The  Consumer  Council  brings  together  consumers  and  businesses 
to  discuss  and  issue  opinions,  as  well  as  to  co-regulate  through  the  development  of 
codes of conduct and recommendations.  

Consumer  associations  include  Tests  Achats/Test-Aankoop,  Ligue  des  Familles  (family 
focus), or Financité (financial sector focus). The two latter ones cover solely Wallonia, 
whereas  Test  Achats/Test-Aankoop  has  a  national  coverage.  These  associations 
represent consumer interest and provide information through websites, magazines and 
newsletters.  As  mentioned  above,  these  associations  also  actively  carry  out  various 
initiatives  (training,  information  events,  and  collective  switching  campaigns)  with  the 
aim of protecting and supporting consumers. For example, in Flanders, the association 
Gezinsbond focuses on the protection of families. 

Incidence of consumer vulnerability  

Indicator of vulnerability 

Mean incidence  

Dimension  1 :  Heightened  risk  of  negative  outcomes 
or impacts on well-being 

Has experienced a problem and took no action 

Overpaid  for  services  due  to  being  unable  to  use  certain 
payment methods 

Dimension 2 : Having characteristics that limit ability 
to maximise well-beings 

Feels vulnerable to 'a great extent' due to health problems 

Feels  vulnerable  to  'a  great  extent'  due  to  financial 
circumstances 

Feels  vulnerable  to  'a  great  extent'  due  to  employment 
situation 

Feels vulnerable to 'a great extent' due to age 

Feels  vulnerable  to  'a  great  extent'  due  to  belonging  to  a 
minority group 

Feels vulnerable to 'a great extent' due to personal issues 

Feels vulnerable to 'a great extent' due to other reasons 

7% 

11% 

6% 

11% 

9% 

5% 

3% 

4% 

3% 

412 

 
 
 
 
 
 
Annex 1│ Country fiches 

Feels  vulnerable  to  'a  great  extent'  due  to  any  personal 
characteristic 

Dimension  3 :  Having  difficulty 
assimilating information 

in  obtaining  or 

Feels  'not  at  all'  informed  about  product  price,  quality, 
conditions,  etc  when  choosing  and  buying  goods  and 
services 

Compares  product  deals  by  using 
advertisements only - energy sector 

information 

from 

Compares  product  deals  by  using 
advertisements only - online sector  

information 

from 

Compares  product  deals  by  using 
advertisements only - finance sector 

information 

from 

Has  problems  comparing  deals  due  to  information-related 
factors - energy sector 

Has  problems  comparing  deals  due  to  information-related 
factors - online sector 

Has  problems  comparing  deals  due  to  information-related 
factors - finance sector 

20. Has an electricity supplier and has not switched due to 
information-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
information-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to 
information-related factors - financial sector 

Dimension  4 :  Inability  or  failure  to  buy,  choose  or 
access suitable products 

Does not compare product deals - energy sector 

Finds  it  very  difficult  to  compare  product  deals  -  energy 
sector 

Does not compare product deals - online sector 

Finds  it  very  difficult  to  compare  product  deals  -  online 
sector 

Does not compare product deals - finance sector 

Finds  it  very  difficult  to  compare  product  deals  -  finance 
sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
energy sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
online sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
financial sector 

413 

19% 

8% 

2% 

3% 

3% 

16% 

11% 

15% 

48% 

16% 

49% 

8% 

62% 

16% 

32% 

26% 

31% 

 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Has  problems  comparing  deals  due  to  market-related 
factors - energy sector 

Has  problems  comparing  deals  due  to  market-related 
factors - online sector 

Has  problems  comparing  deals  due  to  market-related 
factors - financial sector 

Has problems comparing deals due to access-related factors 
- energy sector 

Has problems comparing deals due to access-related factors 
- online sector 

Has problems comparing deals due to access-related factors 
- financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
personal factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
personal factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  personal 
factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
market-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
market-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  market-
related factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
access-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
access-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  access-
related factors - financial sector 

Has not switched due to termination costs 

Has not switched due to bundling 

Excluded from e-commerce due to difficulty of the process 

Excluded  from  e-commerce  due  to  not  having  a  payment 
card 

Declined for a loan 

Dimension  5 :  Higher  susceptibility  to  marketing 
practices 

40% 

32% 

38% 

3% 

2% 

2% 

13% 

24% 

20% 

18% 

Vulnerable  to  a  great  extent  due  to  complexity  of  offers 
(own perception) 

7% 

Experiment choice under the marketing practice treatment - 
energy sector 

Experiment choice under the marketing practice treatment - 

414 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

online sector 

Experiment choice under the marketing practice treatment - 
finance sector 

Experiment choice under the marketing practice treatment - 
cross-cutting 

415 

 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Table 86:  Country fiche - Bulgaria 

Country fiche summary 

Approach  to  consumer  vulnerability:  While  the  concept  of  consumer  vulnerability 
appears  to  be  used  in  specific  sectors,  there  is  no  evidence  of  a  broader  strategic 
approach to tackling consumer vulnerability at national level.  

Measures  addressing  consumer  vulnerability:  There  is  evidence  of  a  range  of  sector-
specific measures addressing consumer vulnerability. 

National consumer protection framework 

The basic elements of the Bulgarian consumer protection framework include ensuring: 

  Consumer safety; 

  Protection of the economic interests of consumers; 

  Protection of individual and collective interests of consumers, including and 

using mechanisms for alternative dispute resolution; and 

  Participation of consumer organizations in the enforcement of consumer 

protection301.  

The  Law  on  Consumer  Protection  is  the  key  legislative  provision  in  the  area  of 
consumer  protection  in  Bulgaria,  along  with  a  set  of  specific  provisions,  such  as  the 
Law  on  Consumer  Credit  and  the  Law  on  Distance  Marketing  of  Consumer  Financial 
Services.  The  Law  on  Consumer  Protection  regulates  all  key  aspects  of  consumer 
protection,  including  information  and  labelling  requirements,  price  indications,  unfair 
contract  terms,  general  product  safety,  product  liability,  consumer  guarantees  and 
warranty,  timeshare  related  contracts,  distance  selling  and  off-premises  contracts, 
actions for injunction, and consumer complaints and redress. The law also transposes 
the UCPD.  

Approach to consumer vulnerability 

Consumer vulnerability within the context of the transposed UCPD  

The concept of consumer vulnerability is present in the Law on Consumer Protection as 
a result of the transposition of the UCPD. The concept has been transposed in line with 
the  Directive’s  wording,  using  the  terms  mental  or  physical  infirmity,  age,  and 
credulity.  Similarly  the  “average  consumer”  concept  has  also  been  transposed  into 
Bulgarian  legislation  through  the  UCPD  and  is  now  applied  within  the  consumer 
protection framework. 

Consumer vulnerability in national consumer policy beyond the UCPD  

Beyond  the  transposition  of  the  UCPD  there  appears  to  be  limited  evidence  of  the 
concept of consumer vulnerability being used within sectoral legislation. One exception 
is  the  energy  sector  where,  according  to  the  CEER  report,  the  concept  of  consumer 
vulnerability is known and used within the context of the energy sector in Bulgaria302. 
Following  a  2012  amendment,  “vulnerable  customers”  are  defined  in  the  Bulgarian 
Energy  Act  as  “household  customers  who  receive  targeted  benefit  for  electricity,  heat 

301 See http://www.mi.government.bg/bg/themes/politika-za-zashtita-na-potrebitelite-317-

325.html 

302 CEER (2012). ‘Status Review of Customer and Retail Market Provisions from the 3rd Package 

as of 1 January 2012’ 

416 

 
 
 
                                                 
Annex 1│ Country fiches 

or  natural  gas  under  Social  Care  Act  and  the  secondary  legislation  on  its 
implementation”303.  
Stakeholder assessment of the national approach to consumer vulnerability. 

According  to  the  consulted  stakeholders,  the  use  of  the  vulnerable  consumer  concept 
within  the  legislation  is  limited,  although  they  recognised  that  it  might  be  used  more 
often in specific contexts, as the case of the energy sector shows. Overall, according to 
the  stakeholders,  there  is  no  unified  approach  to  consumer  vulnerability  in  consumer 
policy, although the average consumer concept stemming from the transposition of the 
UCPD is in use.    

Specific legislative measures 

There  is  little  evidence  of  specific  horizontal  measures  targeting  vulnerability,  with 
consulted  stakeholders  only  able  to  identify  sector-specific  measures  that  are  still 
under  development.  In  the  energy  sector  this  includes  attempts  by  the  energy 
regulator  to  lower  overall  consumer  energy  tariffs  in  favour  of  raising  tariffs  for 
business  users.  In  addition,  there  are  also  support  measures  in  the  energy  sector  in 
the  form  of  financial  help  for  energy  poor.  This  is  implemented  through  the  Winter 
Supplement  Program  (WSP),  with  households  needing  to  earn  below  the  minimum 
wage  for  a  period  of  6  months  to  be  eligible304.  As  noted  above,  the  Energy  Act 
contains  a  definition  of  the  term  ”vulnerable  customers”.  These  are  defined  as 
consumers  who  receive  social  benefits  intended  to  help  them  to  pay  their  electricity, 
central heating and gas bills. 

In the financial sector, a new bill aiming to clarify the use of interest rates in loans, as 
well as prohibiting charges on early credit repayment is currently in progress.  

Non-legislative measures 

There  is  little  evidence  of  additional,  non-legislative  raising  measures  targeting 
vulnerability,  although,  according  to  the  stakeholders,  consumer  education  measures 
are  likely  to  be  implemented  in  the  future  and  could  be  of  relevance  to  consumer 
vulnerability.  

Institutional structure and role of civil society 

Public bodies responsible for consumer policy 

Consumer  policy  is  the  responsibility  of  the  Ministry  of  Economy  and  Energy.  Its 
Consumer Policy Unit is responsible for the overall development of Bulgarian consumer 
policy. 

The Commission for Consumer Protection is the key  enforcement and inspection body 
supported  by  other  sector-specific  regulatory  bodies.  In  addition  to  monitoring  and 
enforcement of legislation, the Commission for Consumer Protection also examines and 
acts on consumer complaints.  

Role of civil society 

The  two  main  Bulgarian  consumer  organisations  include  the  Bulgarian  National 

303 See http://www.me.government.bg/library/index/download/lang/en/fileId/256 
304 See Reach project information on Bulgaria: http://reach-energy.eu/wordpress/wp-

content/uploads/2014/12/D2.2-EAP_EN.pdf 

417 

 
 
 
 
 
                                                 
Annex 1│ Country fiches 

Consumer Association Active Consumers and the Federation of Consumers in Bulgaria 
(FCB).  The  former  organisation  provides  a  wide  range  of  services,  including 
information, advice, testing, and education, but does not explicitly focus on vulnerable 
consumer  groups  through  specific  work  streams  or  projects  The  Federation  of 
Consumers  in  Bulgaria  does  not  appear  to  have  a  specific  work  stream  focusing  on 
consumer  vulnerability,  but  there  are  indications  that  it  covers  vulnerability  through 
some of its actions, in particular in the field of utilities.     

Association  “Consumers’  Help”  is  a  young  organisation  dealing  successfully  with 
consumer  complaints  and  representing  consumers  in  courts.  This  organisation 
represents both the individual and collective interests of consumers bringing actions in 
court for representation and for the protection of collective interests of consumers. 

Incidence of consumer vulnerability  

Indicator of vulnerability 

Mean incidence  

Dimension  1 :  Heightened  risk  of  negative  outcomes 
or impacts on well-being 

Has experienced a problem and took no action 

Overpaid  for  services  due  to  being  unable  to  use  certain 
payment methods 

Dimension 2 : Having characteristics that limit ability 
to maximise well-beings 

Feels vulnerable to 'a great extent' due to health problems 

Feels  vulnerable  to  'a  great  extent'  due  to  financial 
circumstances 

Feels  vulnerable  to  'a  great  extent'  due  to  employment 
situation 

Feels vulnerable to 'a great extent' due to age 

Feels  vulnerable  to  'a  great  extent'  due  to  belonging  to  a 
minority group 

Feels vulnerable to 'a great extent' due to personal issues 

Feels vulnerable to 'a great extent' due to other reasons 

Feels  vulnerable  to  'a  great  extent'  due  to  any  personal 
characteristic 

Dimension  3 :  Having  difficulty 
assimilating information 

in  obtaining  or 

Feels  'not  at  all'  informed  about  product  price,  quality, 
conditions,  etc  when  choosing  and  buying  goods  and 
services 

Compares  product  deals  by  using 
advertisements only - energy sector 

information 

from 

Compares  product  deals  by  using 
advertisements only - online sector  

information 

from 

Compares  product  deals  by  using 
advertisements only - finance sector 

information 

from 

28% 

15% 

4% 

21% 

14% 

9% 

2% 

6% 

6% 

33% 

3% 

4% 

418 

 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Has  problems  comparing  deals  due  to  information-related 
factors - energy sector 

Has  problems  comparing  deals  due  to  information-related 
factors - online sector 

3% 

Has  problems  comparing  deals  due  to  information-related 
factors - finance sector 

20. Has an electricity supplier and has not switched due to 
information-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
information-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to 
information-related factors - financial sector 

Dimension  4 :  Inability  or  failure  to  buy,  choose  or 
access suitable products 

Does not compare product deals - energy sector 

Finds  it  very  difficult  to  compare  product  deals  -  energy 
sector 

Does not compare product deals - online sector 

Finds  it  very  difficult  to  compare  product  deals  -  online 
sector 

Does not compare product deals - finance sector 

Finds  it  very  difficult  to  compare  product  deals  -  finance 
sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
energy sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
online sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
financial sector 

Has  problems  comparing  deals  due  to  market-related 
factors - energy sector 

87% 

37% 

17% 

1% 

61% 

18% 

7% 

Has  problems  comparing  deals  due  to  market-related 
factors - online sector 

10% 

Has  problems  comparing  deals  due  to  market-related 
factors - financial sector 

Has problems comparing deals due to access-related factors 
- energy sector 

Has problems comparing deals due to access-related factors 
- online sector 

3% 

Has problems comparing deals due to access-related factors 
- financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 

419 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

personal factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
personal factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  personal 
factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
market-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
market-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  market-
related factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
access-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
access-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  access-
related factors - financial sector 

Has not switched due to termination costs 

Has not switched due to bundling 

Excluded from e-commerce due to difficulty of the process 

Excluded  from  e-commerce  due  to  not  having  a  payment 
card 

Declined for a loan 

Dimension  5 :  Higher  susceptibility  to  marketing 
practices 

18% 

28% 

29% 

24% 

Vulnerable  to  a  great  extent  due  to  complexity  of  offers 
(own perception) 

13% 

Experiment choice under the marketing practice treatment - 
energy sector 

Experiment choice under the marketing practice treatment - 
online sector 

Experiment choice under the marketing practice treatment - 
finance sector 

Experiment choice under the marketing practice treatment - 
cross-cutting 

420 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Table 87:  Country fiche – Czech Republic 

Country fiche summary 

Approach  to  consumer  vulnerability:  The  term  consumer  vulnerability  appears  to  be 
used  in  a  broader  strategic  consumer  protection  context  beyond  the  transposition  of 
the UCPD.  

Measures  addressing  consumer  vulnerability:  A  number  of  measures  identified  with  a 
particular focus on sales presentation events. 

National consumer protection framework 

The Czech consumer policy priorities for the period 2011-2014 include: 

  Safety of products and services 

  An appropriate level of consumer protection regulation 

 

Improved  consumer  awareness,  development  of  information  and  educational 
activities for the benefit of consumers; environmental education 

  Promoting  self-regulation,  dialogue  between  public  institutions  and  consumers 

and dialogue between businesses and consumers 

 

Increasing the efficiency of market surveillance, strengthening enforcement and 
redress 

  Support for the activities and development of consumer organisations 

  Monitoring  the  development  of  selected  aspects  of  consumer  protection  on  the 

market 
Institutional support for consumer protection305 

 

The  Civil  Code  is  the  cornerstone  for  the  Czech  consumer  protection  legislation.  In 
addition,  consumer  protection  is  also  governed  by  public  law,  primarily  through  the 
Consumer Protection Act306. 

Approach to consumer vulnerability 

Consumer vulnerability within the context of the transposed UCPD  

The term vulnerability is included in the Czech Consumer Protection Act in as far as the 
Act  transposes  the  UCPD.  The  term  “average  consumer”  however  does  not  appear  to 
be directly transposed. 

Consumer vulnerability in national consumer policy beyond the UCPD 

Beyond  the  transposition  of  the  UCPD,  “specific  attention  to  the  needs  and  protection 
of  especially  vulnerable  groups  of  consumers  (children,  seniors,  disabled  persons)”  is 
identified  as  one  of  the  priorities  of  Czech  consumer  policy.  The  term  is  also  used  by 
the  Czech  Trade  Inspection  Authority  when  referring  to  children,  while  the  term 
“members  of  socially  vulnerable  groups”  is  also  used  in  some  specific  contexts.  One 
example is the case of sales presentation events, where the term is used in relation to 
older consumers307. 

305 See http://download.mpo.cz/get/43277/48718/574455/priloha001.pdf 
306 See http://ec.europa.eu/consumers/overview/country_profile/CZ_web_country_profile.pdf 
307 See http://www.coi.cz/pokuty-smejdum-pres-21-milionu-nc998/ 

421 

 
 
 
 
 
                                                 
Annex 1│ Country fiches 

Specific legislative measures 

There  are  examples  of  legislative  measures  addressing  vulnerable  consumer  groups, 
such  as  the  amendment  to  the  Consumer  Protection  Act  focusing  on  rules  concerning 
sales demonstration events targeting vulnerable consumers, in particular seniors. The 
Czech Trade Inspection Authority also carries out specific inspections of firms providing 
“sales demonstration events”, which particularly target older consumers. The authority 
has detected breaches in over 70% of cases308.  
No  specific  legislative  measures  explicitly  targeting  vulnerable  consumers  were 
identified in the energy sector, which is in line with the findings of the CEER report309, 
where  Czech  Republic  is  one  of  the  Member  States  that  do  not  appear  to  use  the 
concept in the context of energy.  

Non-legislative measures 

Projects  and  initiatives  funded  in  the  Czech  Republic  are  primarily  broad  counselling, 
advice,  and  education  projects,  with  selected  projects  having  a  more  direct  link  to 
consumer vulnerability. This included a publicity campaign, which led to the making of 
a  film  concerning  unfair  commercial  practices  employed  during  sales  presentation 
events aimed at seniors.   

Institutional structure and role of civil society 

Public bodies responsible for consumer policy 

The  Ministry  of  Industry  and  Trade  has  the  main  responsibility  for  consumer  policy  in 
the  Czech  Republic.  It  formulates  consumer  policy  and  is  supported  in  specific  policy 
fields by the Ministry of Finance, Ministry of Health, Ministry for Regional Development, 
and  the  Ministry  of  Transport.  The  Ministry  of  Industry  and  Trade  supervises  the 
inspections  relevant  to  the  regulations  within  its  remits  and  gathers  questions  and 
complaints  from  consumers  or  public,  but  does  not  enforce  consumer  complaints, 
which  is  primarily  the  responsibility  of  the  Czech  Trade  Inspection,  along  with  a 
number of other agencies with responsibilities for specific fields. 

Role of civil society 

Czech  Consumer  Association  dTEST  is  currently  the  key  nation-wide  consumer 
organisation. Its main area of work includes product testing, although it also works to 
promote  consumers’  rights  more  broadly  and  is  the  Czech  BEUC  member.  Its  actions 
however  do  not  focus  explicitly  on  consumer  vulnerability,  although  specific  issues  it 
tackles  are  linked  to  consumer  vulnerability,  such  as  for  instance  sales  presentations. 
Other  organisations  also  carry  out  specific  activities  focusing  on  the  protection  of 
vulnerable  groups  (i.e.  children  or  people  with  disabilities).  The  Ministry  of  Industry 
and  Trade  financially  supports  some  of  the  projects  carried  out  by  civil  society 
organisations.  

308 See http://www.coi.cz/pokuty-smejdum-pres-21-milionu-nc998/ 
309 CEER (2012). ‘Status Review of Customer and Retail Market Provisions from the 3rd Package 

as of 1 January 2012’ 

422 

 
 
 
 
 
 
                                                 
Annex 1│ Country fiches 

Incidence of consumer vulnerability  

Indicator of vulnerability 

Mean incidence  

Dimension  1 :  Heightened  risk  of  negative  outcomes 
or impacts on well-being 

Has experienced a problem and took no action 

Overpaid  for  services  due  to  being  unable  to  use  certain 
payment methods 

Dimension 2 : Having characteristics that limit ability 
to maximise well-beings 

Feels vulnerable to 'a great extent' due to health problems 

Feels  vulnerable  to  'a  great  extent'  due  to  financial 
circumstances 

Feels  vulnerable  to  'a  great  extent'  due  to  employment 
situation 

Feels vulnerable to 'a great extent' due to age 

Feels  vulnerable  to  'a  great  extent'  due  to  belonging  to  a 
minority group 

Feels vulnerable to 'a great extent' due to personal issues 

Feels vulnerable to 'a great extent' due to other reasons 

Feels  vulnerable  to  'a  great  extent'  due  to  any  personal 
characteristic 

Dimension  3 :  Having  difficulty 
assimilating information 

in  obtaining  or 

Feels  'not  at  all'  informed  about  product  price,  quality, 
conditions,  etc  when  choosing  and  buying  goods  and 
services 

Compares  product  deals  by  using 
advertisements only - energy sector 

information 

from 

Compares  product  deals  by  using 
advertisements only - online sector  

information 

from 

Compares  product  deals  by  using 
advertisements only - finance sector 

information 

from 

Has  problems  comparing  deals  due  to  information-related 
factors - energy sector 

Has  problems  comparing  deals  due  to  information-related 
factors - online sector 

Has  problems  comparing  deals  due  to  information-related 
factors - finance sector 

20. Has an electricity supplier and has not switched due to 
information-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
information-related factors - online sector 

423 

10% 

6% 

3% 

7% 

4% 

3% 

2% 

2% 

2% 

12% 

3% 

1% 

3% 

3% 

16% 

4% 

9% 

 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Has  a  bank  account  and  has  not  switched  due  to 
information-related factors - financial sector 

Dimension  4 :  Inability  or  failure  to  buy,  choose  or 
access suitable products 

Does not compare product deals - energy sector 

Finds  it  very  difficult  to  compare  product  deals  -  energy 
sector 

Does not compare product deals - online sector 

Finds  it  very  difficult  to  compare  product  deals  -  online 
sector 

Does not compare product deals - finance sector 

Finds  it  very  difficult  to  compare  product  deals  -  finance 
sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
energy sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
online sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
financial sector 

Has  problems  comparing  deals  due  to  market-related 
factors - energy sector 

Has  problems  comparing  deals  due  to  market-related 
factors - online sector 

Has  problems  comparing  deals  due  to  market-related 
factors - financial sector 

Has problems comparing deals due to access-related factors 
- energy sector 

Has problems comparing deals due to access-related factors 
- online sector 

Has problems comparing deals due to access-related factors 
- financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
personal factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
personal factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  personal 
factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
market-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
market-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  market-
related factors - financial sector 

43% 

9% 

38% 

2% 

43% 

7% 

32% 

16% 

27% 

38% 

15% 

33% 

9% 

7% 

2% 

424 

 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Has  an  electricity  supplier  and  has  not  switched  due  to 
access-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
access-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  access-
related factors - financial sector 

Has not switched due to termination costs 

Has not switched due to bundling 

Excluded from e-commerce due to difficulty of the process 

Excluded  from  e-commerce  due  to  not  having  a  payment 
card 

Declined for a loan 

Dimension  5 :  Higher  susceptibility  to  marketing 
practices 

15% 

20% 

25% 

17% 

Vulnerable  to  a  great  extent  due  to  complexity  of  offers 
(own perception) 

8% 

Experiment choice under the marketing practice treatment - 
energy sector 

Experiment choice under the marketing practice treatment - 
online sector 

Experiment choice under the marketing practice treatment - 
finance sector 

Experiment choice under the marketing practice treatment - 
cross-cutting 

425 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Table 88:  Country fiche - Denmark 

Country fiche summary 

Approach to consumer vulnerability: The concept of consumer vulnerability appears to 
be  used  in  specific  sectors  and  the  Danish  consumer  protection  framework  covers 
people  in  vulnerable  situations.  There  appears  to  be  no  broad  policy  approach 
addressing specific vulnerable consumer groups. 

Measures  addressing  consumer  vulnerability:  There  is  evidence  of  a  range  of  sector-
specific measures addressing consumer vulnerability. 

National consumer protection framework 

In  Denmark,  consumer  legislation  establishing  the  general  consumer  protection 
framework can be found in the various Acts such as, inter alia, the Marketing Practices 
Act,  the  Act  on  Contracts,  Act  on  certain  payment  instruments,  Act  on  Consumer 
Complaints, the Act on Certain Consumer Contracts and Act on Credit Agreements. 

Approach to consumer vulnerability 

Consumer vulnerability within the context of the transposed UCPD 

The  relevant  Acts  transposing  the  UCPD  do  not  refer  to  vulnerable  consumers.  The 
appendix of the UCPD is implemented in the Danish Marketing Act as an appendix and 
includes the term “average consumers”310. 
Consumer vulnerability in national consumer policy beyond the UCPD  

The  overall  strategic  approach  in  use  in  Denmark  is  to  focus  on  both  the  vulnerable 
and the strong and independent consumers. The term “vulnerable consumers” appears 
to  generally  not  be  used  in  Denmark.  However,  consumer  protection  is  based  on  the 
assumption that consumers are the weaker part in their relation to businesses. In this 
vein,  consumers  may  in  general  be  considered  vulnerable  in  their  interaction  with 
businesses. Selected acts provide for explicit protection of vulnerable consumers due to 
their age, lack of knowledge, mental state, or health.  
One  of  these  examples  can  be  found  in  the  Act  on  Guardianship311.  In  addition,  the 
Market Practices Act refers to children and young adults and stipulates that businesses 
must  take  into  account  these  consumers’  natural  credulity  and  lack  of  experience  in 
their marketing practices.  

The  Act  on  Social  Services  protects  consumers  with  mental  or  health  problems  and 
stipulates  that  the  municipal  council  shall  grant  support  for  technical aids  for  persons 
with permanent impairment of physical or mental functions where the aid will remedy 
the  permanent  effects  of  the  functional  impairment  significantly312.  Furthermore  the 
municipal council shall provide assistance for the purchase of consumer durables313.  
Other  acts  explicitly  protecting  vulnerable  consumers  include  the  Act  on  Liabilities  in 

310 See 

http://www.europarl.europa.eu/document/activities/cont/201202/20120217ATT38466/2012
0217ATT38466EN.pdf 

311 Compilation of briefing papers on consumer vulnerability. Available at: 

http://www.europarl.europa.eu/document/activities/cont/201202/20120217ATT38466/2012
0217ATT38466EN.pdf  

312 See http://sm.dk/en/files/consolidation-act-on-social-services.pdf, §112 
313 See http://sm.dk/en/files/consolidation-act-on-social-services.pdf, § 113 

426 

 
 
 
                                                 
Annex 1│ Country fiches 

Damages and the Radio and Television Broadcasting Act. 

Specific legislative measures  

There  are  several  legislative  measures  in  place  in  order  to  mitigate  consumer 
vulnerability.  In  the  financial  sector  the  “Executive  Order  on  Risk-Labelling  of 
Investment  Products”  aims  to  protect  investors  with  little  experience  with  regard  to 
investment  products.  It  places  a  requirement  on  financial  undertakings  arranging 
purchases  of  investment  products  to  risk-label  their  investment  products  through  a 
traffic light system indicating the risk level associated with the product. The “Executive 
Order  on  Good  Business  Practices  for  Financial  Undertakings”  states  that  a  bank  or 
mortgage-credit institution may not grant loans against guarantee by a surety, where 
the  guarantee  obligation  is  out  of  proportion  to  the  financial  situation  of  the 
guarantor314.  

Non- legislative measures 

There are specific non-legislative measures in Denmark aimed at mitigating consumer 
vulnerability.  For  example,  in  the  telecommunications  sector,  the  Danish  Consumer 
Ombudsman  and  The  Danish  Consumer  Council  together  with  various  business 
organisations have established a set of guidelines on marketing, terms of contract and 
customer  services  in  the  industry.  Consumers  must  be  provided  with  adequate 
information  as  regards  to,  inter  alia,  the  use  of  equipment,  terms  of  subscription 
periods and prices, especially in connection with marketing of combined offers of both 
equipment  and  services.  In  addition,  a  special  consumer  complaint  board  related  to 
telecommunications has been approved by the Danish Ministry of Business and Growth 
pursuant to the Executive Order on Consumer Complaints.   

Institutional structure and role of civil society 

Public bodies responsible for consumer policy  

Consumer  policy  in  Denmark  falls  within  the  competence  of  the  Ministry  of  Business 
and  Growth.  The  Danish  Competition  and  Consumer  Authority  is  a  public  institution 
under  the  Ministry  of  Business  and  Growth,  responsible  for  the  enforcement  of  a 
number  of  consumer  protection  laws.  In  addition,  the  Authority  contributes  to  the 
development  of  new  consumer  policy  and  regulation,  considers  consumer  complaints, 
performs market analysis and provides information to both consumers and businesses 
with  regard  to  consumer  legislation.  The  Danish  Competition  and  Consumer  Authority 
runs the Secretariat of the Consumer Complaints Board, an independent board dealing 
with consumer complaints related to goods and services purchased from traders.  

In addition to these institutions, the Danish Consumer Ombudsman is an independent 
public  authority  tasked  with  the  supervision  of  compliance  with  primarily  Danish 
Marketing Act, but also the Act on Payment Services, the Act on Legal Counselling, the 
Act on Tobacco Advertising and the E-commerce Act in relation to consumer protection 
issues.  

Role of civil society 

314 See 

http://www.europarl.europa.eu/document/activities/cont/201202/20120217ATT38466/2012
0217ATT38466EN.pdf 

427 

 
 
 
 
 
                                                 
Annex 1│ Country fiches 

The  main  consumer  association  in  Denmark  is  the  Danish  Consumer  Council,  an 
umbrella  organization  with  over  30  different  member  organisations.  The  Danish 
Consumer Council targets all consumers and does have specific work streams focusing 
on vulnerable consumers. 

Incidence of consumer vulnerability  

Indicator of vulnerability 

Mean incidence  

Dimension  1 :  Heightened  risk  of  negative  outcomes 
or impacts on well-being 

Has experienced a problem and took no action 

Overpaid  for  services  due  to  being  unable  to  use  certain 
payment methods 

Dimension 2 : Having characteristics that limit ability 
to maximise well-beings 

Feels vulnerable to 'a great extent' due to health problems 

Feels  vulnerable  to  'a  great  extent'  due  to  financial 
circumstances 

Feels  vulnerable  to  'a  great  extent'  due  to  employment 
situation 

Feels vulnerable to 'a great extent' due to age 

Feels  vulnerable  to  'a  great  extent'  due  to  belonging  to  a 
minority group 

Feels vulnerable to 'a great extent' due to personal issues 

Feels vulnerable to 'a great extent' due to other reasons 

Feels  vulnerable  to  'a  great  extent'  due  to  any  personal 
characteristic 

Dimension  3 :  Having  difficulty 
assimilating information 

in  obtaining  or 

Feels  'not  at  all'  informed  about  product  price,  quality, 
conditions,  etc  when  choosing  and  buying  goods  and 
services 

Compares  product  deals  by  using 
advertisements only - energy sector 

information 

from 

Compares  product  deals  by  using 
advertisements only - online sector  

information 

from 

Compares  product  deals  by  using 
advertisements only - finance sector 

information 

from 

Has  problems  comparing  deals  due  to  information-related 
factors - energy sector 

Has  problems  comparing  deals  due  to  information-related 
factors - online sector 

Has  problems  comparing  deals  due  to  information-related 
factors - finance sector 

7% 

10% 

4% 

8% 

6% 

3% 

2% 

2% 

2% 

14% 

3% 

2% 

3% 

2% 

20% 

7% 

19% 

428 

 
 
 
 
 
 
 
Annex 1│ Country fiches 

20. Has an electricity supplier and has not switched due  to 
information-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
information-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to 
information-related factors - financial sector 

Dimension  4 :  Inability  or  failure  to  buy,  choose  or 
access suitable products 

Does not compare product deals - energy sector 

Finds  it  very  difficult  to  compare  product  deals  -  energy 
sector 

Does not compare product deals - online sector 

Finds  it  very  difficult  to  compare  product  deals  -  online 
sector 

Does not compare product deals - finance sector 

Finds  it  very  difficult  to  compare  product  deals  -  finance 
sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
energy sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
online sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
financial sector 

Has  problems  comparing  deals  due  to  market-related 
factors - energy sector 

Has  problems  comparing  deals  due  to  market-related 
factors - online sector 

Has  problems  comparing  deals  due  to  market-related 
factors - financial sector 

Has problems comparing deals due to access-related factors 
- energy sector 

Has problems comparing deals due to access-related factors 
- online sector 

Has problems comparing deals due to access-related factors 
- financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
personal factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
personal factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  personal 
factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 

429 

2% 

0% 

1% 

53% 

19% 

35% 

6% 

55% 

15% 

35% 

18% 

34% 

42% 

28% 

46% 

15% 

7% 

2% 

9% 

4% 

5% 

15% 

 
 
 
 
 
 
Annex 1│ Country fiches 

market-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
market-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  market-
related factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
access-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
access-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  access-
related factors - financial sector 

Has not switched due to termination costs 

Has not switched due to bundling 

Excluded from e-commerce due to difficulty of the process 

Excluded  from  e-commerce  due  to  not  having  a  payment 
card 

Declined for a loan 

Dimension  5 :  Higher  susceptibility  to  marketing 
practices 

Vulnerable  to  a  great  extent  due  to  complexity  of  offers 
(own perception) 

Experiment choice under the marketing practice treatment - 
energy sector 

Experiment choice under the marketing practice treatment - 
online sector 

Experiment choice under the marketing practice treatment - 
finance sector 

Experiment choice under the marketing practice treatment - 
cross-cutting 

5% 

14% 

10% 

4% 

3% 

9% 

19% 

19% 

10% 

3% 

7% 

85% 

49% 

20% 

64% 

430 

 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Table 89:  Country fiche - Germany 

Country fiche summary 

Approach to consumer vulnerability: To date the concept of consumer vulnerability has 
been  primarily  used  in  projects  carried  out  by  consumer  organisations.  An  overall 
strategic national approach to consumer vulnerability is however in development.  

Measures  addressing  consumer  vulnerability:  There  are  a  number  of  primarily  non-
legislative measures addressing consumer vulnerability.  

National consumer protection framework 

According  to  the  German  Federal  Ministry  of  Justice  and  Consumer  Protection,  the 
federal consumer policy in Germany follows two broad principles: 

  Preventive  consumer  protection,  with  a  priority  given  to  protecting  consumer 

health and safety, followed by their economic interest; and 

  Strengthening of self-determination and personal responsibility. 

Key pieces of legislation include the Consumer Information Act, the Unfair Competition 
Act,  and  the  Unfair  Commercial  Practices  Act  (transposing  the  Unfair  Commercial 
Practices  Directive).  The  main  new  development  is  the  recent  transposition  of  the 
Consumer Rights Directive.315  

Approach to consumer vulnerability 

Consumer vulnerability within the context of the transposed UCPD  

The German transposition of the UCPD introduced the concept of average consumer as 
well  as  vulnerable  consumer  into  German  legislation,  with  the  term  ”vulnerable” 
transposed as  “besonders  schutzbedürftige Verbraucher” (“consumers  in specific need 
of  protection”).  Examples  of  unfair  commercial  practices  provided  include  practices 
suited  to  exploitation  of  a  consumer’s  mental  or  physical  infirmity,  age,  commercial 
inexperience,  credulity  or  fear,  or  the  position  of  constraint  to  which  the  consumer  is 
subject.  The  German  transposition  of  the  UCPD  therefore  identifies  commercial 
inexperience, fear and position of constraint alongside age, physical or mental infirmity 
or credulity as potential sources of vulnerability316.  
Consumer vulnerability in national consumer policy beyond the UCPD  

There is little evidence of the concept of consumer vulnerability being used outside of 
the  context  of  the  UCPD.  The  term  “verletzliche  Verbraucher”,  used  in  practice  by 
German consumer organisations, is also rarely used outside of consumer organisations. 
There  is,  however,  a  degree  of  discussion  at  policy  level  concerning  the  approach  to 
different types of consumers: In 2010 the Scientific Advisory Council to the Ministry of 
Food  and  Agriculture  produced  a  position  paper  distinguishing  between  the  trusting, 
vulnerable, and responsible consumers and calling for a more differentiated consumer 
policy317.  The  2013  government  coalition  agreement  also  included  references  to  a 
differentiated consumer image and consumer protection that is appropriate to age. In 
order  to  establish  an  overall  strategic  national  approach  to  consumer  vulnerability,  a 

315 See http://www.bmjv.de/DE/Themen/Verbraucherschutz/verbraucher_node.html 
316 See UCPD Legal Database 
317 Micklitz, H, W. et al. (2010) 

431 

 
 
 
 
 
                                                 
Annex 1│ Country fiches 

new  unit  called  “Special  Consumer  Groups”  will  be  set  up  within  the  department  of 
“Consumer  Policy”  of  the  Federal  Ministry  of  Justice  and  Consumer  Protection  in  the 
second quarter of 2015. 

The  review  of  consumer  vulnerability  in  German  law  conducted  for  the  European 
Parliament confirms that to date consumer vulnerability has generally not been tackled 
by  German  consumer  policy  outside  of  the  context  of  the  UCPD.  The  authors  do 
however note that German courts touched upon the concept of consumer vulnerability 
prior  to  the  transposition  of  the  UCPD.  An  example  given  is  a  Federal  High  Court 
guidance  stating  that  “a  specific  need  of  protection  is  to  be  guaranteed  when 
consumers  are  unable  to  inform  themselves  about  the  product  or  the  services  in  the 
usual  manner  […]  or  if  they  are  unable  to  understand  and  reflect  upon  the  promoted 
product or service because of below average intellectual capabilities”. This serves as a 
legal  basis  for  special  protection  of  foreign  nationals,  senior  citizens,  or  illiterate 
citizens318.  
Stakeholder assessment of the national approach to consumer vulnerability 

From the perspective of consumer organisations, consumer vulnerability is not seen as 
being  actively  addressed  at  policy  level,  although  it  constitutes  an  area  of  work  for 
consumer  organisations,  with  a  number  of  projects  funded  by  the  Federal  Ministry  of 
Justice  and  Consumer  Protection.  A  particular  concern  identified  by  one  German 
consumer organisation is the perception that the introduction of the concept of average 
consumer  in  the  UCPD  effectively  weakened  consumer  protection  by  raising  the 
standard by which practices should be judged. The current “average consumer” is seen 
as being an informed, competent, and autonomous consumer, which, from the point of 
view of consumer organisations, does not reflect reality319.  

Specific legislative measures  

As noted above, consumer vulnerability is generally not tackled at policy level beyond 
the  context  of  the  UCPD.  While  the  2012  legal  review  identified  special  protection  of 
minors as the main area of activity and pointed to the prohibition of advertising aimed 
directly at children as a key measure, this only reflects the provisions of UCPD.  

In addition in the energy sector, the provisions of Directive 2009/72/EG (as well as the 
respective specifications of Directive 2009/73/EG) have been implemented in Germany 
by a combination of rules of energy law as well as social law. There are several specific 
legislative rules in place especially to protect household customers (e.g. Section 36 et 
seq. of the Energy Act), including customers in need of (financial) help (“hilfebedürftige 
Kunden”), where there is social welfare protection  – it secures a minimum income for 
those  who  are  in  financial  distress,  including  the  coverage  of  electricity  bills  and 
heating. 

Non-legislative measures 

Although the research has not identified non-legislative measures explicitly targeting a 
wide  range  of  vulnerable  consumers,  there  are  examples  of  measures  focusing  on 
specific groups that could potentially be vulnerable. The German government supports 
several  projects  of  consumer  organisations,  which  focus  on  strengthening  and 

318 European Parliament (2012) 
319 See for instance 

http://www.vzbv.de/cps/rde/xbcr/vzbv/Strategie_verbraucherpolitik_Wiss_BeiratBMELV_201
0.pdf 

432 

 
 
 
                                                 
Annex 1│ Country fiches 

protecting  vulnerable  consumers.  These  projects  concern  e.g.  migrants,  seniors  and 
young consumers. One example of a project financed by the German  Federal Ministry 
of Justice and Consumer Protection is a federal programme launched in 2010 aiming at 
strengthening  consumer  skills  among  young  consumers  through  the  school  system. 
Other  examples  are  projects  of  consumer  centres  operating  on  the  Länder-level 
concerning  competences  of  minors  and  young  adults  as  market  participants.  Some  of 
those  projects  are  partly  co-  financed  by  the  Länder-governments).  Other  projects 
include: 

  Project of IAJB (Fachstelle für Internationale Jugendarbeit der Bundesrepublik 
Deutschland e.V. Agency of the Federal Republic of Germany for International 
Youth Work) providing information on consumer and data protection for young 
people. For further details please see www.watchyourweb.de. 

  Project of the consumer centres Berlin, Hamburg and Bremen providing 

information for Turkish and Russian immigrants on consumer rights in digital 
markets320.  

Moreover,  there  are  several  Energy  consulting  programs  that  are  free-of-charge  for 
low-income  household  customers.  They  are  offered  by  consumer  organisations  (vzbv) 
and  are  government-funded  (to  promote  energy  efficiency  and  energy  savings). 
Furthermore,  the  German  government  promotes  several  other  projects  (e.g. 
“Stromspar-Check-Plus”  of  the  German  Caritas/  Bundesverband  der  Energie-  und 
Klimaschutzagenturen  Deutschlands  e.V.)  including  free-of-charge  installation  of 
simple energy saving devices and funding the exchange of old inefficient refrigerators. 

Institutional structure and role of civil society 

Public bodies responsible for consumer policy 

Consumer protection in Germany traditionally fell within the competence of the Federal 
Ministry  of  Food  and  Agriculture,  although  as  of  December  2013  many  of  the 
responsibilities have been transferred to the Federal Ministry of Justice  and Consumer 
Protection.  Other  Ministries  are  responsible  for  specific  elements  of  consumer  policy. 
Supervision and enforcement are primarily carried out on the level of individual Länder 
with  specific  Federal  agencies,  such  as  the  Federal  Office  of  Consumer  Protection  and 
Food Safety having responsibility for specific tasks on federal level. 

In  Germany,  consumer  organisations  have  a  strong  role  in  enforcing  and  protecting 
consumer  law.  In  contrast,  there  are  only  a  few  governmental  and  administrative 
competences in consumer protection. 

Role of civil society 

Verbraucherzentrale  Bundesverband  (vzbv) is  the  federal-level  umbrella  organisation 
grouping individual consumer centres operating on the  Länder-level. While the federal 
level organisation is responsible for involvement in the federal and EU-level consumer 
policy,  the  individual  centres  are  responsible  for  providing  advice  and  consumer 
information.  

Although the centres do not have work  streams addressing specific consumer groups, 
focusing instead on sectors, selected activities target potentially vulnerable consumers. 

320 See http://www.verbraucherzentrale-berlin.de/-20175. 

433 

 
 
 
 
 
                                                 
Annex 1│ Country fiches 

This includes in particular young consumers (e.g. the consumer centre in North Rhine-
Westphalia  carried  out  a  project  aiming  to  educate  young  consumers  about  e-
commerce)  and  older  consumers  (i.e.  the  consumer  centre  in  Hamburg  provides 
seminars  targeting  older  consumers  or  over-indebted  households).  The  consumer 
centres  also  contribute  to  the  debate  concerning  consumer  vulnerability:  in  2013  the 
consumer  centre  in  North  Rhine-Westphalia  organised  a  research  workshop  on  this 
topic321.  

Incidence of consumer vulnerability  

Indicator of vulnerability 

Mean incidence  

Dimension  1 :  Heightened  risk  of  negative  outcomes 
or impacts on well-being 

Has experienced a problem and took no action 

Overpaid  for  services  due  to  being  unable  to  use  certain 
payment methods 

Dimension 2 : Having characteristics that limit ability 
to maximise well-beings 

Feels vulnerable to 'a great extent' due to health problems 

Feels  vulnerable  to  'a  great  extent'  due  to  financial 
circumstances 

Feels  vulnerable  to  'a  great  extent'  due  to  employment 
situation 

Feels vulnerable to 'a great extent' due to age 

Feels  vulnerable  to  'a  great  extent'  due  to  belonging  to  a 
minority group 

Feels vulnerable to 'a great extent' due to personal issues 

Feels vulnerable to 'a great extent' due to other reasons 

Feels  vulnerable  to  'a  great  extent'  due  to  any  personal 
characteristic 

Dimension  3 :  Having  difficulty 
assimilating information 

in  obtaining  or 

Feels  'not  at  all'  informed  about  product  price,  quality, 
conditions,  etc  when  choosing  and  buying  goods  and 
services 

Compares  product  deals  by  using 
advertisements only - energy sector 

information 

from 

Compares  product  deals  by  using 
advertisements only - online sector  

information 

from 

Compares  product  deals  by  using 
advertisements only - finance sector 

information 

from 

321 See http://www.vz-nrw.de/der-verletzliche-verbraucher 

12% 

10% 

2% 

6% 

3% 

3% 

2% 

3% 

2% 

10% 

2% 

2% 

3% 

2% 

434 

 
 
 
 
 
 
 
                                                 
Annex 1│ Country fiches 

Has  problems  comparing  deals  due  to  information-related 
factors - energy sector 

Has  problems  comparing  deals  due  to  information-related 
factors - online sector 

Has  problems  comparing  deals  due  to  information-related 
factors - finance sector 

20. Has an electricity supplier and has not switched due to 
information-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
information-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to 
information-related factors - financial sector 

Dimension  4 :  Inability  or  failure  to  buy,  choose  or 
access suitable products 

Does not compare product deals - energy sector 

Finds  it  very  difficult  to  compare  product  deals  -  energy 
sector 

Does not compare product deals - online sector 

Finds  it  very  difficult  to  compare  product  deals  -  online 
sector 

Does not compare product deals - finance sector 

Finds  it  very  difficult  to  compare  product  deals  -  finance 
sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
energy sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
online sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
financial sector 

Has  problems  comparing  deals  due  to  market-related 
factors - energy sector 

Has  problems  comparing  deals  due  to  market-related 
factors - online sector 

Has  problems  comparing  deals  due  to  market-related 
factors - financial sector 

Has problems comparing deals due to access-related factors 
- energy sector 

Has problems comparing deals due to access-related factors 
- online sector 

Has problems comparing deals due to access-related factors 
- financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 

435 

9% 

8% 

9% 

36% 

7% 

30% 

3% 

50% 

7% 

18% 

15% 

22% 

26% 

26% 

31% 

6% 

3% 

1% 

 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

personal factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
personal factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  personal 
factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
market-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
market-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  market-
related factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
access-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
access-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  access-
related factors - financial sector 

Has not switched due to termination costs 

Has not switched due to bundling 

Excluded from e-commerce due to difficulty of the process 

Excluded  from  e-commerce  due  to  not  having  a  payment 
card 

Declined for a loan 

Dimension  5 :  Higher  susceptibility  to  marketing 
practices 

10% 

17% 

14% 

17% 

Vulnerable  to  a  great  extent  due  to  complexity  of  offers 
(own perception) 

4% 

Experiment choice under the marketing practice treatment - 
energy sector 

Experiment choice under the marketing practice treatment - 
online sector 

Experiment choice under the marketing practice treatment - 
finance sector 

Experiment choice under the marketing practice treatment - 
cross-cutting 

436 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

 Table 90:  Country fiche - Estonia 

Country fiche summary 

Approach  to  consumer  vulnerability:  There  is  no  evidence  of  a  strategic  national 
approach to addressing consumer vulnerability. 

Measures  addressing  consumer  vulnerability:  There  is  evidence  of  a  range  of  sector-
specific measures addressing consumer vulnerability. 

National consumer protection framework 

The  main  legislative  instrument  relating  to  unfair  commercial  practices  and  consumer 
protection  in  Estonia  is  the  Consumer  Protection  Act  (Tarbijakaitseseadus).  The  Act 
sets out “to safeguard consumer rights” and its scope is to regulate “the offer and sale, 
or  marketing in  any  other  manner,  of  goods  or  services  to  consumers  by  traders”,  to 
determine  “the  rights  of  consumers  as  the  purchasers  or  users  of  goods  or  services”, 
and  to  provide  for  “the  organisation  and  supervision  of  consumer  protection  and 
liability for violations of this Act”.  
Under  the  Estonian  law  consumers  are  generally  considered  to  be  weaker  parties  in 
their relationship with businesses.   

Approach to consumer vulnerability 

Consumer vulnerability within the context of the transposed UCPD  
The  term  “vulnerable  consumers”  is  generally  not  used  in  Estonia.  The  Act  amending 
the  Consumer  Protection  Act  and  the  Law  of  Obligations  Act  transposing  the  UCPD 
refer  instead  to  consumers  particularly  “susceptible”  to  certain  practices  due  to  their 
age,  physical  or  mental  infirmity  or  credibility.  The  concept  of  “average  consumer”  is 
present in Estonian legislation through the transposition of the UCPD.   
Consumer vulnerability in national consumer policy beyond the UCPD  
There  is  no  evidence  of  consumer  vulnerability  being  used  in  the  overall  policy 
framework in Estonia outside the UCPD.  
Stakeholder assessment of the national approach to consumer vulnerability 
According  to  the  stakeholder  consulted,  there  seem  to  be  little  awareness  in  Estonia 
around  the  concept  of  consumer  vulnerability  both  at  the  level  of  consumer 
organisations and at policy-level.  

Specific measures  

There are selected specific legislative measures in place in Estonia aiming to counteract 
consumer vulnerability. With regard to the energy sector, there are provisions in place 
in  regard  to  disconnection  of  energy  supply.  However,  there  seems  to  be  no  social 
tariffs available to vulnerable consumers. 

In the financial sector, due to the high prevalence of payday loans, an amendment to 
the General Part Civil Code Act (GPCCA) was introduced in 2009 and revised in 2015. 
This  amendment  stipulates  that  transactions  where  the  APRC  exceeds  the  consumer 
loans  APR  rate  published  by  the  Bank  of  Estonia's  three  times  or  more  are  void.  In 
addition a maximum rate for APR in consumer credit contacts has been set up. 

Traditionally,  it  has  been  relatively  easy  to  apply  for  payday  loans  through,  for 
example,  a  mobile  phone  (SMS  loans)  without  rigorous  checks  of  documentation  and 
proof of identity. Therefore, Estonia is now looking to different measures employed in 

437 

 
 
 
 
 
Annex 1│ Country fiches 

Lithuania  and  Finland  where  access  is  more  restrictive  than  in  the  Estonian  context. 
One example of a recent change is the need to provide identification in order to access 
credit through payday loans.  

Non-legislative measures 

Other  non-legislative  measures  in  the  financial  sector  include  an  information  website 
and  comparison  tool  for  consumers  set  up  by  the  Financial  Supervision  Authority  for 
consumers to better manage their finances and debt repayments.  
In  the  electronic  communication  sector,  particularly  problematic  practices  identified in 
the  Estonian  context  concern  minors  using  mobile  content  services  and  entering  into 
contract/subscriptions.  These  services  are  often  marketed  in  an  aggressive  and 
misleading  way.  Measures  taken  to  counteract  these  aggressive  practices  include  two 
voluntary codes of conduct “Estonian Code of Conduct for Safer Mobile Use by Younger 
Teenagers  and  Children“  and  “the  Advisory  Guidelines  for  the  Provision  of  the 
Periodical Mobile Content Services” 

Institutional structure and role of civil society 

Public bodies responsible for consumer policy 
The  Estonian  Consumer  Protection  Board  is  the  main  public  supervisory  and 
enforcement  authority  falling  under  the  responsibility  of  the  Ministry  for  Economic 
Affairs  and  Communication.  The  main  duty  of  the  Board  is  to  protect  the  legal  rights 
and  interests  of  consumers  and  to  develop  and  implement  consumer  policy  in 
accordance  with  the  Consumer  Protection  Act  and  other  relevant  EU  directives.  The 
Consumer  Complaints  Committee  is  an  extrajudicial  institution  active  in  Estonia  that 
works  independently  at  the  Consumer  Protection  Board  and  solves  disputes  between 
the consumer and the seller. In addition, the regulators such as the Estonian Financial 
Supervision Authority and the Estonian Competition Authorities.  
Role of civil society 
According  to  stakeholder  interviews,  the  consumer  movement  in  Estonia  is  not  very 
strong  relative  to  other  consumer  movements  in  Europe.  The  Estonian  Consumers 
Union  is  the  non-governmental  umbrella  consumer  organisation  with  9  member 
organisations 
Estonia.  
The aim of the organisation is to secure the fundamental rights of consumers - right for 
safety,  information,  choice,  education,  healthy  environment,  redress  and  right  to  be 
heard.  However,  there  appear  to  be  few  activities  related  to  consumer  vulnerability 
except isolated projects and initiatives.  

volunteers 

over 

350 

and 

all 

Incidence of consumer vulnerability  

Indicator of vulnerability 

Mean incidence  

Dimension  1 :  Heightened  risk  of  negative  outcomes 
or impacts on well-being 

Has experienced a problem and took no action 

Overpaid  for  services  due  to  being  unable  to  use  certain 
payment methods 

Dimension 2 : Having characteristics that limit ability 
to maximise well-beings 

17% 

10% 

Feels vulnerable to 'a great extent' due to health problems 

7% 

438 

 
 
 
 
 
 
Annex 1│ Country fiches 

Feels  vulnerable  to  'a  great  extent'  due  to  financial 
circumstances 

Feels  vulnerable  to  'a  great  extent'  due  to  employment 
situation 

Feels vulnerable to 'a great extent' due to age 

Feels  vulnerable  to  'a  great  extent'  due  to  belonging  to  a 
minority group 

Feels vulnerable to 'a great extent' due to personal issues 

Feels vulnerable to 'a great extent' due to other reasons 

Feels  vulnerable  to  'a  great  extent'  due  to  any  personal 
characteristic 

Dimension  3 :  Having  difficulty 
assimilating information 

in  obtaining  or 

Feels  'not  at  all'  informed  about  product  price,  quality, 
conditions,  etc  when  choosing  and  buying  goods  and 
services 

Compares  product  deals  by  using 
advertisements only - energy sector 

information 

from 

Compares  product  deals  by  using 
advertisements only - online sector  

information 

from 

Compares  product  deals  by  using 
advertisements only - finance sector 

information 

from 

Has  problems  comparing  deals  due  to  information-related 
factors - energy sector 

Has  problems  comparing  deals  due  to  information-related 
factors - online sector 

Has  problems  comparing  deals  due  to  information-related 
factors - finance sector 

20. Has an electricity supplier and  has not switched due to 
information-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
information-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to 
information-related factors - financial sector 

Dimension  4 :  Inability  or  failure  to  buy,  choose  or 
access suitable products 

Does not compare product deals - energy sector 

Finds  it  very  difficult  to  compare  product  deals  -  energy 
sector 

Does not compare product deals - online sector 

Finds  it  very  difficult  to  compare  product  deals  -  online 
sector 

439 

19% 

9% 

5% 

3% 

4% 

4% 

29% 

5% 

3% 

6% 

2% 

13% 

5% 

11% 

47% 

9% 

37% 

2% 

 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Does not compare product deals - finance sector 

Finds  it  very  difficult  to  compare  product  deals  -  finance 
sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
energy sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
online sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
financial sector 

Has  problems  comparing  deals  due  to  market-related 
factors - energy sector 

Has  problems  comparing  deals  due  to  market-related 
factors - online sector 

Has  problems  comparing  deals  due  to  market-related 
factors - financial sector 

Has problems comparing deals due to access-related factors 
- energy sector 

Has problems comparing deals due to access-related factors 
- online sector 

Has problems comparing deals due to access-related factors 
- financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
personal factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
personal factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  personal 
factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
market-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
market-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  market-
related factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
access-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
access-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  access-
related factors - financial sector 

Has not switched due to termination costs 

Has not switched due to bundling 

Excluded from e-commerce due to difficulty of the process 

Excluded  from  e-commerce  due  to  not  having  a  payment 

56% 

7% 

35% 

20% 

39% 

36% 

15% 

36% 

17% 

5% 

2% 

19% 

26% 

24% 

16% 

440 

 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

card 

Declined for a loan 

Dimension  5 :  Higher  susceptibility  to  marketing 
practices 

Vulnerable  to  a  great  extent  due  to  complexity  of  offers 
(own perception) 

8% 

Experiment choice under the marketing practice treatment - 
energy sector 

Experiment choice under the marketing practice treatment - 
online sector 

Experiment choice under the marketing practice treatment - 
finance sector 

Experiment choice under the marketing practice treatment - 
cross-cutting 

441 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Table 91:  Country fiche - Ireland 

Country fiche summary 

Approach  to  consumer  vulnerability:  There  appears  to  be  a  broader,  cross-sector 
strategic approach to consumer vulnerability in place. 

Measures  addressing  consumer  vulnerability:   There  is  evidence  of  a  range  of  sector-
specific measures addressing consumer vulnerability. 

National consumer protection framework 

Consumer  Protection  Act  of  2007  constitutes  the  key  piece  of  Irish  consumer 
legislation,  along  with  the  Consumer  Credit  Act  and  a  number  of  other  associated 
pieces  of  legislation.  The  overall  consumer  protection  framework  in  Ireland  is  closely 
linked  to  the  EU  legislative  framework,  with  the  central  pieces  of  legislation  reflecting 
key Directives in the field.   

Approach to consumer vulnerability 

Consumer vulnerability within the context of the transposed UCPD  

The term “vulnerable”, along with the concept of “average consumer” is present in the 
Consumer  Protection  Act,  which  transposes  the  UCPD.  Vulnerability  is  denoted  as 
“mental or physical infirmity, age or credulity” in line with the UCPD.  

Consumer vulnerability in national consumer policy beyond the UCPD  

Prior to the transposition of the UCPD, the concept of vulnerability has been addressed 
in the 2005 Consumer Strategy Group report which defined a vulnerable consumer as 
“one  who  is  at  risk  of  receiving  less  benefit  from  a  transaction  than  average”.  The 
report also recognised that “every  consumer is a vulnerable consumer at some point; 
some  are  persistently  vulnerable”  and  that  “[c]onsumer  vulnerability  can  derive  from 
the  personal  characteristics  of  the  individual  or  from  features  of  the  marketplace 
/product  or  both”.  Specific  groups  recognised  to  be  more  likely  to  be  vulnerable  than 
others include older people, children and young people, people with disabilities, people 
with long-term illnesses, unemployed people, employed people on low incomes, people 
with  no  formal  education/qualifications,  people  from  ethnic  minorities,  the  traveller 
community, rural communities, time-poor, and lone parents working in the home322.  
In  the  financial  services  sector,  the  concept  of  vulnerability  is  also  present  in  the 
National  Bank  of  Ireland  Consumer  Protection  Code,  a  document,  which  is  binding  on 
regulated entities within the financial sector.  The code defines such a consumer as “a 
natural person who: 

a)  Has  the  capacity  to  make  his  or  her  own  decisions  but  who,  because  of 
individual circumstances, may require assistance to do so (for example, hearing 
impaired or visually impaired persons); and/or 

b)  Has  limited  capacity  to  make  his  or  her  own  decisions  and  who  requires 
assistance to do so (for example, persons with intellectual disabilities or mental 
health difficulties)”323 

The  Code  specifies  that  “where  a  regulated  entity  has  identified  that  a  personal 

322 See http://corporate.nca.ie/eng/Research_Zone/Reports/CSG_report.pdf 
323 See http://www.centralbank.ie/regulation/processes/consumer-protection-

code/Documents/Consumer%20Protection%20Code%202012.pdf 

442 

 
 
 
                                                 
Annex 1│ Country fiches 

consumer  is  a  vulnerable  consumer,  the  regulated  entity  must  ensure  that  the 
vulnerable consumer is provided with such reasonable arrangements and/or assistance 
that may be necessary to facilitate him or her in his or her dealings with the regulated 
entity”324. 
In  the  telecommunications  sector,  the  Commission  for  Communications  Regulation 
refers  to  vulnerable  groups  in  its  Code  of  Practice  on  Premium  Rate  Services 325 
(services  that  provide  the  contents  of  communications  through  an  electronic 
communications network or by using an electronic communications service), published 
in  accordance  with  section  15  of  the  Communications  Regulation  (Premium  Rate 
Services and Electronic Communications Infrastructure) Act 2010. The Code of Practice 
prohibits Premium Rate Services (PRS) and promotional material from seeking to take 
unfair advantage of any characteristic or circumstance which may make, or is likely to 
make, end-users vulnerable. 

In  the  energy  sector,  the  Commission  for  Energy  Regulation  has  developed  an 
Electricity  and  Gas  Code  of  Practice  for  Suppliers  which  sets  out  that  vulnerable 
customers should be categorised as those customers vulnerable to supply interruption 
and those who have special communication requirements.  

Specific legislative measures 

A number of specific requirements are present. In the energy sector, the Electricity and 
Gas Code of Practice requires suppliers to provide details of how to contact the supplier 
to  make  a  complaint,  including  any  special  arrangements  for  customers  who  are 
vulnerable  and  those  whose  first  language  is  not  English,  if  available.  In  addition,  all 
suppliers  of  domestic  customers  are  required  to  put  special  communications 
arrangements  in  place  for  customers  with  visual  and  hearing  impairments  and 
customers who are vulnerable in the home (elderly, mobility & visually impaired). 

In  the  telecommunications  sector,  the  Code  of  Practice  on  Premium  Rate  Services 
provides  that,  if  a  PRS  provider  can  reasonably  foresee  that  a  promotion  is  likely  to 
affect a vulnerable group only, the "average end-user" is an average end-user of that 
group.  The  Code  prohibits  access  by  children  to  a  number  of  PRSs  and  promotions, 
including sexual entertainment services, chat lines, virtual chat, and contact and dating 
services.  Promotional  material  for  children's  services  and  competition  services  which 
are  aimed  at,  or  would  reasonably  be  expected  to  prove  particularly  attractive  to, 
children must also comply with certain requirements. 

Non-legislative measures 

The  Competition  and  Consumer  Protection  Commission  (and  previously  the  National 
Consumer Agency) has a particular role in empowering consumers through a variety of 
channels.  These  include  a  consumer  website  which  provides  information  and  tools  to 
consumers on their consumer rights and information to assist them in making personal 
finance decisions, a telephone  consumer helpline, social media, a monthly newsletter, 
public  awareness  campaigns  (general  and  issue  specific),  and  providing  financial 
education  programmes  directly  to  consumers  (workplace  programme  and  initiatives 
targeted  at  new  parents  and  2nd level  students).  Some  of  the  issues  addressed  relate 

324 See http://www.centralbank.ie/regulation/processes/consumer-protection-

code/Documents/Consumer%20Protection%20Code%202012.pdf 
325 See http://www.comreg.ie/_fileupload/publications/ComReg1445.pdf 

443 

 
 
 
 
 
                                                 
Annex 1│ Country fiches 

directly  to  consumer  vulnerability  drivers,  such  as  life  stages  (including  losing  one’s 
job, separating or divorcing, or retiring), as well as tackling debt326.  

Institutional structure and role of civil society 

Public bodies responsible for consumer policy 

Consumer policy in Ireland is primarily the responsibility of the Commerce, Consumer 
and Competition Division within the Department of Jobs, Enterprise and Innovation. In 
2007,  with  the  introduction  of  the  Consumer  Protection  Act,  the  Department 
established  the  National  Consumer  Agency.  The  National  Consumer  Agency  is 
responsible for enforcement, advocacy, research, as well as informing, educating, and 
awareness  raising  among  consumers.  Although  the  Agency  has  a  wide  remit  across  a 
number  of  policy  areas,  The  Central  Bank  of  Ireland  retains  the  responsibility  for 
financial sector regulation. The Agency operates, in addition to its corporate website, a 
consumer-oriented Consumer Help website, along with a service dedicated to financial 
education. 

Role of civil society 

The Consumers’ Association of Ireland (CAI) is the main consumer organisation, which 
protects,  promotes,  and  represents  Irish  consumers.  The  organisation  however  does 
not  have  a  specific  consumer  vulnerability-oriented  work  stream,  nor  does  it 
specifically target vulnerability in its activities.   

Incidence of consumer vulnerability  

Indicator of vulnerability 

Mean incidence  

Dimension  1 :  Heightened  risk  of  negative  outcomes 
or impacts on well-being 

Has experienced a problem and took no action 

Overpaid  for  services  due  to  being  unable  to  use  certain 
payment methods 

Dimension 2 : Having characteristics that limit ability 
to maximise well-beings 

Feels vulnerable to 'a great extent' due to health problems 

Feels  vulnerable  to  'a  great  extent'  due  to  financial 
circumstances 

Feels  vulnerable  to  'a  great  extent'  due  to  employment 
situation 

Feels vulnerable to 'a great extent' due to age 

Feels  vulnerable  to  'a  great  extent'  due  to  belonging  to  a 
minority group 

Feels vulnerable to 'a great extent' due to personal issues 

Feels vulnerable to 'a great extent' due to other reasons 

Feels  vulnerable  to  'a  great  extent'  due  to  any  personal 

11% 

22% 

3% 

14% 

13% 

4% 

3% 

3% 

3% 

25% 

326 See http://www.consumerhelp.ie/life-stages and http://www.consumerhelp.ie/tackling-debt 

444 

 
 
 
 
 
 
                                                 
Annex 1│ Country fiches 

characteristic 

Dimension  3 :  Having  difficulty 
assimilating information 

in  obtaining  or 

Feels  'not  at  all'  informed  about  product  price,  quality, 
conditions,  etc  when  choosing  and  buying  goods  and 
services 

Compares  product  deals  by  using 
advertisements only - energy sector 

information 

from 

Compares  product  deals  by  using 
advertisements only - online sector  

information 

from 

Compares  product  deals  by  using 
advertisements only - finance sector 

information 

from 

Has  problems  comparing  deals  due  to  information-related 
factors - energy sector 

Has  problems  comparing  deals  due  to  information-related 
factors - online sector 

Has  problems  comparing  deals  due  to  information-related 
factors - finance sector 

20. Has an electricity supplier and has not switched due to 
information-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
information-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to 
information-related factors - financial sector 

Dimension  4 :  Inability  or  failure  to  buy,  choose  or 
access suitable products 

Does not compare product deals - energy sector 

Finds  it  very  difficult  to  compare  product  deals  -  energy 
sector 

Does not compare product deals - online sector 

Finds  it  very  difficult  to  compare  product  deals  -  online 
sector 

Does not compare product deals - finance sector 

Finds  it  very  difficult  to  compare  product  deals  -  finance 
sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
energy sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
online sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
financial sector 

Has  problems  comparing  deals  due  to  market-related 
factors - energy sector 

445 

5% 

4% 

4% 

4% 

13% 

8% 

14% 

33% 

9% 

20% 

4% 

54% 

13% 

17% 

11% 

28% 

26% 

 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Has  problems  comparing  deals  due  to  market-related 
factors - online sector 

Has  problems  comparing  deals  due  to  market-related 
factors - financial sector 

Has problems comparing deals due to access-related factors 
- energy sector 

Has problems comparing deals due to access-related factors 
- online sector 

Has problems comparing deals due to access-related factors 
- financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
personal factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
personal factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  personal 
factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
market-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
market-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  market-
related factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
access-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
access-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  access-
related factors - financial sector 

Has not switched due to termination costs 

Has not switched due to bundling 

Excluded from e-commerce due to difficulty of the process 

Excluded  from  e-commerce  due  to  not  having  a  payment 
card 

Declined for a loan 

Dimension  5 :  Higher  susceptibility  to  marketing 
practices 

16% 

35% 

6% 

6% 

3% 

21% 

28% 

26% 

15% 

Vulnerable  to  a  great  extent  due  to  complexity  of  offers 
(own perception) 

9% 

Experiment choice under the marketing practice treatment - 
energy sector 

Experiment choice under the marketing practice treatment - 
online sector 

Experiment choice under the marketing practice treatment - 

446 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

finance sector 

Experiment choice under the marketing practice treatment - 
cross-cutting 

447 

 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Table 92:  Country fiche - Greece 

Country fiche summary 

Approach  to  consumer  vulnerability:  There  is  limited  evidence  of  the  concept  of 
consumer  vulnerability  being  used  in  national  consumer  protection  policy  beyond  the 
transposition of the UCPD. 

Measures  addressing  consumer  vulnerability:  There  is  evidence  of  a  range  of  sector-
specific measures addressing consumer vulnerability. 

National consumer protection framework 

The overall approach to consumer protection is established in the Law 2251/1994 (as 
amended  by  law  3587/2007)  on  the  Protection  of  Consumers.  It  sets  out  regulation 
aiming to protect consumer with regard to, inter alia, general terms and conditions of 
consumer  contracts,  unfair  contract  terms,  distance  selling,  doorstep  selling, 
misleading and comparative advertising, and distance marketing of consumer financial 
services as well as product liability.  

Approach to consumer vulnerability 

Consumer vulnerability within the context of the transposed UCPD  
The term “vulnerable” is present in Law 3587 Amendment and supplementation of Law 
2251/1994  on  Consumer  protection  through  the  transposition  of  the  UCPD.  The 
concept of vulnerability has been transposed in line with the Directive’s wording, using 
the terms mental or physical infirmity, age, and credulity. In addition, the law sets out 
that  the  State  is  to  ensure  the  provision  of  information  to  and  training  of  particularly 
vulnerable  groups  of  consumers  on  matters  concerning  the  market,  competition, 
consumers,  environmental  protection,  and  promoting  sustainable  consumption327. 
Similarly, the “average consumer” term has also been transposed into Greek legislation 
through the UCPD and is applied within the consumer protection framework.   
Consumer vulnerability in national consumer policy beyond the UCPD 
Beyond  the  UCPD,  the  concept  of  vulnerable  consumers  figures  in  Law  4001/2011 
(Article  52  paragraph  1  (a-d))  in  regards  to  the  “Operation  of  Electricity  and  Gas 
Energy  Markets, 
transmission  networks  of 
Hydrocarbons  and  other  provisions”328.  In  addition,  in  the  context  of  a  new  Supply 
Code  for  Electricity  introduced  in  2013,  a  ministerial  decision  defined  the  categories 
and  criteria  for  the  Registry  of  Vulnerable  Consumers,  which  aims  to  support  special 
service benefits to vulnerable consumers.   
Stakeholder assessment of the national approach to consumer vulnerability   
According to the stakeholder consulted, any consumer  can be vulnerable at any point 
in  time  and  is,  as  such,  difficult  and  not  clearly  defined.  In  particular,  in  light  of  the 
economic crisis – vulnerability should also include those consumers that are in financial 
difficulties.  

for  Exploration,  Production  and 

Specific measures  

There is a range of specific measures relevant to consumer vulnerability in Greece. In 
the  energy  sector,  approximately  11%  of  Greek  households  benefit  from  Social 

327 Article 1(3)(e)  
328 See http://www.energy-community.org/pls/portal/docs/1296177.PDF  

448 

 
 
 
                                                 
Annex 1│ Country fiches 

Residential  Tariff.  Typically  six  categories  of  consumers  may  benefit  from  these 
reduced tariffs: families on low income, families with three or more children, the long-
term  unemployed  (for  a  continuous  period  of  at  least  6  months),  short-term 
unemployed  (for  a  period  of  at  least  3  months),  chronically  ill  and  people  with 
disabilities, and people living on mechanical support. The Social Residential Tariff came 
into effect on 1 January 2011. Besides financial support, the eligible consumers benefit 
from measures such as: 

  Suspended disconnections during critical periods (cold winter months and hot 

summer) months; 

  Extended bill payment deadlines; 
  Automatic deadline extension of 30-days in the case of missed payment prior to 

requesting disconnection orders; and 

  Beneficial debt settlement terms. 

In the finance sector, new legislation came into effect on September 1, 2010 enabling 
over-indebted  individuals  to  benefit  from  debt  arrangements  under  specific  conditions 
(Law 3869/2010). Previously, consumer debtors have not been eligible for insolvency, 
however, the new legislation establishes bankruptcy procedures in three phases: out of 
court  settlement,  in-Court  Compromise,  and,  if  the  previous  stages  fail,  judicial 
settlement (debt discharge).  

Non-legislative measures  

No  non-legislative  measures  specifically  targeting  consumer  vulnerability  have  been 
identified in Greece.  

Institutional structure and role of civil society 

Public bodies responsible for consumer policy 
The  General  Secretariat  of  Consumer  Affairs  under  the  Ministry  of  Development  and 
Competitiveness  is  responsible  for,  inter  alia,  the  development  of  consumer  policy, 
harmonisation  of  Greek  legislation  in  line  with  community  law  and  monitoring  of 
legislation  at  national  level  and  abroad  with  regard  to  consumer  protection. 
Enforcement  of  the  consumer  protection  regulation  set  out  in  Law  2251/1994  (as 
amended by law 3587/2007) on Consumer Protection is also within the responsibilities 
of  the  General  Secretariat.  In  addition,  the  General  Secretariat  is  also  charged  with 
providing consumer information and education. In 2004, a Consumer Ombudsman was 
established,  forming  an  independent  agency  of  extrajudicial  dispute  resolution  in  the 
area of consumer disputes. 
Role of civil society 
There  are  multiple  consumer  organisations  representing  the  interest  of  consumers. 
Those representing consumer at a national-level include, amongst others:  

INKA (Institute of Consumer Protection) 

  Consumers’ Protection Centre-KEPKA 
 
  E.K.PI.ZO (Consumers’ Association “Quality of Life”) 
  Union for Consumer’s & Citizen’s rights 
  Citizen’s Association General Consumer Organization of Greece (Eurozoni) 
  Consumers’ Union of Greece 

Many of these organisations have activities that address vulnerable consumers, with an 

449 

 
 
 
 
 
Annex 1│ Country fiches 

important target group being consumers in financial difficulties. 

Incidence of consumer vulnerability  

Indicator of vulnerability 

Mean incidence  

Dimension  1 :  Heightened  risk  of  negative  outcomes 
or impacts on well-being 

Has experienced a problem and took no action 

Overpaid  for  services  due  to  being  unable  to  use  certain 
payment methods 

Dimension 2 : Having characteristics that limit ability 
to maximise well-beings 

Feels vulnerable to 'a great extent' due to health problems 

Feels  vulnerable  to  'a  great  extent'  due  to  financial 
circumstances 

Feels  vulnerable  to  'a  great  extent'  due  to  employment 
situation 

Feels vulnerable to 'a great extent' due to age 

Feels  vulnerable  to  'a  great  extent'  due  to  belonging  to  a 
minority group 

Feels vulnerable to 'a great extent' due to personal issues 

Feels vulnerable to 'a great extent' due to other reasons 

Feels  vulnerable  to  'a  great  extent'  due  to  any  personal 
characteristic 

Dimension  3 :  Having  difficulty 
assimilating information 

in  obtaining  or 

Feels  'not  at  all'  informed  about  product  price,  quality, 
conditions,  etc  when  choosing  and  buying  goods  and 
services 

Compares  product  deals  by  using 
advertisements only - energy sector 

information 

from 

Compares  product  deals  by  using 
advertisements only - online sector  

information 

from 

Compares  product  deals  by  using 
advertisements only - finance sector 

information 

from 

Has  problems  comparing  deals  due  to  information-related 
factors - energy sector 

Has  problems  comparing  deals  due  to  information-related 
factors - online sector 

Has  problems  comparing  deals  due  to  information-related 
factors - finance sector 

20. Has an electricity supplier and has not switched due to 
information-related factors - energy sector 

15% 

18% 

6% 

35% 

27% 

6% 

2% 

5% 

3% 

46% 

2% 

5% 

5% 

450 

 
 
 
   
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Has  an  internet  provider  and  has  not  switched  due  to 
information-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to 
information-related factors - financial sector 

Dimension  4 :  Inability  or  failure  to  buy,  choose  or 
access suitable products 

Does not compare product deals - energy sector 

Finds  it  very  difficult  to  compare  product  deals  -  energy 
sector 

Does not compare product deals - online sector 

Finds  it  very  difficult  to  compare  product  deals  -  online 
sector 

Does not compare product deals - finance sector 

Finds  it  very  difficult  to  compare  product  deals  -  finance 
sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
energy sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
online sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
financial sector 

Has  problems  comparing  deals  due  to  market-related 
factors - energy sector 

67% 

16% 

12% 

0% 

61% 

15% 

7% 

Has  problems  comparing  deals  due  to  market-related 
factors - online sector 

10% 

Has  problems  comparing  deals  due  to  market-related 
factors - financial sector 

Has problems comparing deals due to access-related factors 
- energy sector 

Has problems comparing deals due to access-related factors 
- online sector 

2% 

Has problems comparing deals due to access-related factors 
- financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
personal factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
personal factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  personal 
factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
market-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 

451 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

market-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  market-
related factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
access-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
access-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  access-
related factors - financial sector 

Has not switched due to termination costs 

Has not switched due to bundling 

Excluded from e-commerce due to difficulty of the process 

Excluded  from  e-commerce  due  to  not  having  a  payment 
card 

Declined for a loan 

Dimension  5 :  Higher  susceptibility  to  marketing 
practices 

25% 

30% 

31% 

36% 

Vulnerable  to  a  great  extent  due  to  complexity  of  offers 
(own perception) 

13% 

Experiment choice under the marketing practice treatment - 
energy sector 

Experiment choice under the marketing practice treatment - 
online sector 

Experiment choice under the marketing practice treatment - 
finance sector 

Experiment choice under the marketing practice treatment - 
cross-cutting 

452 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Table 93: Country fiche - Spain 

Country fiche summary 

Approach to consumer vulnerability: The concept of consumer vulnerability appears to 
not be used within Spanish consumer policy.  

Measures  addressing  consumer  vulnerability:  There  is  evidence  of  a  range  of  sector-
specific measures addressing consumer vulnerability. 

National consumer protection framework 

In Spain, consumer protection is not a matter expressly included in the distribution of 
legislative competences between the State and the regional Autonomous Communities 
(Communidades  Autonomas).  Thus,  the  17  Autonomous  Communities  have  their  own 
legislation  on  consumer  protection.  At  national  level,  consumer  competences  now  lie 
with  La  Agencia  Española  de  Consumo,  Seguridad  Alimentaria  y  Nutrición after 
governmental restructuring in February 2014. This agency is charged with coordinating 
consumer protection 

Approach to consumer vulnerability 

Consumer vulnerability within the context of the transposed UCPD  
The term “vulnerable” is present in the Law 29/2009 of 30 December 2009  amending 
the  statutory  regime  of  unfair  competition  and  advertising  in  order  to  enhance 
protection  afforded  to  consumers  and  users  as  a  result  of  the  transposition  of  the 
UCPD.  The  concept  of  vulnerability  has  been  transposed  in  line  with  the  Directive’s 
wording, using the terms mental or physical infirmity, age, and credulity. Similarly the 
“average consumer” term has also been transposed into Spanish legislative framework, 
but is not commonly used. 
Consumer vulnerability in national consumer policy beyond the UCPD  
The  term  “vulnerable  consumers”  (“consumidores  vulnerables”)  is  not  a  widely  used 
concept in Spain. This direct term is derived from the UCPD as well as imported from 
other Member States such as the UK. However, the notion that some consumers are in 
need of further protection has developed in Spain during the past 10 years. According 
to  the  regional  statutes,  special  treatment  is  to  be  granted  by  public  authorities  to 
groups  in  situations  of  disadvantage,  helplessness  or  lack  of  protection,  such  as 
children, people with disabilities, elderly, pregnant women, large families, economically 
and  socially  disadvantaged  consumers,  immigrants  and  unemployed  constituting  the 
so-called “special protection groups” (“colectivos de protección especial”).  
Stakeholder assessment of the national approach to consumer vulnerability 
According to the consulted stakeholder, the overall policy framework in Spain is geared 
towards competitiveness and recovery from the financial crisis. Thus, there is a limited 
focus on consumer vulnerability in current policy measures. 

Specific legislative measures  

There  are  some  specific  measures  in  place  in  Spain  with  regards  to  vulnerable 
consumers.  In  the  energy  sector,  some  consumers  may  benefit  from  reduced  tariffs. 
These  include  consumers  who  use  less  than  3KW  energy  per  annum,  pensioners  on 
benefits, families with more than 3 children and households facing unemployment. One 
criticism  of  these  measures  relates  to  the  lack  of  income  criteria  for  families.  It  has 
been  argued  that  families  with  three  or  more  children  are  not  necessarily  vulnerable 

453 

 
 
 
 
 
Annex 1│ Country fiches 

and there are no income index parameters. 

Non-legislative measures  

Few  non-legislative  measures  have  been  identified  in  the  Spanish  context.  One 
example  in  the  online  environment  includes  the  Code  of  Ethics  in  E-Commerce  and 
Interactive Advertising (Código Ético de comercio electrónico y publicidad interactiva), 
which specifies rules related to the protection of minors in the area of advertising and 
data processing.  

Institutional structure and role of civil society 

Public bodies responsible for consumer policy  

As  pointed  out  above,  consumer  protection  falls  both  under  the  competences  of  the 
State as well as the Autonomous Communities.  At national level, La Agencia Española 
de  Consumo,  Seguridad  Alimentaria  y  Nutrición  has  the  overarching  responsibility  for 
consumer protection. The Agency coordinates and integrates the consumer work done 
by  different  governmental  agencies  including  regulators  involved  in  consumer 
protection.  

At regional level, the Autonomous Communities are responsible for the enforcement of 
consumer legislation.  

The role of civil society 

In  addition  to  the  public  bodies  described  above,  there  are  numerous  civil  society 
organisations  working  on  consumer  issues,  including  sector-specific  organisations. 
These  organisations  strive  to  ensure  that  the  economic  interests  of  consumers  are 
being  protected  and  that  they  have  the  right  to  information  around  products  and 
services. These include, among others:  

  Organización de Consumidores y Usuarios (OCU) 
  Unión de Consumidores de España (UCE)  
  Asociación General de Consumidores (ASGECO) 
  ADICAE (Spanish Association of Users of Banks, Savings Banks and Insurances) 
  Asociación  de  Usuarios  de  la  Comunicación  (AUC)  (Publicity  and  Commercial 

Communication) 

  Confederación  Española  de  Cooperativas  de  Consumidores  y  Usuarios 

(HISPACOOP) 

  Confederación  Española  de  Organizaciones  de  Amas  de  Casa  Consumidores  y 

Usarios (CEACCU)  

  Federación de Usuarios y Consumidores Independientes (FUCI) 
  Consumidores en Acción (FACUA) 

Incidence of consumer vulnerability  

Indicator of vulnerability 

Mean incidence  

Dimension  1 :  Heightened  risk  of  negative  outcomes 
or impacts on well-being 

Has experienced a problem and took no action 

Overpaid  for  services  due  to  being  unable  to  use  certain 
payment methods 

9% 

9% 

454 

 
 
 
 
 
Annex 1│ Country fiches 

Dimension 2 : Having characteristics that limit ability 
to maximise well-beings 

Feels vulnerable to 'a great extent' due to health problems 

Feels  vulnerable  to  'a  great  extent'  due  to  financial 
circumstances 

Feels  vulnerable  to  'a  great  extent'  due  to  employment 
situation 

Feels vulnerable to 'a great extent' due to age 

Feels  vulnerable  to  'a  great  extent'  due  to  belonging  to  a 
minority group 

Feels vulnerable to 'a great extent' due to personal issues 

Feels vulnerable to 'a great extent' due to other reasons 

Feels  vulnerable  to  'a  great  extent'  due  to  any  personal 
characteristic 

Dimension  3 :  Having  difficulty 
assimilating information 

in  obtaining  or 

Feels  'not  at  all'  informed  about  product  price,  quality, 
conditions,  etc  when  choosing  and  buying  goods  and 
services 

Compares  product  deals  by  using 
advertisements only - energy sector 

information 

from 

Compares  product  deals  by  using 
advertisements only - online sector  

information 

from 

Compares  product  deals  by  using 
advertisements only - finance sector 

information 

from 

Has  problems  comparing  deals  due  to  information-related 
factors - energy sector 

5% 

20% 

19% 

8% 

5% 

5% 

5% 

32% 

6% 

6% 

Has  problems  comparing  deals  due  to  information-related 
factors - online sector 

13% 

Has  problems  comparing  deals  due  to  information-related 
factors - finance sector 

20. Has an electricity supplier and has not switched due to 
information-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
information-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to 
information-related factors - financial sector 

Dimension  4 :  Inability  or  failure  to  buy,  choose  or 
access suitable products 

Does not compare product deals - energy sector 

Finds  it  very  difficult  to  compare  product  deals  -  energy 
sector 

53% 

19% 

455 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Does not compare product deals - online sector 

Finds  it  very  difficult  to  compare  product  deals  -  online 
sector 

Does not compare product deals - finance sector 

Finds  it  very  difficult  to  compare  product  deals  -  finance 
sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
energy sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
online sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
financial sector 

Has  problems  comparing  deals  due  to  market-related 
factors - energy sector 

23% 

3% 

55% 

16% 

16% 

Has  problems  comparing  deals  due  to  market-related 
factors - online sector 

25% 

Has  problems  comparing  deals  due  to  market-related 
factors - financial sector 

Has problems comparing deals due to access-related factors 
- energy sector 

Has problems comparing deals due to access-related factors 
- online sector 

2% 

Has problems comparing deals due to access-related factors 
- financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
personal factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
personal factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  personal 
factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
market-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
market-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  market-
related factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
access-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
access-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  access-
related factors - financial sector 

Has not switched due to termination costs 

16% 

456 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Has not switched due to bundling 

Excluded from e-commerce due to difficulty of the process 

Excluded  from  e-commerce  due  to  not  having  a  payment 
card 

23% 

19% 

12% 

Declined for a loan 

Dimension  5 :  Higher  susceptibility  to  marketing 
practices 

Vulnerable  to  a  great  extent  due  to  complexity  of  offers 
(own perception) 

15% 

Experiment choice under the marketing practice treatment - 
energy sector 

Experiment choice under the marketing practice treatment - 
online sector 

Experiment choice under the marketing practice treatment - 
finance sector 

Experiment choice under the marketing practice treatment - 
cross-cutting 

457 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Table 94:  Country fiche - France 

Country fiche summary 

Approach  to  consumer  vulnerability:  While  the  concept  of  consumer  vulnerability 
appears  to  be  used  in  specific  sectors,  there  is  no  evidence  of  a  broader  strategic 
approach to tackling consumer vulnerability at national level.  

Measures  addressing  consumer  vulnerability:  There  is  evidence  of  a  range  of  sector-
specific measures addressing consumer vulnerability. 

National consumer protection framework 

The  consumer  code  (1993)  is  the  principal  French  legislation  providing  for  consumer 
protection.  The  code  is  supplemented  by  sector-specific  legislations.  The  Loi  Chatel 
(2007)  is  also  of  great  importance  in  terms  of  consumer  protection  in  France.  The 
legislative  framework  is  often  updated,  with  the  latest  legislation  coming into  force  in 
2014. 

Approach to consumer vulnerability 

Consumer vulnerability within the context of the transposed UCPD  

The  concept  of  “vulnerable  consumer”  has  been  introduced  in  French  legislation 
through  the  transposition  of  the  UCPD.  The  Law  2008-776  of  4  August  2008  on 
modernisation  of  the  economy  includes  the  definition  in  its  Article  83.  The  concept  of 
vulnerability has been transposed in line with the Directive’s wording, using the terms 
mental or physical infirmity, age and credulity.  

The  notion  of  average  consumer  is  not  used,  while  “average  capacity”  is  mentioned 
once without providing a specific definition (Article 83).  

Consumer vulnerability in national consumer policy beyond the UCPD  

One  area  where  a  notion  of  consumer  vulnerability  is  also  present  is  the  financial 
sector, with the concept of “personnes en situation de fragilité financière”. The concept 
of consumer vulnerability is also defined in the energy sector329.  
Stakeholder assessment of the national approach to consumer vulnerability 

Despite existing definitions, the notion “vulnerable consumer” is not commonly used in 
France  and  the  current  legislation  aims  to  provide  protection  for  all  consumers. 
However, some groups, e.g. elderly or children/teenagers might be seen as particularly 
vulnerable  in  some  contexts.  The  concept  of  “average  consumer”  is  used,  but  the 
interpretation differs from sector to sector. 

Specific legislative measures  

A number of specific legislative measures have been identified. In the  energy sector 
these cover 

  Social  tariffs  (telecommunications,  gas,  electricity)  exist  for  e.g.  low-income 
consumers, people with disabilities and elderly people. However, these are not 
always relevant, since  due to price  competition and lower prices market prices 

329 ACER (2013). Annual Report on the Results of Monitoring the Internal Electricity and Gas 

Markets in 2012. Available at: 
http://www.acer.europa.eu/Electricity/Market%20monitoring/Pages/Reports.aspx 

458 

 
 
 
                                                 
Annex 1│ Country fiches 

are sometimes lower than the social tariffs; 

  Provisions  set  out  in  a  2008  decree330  state  that  providers  cannot  cut  off  the 
energy  supply  in  the  winter  months  even  if  the  consumer  is  not  able  to  pay 
his/her bills. The temperature  and allowances  can be lowered but the  operator 
must not stop the supply. 

In the financial sector specific measures include: 

  New  legislation  from  March  2014  targets  inter  alia  abuse  of  weakness  and 
vulnerability  in  the  field  of  consumer  credit.  For  example,  contracts  that  have 
been  concluded  abusing  vulnerability  can  be  annulled.  Furthermore,  the 
legislation  obliges  the  salesman  to  suggest  two  types  of  credit  in  case  of  a 
purchase exceeding 1,000 euros in a shop or through distance selling. This is to 
avoid  the  abuse  of  revolving  credit,  which  has  proven  difficult  for  many 
consumers. Amortising credit shall also be presented as an alternative allowing 
for  a  progressive  reimbursement  ending  as  soon  as  the  sum  has  been 
reimbursed.  In  order  to  combat  over-indebtedness,  a  national  register  of 
individual loans will be established for credit providers. The 2014 legislation also 
enables consumers to sign up to not receive any sale offers by phone calls, and 
salesmen who are using this method will be prohibited from using unknown or 
private numbers; 

  Banking law of July 2013 specifies that banks must now send out bills to inform 
the consumer of any non-regular fees (like overdraft fees), at the latest 15 days 
prior  to  debit  the  account.  This  provides  protection  to  low-income  consumers 
who  may  have  a  limited  amount  of  money  on  their  account  and  might  be 
obliged  to  pay  overdraft  fees.  The  law  also  limits  the  banking  overdraft  fees, 
establishing a lower fee for “vulnerable groups”; 

  Provisions regarding access to  finance and banking stipulate that banks should 
provide  specific  packages  with  low  tariffs  to  the  “personnes  en  situation  de 
fragilité  financière”  (weak  groups  /  vulnerable  or  fragile  consumers).  These 
accounts would have different overdraft limits and penalty fees; 

Non legislative-measures 

Non-legislative  measures  include  information  and  communication  measures.  For 
example,  in  the  financial  sector  “Les  Cles  de  la  Banque”  (Keys  to  the  bank)  is  a 
website  of  the  French  Banking  Federation  (Fédération  Bancaire  Française,  FBF)  which 
provides  information  on  banking  and  finance  services.  A  specific  section  focuses  on 
difficulties that consumers can encounter, with sub-sections focusing on issues such as 
disability,  long-term  illness  or  emotional  stress  due  to  disruptive  events  in  life.  The 
Bank of France also has a dedicated website, named The City of the Economy and the 
Money  (“La  Cité  de  l’Economie  et  de  la  Monnaie”)  which  offers  a  strong  educational 
value.  This  website  is  currently  updated.  The  website  “Finance  for  all”,  promoted  by 
the AMF, aims also at making finance accessible to all by providing basic knowledge on 
managing a budget. 

330 See 

http://www.legifrance.gouv.fr/affichTexte.do?cidTexte=JORFTEXT000019325694&categorieLi
en=cid 

459 

 
 
 
 
 
                                                 
Annex 1│ Country fiches 

Institutional structure and role of civil society 

Public bodies responsible for consumer policy 

The  Minister  for  the  Economy,  Industry  and  Employment  is  responsible  for  the 
development  and  implementation  of  consumer  policy  in  France.  The  DGCCRF 
(Directorate-General  for  Competition,  Consumer  Affairs  and  Fraud  Repression) 
implements  government  consumer  policy.  A  variety  of  independent  administrative 
authorities  are  also  involved  in  consumer  protection.  In  addition,  other  ministries  are 
involved  in  consumer  policy  in  specific  sector,  e.g.  the  Ministry  of  Energy  and 
Sustainable Development.  

The  Direction  Départementale  de  la  Protection  de  la  Population  or  DDPP  (Department 
Services for Protection of the Population) is the main enforcement authority. The DDPP 
is under the authority of each department’s Préfet, i.e. the state representative in the 
department. 

In  terms  of  complaints  and  procedures,  DGCCRF  is  the  responsible  authority.  The 
recent  consumer  law  (March  2014)  introduces  the  possibility  of  a  group  redress 
procedure where a number  of consumers having suffered the same  unfair practice on 
the  market  can,  with  the  help  of  a  national  consumer  association,  take  the  case  to 
court.  

Sectorial ombudsmen play a role in handling individual contractual complaints (energy, 
internet, telecommunication, insurance).  

Commissions for indebtedness (Commission de Surendettement) which are part of the 
Bank  of  France  assist  the  consumers  in  economic  difficulties.  This  is  done  through 
payment  plans  which  are  developed  together  with  the  consumers  and  creditors,  and 
which are adapted to the consumer’s situation.  

Role of civil society 

There are 15 consumer associations recognised by the government. Organisations with 
a  general  consumer  focus  include  the  Association  Consommation,  Logement  et  Cadre 
de  Vie  (CLCV)  and  the  Union  Fédérale  des  Consommateurs-  Que  choisir  (UFC-Que 
Choisir). They provide information, training on consumers’ rights, consumer assistance 
in disputes and litigations, and perform lobbying activities, although these activities do 
not focus specifically on vulnerable consumers.  

The  national  consumer  institute  (Institut  National  de  la  Consommation)  is  a  body 
supporting  the  consumer  associations  with  their  communication  and  information 
activities.  The  national  consumer  council  (Conseil  National  de  la  Consommation) 
provides  for  a  space  for  discussion  and  debate  between  consumer  organisations, 
industry and professionals, as well as government representatives.  

Incidence of consumer vulnerability  

Indicator of vulnerability 

Mean incidence  

Dimension  1 :  Heightened  risk  of  negative  outcomes 
or impacts on well-being 

Has experienced a problem and took no action 

Overpaid  for  services  due  to  being  unable  to  use  certain 
payment methods 

Dimension 2 : Having characteristics that limit ability 
to maximise well-beings 

5% 

8% 

460 

 
 
 
 
 
 
Annex 1│ Country fiches 

Feels vulnerable to 'a great extent' due to health problems 

Feels  vulnerable  to  'a  great  extent'  due  to  financial 
circumstances 

Feels  vulnerable  to  'a  great  extent'  due  to  employment 
situation 

Feels vulnerable to 'a great extent' due to age 

Feels  vulnerable  to  'a  great  extent'  due  to  belonging  to  a 
minority group 

Feels vulnerable to 'a great extent' due to personal issues 

Feels vulnerable to 'a great extent' due to other reasons 

Feels  vulnerable  to  'a  great  extent'  due  to  any  personal 
characteristic 

Dimension  3 :  Having  difficulty 
assimilating information 

in  obtaining  or 

Feels  'not  at  all'  informed  about  product  price,  quality, 
conditions,  etc  when  choosing  and  buying  goods  and 
services 

Compares  product  deals  by  using 
advertisements only - energy sector 

information 

from 

Compares  product  deals  by  using 
advertisements only - online sector  

information 

from 

Compares  product  deals  by  using 
advertisements only - finance sector 

information 

from 

Has  problems  comparing  deals  due  to  information-related 
factors - energy sector 

Has  problems  comparing  deals  due  to  information-related 
factors - online sector 

Has  problems  comparing  deals  due  to  information-related 
factors - finance sector 

20. Has an electricity supplier and has not switched due to 
information-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
information-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to 
information-related factors - financial sector 

Dimension  4 :  Inability  or  failure  to  buy,  choose  or 
access suitable products 

Does not compare product deals - energy sector 

Finds  it  very  difficult  to  compare  product  deals  -  energy 
sector 

Does not compare product deals - online sector 

Finds  it  very  difficult  to  compare  product  deals  -  online 

461 

5% 

19% 

14% 

6% 

4% 

4% 

5% 

28% 

4% 

3% 

4% 

1% 

27% 

10% 

12% 

76% 

17% 

43% 

8% 

 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

sector 

Does not compare product deals - finance sector 

Finds  it  very  difficult  to  compare  product  deals  -  finance 
sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
energy sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
online sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
financial sector 

Has  problems  comparing  deals  due  to  market-related 
factors - energy sector 

Has  problems  comparing  deals  due  to  market-related 
factors - online sector 

Has  problems  comparing  deals  due  to  market-related 
factors - financial sector 

Has problems comparing deals due to access-related factors 
- energy sector 

Has problems comparing deals due to access-related factors 
- online sector 

Has problems comparing deals due to access-related factors 
- financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
personal factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
personal factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  personal 
factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
market-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
market-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  market-
related factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
access-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
access-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  access-
related factors - financial sector 

Has not switched due to termination costs 

Has not switched due to bundling 

Excluded from e-commerce due to difficulty of the process 

67% 

15% 

41% 

19% 

34% 

47% 

30% 

46% 

10% 

2% 

1% 

16% 

24% 

21% 

462 

 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Excluded  from  e-commerce  due  to  not  having  a  payment 
card 

8% 

Declined for a loan 

Dimension  5 :  Higher  susceptibility  to  marketing 
practices 

Vulnerable  to  a  great  extent  due  to  complexity  of  offers 
(own perception) 

12% 

Experiment choice under the marketing practice treatment - 
energy sector 

Experiment choice under the marketing practice treatment - 
online sector 

Experiment choice under the marketing practice treatment - 
finance sector 

Experiment choice under the marketing practice treatment - 
cross-cutting 

463 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Table 95:  Country fiche - Croatia 

Country fiche summary 

Approach  to  consumer  vulnerability:  The  consumer  vulnerability  concept  appears  to 
not be in use beyond the transposition of the UCPD. 

Measures  addressing  consumer  vulnerability:  Only  a  limited  number  of  measures 
relevant to consumer vulnerability have been identified.  

National consumer protection framework 

The  Consumer  Protection  Act  is  the  key  element  of  the  Croatian  consumer  protection 
framework.  It  sets  out  consumer  rights  in  the  marketplace,  the  framework  for 
consumer  education,  and  the  broader  institutional  structure.  The  government 
consumer protection priorities include: 

  Creating a legal basis for consumer protection; 

  Strengthening the market surveillance system; 

Increasing consumers’ knowledge about their rights; and 

 
  Supporting the work of consumer associations and counselling centres331. 

The  key  recent  development  in  Croatian  consumer  policy  has  been  the  harmonisation 
of  national  provisions  with  existing  EU  legislation  in  conjunction  with  Croatia’s  EU 
accession in 2013.  

Approach to consumer vulnerability 

Consumer vulnerability within the context of the transposed UCPD  

The concept of vulnerable consumer has been transposed into Croatian legislation as a 
result  of  the  harmonisation  of  national  legislation  with  the  UCPD,  although  the 
translated  legislation  uses  the  text  “consumers  who  are  particularly  exposed  to  a 
certain business practice or the particular product because  of their physical or mental 
infirmity, age or credulity” instead of the term “vulnerable”.  

The  term  “average  consumer”  is  present  in  the  legislation  as  a  result  of  the 
harmonisation with the UCPD. 

Consumer vulnerability in national consumer policy beyond the UCPD  

The  consumer  vulnerability  concept  appears  to  not  be  in  use  in  Croatia  beyond  the 
transposition of the UCPD.  

Stakeholder assessment of the national approach to consumer vulnerability 

From  the  point  of  view  of  consulted  stakeholders,  in  practice  the  existing  legislation 
does not single out specific groups and aims to ensure coverage of all consumers. An 
“average consumer” is viewed by consumer associations as an “active consumer”, with 
the term being used by the associations as an objective rather than a definition.  

Specific legislative measures 

Having  transposed  EU  legislation,  Croatian  government  is  currently  working  to 
effectively  implement  the  legal  framework.  As  a  result,  the  policy  environment  is  a 
dynamic  one  and  at  the  moment  no  legislative  measures  targeting  vulnerable 
consumers have been identified.  

331 See http://potrosac.mingo.hr/en/potrosac/clanak.php?id=12362 

464 

 
 
 
                                                 
Annex 1│ Country fiches 

Non-legislative measures 

According to the interviews, specific projects aiming to inform and educate consumers 
also partially address potentially vulnerable groups. These information, education, and 
advisory  projects  are  funded  by  the  state  and  carried  out  by  consumer  organisations 
and other civil society organisations.  

Institutional structure and role of civil society 

Public bodies responsible for consumer policy 

The  Consumer  Protection  Department  of  the  Ministry  of  Economy  has  the  main 
responsibility  for  consumer  protection.  The  National  Consumer  Protection  Council, 
consisting  of  representatives  of  government  bodies  involved  in  consumer  protection 
along  with  key  civil  society  organisations,  has  an  advisory  and  supportive  role  in  the 
formulation  of  Croatian  consumer  policy.  The  enforcement  is  carried  out  by  individual 
agencies in relevant sectors. 

Role of civil society 

There  are  four  consumer  advice  centres  in  Croatia.  These  are  NGOs  receiving 
government  funding  and  responsible  for  providing  advice  to  consumers.  In  addition, 
there  are  over  20  individual  NGOs  operating  in  the  area  and  representing  and 
supporting  consumers  on  local  and  regional  level.  The  organisations  do  not  carry  out 
activities focusing specifically on consumer vulnerability.  

Incidence of consumer vulnerability  

Indicator of vulnerability 

Mean incidence  

Dimension  1 :  Heightened  risk  of  negative  outcomes 
or impacts on well-being 

Has experienced a problem and took no action 

Overpaid  for  services  due  to  being  unable  to  use  certain 
payment methods 

Dimension 2 : Having characteristics that limit ability 
to maximise well-beings 

Feels vulnerable to 'a great extent' due to health problems 

Feels  vulnerable  to  'a  great  extent'  due  to  financial 
circumstances 

Feels  vulnerable  to  'a  great  extent'  due  to  employment 
situation 

Feels vulnerable to 'a great extent' due to age 

Feels  vulnerable  to  'a  great  extent'  due  to  belonging  to  a 
minority group 

Feels vulnerable to 'a great extent' due to personal issues 

Feels vulnerable to 'a great extent' due to other reasons 

Feels  vulnerable  to  'a  great  extent'  due  to  any  personal 
characteristic 

15% 

18% 

11% 

19% 

13% 

6% 

3% 

6% 

6% 

31% 

465 

 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Dimension  3 :  Having  difficulty 
assimilating information 

in  obtaining  or 

Feels  'not  at  all'  informed  about  product  price,  quality, 
conditions,  etc  when  choosing  and  buying  goods  and 
services 

Compares  product  deals  by  using 
advertisements only - energy sector 

information 

from 

Compares  product  deals  by  using 
advertisements only - online sector  

information 

from 

Compares  product  deals  by  using 
advertisements only - finance sector 

information 

from 

Has  problems  comparing  deals  due  to  information-related 
factors - energy sector 

9% 

2% 

Has  problems  comparing  deals  due  to  information-related 
factors - online sector 

11% 

Has  problems  comparing  deals  due  to  information-related 
factors - finance sector 

20. Has an electricity supplier and has not switched due to 
information-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
information-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to 
information-related factors - financial sector 

Dimension  4 :  Inability  or  failure  to  buy,  choose  or 
access suitable products 

Does not compare product deals - energy sector 

Finds  it  very  difficult  to  compare  product  deals  -  energy 
sector 

Does not compare product deals - online sector 

Finds  it  very  difficult  to  compare  product  deals  -  online 
sector 

Does not compare product deals - finance sector 

Finds  it  very  difficult  to  compare  product  deals  -  finance 
sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
energy sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
online sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
financial sector 

Has  problems  comparing  deals  due  to  market-related 
factors - energy sector 

52% 

23% 

15% 

4% 

62% 

23% 

13% 

Has  problems  comparing  deals  due  to  market-related 
factors - online sector 

22% 

466 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Has  problems  comparing  deals  due  to  market-related 
factors - financial sector 

Has problems comparing deals due to access-related factors 
- energy sector 

Has problems comparing deals due to access-related factors 
- online sector 

3% 

Has problems comparing deals due to access-related factors 
- financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
personal factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
personal factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  personal 
factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
market-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
market-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  market-
related factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
access-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
access-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  access-
related factors - financial sector 

Has not switched due to termination costs 

Has not switched due to bundling 

Excluded from e-commerce due to difficulty of the process 

Excluded  from  e-commerce  due  to  not  having  a  payment 
card 

Declined for a loan 

Dimension  5 :  Higher  susceptibility  to  marketing 
practices 

25% 

38% 

39% 

35% 

Vulnerable  to  a  great  extent  due  to  complexity  of  offers 
(own perception) 

9% 

Experiment choice under the marketing practice treatment - 
energy sector 

Experiment choice under the marketing practice treatment - 
online sector 

Experiment choice under the marketing practice treatment - 
finance sector 

467 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Experiment choice under the marketing practice treatment - 
cross-cutting 

468 

 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Table 96:  Country fiche - Italy 

Country fiche summary 

Approach  to  consumer  vulnerability:  There  appears  to  be  no  broader  strategic 
approach to consumer vulnerability in place. 

Measures  addressing  consumer  vulnerability:  There  is  evidence  of  a  range  of  sector-
specific measures addressing consumer vulnerability. 

National consumer protection framework 

Il  Codice  del  Consumo,  the  Italian  consumer  code  is  based  on,  and  reflects,  the  EU 
consumer protection legislation (September 2005), and aims to ensure a high level of 
protection to consumers and users.  

The  competent  national  authorities  have  the  obligation  to  ensure  respect  for  the 
principles  and  precepts  contained  in  the  Code  and  the  objective  of  protecting 
consumers from unfair commercial practices and unfair contract terms used by traders.  

Approach to consumer vulnerability 

Consumer vulnerability within the context of the transposed UCPD  

The concept of vulnerable consumer is present in the Italian transposition of the UCPD 
through  the  notion  of  “group  of  consumers  who  are  particularly  vulnerable”,  used  in 
the legislation (article 20 of the consumers’ code). The rest of the article is, however, 
transposed  in  line  with  the  Directive’s  wording,  using  the  terms  mental  or  physical 
infirmity, age, and credulity. The concept of average consumer (“consumatore medio”) 
is  also  used  in  the  Consumers’  Code,  and  is  considered  to  be  a  generally  informed, 
reasonably rational and insightful consumer.  

Consumer vulnerability in national consumer policy beyond the UCPD  

No uses of the consumer vulnerability concept have been identified in Italian consumer 
legislation in addition to the UCPD transposition.  

Stakeholder assessment of the national approach to consumer vulnerability 

While the concept of  consumer  vulnerability does not appear to be used as  such, one 
interviewee  highlighted  elderly  people  and  those  who  are  not  able  to  use  online 
services and access information available online, as particularly vulnerable groups. 

Specific legislative measures  

There are specific measures relevant to consumer vulnerability in place in Italy. In the 
energy sector, low-income households can be  eligible for an  energy/gas bonus, which 
can  help  them  pay  some  of  their  energy  costs.  In  terms  of  essential  services, 
consumers  with  disabilities  may  receive  special  protection  through  reduced  energy 
tariffs,  phone  and  internet  subscriptions,  as  well  as  TV  and  radio.  In  particular, 
disabilities  requiring  the  assistance  from  health  safety  equipment  are  included  in  the 
support system for electricity. There is also protection against disconnection in case of 
non-payment of bills due to low income332. 
In  the  financial  sector,  the  June  2012  legislation  stipulates  that  anyone  should  have 

332 CEER (2012). ‘Status Review of Customer and Retail Market Provisions from the 3rd Package 

as of 1 January 2012’  

469 

 
 
 
 
 
                                                 
Annex 1│ Country fiches 

access  to  a  low  cost  bank  account  providing  only  the  essential  banking  services.  This 
targets  in  particular  low-income  citizens  and  pensioners.  However,  it  is  important  to 
note that the measure is generally viewed as having had limited impact to date333.    
Another  specific  measure  is  the  planned  introduction  of  a  fund  giving  access  to  loans 
for  young  precarious  people  who  do  not  have  guarantees  or  parents  able  to  provide 
guarantees  in  order  to  get  a  loan334.  Furthermore,  in  this  sector  a  crisis-related 
measure allows additional time to low-income families for the repayment of loans and 
mortgages. 

Non-legislative measures 

Non-legislative measures include the project “Carta degli investitori” (Investors’ Chart), 
an  initiative  by  CONSOB  and  selected  consumer  associations  with  the  objective  to 
provide  consumers  and  investors  with  tools  to  make  more  informed  decisions  in 
regards to investments, and knowledge about their rights335.  
The association Cittadinanza Attiva has performed an investigation on energy poverty, 
which  is  growing  in  Italy  due  to  increasing  prices  and  the  economic  crisis.  Energy 
poverty relates to families that spend more than 10% of their income on energy. This 
investigation has been presented to the Italian Senate336. 

Institutional structure and role of civil society 

Public bodies responsible for consumer policy 

The  ministry  responsible  for  consumer  policy  in  Italy  is  the  Ministry  for  Economic 
Development. The main task of the ministry is to guarantee protection of consumers in 
the  competition  and  market  area.  In  addition,  there  are  other  sector-based  activities 
carried  out  by  other  ministries,  public  institutes  and  independent  authorities.  Key 
institutions include: 

  Autorita’ Garante della Concorrenza e del Mercato, which has an overall 

enforcement remit in the area of consumer protection; 

  Commissione Nazionale per le Societa’ e la Borsa (CONSOB), which is public 

authority responsible for regulating the Italian securities market. One of its 
tasks is the protection of investors; 

  Autorità per l’Energia Elettrica, il Gas, e il Sistema Idrico, which is responsible 

for enforcement in the in the field of energy; 

  Autorità delle Telecommunicazioni, which is responsible for enforcement in the 
field of communication. The authority has the competence to resolve disputes 
between consumers and operators in the sector; 

  Autorità italiana del farmaco (AIFA) which is the competent authority for the 

regulation of medicines; 

333 Altroconsumo, http://www.altroconsumo.it/soldi/conti-correnti/news/conto-base-introvabile 
334 Altroconsumo, mutui-per-giovani-precari-il-fondo-del-governo-fa-da-garante, 

http://www.altroconsumo.it/soldi/comprare-vendere-casa/news/mutui-per-giovani-precari-
il-fondo-del-governo-fa-da-garante 

335 See http://www.consumatoridirittimercato.it/assicurazioni-banche-e-servizi-

finanziari/progetto-carta-degli-investitori/ 
336 Press release, Cittadinanza Attiva, July 3, 2012, 

http://www.cittadinanzattiva.it/comunicati/salute/malattie-croniche-e-rare/3716-disabili-e-
malati-cronici-bolletta-energetica-da-oltre-1-150-all-anno.html 

470 

 
 
 
                                                 
Annex 1│ Country fiches 

  Autorità Garante per la protezione dei dati personali (AGPDP) which is the 

competent authority for data protection, implementing the Directive 95/46 / EC; 

  Bank of Italy which is the competent authority for the monetary and financial 

system; 

  Autorità di Regolazione dei Trasporti  (ART) which is the competent authority for 
the regulation in the transport sector and access to infrastructure and related 
ancillary services; 

 

Istituto per la Vigilanza delle Assicurazioni (IVASS) which is the competent 
authority to ensure the stability of the insurance market and consumer 
protection; 

  Ente Nazionale per l’Aviazione Civile (ENAC), which is the technical regulation, 
certification, supervision and control authority in the field of civil aviation; and 

  Ministry of Health, which is the national authority that governs promotes and 

protects health as a basic right and interest of the community. 

National  Council  of  Consumers  and  Users  (CNCU)  is  the  public  body  representing  the 
consumers’  and  users’  associations  on  the  national  level.  The  task  of  the  CNCU  is  to 
improve  and  strengthen  consumers’  position  in  the  market.  The  CNCU  forms  part  of 
the  Ministry  for  Economic  Development  and  performs  consultative,  informative  and 
promotional activities. 

Role of civil society 

There  are  19  recognised  consumer  associations  at  the  national  level,  with 
Altroconsumo being the largest one. Adusbef is an association focusing in particular on 
the financial sector. As has been mentioned above, the consumer associations have an 
important  role  in  regards  to  the  protection  of  consumers,  including  vulnerable 
consumers. 

Incidence of consumer vulnerability  

Indicator of vulnerability 

Mean incidence  

Dimension  1 :  Heightened  risk  of  negative  outcomes 
or impacts on well-being 

Has experienced a problem and took no action 

Overpaid  for  services  due  to  being  unable  to  use  certain 
payment methods 

Dimension 2 : Having characteristics that limit ability 
to maximise well-beings 

Feels vulnerable to 'a great extent' due to health problems 

Feels  vulnerable  to  'a  great  extent'  due  to  financial 
circumstances 

Feels  vulnerable  to  'a  great  extent'  due  to  employment 
situation 

Feels vulnerable to 'a great extent' due to age 

Feels  vulnerable  to  'a  great  extent'  due  to  belonging  to  a 
minority group 

7% 

12% 

5% 

20% 

17% 

6% 

4% 

471 

 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Feels vulnerable to 'a great extent' due to personal issues 

Feels vulnerable to 'a great extent' due to other reasons 

Feels  vulnerable  to  'a  great  extent'  due  to  any  personal 
characteristic 

Dimension  3 :  Having  difficulty 
assimilating information 

in  obtaining  or 

Feels  'not  at  all'  informed  about  product  price,  quality, 
conditions,  etc  when  choosing  and  buying  goods  and 
services 

Compares  product  deals  by  using 
advertisements only - energy sector 

information 

from 

Compares  product  deals  by  using 
advertisements only - online sector  

information 

from 

Compares  product  deals  by  using 
advertisements only - finance sector 

information 

from 

Has  problems  comparing  deals  due  to  information-related 
factors - energy sector 

7% 

5% 

30% 

3% 

4% 

Has  problems  comparing  deals  due  to  information-related 
factors - online sector 

11% 

Has  problems  comparing  deals  due  to  information-related 
factors - finance sector 

20. Has an electricity supplier and has not switched due to 
information-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
information-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to 
information-related factors - financial sector 

Dimension  4 :  Inability  or  failure  to  buy,  choose  or 
access suitable products 

Does not compare product deals - energy sector 

Finds  it  very  difficult  to  compare  product  deals  -  energy 
sector 

Does not compare product deals - online sector 

Finds  it  very  difficult  to  compare  product  deals  -  online 
sector 

Does not compare product deals - finance sector 

Finds  it  very  difficult  to  compare  product  deals  -  finance 
sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
energy sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
online sector 

Has  problems  comparing  deals  due  to  personal  factors  - 

48% 

22% 

22% 

5% 

60% 

22% 

16% 

472 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

financial sector 

Has  problems  comparing  deals  due  to  market-related 
factors - energy sector 

Has  problems  comparing  deals  due  to  market-related 
factors - online sector 

23% 

Has  problems  comparing  deals  due  to  market-related 
factors - financial sector 

Has problems comparing deals due to access-related factors 
- energy sector 

Has problems comparing deals due to access-related factors 
- online sector 

3% 

Has problems comparing deals due to access-related factors 
- financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
personal factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
personal factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  personal 
factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
market-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
market-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  market-
related factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
access-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
access-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  access-
related factors - financial sector 

Has not switched due to termination costs 

Has not switched due to bundling 

Excluded from e-commerce due to difficulty of the process 

Excluded  from  e-commerce  due  to  not  having  a  payment 
card 

Declined for a loan 

Dimension  5 :  Higher  susceptibility  to  marketing 
practices 

22% 

26% 

19% 

19% 

Vulnerable  to  a  great  extent  due  to  complexity  of  offers 
(own perception) 

12% 

Experiment choice under the marketing practice treatment - 

473 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

energy sector 

Experiment choice under the marketing practice treatment - 
online sector 

Experiment choice under the marketing practice treatment - 
finance sector 

Experiment choice under the marketing practice treatment - 
cross-cutting 

474 

 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Table 97:  Country fiche - Cyprus 

Country fiche summary 

Approach  to  consumer  vulnerability:    While  the  concept  of  consumer  vulnerability 
appears  to  be  used  in  specific  sectors,  there  is  no  evidence  of  a  broader  strategic 
approach to tackling consumer vulnerability at national level.  

Measures  addressing  consumer  vulnerability:  There  is  evidence  of  a  range  of  sector-
specific measures addressing consumer vulnerability. 

National consumer protection framework 

The main piece of legislation regulating consumer protection in Cyprus is the business-
to-consumer  commercial  practices 
implementing  the  UCPD.  The 
enforcement  of  the  legislation  is  the  responsibility  of  the  Director  of  the  Competition 
and  Consumer  Protection  Service  of  the  Ministry  of  Energy,  Commerce,  Industry  and 
Tourism. The Consumer Protection Service is also the competent authority for receiving 
administrative complaints.  

law  (2007) 

The  main  objective  of  consumer  legislation  in  Cyprus,  and  for  related  responsible 
authorities,  is  to  protect  health,  safety  and  economic  interests  of  the  consumers,  as 
well as providing education and information on relevant issues.  

Approach to consumer vulnerability 

Consumer vulnerability within the context of the transposed UCPD  

In  Cyprus  the  term  “vulnerable”  is  in  place  in  consumer  legislation  through  the 
transposition  of  the  UCPD.  The  concept  of  vulnerability  has  been  transposed  in  line 
with  the  Directive’s  wording,  using  the  terms  mental  or  physical  infirmity,  age,  and 
credulity.  

The  concept  of  “average  consumer”  has  been  transposed  into  the  Cypriot  legislation 
based on the definition in the UCPD, and thus also takes into consideration “any social, 
cultural  and  language  factors,  as  well  as  personal  characteristics,  that  makes  them 
particularly  vulnerable 
in  unfair  commercial  practices”  (Business-to-Consumer 
Commercial  Practices  Law  2007,  part  1,  paragraph  2).  Furthermore,  contractual 
capacity  by  age,  mental  incapacity  and  other  legal  situations  of  the  person  are 
mentioned.  

Consumer vulnerability in national consumer policy beyond the UCPD  

In 2006, the Cyprus Energy Regulatory Authority (CERA) established a working group 
for  vulnerable  consumers,  aiming  at  the  preparation  and  publication  of  guidelines  for 
service  provision  (in  particular  relating  to  essential  services)  to  vulnerable  groups.  
However,  this  effort  was  not  completed  and  no  further  official  action  has  been  taken 
since then337.  
Stakeholder assessment of the national approach to consumer vulnerability 

Overall, according to the stakeholders, and in particular following the economic crisis, 

337 The Protection of Consumers with Disabilities in the European Union: Persons with Disabilities 

as Active Participants in the Internal Market, Academic Network of European Disability 
Experts (2012) 

475 

 
 
 
 
 
                                                 
Annex 1│ Country fiches 

in Cyprus vulnerable consumers are considered to mean those who do not have a job 
or an income and therefore encounter difficulties in satisfying their daily needs such as 
accommodation,  electricity  and  food,  as  well  as  regarding  debt  reimbursements.  This 
definition is, however, not present in the legislation. According to one interviewee, the 
notion  of  vulnerable  consumers  in  Cyprus  always  includes  people  receiving  public 
assistance (i.e. welfare and social services). 

Specific legislative measures  

Only  few  specific  legislative  measures  addressing  consumer  vulnerability  have  been 
identified, with the main existing measures being support measures in the energy and 
electronic  communication  sectors.  In  the  energy  sector,  in  the  case  of  consumers 
encountering  difficulties  in  paying  for  electricity,  the  electricity  authority  and  Energy 
Ministry  guarantee  that  energy  supply  to  these  consumers  cannot  be  disconnected 
even though the bills are not paid. Furthermore, the Electricity Authority in Cyprus has 
set  a  special  rate  for  large  and  disadvantaged  families.  The  Electricity  Market  Law 
(2003) covers the requirements of the Electricity Directive under Art. 3. 

In  the  electronic  communications  sector,  based  on  the  electronic  communications 
and  postal  services  law  (Law  112  (I)  2004,  articles  113  and  114)  and  internal 
regulations  and  practices,  the  Cyprus  Telecommunications  Authority  (CYTA)  offers 
special  provisions  for  various  groups,  including  people  with  disabilities.  These  include 
reduced  fees  for  home  and  mobile  telephony  and  internet  connection,  as  well  as 
reductions on video calls for people with hearing impairments. 

Non-legislative measures 

Consumer education has been highlighted in the interviews as an important measure in 
Cyprus.  A  Consumers’  School  organised  by  the  Cyprus  Consumers  Association  (CCA) 
provides several seminars across the country for children and adults.  

Institutional structure and role of civil society 

Public bodies responsible for consumer policy 

The general enforcement of consumer legislation is the responsibility of the Director of 
the  Competition  and  Consumer  Protection  Service  of  the  Ministry  of  Energy, 
Commerce,  Industry  and  Tourism.  The  Consumer  Protection  Service  enforces  this law 
and is responsible for receiving administrative complaints. Other authorities involved in 
the  area  of  consumer  protection  include  the  Cyprus  Energy  Regulatory  Authority 
(CERA),  the  Competition  Ministry,  the  Electricity  Authority  in  Cyprus  (ECA)  and  the 
Cyprus Telecommunications Authority (CYTA). 

Role of civil society 

The main consumer organisation is the Cyprus Consumers Association (CCA). The CCA 
provides  general  consumer  protection  to  any  consumer  encountering  difficulties. 
Through  a  call  centre,  and  an  information  and  training  offer  they  provide  advice  on 
rights and regulations with the aim of enhancing consumer protection. One example is 
consumer training with particular focus on the protection of children. The Pan-Cyprian 
Consumers and Quality of Life Union is another organisation engaged in the field. 

With regard to consumers with disabilities, The Cyprus Confederation of Organisations 
for the Disabled (CCOD) works inter alia with information and protection of consumers 
with  disabilities.  Finally,  the  Association  for  Protection  of  Debtors  for  Banks  provides 
assistance and advice to consumers who are not capable of repaying their loans. 

476 

 
 
 
 
 
Annex 1│ Country fiches 

Incidence of consumer vulnerability  

Indicator of vulnerability 

Mean incidence  

Dimension  1 :  Heightened  risk  of  negative  outcomes 
or impacts on well-being 

Has experienced a problem and took no action 

Overpaid  for  services  due  to  being  unable  to  use  certain 
payment methods 

Dimension 2 : Having characteristics that limit ability 
to maximise well-beings 

Feels vulnerable to 'a great extent' due to health problems 

Feels  vulnerable  to  'a  great  extent'  due  to  financial 
circumstances 

Feels  vulnerable  to  'a  great  extent'  due  to  employment 
situation 

Feels vulnerable to 'a great extent' due to age 

Feels  vulnerable  to  'a  great  extent'  due  to  belonging  to  a 
minority group 

Feels vulnerable to 'a great extent' due to personal issues 

Feels vulnerable to 'a great extent' due to other reasons 

Feels  vulnerable  to  'a  great  extent'  due  to  any  personal 
characteristic 

Dimension  3 :  Having  difficulty 
assimilating information 

in  obtaining  or 

Feels  'not  at  all'  informed  about  product  price,  quality, 
conditions,  etc  when  choosing  and  buying  goods  and 
services 

Compares  product  deals  by  using 
advertisements only - energy sector 

information 

from 

Compares  product  deals  by  using 
advertisements only - online sector  

information 

from 

Compares  product  deals  by  using 
advertisements only - finance sector 

information 

from 

Has  problems  comparing  deals  due  to  information-related 
factors - energy sector 

12% 

23% 

9% 

33% 

25% 

13% 

10% 

8% 

8% 

47% 

10% 

18% 

Has  problems  comparing  deals  due  to  information-related 
factors - online sector 

9% 

Has  problems  comparing  deals  due  to  information-related 
factors - finance sector 

20. Has an electricity supplier and has not switched due to 
information-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
information-related factors - online sector 

477 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Has  a  bank  account  and  has  not  switched  due  to 
information-related factors - financial sector 

Dimension  4 :  Inability  or  failure  to  buy,  choose  or 
access suitable products 

Does not compare product deals - energy sector 

Finds  it  very  difficult  to  compare  product  deals  -  energy 
sector 

Does not compare product deals - online sector 

Finds  it  very  difficult  to  compare  product  deals  -  online 
sector 

Does not compare product deals - finance sector 

Finds  it  very  difficult  to  compare  product  deals  -  finance 
sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
energy sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
online sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
financial sector 

Has  problems  comparing  deals  due  to  market-related 
factors - energy sector 

85% 

47% 

36% 

11% 

65% 

21% 

31% 

Has  problems  comparing  deals  due  to  market-related 
factors - online sector 

17% 

Has  problems  comparing  deals  due  to  market-related 
factors - financial sector 

Has problems comparing deals due to access-related factors 
- energy sector 

Has problems comparing deals due to access-related factors 
- online sector 

2% 

Has problems comparing deals due to access-related factors 
- financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
personal factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
personal factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  personal 
factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
market-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
market-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  market-
related factors - financial sector 

478 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Has  an  electricity  supplier  and  has  not  switched  due  to 
access-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
access-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  access-
related factors - financial sector 

Has not switched due to termination costs 

Has not switched due to bundling 

Excluded from e-commerce due to difficulty of the process 

Excluded  from  e-commerce  due  to  not  having  a  payment 
card 

Declined for a loan 

Dimension  5 :  Higher  susceptibility  to  marketing 
practices 

17% 

30% 

31% 

28% 

Vulnerable  to  a  great  extent  due  to  complexity  of  offers 
(own perception) 

26% 

Experiment choice under the marketing practice treatment - 
energy sector 

Experiment choice under the marketing practice treatment - 
online sector 

Experiment choice under the marketing practice treatment - 
finance sector 

Experiment choice under the marketing practice treatment - 
cross-cutting 

479 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Table 98:  Country fiche - Latvia 

Country fiche summary 

Approach  to  consumer  vulnerability:  The  consumer  vulnerability  concept  appears  to 
not be in use beyond the transposition of the UCPD. 

Measures  addressing  consumer  vulnerability:  Only  a  limited  number  of  measures 
relevant to consumer vulnerability have been identified.  

National consumer protection framework 

The key pieces of legislation within the Latvian consumer protection framework include 
the Consumer Rights Protection Law, Advertising Law, Law on the Safety of Goods and 
Services,  and  the  Law  On  Information  Society  Services338.  The  Consumer  Rights 
Protection  Law  sets  out  key  parameters  of  the  consumer  protection  system,  including 
consumer rights, requirements for contracts and information provision, and the roles of 
consumer protection institutions.  

Approach to consumer vulnerability 

Consumer vulnerability within the context of the transposed UCPD  

The  term  “vulnerable”  is  not  used  as  such  in  the  Law  on  the  Prohibition  of  Unfair 
Commercial  Practices 
term  “especially 
unprotected” is used and the indicators are stated as: “mental or physical state, age or 
unreasonable credulity (including through a lack of experience or knowledge)”.  

the  UCPD,  although 

transposing 

the 

The  term  “average  consumer”  is  present  in  the  Law  on  the  Prohibition  of  Unfair 
Commercial Practices. 

Consumer vulnerability in national consumer policy beyond the UCPD  

There  is  little  evidence  of  broader  approach  to  consumer  vulnerability  beyond  the 
transposition  of  the  UCPD,  although  the  Consumer  Rights  Protection  Law  prohibits 
differential treatment based on sex, race, ethnic belonging or disability.  

Specific legislative measures 

No  specific  legislative  measures  targeting  consumer  vulnerability  in  Latvia  have  been 
identified.  

Non-legislative measures 

There  are  selected  non-legislative  measures  of  relevance  to  vulnerability  in  place  in 
Latvia.  The  Latvian  Consumer  Rights  Protection  Centre,  for  example,  provides 
information to consumers on specific practices, which includes also doorstep selling in 
the  financial  sector339.  The  Latvian  Association  for  Consumer  Protection  (LPIAA) 
provides  a  range  of  services  focusing  in  particular  on  navigating  the  financial  and 
energy markets, although these do not have specific vulnerability focus. 

Institutional structure and role of civil society 

Public bodies responsible for consumer policy 

The  Ministry  of  Economics  is  the  main  with  an  overall  responsibility  for  consumer 
policy,  while  the  Consumer  Rights  Protection  Centre  (CRPC)  is  responsible  for  the 

338 See http://www.ptac.gov.lv/page/263 
339 See http://www.ptac.gov.lv/page/263 

480 

 
 
 
                                                 
Annex 1│ Country fiches 

implementation of consumer policy. Its primary tasks include supervising trade of non-
food  products,  handling  of  consumer  complaints,  and  providing  legal  advice  for 
consumers340. 
Role of civil society 
The  Latvian  Association  for  Consumer  Protection  (LPIAA)  is  the  main  consumer 
organisation  representing  consumers,  raising  awareness,  and  providing  consumers 
with  legal  advice.  It  organises  regular  seminars  in  Riga  and  other  Latvian  regions, 
although these generally do not focus on specific vulnerable consumer groups.   

Incidence of consumer vulnerability  

Indicator of vulnerability 

Mean incidence  

Dimension  1 :  Heightened  risk  of  negative  outcomes 
or impacts on well-being 

Has experienced a problem and took no action 

Overpaid  for  services  due  to  being  unable  to  use  certain 
payment methods 

Dimension 2 : Having characteristics that limit ability 
to maximise well-beings 

Feels vulnerable to 'a great extent' due to health problems 

Feels  vulnerable  to  'a  great  extent'  due  to  financial 
circumstances 

Feels  vulnerable  to  'a  great  extent'  due  to  employment 
situation 

Feels vulnerable to 'a great extent' due to age 

Feels  vulnerable  to  'a  great  extent'  due  to  belonging  to  a 
minority group 

Feels vulnerable to 'a great extent' due to personal issues 

Feels vulnerable to 'a great extent' due to other reasons 

Feels  vulnerable  to  'a  great  extent'  due  to  any  personal 
characteristic 

Dimension  3 :  Having  difficulty 
assimilating information 

in  obtaining  or 

Feels  'not  at  all'  informed  about  product  price,  quality, 
conditions,  etc  when  choosing  and  buying  goods  and 
services 

Compares  product  deals  by  using 
advertisements only - energy sector 

information 

from 

Compares  product  deals  by  using 

information 

from 

340 See http://www.ptac.gov.lv/page/251 

481 

16% 

18% 

10% 

21% 

14% 

8% 

4% 

5% 

5% 

34% 

2% 

12% 

 
 
 
 
 
 
 
 
 
 
                                                 
Annex 1│ Country fiches 

advertisements only - online sector  

Compares  product  deals  by  using 
advertisements only - finance sector 

information 

from 

Has  problems  comparing  deals  due  to  information-related 
factors - energy sector 

Has  problems  comparing  deals  due  to  information-related 
factors - online sector 

7% 

Has  problems  comparing  deals  due  to  information-related 
factors - finance sector 

20. Has an electricity supplier and has not switched due to 
information-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
information-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to 
information-related factors - financial sector 

Dimension  4 :  Inability  or  failure  to  buy,  choose  or 
access suitable products 

Does not compare product deals - energy sector 

Finds  it  very  difficult  to  compare  product  deals  -  energy 
sector 

Does not compare product deals - online sector 

Finds  it  very  difficult  to  compare  product  deals  -  online 
sector 

Does not compare product deals - finance sector 

Finds  it  very  difficult  to  compare  product  deals  -  finance 
sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
energy sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
online sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
financial sector 

Has  problems  comparing  deals  due  to  market-related 
factors - energy sector 

59% 

15% 

32% 

2% 

63% 

10% 

18% 

Has  problems  comparing  deals  due  to  market-related 
factors - online sector 

18% 

Has  problems  comparing  deals  due  to  market-related 
factors - financial sector 

Has problems comparing deals due to access-related factors 
- energy sector 

Has problems comparing deals due to access-related factors 
- online sector 

6% 

Has problems comparing deals due to access-related factors 
- financial sector 

482 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Has  an  electricity  supplier  and  has  not  switched  due  to 
personal factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
personal factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  personal 
factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
market-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
market-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  market-
related factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
access-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
access-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  access-
related factors - financial sector 

Has not switched due to termination costs 

Has not switched due to bundling 

Excluded from e-commerce due to difficulty of the process 

Excluded  from  e-commerce  due  to  not  having  a  payment 
card 

Declined for a loan 

Dimension  5 :  Higher  susceptibility  to  marketing 
practices 

22% 

23% 

28% 

15% 

Vulnerable  to  a  great  extent  due  to  complexity  of  offers 
(own perception) 

12% 

Experiment choice under the marketing practice treatment - 
energy sector 

Experiment choice under the marketing practice treatment - 
online sector 

Experiment choice under the marketing practice treatment - 
finance sector 

Experiment choice under the marketing practice treatment - 
cross-cutting 

483 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Table 99:  Country fiche - Lithuania 

Country fiche summary 

Approach  to  consumer  vulnerability:  The  consumer  vulnerability  concept  appears  to 
not be in use beyond the transposition of the UCPD. 

Measures  addressing  consumer  vulnerability:  Only  a  limited  number  of  measures 
relevant to consumer vulnerability have been identified.  

National consumer protection framework 

The  Law  on  Consumer  Protection  is  the  cornerstone  of  consumer  protection  in 
Lithuania,  setting  out  consumer  rights  and  the  means  for  protecting  them,  including 
the  institutional  system.    Another  key  element  of  the  consumer  protection  framework 
is the Civil Code, which sets out provisions related to contracts, as well as consumers’ 
and  suppliers  rights  and  obligations.  These  two  key  elements  are  supplemented  by  a 
range  of  specific  laws,  including,  among  others,  laws  on  advertising  and  product 
safety,  as  well  as  the  law  on  prohibition  of  unfair  business  to  consumer  commercial 
practices, which transposes the UCPD. 

Approach to consumer vulnerability 

Consumer vulnerability within the context of the transposed UCPD  

The concept of consumer vulnerability is present in the Lithuanian transposition UCPD, 
which uses the UCPD terms of “mental or physical infirmity, age or credulity”. The term 
“vulnerable  consumers”  (“pažeidžiami  vartotojai”)  is  not  used  more  widely,  although 
other  legal  instruments  do  make  references  to  specific  consumer  groups:  The  law  on 
advertising for instance prohibits causing “moral and physical detrimental influence to 
children”.  

The  term  “average  consumer”  (“vidutinis  vartotojas”)  has  also  been  transposed 
through  the  law  on  prohibition  of  unfair  business-to-consumer  commercial  practices 
and  means  “a  consumer  who  is  reasonably  well-informed  and  reasonably  observant 
and circumspect, taking into account social, cultural and linguistic factors”. 

Consumer vulnerability in national consumer policy beyond the UCPD  

Although  there  is  little  evidence  of  the  concept  of  consumer  vulnerability  being  used 
beyond  the  UCPD,  the  consulted  stakeholders  noted  that  there  are  measures  in  the 
energy  and  financial  sectors  that  target  specific  consumer  groups.  These  groups 
include old people, children, and low-income individuals.  

Specific legislative measures 

There  are  no  specific  programmes  dedicated  to  vulnerable  consumers,  or  specific 
consumer groups, but some of the measures in specific sectors implicitly include older 
consumers, children, and low-income consumers as target groups. For instance, in the 
energy  sector,  social  support  to  low-income  consumers  for  paying  energy  bills  is 
available341.  

Non-legislative measures 

While  the  State  Consumer  Rights  Protection  Authority  provides  range  of  information 

341 See http://www.disability-europe.net/content/aned/media/ANED%202012%20-
%20Task%203%20-%20LT%20-%20Consumer%20Protection%20final.doc 

484 

 
 
 
                                                 
Annex 1│ Country fiches 

and advice to consumers, this generally does not include specific activities focusing on 
consumer  vulnerability.  Campaigns  and  other  awareness-raising  measures  are 
primarily conducted by consumer organisations and do not include actions with specific 
vulnerability focus.  

Institutional structure and role of civil society 

Public bodies responsible for consumer policy 

The Ministry of Justice is responsible for the Lithuanian consumer policy, with the State 
Consumer  Rights  Protection  Authority  having  the  central  enforcement  role.  The 
Authority’s  role  includes  coordination  of  activities  of  other  national  agencies  in  the 
area, carrying out of controls, as well analysing and acting on consumer complaints. It 
also has the competence to impose fines on suppliers in breach of the legal provisions.  

Role of civil society 

Alliance  of  Lithuanian  Consumer  Organisations,  formed  in  2012,  groups  together 
Lithuanian  consumer  organisations.  It  focuses  on  ensuring  effective  representation  of 
consumers’ interests within Lithuanian policymaking, as well as awareness raising and 
education.  The  Lithuanian  National  Consumer  Confederation 
is  a  consumer 
organisation  focusing  on  representing  consumers,  research,  collecting  information  on 
consumer  rights,  and  working  to  influence  public  policy  in  favour  of  consumer  rights. 
The Lithuanian Consumer  Institute, in addition to also acting to represent consumers, 
provides consumer education and advice, as well as the testing of goods and services.  
While  the  consumer  organisations  do  not  have  specific  work  streams  focusing  on 
consumer  vulnerability,  there  are  examples  of  activities  linked  to  consumer 
vulnerability, such as for example the National Consumer Confederation’s 2008 project 
on internet usage by children. 

Incidence of consumer vulnerability  

Indicator of vulnerability 

Mean incidence  

Dimension  1 :  Heightened  risk  of  negative  outcomes 
or impacts on well-being 

Has experienced a problem and took no action 

Overpaid  for  services  due  to  being  unable  to  use  certain 
payment methods 

Dimension 2 : Having characteristics that limit ability 
to maximise well-beings 

Feels vulnerable to 'a great extent' due to health problems 

Feels  vulnerable  to  'a  great  extent'  due  to  financial 
circumstances 

Feels  vulnerable  to  'a  great  extent'  due  to  employment 
situation 

Feels vulnerable to 'a great extent' due to age 

Feels  vulnerable  to  'a  great  extent'  due  to  belonging  to  a 
minority group 

Feels vulnerable to 'a great extent' due to personal issues 

11% 

13% 

8% 

25% 

17% 

7% 

3% 

5% 

485 

 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Feels vulnerable to 'a great extent' due to other reasons 

Feels  vulnerable  to  'a  great  extent'  due  to  any  personal 
characteristic 

Dimension  3 :  Having  difficulty 
assimilating information 

in  obtaining  or 

Feels  'not  at  all'  informed  about  product  price,  quality, 
conditions,  etc  when  choosing  and  buying  goods  and 
services 

Compares  product  deals  by  using 
advertisements only - energy sector 

information 

from 

Compares  product  deals  by  using 
advertisements only - online sector  

information 

from 

Compares  product  deals  by  using 
advertisements only - finance sector 

information 

from 

Has  problems  comparing  deals  due  to  information-related 
factors - energy sector 

Has  problems  comparing  deals  due  to  information-related 
factors - online sector 

Has  problems  comparing  deals  due  to  information-related 
factors - finance sector 

20. Has an electricity supplier and has not switched due to 
information-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
information-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to 
information-related factors - financial sector 

Dimension  4 :  Inability  or  failure  to  buy,  choose  or 
access suitable products 

Does not compare product deals - energy sector 

Finds  it  very  difficult  to  compare  product  deals  -  energy 
sector 

Does not compare product deals - online sector 

Finds  it  very  difficult  to  compare  product  deals  -  online 
sector 

Does not compare product deals - finance sector 

Finds  it  very  difficult  to  compare  product  deals  -  finance 
sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
energy sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
online sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
financial sector 

5% 

34% 

8% 

1% 

3% 

1% 

16% 

3% 

9% 

2% 

0% 

0% 

83% 

24% 

24% 

1% 

66% 

11% 

33% 

12% 

45% 

486 

 
 
 
 
 
Annex 1│ Country fiches 

Has  problems  comparing  deals  due  to  market-related 
factors - energy sector 

Has  problems  comparing  deals  due  to  market-related 
factors - online sector 

Has  problems  comparing  deals  due  to  market-related 
factors - financial sector 

Has problems comparing deals due to access-related factors 
- energy sector 

Has problems comparing deals due to access-related factors 
- online sector 

Has problems comparing deals due to access-related factors 
- financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
personal factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
personal factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  personal 
factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
market-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
market-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  market-
related factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
access-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
access-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  access-
related factors - financial sector 

Has not switched due to termination costs 

Has not switched due to bundling 

Excluded from e-commerce due to difficulty of the process 

Excluded  from  e-commerce  due  to  not  having  a  payment 
card 

Declined for a loan 

Dimension  5 :  Higher  susceptibility  to  marketing 
practices 

Vulnerable  to  a  great  extent  due  to  complexity  of  offers 
(own perception) 

Experiment choice under the marketing practice treatment - 
energy sector 

487 

15% 

8% 

30% 

55% 

4% 

6% 

21% 

3% 

8% 

4% 

2% 

12% 

42% 

6% 

4% 

17% 

18% 

21% 

20% 

1% 

15% 

90% 

 
 
 
 
 
 
Annex 1│ Country fiches 

Experiment choice under the marketing practice treatment - 
online sector 

Experiment choice under the marketing practice treatment - 
finance sector 

Experiment choice under the marketing practice treatment - 
cross-cutting 

57% 

20% 

73% 

488 

 
 
 
 
 
 
 
Annex 1│ Country fiches 

Table 100:  Country fiche - Luxembourg 

Country fiche summary 

Approach  to  consumer  vulnerability:  The  consumer  vulnerability  concept  appears  to 
not be in use beyond the transposition of the UCPD. 

Measures  addressing  consumer  vulnerability:  Only  a  limited  number  of  measures 
relevant to consumer vulnerability have been identified.  

National consumer protection framework 

In Luxembourg, the basis of the national consumer protection framework can be found 
in the Luxembourg Consumer Code. The Consumer Code came into force in 2011 and 
includes regulations in regards to, inter alia, consumer credit, unfair contractual terms, 
unfair commercial practices, price indications and internet sales.  

Approach to consumer vulnerability 

Consumer vulnerability within the context of the transposed UCPD  
The  term  “vulnerable”  is  present  in  the  Law  of  29  April  2009  on  Unfair  commercial 
practices through the transposition of the UCPD. The concept of vulnerability has been 
transposed  in  line  with  the  Directive’s  wording,  using  the  terms  mental  or  physical 
infirmity,  age,  and  credulity.  Similarly,  the  “average  consumer”  term  has  also  been 
transposed into Luxembourg legislation through the UCPD.   
Consumer vulnerability in national consumer policy beyond the UCPD 
There is little evidence  that the vulnerable consumer concept being used more widely 
in Luxembourg. For example, while the laws organising the electricity and gas market 
incorporate references to vulnerable consumers, the concept is not explicitly defined.    

Specific measures  

There  are  few  specific  measures  in  regards  to  consumer  vulnerability  in  Luxembourg. 
In the energy sector, there are no social tariffs available, but for those consumers that 
have difficulties paying their bills there are some provisions in place  administrated by 
social and welfare services.  

Non-legislative measures 

No  non-legislative  public  measures  targeting  consumer  vulnerability  have  been 
identified in Luxembourg. Similarly, the ULC, which is the only consumer association in 
Luxembourg, does not target vulnerable consumers in its activities.   

Institutional structure and role of civil society 

Public bodies responsible for consumer policy  
The  Ministry  responsible  for  the  formulation  of  consumer  policy  is  the  Ministry  of 
Economy and Foreign Trade. The tasks of the ministry include: 

  The development of legislation and regulations on consumer policy as well as 
the transposition of directives and implementation of Community regulations; 
  The protection of consumer interest of consumers and ensure the rights of air 

passengers; and 

  Managing relationships with the Luxembourg Union of Consumers (ULC) and 

the European Consumer Centre (ECC).  

489 

 
 
 
 
 
Annex 1│ Country fiches 

Moreover,  the  Ministry  is  responsible  for  the  Consumer  Council,  an  advisory  body 
from  government,  consumer  organizations  and 
consisting  of  representatives 
employers'  organizations.  In  addition,  individual  sector  regulators  address  consumer 
issues in accordance with their competences.   
Role of civil society 
The  Luxembourg  Consumers  Union  (ULC)  is  the  only  consumer  association  in 
Luxembourg and is represented in the Consumer Council. The ULC works on a range of 
areas  with  the  aim  to  protect  and  defend  consumers,  as  well  as  provide  information 
and education. The ULC also represents the consumer interest before public bodies and 
in the process of policy-formulation. However, the Consumer Union does not carry out 
any particular activities directed towards vulnerable consumers. 

Incidence of consumer vulnerability  

Indicator of vulnerability 

Mean incidence  

Dimension  1 :  Heightened  risk  of  negative  outcomes 
or impacts on well-being 

Has experienced a problem and took no action 

Overpaid  for  services  due  to  being  unable  to  use  certain 
payment methods 

Dimension 2 : Having characteristics that limit ability 
to maximise well-beings 

Feels vulnerable to 'a great extent' due to health problems 

Feels  vulnerable  to  'a  great  extent'  due  to  financial 
circumstances 

Feels  vulnerable  to  'a  great  extent'  due  to  employment 
situation 

Feels vulnerable to 'a great extent' due to age 

Feels  vulnerable  to  'a  great  extent'  due  to  belonging  to  a 
minority group 

Feels vulnerable to 'a great extent' due to personal issues 

Feels vulnerable to 'a great extent' due to other reasons 

Feels  vulnerable  to  'a  great  extent'  due  to  any  personal 
characteristic 

Dimension  3 :  Having  difficulty 
assimilating information 

in  obtaining  or 

Feels  'not  at  all'  informed  about  product  price,  quality, 
conditions,  etc  when  choosing  and  buying  goods  and 
services 

Compares  product  deals  by  using 
advertisements only - energy sector 

information 

from 

Compares  product  deals  by  using 
advertisements only - online sector  

information 

from 

7% 

12% 

2% 

7% 

4% 

3% 

3% 

3% 

3% 

14% 

3% 

4% 

12% 

490 

 
 
 
 
 
 
 
Annex 1│ Country fiches 

Compares  product  deals  by  using 
advertisements only - finance sector 

information 

from 

Has  problems  comparing  deals  due  to  information-related 
factors - energy sector 

Has  problems  comparing  deals  due  to  information-related 
factors - online sector 

Has  problems  comparing  deals  due  to  information-related 
factors - finance sector 

20. Has an electricity supplier and has not switched due to 
information-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
information-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to 
information-related factors - financial sector 

Dimension  4 :  Inability  or  failure  to  buy,  choose  or 
access suitable products 

Does not compare product deals - energy sector 

Finds  it  very  difficult  to  compare  product  deals  -  energy 
sector 

Does not compare product deals - online sector 

Finds  it  very  difficult  to  compare  product  deals  -  online 
sector 

Does not compare product deals - finance sector 

Finds  it  very  difficult  to  compare  product  deals  -  finance 
sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
energy sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
online sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
financial sector 

Has  problems  comparing  deals  due  to  market-related 
factors - energy sector 

Has  problems  comparing  deals  due  to  market-related 
factors - online sector 

Has  problems  comparing  deals  due  to  market-related 
factors - financial sector 

Has problems comparing deals due to access-related factors 
- energy sector 

Has problems comparing deals due to access-related factors 
- online sector 

Has problems comparing deals due to access-related factors 

491 

5% 

29% 

16% 

15% 

82% 

19% 

51% 

8% 

62% 

8% 

49% 

29% 

29% 

42% 

39% 

38% 

29% 

3% 

0% 

 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

- financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
personal factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
personal factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  personal 
factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
market-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
market-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  market-
related factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
access-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
access-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  access-
related factors - financial sector 

Has not switched due to termination costs 

Has not switched due to bundling 

Excluded from e-commerce due to difficulty of the process 

Excluded  from  e-commerce  due  to  not  having  a  payment 
card 

Declined for a loan 

Dimension  5 :  Higher  susceptibility  to  marketing 
practices 

19% 

26% 

29% 

14% 

Vulnerable  to  a  great  extent  due  to  complexity  of  offers 
(own perception) 

9% 

Experiment choice under the marketing practice treatment - 
energy sector 

Experiment choice under the marketing practice treatment - 
online sector 

Experiment choice under the marketing practice treatment - 
finance sector 

Experiment choice under the marketing practice treatment - 
cross-cutting 

492 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Table 101:  Country fiche - Hungary 

Country fiche summary 

Approach to consumer vulnerability: The concept of consumer vulnerability appears to 
have  been  in  place  within  national  consumer  policy  independently  of  the  UCPD 
transposition.  

Measures  addressing  consumer  vulnerability:  There  is  evidence  of  a  range  of  sector-
specific measures addressing consumer vulnerability. 

National consumer protection framework 

The main piece of legislation laying down the basic consumer protection framework in 
Hungary  is  the  Consumer  Protection  Act.  In  January  2014,  amendments  to  the  Act 
were  implemented  in  order  to  strengthen  the  protection  of  vulnerable  consumers,  in 
particular, the elderly, disabled and younger consumers.  

Approach to consumer vulnerability 

is 

Consumer vulnerability within the context of the transposed UCPD 
The  “average  consumer”  term  is  not  explicitly  included  in  the  Hungarian  consumer 
protection  legislation.  However,  Act  XLVII  of  2008  on  the  Prohibition  of  Unfair 
transposing  Directive 
Business-to-Consumer  Commercial  Practices,  which 
2005/29/EC  (UCP  Directive)  into  the  Hungarian  law,  clearly  defines  the  term  on  the 
basis of Preamble (18) of Directive 2005/29/EC as in Section 4(1) of Act XLVII of 2008. 
Section 4(2) of Act XLVII of 2008 complements the definition of the average consumer 
since  it  pays  special  attention  to  vulnerable  consumer  groups  in  line  with  the  UCPD 
definition.  
Consumer vulnerability in national consumer policy beyond the UCPD  
The  1997  act  on  Consumer  Protection  was  recently  amended  to  include  provisions 
concerning  the  protection  of  vulnerable  consumers.  According  to  the  new  provisions, 
which came into force in January 2014, the consumer protection authority is obliged to 
impose a fine if identified infringements concern vulnerable consumers. This illustrates 
that  the  protection  of  vulnerable  consumers  is  increasingly  becoming  a  priority  within 
consumer policy. 

Specific legislative measures  

There  are  a  number  of  specific  measures  relevant  to  consumer  vulnerability  in 
Hungary.  These  relate  mainly  to  the  energy  sector.  For  consumers  with  financial 
difficulties,  measures  include  deferred  payment,  payment  facilities  and  prepayment 
metering  devices  to  help  mitigate  economic  vulnerability.  For  consumers  with 
disabilities special provisions are in place with regards to metering, reading and billing 
procedures  and  payment  terms.  For  those  consumers  with  disabilities  whose  life  or 
health  is  directly  jeopardized  if  disconnected  from  the  electricity  supply  system, 
disconnection  is  not  allowed.  In  addition,  new  regulation  forbids  utility  service 
providers  to  charge  an  extra  fee  for  paying  through  postal  payment  order  or  for 
presenting a paper invoice, which may help elderly consumers who do not have access 
to digital forms of payment.  
In general, the 2014 amendments to the Consumer Protection Act enable authorities to 
impose immediate fines on companies if the rights of older people and those living with 
disabilities are harmed.  

493 

 
 
 
 
 
Annex 1│ Country fiches 

Non-legislative measures 

Beside  the  specific legislative  measures  set  out  above,  there  is  little  evidence  of  non-
legislative measures currently in place in Hungary.  

Institutional structure and role of civil society 

Public bodies responsible for consumer policy 
In  Hungary,  consumer  policy  falls  under  the  competences  of  the  Ministry  of  National 
Development.  The  Hungarian  Authority  for  Consumer  Protection  is  the  authority 
responsible  for  carrying  out  the  primary  tasks  with  regard  to  consumer  protection.  In 
addition, the  Hungarian  Competition  Authority,  the  Central  Bank  of  Hungary,  National 
Food  Chain  Safety  Office,  Hungarian  Energy  and  Public  Utility  Regulatory  Authority, 
National Media and Infocommunications Authority Hungary, National Public Health and 
Medical  Officer  Service-Office  of  the  Chief  Medical  Officer,  Hungarian  Trade  Licensing 
Office,  Hungarian  Transport  Authority  all  have  competences  related  to  consumer 
protection.  
Role of civil society 
The National Association for Consumer Protection in Hungary is one of the several non-
governmental  organizations  for  consumer  protection,  however  as  an  umbrella 
organization  covers  the  entire  country  and  a  wide  range  of  consumers’  issues.  Based 
on  the  research  conducted,  the  association  does  not  specifically  target  vulnerable 
consumers  through  its  activities.  Association  of  Conscious  Consumers  is  an  NGO 
organises  campaigns  focusing  on  informing  consumers  and  changing  consumers’ 
behaviour. In addition there are other NGOs in Hungary, which are active in the area of 
consumer information. 

Incidence of consumer vulnerability  

Indicator of vulnerability 

Mean incidence  

Dimension  1 :  Heightened  risk  of  negative  outcomes 
or impacts on well-being 

Has experienced a problem and took no action 

Overpaid  for  services  due  to  being  unable  to  use  certain 
payment methods 

Dimension 2 : Having characteristics that limit ability 
to maximise well-beings 

Feels vulnerable to 'a great extent' due to health problems 

Feels  vulnerable  to  'a  great  extent'  due  to  financial 
circumstances 

Feels  vulnerable  to  'a  great  extent'  due  to  employment 
situation 

Feels vulnerable to 'a great extent' due to age 

Feels  vulnerable  to  'a  great  extent'  due  to  belonging  to  a 
minority group 

Feels vulnerable to 'a great extent' due to personal issues 

Feels vulnerable to 'a great extent' due to other reasons 

26% 

13% 

8% 

22% 

12% 

7% 

5% 

7% 

6% 

494 

 
 
 
 
 
 
Annex 1│ Country fiches 

Feels  vulnerable  to  'a  great  extent'  due  to  any  personal 
characteristic 

34% 

Dimension  3 :  Having  difficulty 
assimilating information 

in  obtaining  or 

Feels  'not  at  all'  informed  about  product  price,  quality, 
conditions,  etc  when  choosing  and  buying  goods  and 
services 

Compares  product  deals  by  using 
advertisements only - energy sector 

information 

from 

Compares  product  deals  by  using 
advertisements only - online sector  

information 

from 

Compares  product  deals  by  using 
advertisements only - finance sector 

information 

from 

Has  problems  comparing  deals  due  to  information-related 
factors - energy sector 

Has  problems  comparing  deals  due  to  information-related 
factors - online sector 

Has  problems  comparing  deals  due  to  information-related 
factors - finance sector 

20. Has an electricity supplier and has not switched due to 
information-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
information-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to 
information-related factors - financial sector 

Dimension  4 :  Inability  or  failure  to  buy,  choose  or 
access suitable products 

Does not compare product deals - energy sector 

Finds  it  very  difficult  to  compare  product  deals  -  energy 
sector 

Does not compare product deals - online sector 

Finds  it  very  difficult  to  compare  product  deals  -  online 
sector 

Does not compare product deals - finance sector 

Finds  it  very  difficult  to  compare  product  deals  -  finance 
sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
energy sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
online sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
financial sector 

495 

4% 

8% 

7% 

72% 

19% 

26% 

2% 

54% 

16% 

17% 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Has  problems  comparing  deals  due  to  market-related 
factors - energy sector 

Has  problems  comparing  deals  due  to  market-related 
factors - online sector 

22% 

Has  problems  comparing  deals  due  to  market-related 
factors - financial sector 

Has problems comparing deals due to access-related factors 
- energy sector 

Has problems comparing deals due to access-related factors 
- online sector 

7% 

Has problems comparing deals due to access-related factors 
- financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
personal factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
personal factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  personal 
factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
market-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
market-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  market-
related factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
access-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
access-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  access-
related factors - financial sector 

Has not switched due to termination costs 

Has not switched due to bundling 

Excluded from e-commerce due to difficulty of the process 

Excluded  from  e-commerce  due  to  not  having  a  payment 
card 

Declined for a loan 

Dimension  5 :  Higher  susceptibility  to  marketing 
practices 

22% 

24% 

24% 

20% 

Vulnerable  to  a  great  extent  due  to  complexity  of  offers 
(own perception) 

8% 

Experiment choice under the marketing practice treatment - 
energy sector 

Experiment choice under the marketing practice treatment - 

496 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

online sector 

Experiment choice under the marketing practice treatment - 
finance sector 

Experiment choice under the marketing practice treatment - 
cross-cutting 

497 

 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Table 102:  Country fiche - Malta 

Country fiche summary 

Approach  to  consumer  vulnerability:  The  consumer  vulnerability  concept  appears  to 
not be in use beyond the transposition of the UCPD. 

Measures  addressing  consumer  vulnerability:  There  is  evidence  of  a  range  of  sector-
specific measures addressing consumer vulnerability. 

National consumer protection framework 

The  main  piece  of  legislation  regulating  the  protection  of  consumers  is  the  Consumer 
Affairs  Act  from  1994.  The  activities  carried  out  within  the  framework  of  Maltese 
consumer  policy  relate  mainly  to  compliance,  enforcement  and  information  on 
consumer rights.  

Approach to consumer vulnerability 

Consumer vulnerability within the context of the transposed UCPD  

The term vulnerable consumer is used in the Consumer Affairs Act, Chapter 378 of the 
Laws  of  Malta  Article  51B  –  Unfair  Commercial  Practices  transposing  the  UCPD.  The 
Consumer  Affairs  Act  refers  to  vulnerable  consumers  by  referring  to  their  mental  or 
physical  infirmities,  their  age  or  credulity  in  a  manner,  which  is  easily  seen  by  the 
trader.  In  addition,  the  term  is  also  used  in  the  Second  Schedule  to  the  Act  – 
Administrative  fines  Article  4  setting  out  the  amount  of  fine  for  infringement342.  The 
term “average” consumer has also been transposed into the Consumer Affairs Act.  

Consumer vulnerability in national consumer policy beyond the UCPD  

There is little evidence that the concept of consumer vulnerability being used outside of 
the  context  of  the  UCPD.  The  Laws  in  Malta  are  directed  towards  consumers  at  large 
and do not differentiate between the various degrees of consumer vulnerability.  

Specific legislative measures  

No  specific  national  legislative  measures  mitigating  consumer  vulnerability  have  been 
identified  in  Malta  except  those  set  out  in  European  Directives  such  as  the  Universal 
Services  Directive.  Some  consumers  in  the  energy  sector,  within  the  social  policy 
framework, are eligible to receive benefits in order to ensure access to energy. These 
consumer  groups  include  families  with  low  annual  income  (less  than  €8,158.81), 
persons with disabilities, families on social assistance or special unemployment benefit, 
persons with an age pension or a carer’s pension. In addition, if consumers experience 
difficulties in keeping up with their bills, they are allowed to pay their bill in instalments 
to  avoid  disconnection.  This  has  reduced  the  number  of  disconnection  from  846  in 
2011 to 488 in 2012343.  

Non-legislative measures 

342 “Without prejudice to the generality of rule 3, the following shall in all cases be deemed as 
aggravating circumstances: where the practice or scheme constituting the infringement is 
specifically targeting vulnerable consumers, the basic amount shall be increased by two 
thousand, three hundred and fifty euro (€2,350).” 

343 See 

http://www.ceer.eu/portal/page/portal/EER_HOME/EER_PUBLICATIONS/NATIONAL_REPORT
S/National%20Reporting%202013/NR_En/C13_NR_Malta-EN.pdf  

498 

 
 
 
                                                 
Annex 1│ Country fiches 

With regard to the online environment, Malta Communications Authority (MCA), jointly 
with the Office of the Commissioner for Children and the National Agency for children, 
families  and  the  community  (Aġenzija  Appoġġ)  has  been  running  a  project  entitled 
“BeSmartOnline!”.  Through  the  project,  over  8000  children  from  all  schools  across 
Malta and Gozo have taken part in lessons on Digital Citizenship and Digital Footprints. 
During  these  lessons,  children  explored  how  their  online  activity  impacts  their  self-
image  as  well  as  how  their  digital  behaviour  affected  others.  Aspects  such  as 
cyberbullying,  privacy  and  being  a  respectful  online  citizen  were  discussed. 
Furthermore,  over  2000  professionals  have  taken  part  in  development  sessions 
specifically  addressing  the  issue  of  Internet  Safety.  A  number  of  Information  Days 
were  also  organised  for  families,  with  experts  available  on  the  day  to  address  any 
questions  or  concerns  parents  or  carers  may  have  on  the  subject.  At  present,  the 
MCCA, through the BeSmartOnline! project is also organising digital parenting sessions 
which are being held at various Local Councils across Malta and Gozo. 

In  addition,  the  MCA  recently  launched  a  campaign  “Live  ICT”  which  was  aimed  at 
raising  awareness  amongst  older  generations  about  the  true  potential  of  ICT  and  to 
challenge the perception that computers and internet use are exclusively for the young 
or  those  in  employment.  Throughout  2013,  980  individuals  aged  65+  attended  basic 
ICT training courses. 

Institutional structure and role of civil society 

Public bodies responsible for consumer policy 

Consumer  policy  falls  under  the  competence  of  the  Ministry  for  Social  Dialogue, 
Consumer  Affairs  and  Civil  Liberties.  The  Malta  Competition  and  Consumer  Affairs 
Authority  (MCCAA)  was  established  on  23  May  2011  and  is  a  governmental  agency 
operating under the aforementioned Ministry. The tasks of the MCCAA are to safeguard 
consumer  economic  interests  and  enhance  consumer  welfare.  This  is  carried  out 
through  mediation  of  consumer  complaints;  enforcement  of  consumer  protection 
legislation;  and  educating  and 
informing  consumers  as  to  their  rights  and 
responsibilities with the aim of enlightening consumers how to act in the market place. 
In addition, the MCCAA is charged with, inter alia, promoting sound business practices, 
adopting and co-ordinating standards in relation to products or services.  

In addition to the MCCAA, the Consumer Affairs Council is an independent body with its 
functions and duties set out in the Consumer Affairs Act. The Council has a consultative 
function  and  advises  the  Minister  on  measures  for  the  promotion  and  protection  of 
consumer  interests.  In  turn,  the  Minister  consults  with  the  Council  when  making 
regulations about any matter relating to consumer affairs. 

Role of civil society 

The Maltese Consumer Association (L-Ghaqda tal-Konsumaturi) is the only consumers’ 
association  in  Malta  and  is  recognised  under  the  Consumers’  Affairs  Act  of  1994.  The 
Association’s principal activities include: 

  Consumer representation;  

  Consumer education and information; and 

  Advocacy 

The  Association  does  not  appear  to  carry  out  activities  directed  specifically  at 
vulnerable consumers.  

499 

 
 
 
 
 
 
Annex 1│ Country fiches 

Incidence of consumer vulnerability  

Indicator of vulnerability 

Mean incidence  

Dimension  1 :  Heightened  risk  of  negative  outcomes 
or impacts on well-being 

Has experienced a problem and took no action 

Overpaid  for  services  due  to  being  unable  to  use  certain 
payment methods 

Dimension 2 : Having characteristics that limit ability 
to maximise well-beings 

Feels vulnerable to 'a great extent' due to health problems 

Feels  vulnerable  to  'a  great  extent'  due  to  financial 
circumstances 

Feels  vulnerable  to  'a  great  extent'  due  to  employment 
situation 

Feels vulnerable to 'a great extent' due to age 

Feels  vulnerable  to  'a  great  extent'  due  to  belonging  to  a 
minority group 

Feels vulnerable to 'a great extent' due to personal issues 

Feels vulnerable to 'a great extent' due to other reasons 

Feels  vulnerable  to  'a  great  extent'  due  to  any  personal 
characteristic 

Dimension  3 :  Having  difficulty 
assimilating information 

in  obtaining  or 

Feels  'not  at  all'  informed  about  product  price,  quality, 
conditions,  etc  when  choosing  and  buying  goods  and 
services 

Compares  product  deals  by  using 
advertisements only - energy sector 

information 

from 

Compares  product  deals  by  using 
advertisements only - online sector  

information 

from 

Compares  product  deals  by  using 
advertisements only - finance sector 

information 

from 

Has  problems  comparing  deals  due  to  information-related 
factors - energy sector 

6% 

8% 

4% 

9% 

6% 

5% 

3% 

4% 

4% 

18% 

7% 

11% 

Has  problems  comparing  deals  due  to  information-related 
factors - online sector 

6% 

Has  problems  comparing  deals  due  to  information-related 
factors - finance sector 

20. Has an electricity supplier and has not switched due to 
information-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
information-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to 

500 

 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

information-related factors - financial sector 

Dimension  4 :  Inability  or  failure  to  buy,  choose  or 
access suitable products 

Does not compare product deals - energy sector 

Finds  it  very  difficult  to  compare  product  deals  -  energy 
sector 

Does not compare product deals - online sector 

Finds  it  very  difficult  to  compare  product  deals  -  online 
sector 

Does not compare product deals - finance sector 

Finds  it  very  difficult  to  compare  product  deals  -  finance 
sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
energy sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
online sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
financial sector 

Has  problems  comparing  deals  due  to  market-related 
factors - energy sector 

85% 

29% 

51% 

17% 

57% 

19% 

19% 

Has  problems  comparing  deals  due  to  market-related 
factors - online sector 

12% 

Has  problems  comparing  deals  due  to  market-related 
factors - financial sector 

Has problems comparing deals due to access-related factors 
- energy sector 

Has problems comparing deals due to access-related factors 
- online sector 

0% 

Has problems comparing deals due to access-related factors 
- financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
personal factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
personal factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  personal 
factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
market-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
market-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  market-
related factors - financial sector 

501 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Has  an  electricity  supplier  and  has  not  switched  due  to 
access-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
access-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  access-
related factors - financial sector 

Has not switched due to termination costs 

Has not switched due to bundling 

Excluded from e-commerce due to difficulty of the process 

Excluded  from  e-commerce  due  to  not  having  a  payment 
card 

Declined for a loan 

Dimension  5 :  Higher  susceptibility  to  marketing 
practices 

10% 

26% 

13% 

9% 

Vulnerable  to  a  great  extent  due  to  complexity  of  offers 
(own perception) 

13% 

Experiment choice under the marketing practice treatment - 
energy sector 

Experiment choice under the marketing practice treatment - 
online sector 

Experiment choice under the marketing practice treatment - 
finance sector 

Experiment choice under the marketing practice treatment - 
cross-cutting 

502 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Table 103:  Country fiche - Netherlands 

Country fiche summary 

Approach  to  consumer  vulnerability:  The  consumer  vulnerability  concept  appears  to 
not be in use beyond the transposition of the UCPD, with the national consumer policy 
taking a broader approach.  

Measures  addressing  consumer  vulnerability:  While  consumer  policy  is  not  based 
around  new  legislative  measures,  there  is  evidence  of  investigations  into  numerous 
sectors, which indirectly address particular vulnerabilities. 

National consumer protection framework 

The  overall  approach  to  consumer  protection  in  the  Netherlands  is  closely  linked  to 
competition  and  market  regulation,  reflected  in  the  recently  created  Netherlands 
Authority  for  Consumers  and  Markets  (ACM),  with  the  ACM  strategy  document344 
highlighting that  safeguarding  competition is  central  to its  consumer  protection  tasks.  
According  to  interviewed  stakeholders,  the  consumer  protection  approach  in  the 
Netherlands  is  currently  based  on  establishing  dialogue  with  relevant  sectors  in  order 
to ensure better outcomes for consumers rather than introducing new legislation.  

Approach to consumer vulnerability 

Consumer vulnerability within the context of the transposed UCPD  

The transposition of the UCPD makes note of the mental or physical limitations, age or 
credulity  of  potential  target  groups  of  the  trader  in  line  with  the  text  of  the  Directive 
without  using  the  term  “vulnerable”.  The  term  “average  consumer”  is  in  use  and  is 
defined  in  the  Act  of  25  September  2008  bringing  Volumes  3  and  6  of  the  Civil  Code 
and  other  Acts  into  line  with  the  Directive  concerning  unfair  business-to  consumer 
commercial  practices  in  the  internal  market.  “Average  consumer”  is  defined  as  the 
average member of the specific group targeted by the trader, taking into account that 
in specific groups these members can display the characteristics noted above.  

Consumer vulnerability in national consumer policy beyond the UCPD  

The term “vulnerable consumer” is not explicitly used in the transposition of the UCPD 
and there is no evidence of it being in use in other elements of Dutch consumer policy, 
with the exception of the energy sector345.  
Stakeholder assessment of the national approach to consumer vulnerability 

According  to  the  stakeholder  interviews,  there  are  few  overarching  vulnerability-
oriented measures and addressing consumer vulnerability is primarily seen as the role 
of  the  welfare  state  rather  than  consumer  protection  policy.  Nevertheless,  the 
regulators  in  the  Netherlands  implicitly  focus  on  specific  types  of  vulnerabilities  when 
investigating individual sectors (i.e. marketing to children and young adults).  

Specific legislative measures 

344 See https://www.acm.nl/en/download/publication/?id=11993 
345 ACER (2013). Annual Report on the Results of Monitoring the Internal Electricity and Gas 

Markets in 2012. Available at: 
http://www.acer.europa.eu/Electricity/Market%20monitoring/Pages/Reports.aspx 

503 

 
 
 
 
 
                                                 
Annex 1│ Country fiches 

No  legislative  provisions  specifically  targeting  vulnerable  consumers  have  been 
identified  in  the  Netherlands.  In  the  energy  sector  the  Netherlands,  like  many  other 
Member States, does however  offer protection against disconnection of  electricity and 
gas,  which  serves  as  a  means  of  supporting  vulnerable  consumers  in  the  energy 
sector.  

Non-legislative measures 

The  Netherlands  Authority  for  Consumers  and  Markets  has  launched  a  range  of 
campaigns  and  market  scans  focusing  on  specific  consumer  issues.  While  selected 
actions  are  linked  to  consumer  vulnerability,  such  as  the  market  scan  into  online 
games  and  their  use  by  young  consumers346,  there  are  no  work  streams  or  actions 
explicitly targeting consumer vulnerability.  In the financial sector, the regulator uses a 
risk assessment approach to investigate specific products  and, while there is no focus 
on  specific  consumer  groups,  in  some  cases  the  analysis  does  address  specific 
vulnerabilities,  such  as  those  related  to  retirement  products  or  the  situation  of 
consumers who may not be disciplined in financial affairs.  

Institutional structure and role of civil society 

Public bodies responsible for consumer policy 

In  the  Netherlands,  overall  responsibility  for  consumer  policy  falls  within  the  remit  of 
the  Ministry  of  Economic  Affairs,  with  specific  aspects  of  consumer  policy  being  the 
competence of Ministry of Justice, the Ministry of Health and the Ministry of Finance.  

The key enforcement agency is the Netherlands Authority for Consumers and Markets 
(ACM).  The  ACM  has  been  created  in  2013  by  joining  the  Netherlands  Consumer 
Authority  (CA),  the  Netherlands  Independent  Post  and  Telecommunication  Authority 
(OPTA)  and  the  Netherlands  Competition  Authority  (NMA).  The  ACM  has  specific 
enforcement  responsibilities in the  areas  of  energy,  telecommunication,  transport  and 
postal services. It can impose administrative penalties or order subject to penalties. In 
addition,  the  Netherlands  Authority  For  the  Financial  Markets  has  enforcement 
competence in the field of financial services. Consuwijzer is the public body ran by the 
Netherlands  Authority  for  Consumers  and  Markets  which  that  consumer  advice  and 
serves as a central point for the lodging of consumer complaints.  

Role of civil society 

Consumentenbond  is  the  largest  general  consumer  organisation  representing  Dutch 
consumers.  It  provides  consumer  information  and  advice,  as  well  as  services  such  as 
testing.  It  however  does  not  carry  out  activities  focusing  specifically  on  vulnerable 
consumers.  

Incidence of consumer vulnerability  

Indicator of vulnerability 

Mean incidence  

Dimension  1 :  Heightened  risk  of  negative  outcomes 
or impacts on well-being 

Has experienced a problem and took no action 

Overpaid  for  services  due  to  being  unable  to  use  certain 

5% 

7% 

346 See https://www.acm.nl/en/publications/publication/11847/Market-scan-into-online-games/ 

504 

 
 
 
 
 
                                                 
Annex 1│ Country fiches 

payment methods 

Dimension 2 : Having characteristics that limit ability 
to maximise well-beings 

Feels vulnerable to 'a great extent' due to health problems 

Feels  vulnerable  to  'a  great  extent'  due  to  financial 
circumstances 

Feels  vulnerable  to  'a  great  extent'  due  to  employment 
situation 

Feels vulnerable to 'a great extent' due to age 

Feels  vulnerable  to  'a  great  extent'  due  to  belonging  to  a 
minority group 

Feels vulnerable to 'a great extent' due to personal issues 

Feels vulnerable to 'a great extent' due to other reasons 

Feels  vulnerable  to  'a  great  extent'  due  to  any  personal 
characteristic 

Dimension  3 :  Having  difficulty 
assimilating information 

in  obtaining  or 

Feels  'not  at  all'  informed  about  product  price,  quality, 
conditions,  etc  when  choosing  and  buying  goods  and 
services 

Compares  product  deals  by  using 
advertisements only - energy sector 

information 

from 

Compares  product  deals  by  using 
advertisements only - online sector  

information 

from 

Compares  product  deals  by  using 
advertisements only - finance sector 

information 

from 

Has  problems  comparing  deals  due  to  information-related 
factors - energy sector 

Has  problems  comparing  deals  due  to  information-related 
factors - online sector 

Has  problems  comparing  deals  due  to  information-related 
factors - finance sector 

20. Has an electricity supplier and has not switched due to 
information-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
information-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to 
information-related factors - financial sector 

Dimension  4 :  Inability  or  failure  to  buy,  choose  or 
access suitable products 

Does not compare product deals - energy sector 

Finds  it  very  difficult  to  compare  product  deals  -  energy 

505 

3% 

5% 

3% 

2% 

2% 

2% 

2% 

10% 

4% 

1% 

2% 

1% 

13% 

5% 

12% 

46% 

12% 

 
 
 
 
 
 
 
 
 
 
 
46% 

4% 

73% 

8% 

29% 

21% 

34% 

34% 

23% 

27% 

3% 

3% 

2% 

Annex 1│ Country fiches 

sector 

Does not compare product deals - online sector 

Finds  it  very  difficult  to  compare  product  deals  -  online 
sector 

Does not compare product deals - finance sector 

Finds  it  very  difficult  to  compare  product  deals  -  finance 
sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
energy sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
online sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
financial sector 

Has  problems  comparing  deals  due  to  market-related 
factors - energy sector 

Has  problems  comparing  deals  due  to  market-related 
factors - online sector 

Has  problems  comparing  deals  due  to  market-related 
factors - financial sector 

Has problems comparing deals due to access-related factors 
- energy sector 

Has problems comparing deals due to access-related factors 
- online sector 

Has problems comparing deals due to access-related factors 
- financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
personal factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
personal factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  personal 
factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
market-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
market-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  market-
related factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
access-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
access-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  access-
related factors - financial sector 

Has not switched due to termination costs 

10% 

506 

 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Has not switched due to bundling 

Excluded from e-commerce due to difficulty of the process 

Excluded  from  e-commerce  due  to  not  having  a  payment 
card 

15% 

10% 

6% 

Declined for a loan 

Dimension  5 :  Higher  susceptibility  to  marketing 
practices 

Vulnerable  to  a  great  extent  due  to  complexity  of  offers 
(own perception) 

4% 

Experiment choice under the marketing practice treatment - 
energy sector 

Experiment choice under the marketing practice treatment - 
online sector 

Experiment choice under the marketing practice treatment - 
finance sector 

Experiment choice under the marketing practice treatment - 
cross-cutting 

507 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Table 104:  Country fiche - Austria 

Country fiche summary 

Approach  to  consumer  vulnerability:  Evidence  suggests  that  national  consumer  policy 
takes a neutral approach and hence does not involve a specific approach to consumer 
vulnerability.  

Measures  addressing  consumer  vulnerability:  There  appears  to  be  only  a  limited 
number  of  consumer  policy  measures  addressing  consumer  vulnerability,  with 
consumer vulnerability addressed primarily by social policy measures.   

National consumer protection framework 

Austrian consumer protection policy is set out in a wide range of legislation: 

  Consumer Protection Act (KSchG) 
  Unfair Competition Act (UWG) 
  e-Commerce Law (E-commerce-G) 
  Distance Selling and Off-Premises Contracts Act (FAGG) 
  Civil Code (ABGB) 
  Banking Act (BWG) 
  Price Indication Act (PrAG) 
  Financial Services Distance Selling Act (FernFinG) 
  Consumer Credit Act (VKrG) 
  Payment Services Act (ZaDiG) 
  Securities Supervision Act (WAG 2007) 
 
  Product Safety Act (PSG 2004) 
  Telecommunications Act (TKG 2003) 
  Electricity Management and Organisation Act (ElWOG 2010) 
  Gas Management Act (GWG 2011) 
 
Industrial Code (GewO 1994) 
  Human Medicines Act (AMG) 

Insurance Contract Act (VersVG) 

Approach to consumer vulnerability 

Consumer vulnerability within the context of the transposed UCPD  

The concept of  consumer  vulnerability is present in the Unfair Competition Act due to 
the  transposition  of  the  UCPD  and  use  the  UCPD  terminology  of  “mental  or  physical 
infirmity, age or credulity”. Similarly, the average consumer concept is also present in 
the legislation as a result of the transposition of the UCPD. 

Consumer vulnerability in national consumer policy beyond the UCPD  

While beyond the UCPD Austrian consumer policy provisions generally do not explicitly 
target  specific  vulnerable  consumer  groups  (this  is  primarily  done  by  social  policy 
measures), they do in some cases indirectly address consumer vulnerability. There are 
however also selected provisions applying to specific consumer groups, such as specific 
protection extended to children and young adults through the use of the legal capacity 
provisions (“Geschäftsfähigkeit”).  

Stakeholder assessment of the national approach to consumer vulnerability 

Overall,  as  noted  by  the  interviewees,  the  consumer  protection  framework  in  Austria 
generally  tends  to  take  a  neutral  approach,  without  singling  out  specific  vulnerable 

508 

 
 
 
Annex 1│ Country fiches 

consumer groups.  

Specific legislative measures  

As noted above, the existing measures in Austria tend to be neutral and directed to a 
wide  range  of  consumer  groups.  There  are  some  specific  provisions  identified  by 
consulted  stakeholders,  which  address  specific  practices  exploiting  consumer 
vulnerabilities.  An  example  includes  the  outlawing  of  the  practice  of  “cold  calling”, 
although this broadly reflects the existing examples of aggressive commercial practices 
present  in the  UCPD.    Another  states  that  consumers  are  not  bound  by  a  contract  on 
prize notifications or gaming and lottery services, if the contract was concluded during 
a phone call that was initiated by the trader (covers not only active calls of traders but 
also ping calls). 

Especially  in  the  energy  sector,  there  are  several  measures  that  benefit  vulnerable 
consumer groups. The so-called “Grundversorgung” obliges every energy supplier and 
distribution system operator to conclude a contract with a consumer who refers to this 
system of basic supply. This mainly benefits vulnerable consumer groups such as e.g. 
low-income  households.  To  assist  consumers  in  energy  poverty,  there  are  social 
security  benefits  for  low-income  or  otherwise  vulnerable  people  (e.g.  unemployed, 
chronically  ill,  old  age)  as  well  as  earmarked  financial  benefits  to  cover  energy/gas 
costs (“Heizkostenzuschuss”). 

Another  example  of  provisions  that  benefits  a  vulnerable  consumer  group  (elderly 
people)  is  the  interdiction  of  specific  promotional  activities  and  trips  in  respect  to 
certain product groups (i.e. medicines, food supplements, therapeutic products). 

Non-legislative measures 

There  is  a  range  of  non-legislative  measures  related  to  consumer  vulnerability.  The 
Federal  Ministry  for  Labour,  Social  Affairs  and Consumer  Protection  has  the  education 
of young consumers as one of its priorities and provides specific educational materials 
to be used by teachers. The Ministry also operates a consumer web portal, which, while 
it  does  not  focus  on  specific  groups,  includes  consumer  information  and  advice  on 
issues related to consumer vulnerability, such as over-indebtedness.  

In  addition,  there  are  also  projects  that  intend  to  promote  the  dialogue  between 
entrepreneurs and various stakeholders in the field of consumer protection in order to 
find  solutions  for  vulnerable  consumer  groups  (e.g.  annual  conference  on  energy 
poverty). 

Institutional structure and role of civil society 

Public bodies responsible for consumer policy 

The  responsibility  for  consumer  protection  in  Austria  is  split  between  a  number  of 
ministries,  as  well  as  between  the  federal  and  Länder-level.  Federal  Ministry  for 
Labour,  Social  Affairs  and  Consumer  Protection  plays  a  key  role  in  setting  out 
consumer  policy  in  cooperation  with  the  Federal  Ministry  of  Science,  Research  and 
Economy,  the  Federal  Ministry  of  Family  and  Youth,  the  Federal  Ministry  of  Education 
and  Women’s  Affairs,  the  Federal  Ministry  for  Justice,  Federal  Ministry  for  Finance, 
Federal  Ministry  for  Health,  the  Federal  Ministry  for  Transport,  Innovation  and 
Technologies,  and  the  Ministry  for  Arts  and  Culture,  Constitution  and  Media. 
Enforcement is primarily the responsibility of Länder governments, along with national 
agencies  responsible  for  specific  policy  fields.  There  is  however  no  federal-level 
consumer agency)  

509 

 
 
 
 
 
Annex 1│ Country fiches 

Role of civil society 

The  Consumer  Information  Association  (VKI)  is  the  main  consumer  organisation  in 
Austria,  financed  in  a large  part  by  the  Federal  Ministry  for  Labour,  Social  Affairs  and 
Consumer Protection and entitled to initiate legal proceedings against suppliers. It also 
provides a range  of other services such as product testing. It however does not carry 
out activities focusing specifically on vulnerable consumers.  

The  Chamber  of  Labour,  although  representing  Austrian  workers  and  employees,  is 
another  organisation  active  within  the  consumer  protection  field  in  Austria,  with 
selected  activities  being  of  relevance  to  consumer  vulnerability.  These  include  for 
instance  information  campaigns  directed  at  immigrant  groups  or  younger  consumers 
(i.e. by using YouTube videos).   

Both  the  Consumer  Information  Association  (together  with  the  Federal  Ministry  of 
Labour,  Social  Affairs  and  Consumer  Protection)  as  well  as  the  Chamber  of  Labour 
regularly  work  to  obtain  pertinent  court  rulings  in  the  field  of  consumer  protection, 
which in some cases concerns specific vulnerable consumer groups (e.g. advertising in 
schools, debt-collecting-agencies).   

Incidence of consumer vulnerability  

Indicator of vulnerability 

Mean incidence  

Dimension  1 :  Heightened  risk  of  negative  outcomes 
or impacts on well-being 

Has experienced a problem and took no action 

Overpaid  for  services  due  to  being  unable  to  use  certain 
payment methods 

Dimension 2 : Having characteristics that limit ability 
to maximise well-beings 

Feels vulnerable to 'a great extent' due to health problems 

Feels  vulnerable  to  'a  great  extent'  due  to  financial 
circumstances 

Feels  vulnerable  to  'a  great  extent'  due  to  employment 
situation 

Feels vulnerable to 'a great extent' due to age 

Feels  vulnerable  to  'a  great  extent'  due  to  belonging  to  a 
minority group 

Feels vulnerable to 'a great extent' due to personal issues 

Feels vulnerable to 'a great extent' due to other reasons 

Feels  vulnerable  to  'a  great  extent'  due  to  any  personal 
characteristic 

Dimension  3 :  Having  difficulty 
assimilating information 

in  obtaining  or 

Feels  'not  at  all'  informed  about  product  price,  quality, 
conditions,  etc  when  choosing  and  buying  goods  and 
services 

Compares  product  deals  by  using 

information 

from 

8% 

10% 

3% 

6% 

2% 

3% 

3% 

2% 

2% 

10% 

2% 

3% 

510 

 
 
 
 
 
 
 
Annex 1│ Country fiches 

advertisements only - energy sector 

Compares  product  deals  by  using 
advertisements only - online sector  

information 

from 

Compares  product  deals  by  using 
advertisements only - finance sector 

information 

from 

Has  problems  comparing  deals  due  to  information-related 
factors - energy sector 

Has  problems  comparing  deals  due  to  information-related 
factors - online sector 

Has  problems  comparing  deals  due  to  information-related 
factors - finance sector 

20. Has an electricity supplier and has not switched due to 
information-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
information-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to 
information-related factors - financial sector 

Dimension  4 :  Inability  or  failure  to  buy,  choose  or 
access suitable products 

Does not compare product deals - energy sector 

Finds  it  very  difficult  to  compare  product  deals  -  energy 
sector 

Does not compare product deals - online sector 

Finds  it  very  difficult  to  compare  product  deals  -  online 
sector 

Does not compare product deals - finance sector 

Finds  it  very  difficult  to  compare  product  deals  -  finance 
sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
energy sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
online sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
financial sector 

Has  problems  comparing  deals  due  to  market-related 
factors - energy sector 

Has  problems  comparing  deals  due  to  market-related 
factors - online sector 

Has  problems  comparing  deals  due  to  market-related 
factors - financial sector 

Has problems comparing deals due to access-related factors 
- energy sector 

511 

6% 

2% 

14% 

8% 

12% 

43% 

9% 

29% 

5% 

46% 

8% 

28% 

17% 

20% 

32% 

27% 

41% 

12% 

 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Has problems comparing deals due to access-related factors 
- online sector 

Has problems comparing deals due to access-related factors 
- financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
personal factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
personal factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  personal 
factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
market-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
market-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  market-
related factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
access-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
access-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  access-
related factors - financial sector 

Has not switched due to termination costs 

Has not switched due to bundling 

Excluded from e-commerce due to difficulty of the process 

Excluded  from  e-commerce  due  to  not  having  a  payment 
card 

Declined for a loan 

Dimension  5 :  Higher  susceptibility  to  marketing 
practices 

3% 

1% 

16% 

19% 

17% 

27% 

Vulnerable  to  a  great  extent  due  to  complexity  of  offers 
(own perception) 

4% 

Experiment choice under the marketing practice treatment - 
energy sector 

Experiment choice under the marketing practice treatment - 
online sector 

Experiment choice under the marketing practice treatment - 
finance sector 

Experiment choice under the marketing practice treatment - 
cross-cutting 

512 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Table 105:  Country fiche - Poland 

Country fiche summary 

Approach to consumer vulnerability: The concept of consumer vulnerability appears to 
have  been  in  place  within  national  consumer  policy  independently  of  the  UCPD 
transposition.  

Measures  addressing  consumer  vulnerability:  There  is  evidence  of  a  range  of  sector-
specific measures addressing consumer vulnerability. 

National consumer protection framework 

The latest consumer policy strategy published in 2015 by the Office of Competition and 
Consumer  Protection  takes  an  integrated  approach  to  ensuring  competition  and 
protecting  consumers’  interests.  The  main  priorities  in  terms  of  consumer  protection 
include:  

  The monitoring of protection of consumer rights; 

  Quick response and elimination of problematic outcomes; 

Maximising the effect of education measures; and 
Improving competences and knowledge of institutions347. 

 

The  central  element  of  the  Polish  consumer  protection  framework  is  the  Act  of  16 
February 2007 on competition and consumer protection (Journal of Laws of 2007, No. 
50, item 331), along with the Act of 23 August 2007 on combating unfair commercial 
practices (Journal of Laws No. 171 of 2007, item 1206), which transposes the UCPD.  

Approach to consumer vulnerability 

Consumer vulnerability within the context of the transposed UCPD  

The UCPD concept of a vulnerable consumer group is transposed into Polish legislation 
as  a  group  that  is  “particularly  receptive  to  the  influence  of  a  commercial  practice  or 
the product to which the commercial practice applies”, with specific examples provided 
being  age,  physical  or  mental  disability  (the  term  “credulity”  is  not  used).  The  term 
“average  consumer”  has  been  transposed  together  with  the  UCPD  and  is  used 
extensively throughout the Act on combatting unfair commercial practices and defined 
as “a consumer who is adequately informed, attentive and careful”.  

Consumer vulnerability in national consumer policy beyond the UCPD  

Beyond  the  transposition  of  the  UCPD,  the  vulnerability  concept  is  also  used  in  the 
latest consumer policy strategy document published in 2015. Older consumers as well 
as children and youth have been identified as groups particularly vulnerable to certain 
marketing practices. The strategy acknowledges that both groups need to be targeted 
by  appropriate  communication  and  education  actions.  It  also  identifies  online 
marketing as a particularly problematic area for children and youth348. According to the 
CEER report on consumer vulnerability349, the concept of consumer vulnerability is also 
present  in  the  energy  sector,  where  vulnerable  consumer  groups  are  entitled  to 

347 See https://uokik.gov.pl/download.php?plik=16694 
348 See https://uokik.gov.pl/download.php?plik=16694 
349 CEER (2012). ‘Status Review of Customer and Retail Market Provisions from the 3rd Package 

as of 1 January 2012’ 

513 

 
 
 
 
 
                                                 
Annex 1│ Country fiches 

financial support.  

It is worth noting that the concept of average consumer is also used in the context of 
payday  loan  advertising:  The  Office  of  Competition  and  Consumer  Protection  makes 
use  the  notion  of  “average  consumer”  to  identify  problematic  practices  in  the  field, 
singling out advertising that is likely to mislead such a consumer350. 
Stakeholder assessment of the national approach to consumer vulnerability 

Despite  the  vulnerable  consumer  concept  being  in  use  and  specific  consumer  groups 
being identified as target groups for consumer policy, the consulted stakeholders noted 
that  there  Is  no  clear  definition  of  both  consumer  vulnerability  and  the  notion  of 
average consumer, which could be interpreted in a number of ways depending on the 
situation.  Consulted  stakeholders  also  noted  that  the  terms  are  generally  not  used  in 
practice by consumer protection bodies.   

Specific measures  

Although  no  legislative  instruments  specifically  targeting  particular  consumer  groups 
have been identified, there are selected measures that take potential vulnerability into 
account.  These  include  provisions  in  the  telecommunications  sector,  which  require 
contracts  to  be  presented  in  a  clear  and  easily  accessible  manner.  In  the  energy 
sector,  vulnerable  consumers  are  eligible  for  financial  support,  while  in  the  financial 
sector  new  legislation  is  in  progress,  which  will  focus,  among  others,  on  “reverse 
mortgages”. 

The  Office  of  Competition  and  Consumer  Protection  has  also  taken  action  in  specific 
cases linked to consumer vulnerability. This included in particular taking action against 
suppliers  using  small  and  illegible  text  in  advertising  aimed  at  older  consumers 351, 
actions  addressing  complex  contracts  in  the  construction  industry352,  and  actions 
against misleading information provided by the payday loan industry353.  

Non-legislative measures 

Non-legislative measures include information campaigns aimed at older consumers, as 
well  as  children  and  youth  organised  by  the  Office  of  Competition  and  Consumer 
Protection.  The  ”My  Consumer  ABC”  campaign,  for  instance,  specifically  targeted 
younger consumers. The “Before you sign” campaign targets consumers over 50 in the 
financial sector.   

Institutional structure and role of civil society 

Public bodies responsible for consumer policy 

The  Office  of  Competition  and  Consumer  Protection  (UOKIK)  is  the  key  institution 
within  the  Polish  consumer  policy  framework.  It  implements  and  enforces  consumer 
policy  in  Poland,  can  initiate  administrative  proceedings  against  suppliers,  as  well  as 
monitor  contract  terms.  It  also  provides  opinions  on  Polish  legislation  to  ensure  that 
consumer  protection  principles  are  sufficiently  addressed  and  can  also  initiate 
legislative  measures  in  the  area.  In  addition  to  its  implementation  and  enforcement 
role,  the  Office  also  provides  consumer  advice,  including  by  email  and  on  the  phone, 
and  runs  awareness-raising  campaigns.  On  local  and  regional  level,  the  consumer 

350 See http://www.uokik.gov.pl/download.php?plik=13278 
351 See http://www.uokik.gov.pl/download.php?plik=12543 
352 See http://www.uokik.gov.pl/download.php?plik=14600 
353 See http://www.uokik.gov.pl/download.php?plik=13504 

514 

 
 
 
                                                 
Annex 1│ Country fiches 

ombudsmen provide advice to individual consumers in cooperation with UOKIK.  

Role of civil society 

Polish  Consumer  Federation  provides  advice  and  legal  aid  to  consumers  through  its 
branch offices. It also carries out projects and campaigns related to vulnerability, such 
as  a  project  focusing  on  older  consumers’  rights,  which  included  adapting  advisory 
services  to  needs  of  older  consumers,  as  well  as  consumer  training  and  information 
materials  targeted  at  older  consumers.  Association  of  Polish  Consumers  is  another 
consumer organisation also with a focus on consumer advice and education.   

Incidence of consumer vulnerability  

Indicator of vulnerability 

Mean incidence  

Dimension  1 :  Heightened  risk  of  negative  outcomes 
or impacts on well-being 

Has experienced a problem and took no action 

Overpaid  for  services  due  to  being  unable  to  use  certain 
payment methods 

Dimension 2 : Having characteristics that limit ability 
to maximise well-beings 

Feels vulnerable to 'a great extent' due to health problems 

Feels  vulnerable  to  'a  great  extent'  due  to  financial 
circumstances 

Feels  vulnerable  to  'a  great  extent'  due  to  employment 
situation 

Feels vulnerable to 'a great extent' due to age 

Feels  vulnerable  to  'a  great  extent'  due  to  belonging  to  a 
minority group 

Feels vulnerable to 'a great extent' due to personal issues 

Feels vulnerable to 'a great extent' due to other reasons 

Feels  vulnerable  to  'a  great  extent'  due  to  any  personal 
characteristic 

Dimension  3 :  Having  difficulty 
assimilating information 

in  obtaining  or 

Feels  'not  at  all'  informed  about  product  price,  quality, 
conditions,  etc  when  choosing  and  buying  goods  and 
services 

Compares  product  deals  by  using 
advertisements only - energy sector 

information 

from 

Compares  product  deals  by  using 
advertisements only - online sector  

information 

from 

Compares  product  deals  by  using 
advertisements only - finance sector 

information 

from 

Has  problems  comparing  deals  due  to  information-related 

515 

15% 

18% 

6% 

12% 

10% 

5% 

3% 

4% 

3% 

22% 

4% 

7% 

 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

factors - energy sector 

Has  problems  comparing  deals  due  to  information-related 
factors - online sector 

9% 

Has  problems  comparing  deals  due  to  information-related 
factors - finance sector 

20. Has an electricity supplier and has not switched due to 
information-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
information-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to 
information-related factors - financial sector 

Dimension  4 :  Inability  or  failure  to  buy,  choose  or 
access suitable products 

Does not compare product deals - energy sector 

Finds  it  very  difficult  to  compare  product  deals  -  energy 
sector 

Does not compare product deals - online sector 

Finds  it  very  difficult  to  compare  product  deals  -  online 
sector 

Does not compare product deals - finance sector 

Finds  it  very  difficult  to  compare  product  deals  -  finance 
sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
energy sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
online sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
financial sector 

Has  problems  comparing  deals  due  to  market-related 
factors - energy sector 

61% 

12% 

20% 

2% 

49% 

8% 

16% 

Has  problems  comparing  deals  due  to  market-related 
factors - online sector 

16% 

Has  problems  comparing  deals  due  to  market-related 
factors - financial sector 

Has problems comparing deals due to access-related factors 
- energy sector 

Has problems comparing deals due to access-related factors 
- online sector 

4% 

Has problems comparing deals due to access-related factors 
- financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
personal factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
personal factors - online sector 

516 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Has  a  bank  account  and  has  not  switched  due  to  personal 
factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
market-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
market-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  market-
related factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
access-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
access-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  access-
related factors - financial sector 

Has not switched due to termination costs 

Has not switched due to bundling 

Excluded from e-commerce due to difficulty of the process 

Excluded  from  e-commerce  due  to  not  having  a  payment 
card 

Declined for a loan 

Dimension  5 :  Higher  susceptibility  to  marketing 
practices 

21% 

29% 

21% 

22% 

Vulnerable  to  a  great  extent  due  to  complexity  of  offers 
(own perception) 

12% 

Experiment choice under the marketing practice treatment - 
energy sector 

Experiment choice under the marketing practice treatment - 
online sector 

Experiment choice under the marketing practice treatment - 
finance sector 

Experiment choice under the marketing practice treatment - 
cross-cutting 

517 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Table 106:  Country fiche - Portugal 

Country fiche summary 

Approach  to  consumer  vulnerability:  While  concepts  similar  to  consumer  vulnerability 
are in use, there appears to be no strategic approach to consumer vulnerability.   

Measures  addressing  consumer  vulnerability:  There  is  evidence  of  a  range  of  sector-
specific measures addressing consumer vulnerability. 

National consumer protection framework 

In  Portugal,  consumer  protection  is  laid  down  in  the  Constitution  and  the  Law  on  the 
Protection  of  the  Consumer  (Lei  de  Defesa  do  Consumidor,  Lei  24/96  de  31  de 
Julho).  Article 60 of the Portuguese Constitution lists the rights of consumers in terms 
of protecting their quality of life with regard to the good quality of goods and services, 
its competitive and balanced price, and the protection of health and safety. 

Approach to consumer vulnerability 

Consumer vulnerability within the context of the transposed UCPD  

The  term  “vulnerable”  is  present  in  the  Decree-Law  No.  57/2008  through  the 
transposition  of  the  UCPD.  The  concept  of  vulnerability  has  been  transposed  in  line 
with  the  Directive’s  wording,  using  the  terms  mental  or  physical  infirmity,  age,  and 
credulity.  Similarly  the  “average  consumer”  term  has  also  been  transposed  into 
Portuguese  legislation  through  the  UCPD  and  is  now  applied  within  the  consumer 
protection framework.   

Consumer vulnerability in national consumer policy beyond the UCPD  

The  term  “vulnerable  consumers”  is  generally not  widely  used  in  Portugal  beyond  the 
transposition  of  the  UCPD.  However,  the  terms  “economically  vulnerable  consumers” 
(“consumidores  economicamente  vulneráveis”)  or  “consumers  with  special  needs” 
(“consumidores  com  necessidades  especiais”)  are  used  in  some  contexts.  There  is 
evidence of consumer vulnerability being defined in the energy sector354.  
Stakeholder assessment of the national approach to consumer vulnerability 

The  stakeholders  consulted  were  familiar  with  the  concept  of  consumer  vulnerability 
particularly in relation to the definitions described above. They stressed the importance 
of  considering  consumers  in  financial  difficulties  as  vulnerable  consumers.  This  has 
become increasingly important in light of the financial crisis when many consumers are 
experiencing exclusion due to low-income and debt.    

Specific measures  

There are a number of specific legislative measures relevant to consumer vulnerability 
in place in Portugal. Measures in the energy sector include the provision of social tariffs 
and Extraordinary Social Support for Energy Consumers. These tariffs are available to 
those  receiving  one  of  the  following  benefits:  income  support  for  the  elderly,  social 
insertion  income,  unemployment  benefits,  1st  tier  of  family  allowance  or  disability 
living  allowance.  There  are  also  conditions  attached  to  this  tariff  with  regard  to 

354 ACER (2013). Annual Report on the Results of Monitoring the Internal Electricity and Gas 

Markets in 2012. Available at: 
http://www.acer.europa.eu/Electricity/Market%20monitoring/Pages/Reports.aspx 

518 

 
 
 
                                                 
Annex 1│ Country fiches 

maximum usage (4.6Kw in the case of electricity). 

In  the  financial  sector,  a  new  legislative  framework  establishes  an  extraordinary 
regime  for  the  protection  of  housing  loan  borrowers  in  very  difficult  economic 
circumstances  (the  arrears  settlement  extraordinary  regime).  This  includes  a 
requirement  for  credit  institutions  to  implement  procedures  allowing  for  regular 
monitoring  of  credit  agreements,  so  as  to  prevent  customers  from  entering  into 
arrears.  This  obliges  credit  institutions  to  develop  a  Pre-Arrears  Action  Plan  (PRAP) 
(Decree-Law  no.  227/2012,  of  25  October)355.  In  addition,  according  to  stakeholder 
interviews,  consumers  with  disabilities  have  the  right  to  access  a  more  economically 
advantageous form of credit per national regulations.  

In  the  telecommunications  sector,  according  to  the  interviews,  steps  were  taken  to 
limit  the  use  of  premium  mobile  text  messages,  but  making  it  more  difficult  for 
subscriptions to take place without consumers’ explicit consent.  

Non-legislative measures 

There  have  been  few  non-legislative  initiatives  identified  related  to  mitigating 
consumer  vulnerability.  In  the  financial  sector,  this  includes  comparison  tools  and  a 
helpline by DECO (see below) for over-indebted consumers 

Institutional structure and role of civil society 

Public bodies responsible for consumer policy 

The Directorate General of Consumers is the institution charged with the development, 
formulation  and implementation  of  consumer  policy  in  order  to  ensure  a  high level  of 
protection.  In  addition,  the  Directorate  General  has  been  entrusted  with  the 
responsibility  of  guaranteeing  consumers’  access  to  alternative  means  of  dispute 
resolution  (ADR)  as  well  as  informing  consumers  of  their  rights  under  the  applicable 
laws and regulations and of directing consumer complaints to sector regulators in the 
areas  submitted  to  economic  regulation  of  sectorial  nature.  In  addition,  the  different 
regulators  are  responsible  for  protecting  consumer  rights  and  interests  in  regards  to 
prices,  services  and  service  quality  as  well  as  monitoring  compliance  with  relevant 
regulations.   

Role of civil society 

There  are  several  national  organisations  representing  consumer  interests.  These 
include:  

  Associação de Consumidores de Portugal (ACOP) 

  Associação Portuguesa para a Defesa do Consumidor (DECO) 

  União Geral de Consumidores (UGC) 

  Federação Nacional das Cooperativas de Consumidores (FENACOOP) 

These  organisations  are  working  on  various  issues  touching  upon  consumer 
vulnerability.  As  seen  above,  DECO  runs  a  unit  and  helpline  for  indebted  consumers. 
Similarly,  UGC  are  also  providing  help  and  legal  advice  for  indebted  consumers  and 
those consumers who experience problems with contracts and public services.  

355 More information available at: 

http://clientebancario.bportugal.pt/SiteCollectionDocuments/Decree-
Law%20No%20227_2012%20of%2025%20October.pdf  

519 

 
 
 
 
 
                                                 
Annex 1│ Country fiches 

Incidence of consumer vulnerability  

Indicator of vulnerability 

Mean incidence  

Dimension  1 :  Heightened  risk  of  negative  outcomes 
or impacts on well-being 

Has experienced a problem and took no action 

Overpaid  for  services  due  to  being  unable  to  use  certain 
payment methods 

Dimension 2 : Having characteristics that limit ability 
to maximise well-beings 

Feels vulnerable to 'a great extent' due to health problems 

Feels  vulnerable  to  'a  great  extent'  due  to  financial 
circumstances 

Feels  vulnerable  to  'a  great  extent'  due  to  employment 
situation 

Feels vulnerable to 'a great extent' due to age 

Feels  vulnerable  to  'a  great  extent'  due  to  belonging  to  a 
minority group 

Feels vulnerable to 'a great extent' due to personal issues 

Feels vulnerable to 'a great extent' due to other reasons 

Feels  vulnerable  to  'a  great  extent'  due  to  any  personal 
characteristic 

Dimension  3 :  Having  difficulty 
assimilating information 

in  obtaining  or 

Feels  'not  at  all'  informed  about  product  price,  quality, 
conditions,  etc  when  choosing  and  buying  goods  and 
services 

Compares  product  deals  by  using 
advertisements only - energy sector 

information 

from 

Compares  product  deals  by  using 
advertisements only - online sector  

information 

from 

Compares  product  deals  by  using 
advertisements only - finance sector 

information 

from 

Has  problems  comparing  deals  due  to  information-related 
factors - energy sector 

Has  problems  comparing  deals  due  to  information-related 
factors - online sector 

Has  problems  comparing  deals  due  to  information-related 
factors - finance sector 

20. Has an electricity supplier and has not switched due to 
information-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
information-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to 

9% 

14% 

8% 

24% 

17% 

7% 

6% 

8% 

6% 

34% 

6% 

6% 

4% 

2% 

16% 

4% 

12% 

3% 

0% 

2% 

520 

 
 
 
 
 
 
Annex 1│ Country fiches 

information-related factors - financial sector 

Dimension  4 :  Inability  or  failure  to  buy,  choose  or 
access suitable products 

Does not compare product deals - energy sector 

Finds  it  very  difficult  to  compare  product  deals  -  energy 
sector 

Does not compare product deals - online sector 

Finds  it  very  difficult  to  compare  product  deals  -  online 
sector 

Does not compare product deals - finance sector 

Finds  it  very  difficult  to  compare  product  deals  -  finance 
sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
energy sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
online sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
financial sector 

Has  problems  comparing  deals  due  to  market-related 
factors - energy sector 

Has  problems  comparing  deals  due  to  market-related 
factors - online sector 

Has  problems  comparing  deals  due  to  market-related 
factors - financial sector 

Has problems comparing deals due to access-related factors 
- energy sector 

Has problems comparing deals due to access-related factors 
- online sector 

Has problems comparing deals due to access-related factors 
- financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
personal factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
personal factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  personal 
factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
market-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
market-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  market-
related factors - financial sector 

521 

46% 

15% 

13% 

1% 

63% 

20% 

28% 

6% 

39% 

25% 

13% 

35% 

10% 

2% 

3% 

11% 

1% 

12% 

12% 

6% 

12% 

 
 
 
 
 
 
Annex 1│ Country fiches 

Has  an  electricity  supplier  and  has  not  switched  due  to 
access-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
access-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  access-
related factors - financial sector 

Has not switched due to termination costs 

Has not switched due to bundling 

Excluded from e-commerce due to difficulty of the process 

Excluded  from  e-commerce  due  to  not  having  a  payment 
card 

Declined for a loan 

Dimension  5 :  Higher  susceptibility  to  marketing 
practices 

Vulnerable  to  a  great  extent  due  to  complexity  of  offers 
(own perception) 

Experiment choice under the marketing practice treatment - 
energy sector 

Experiment choice under the marketing practice treatment - 
online sector 

Experiment choice under the marketing practice treatment - 
finance sector 

Experiment choice under the marketing practice treatment - 
cross-cutting 

7% 

2% 

3% 

18% 

27% 

23% 

27% 

5% 

14% 

90% 

58% 

35% 

74% 

522 

 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Table 107:  Country fiche – Romania 

Country fiche summary 

Approach  to  consumer  vulnerability:  Elements  of  consumer  vulnerability  are  singled 
out in national consumer legislation also independent of the transposition of the UCPD. 

Measures  addressing  consumer  vulnerability:  Only  a  limited  number  of  measures 
addressing consumer vulnerability have been identified.  

National consumer protection framework 

Key pieces of legislation regarding consumer protection include the GO 21/1992, which 
specifies the fundamental rights of the consumers (amended in 2008), Law 193/2000 
regarding  abusive  practices,  Law  363/2007  regarding  unfair  commercial  practices 
which  transposes  the  UCPD,  and  the  Law  of  energy  and  gas  123/2012  which  defines 
vulnerable  consumers.  Several  other  rules  and  regulations  were  implemented 
concerning: consumer contracts negotiated away from business premises, protection of 
consumers  in  the  execution  and  performance  of  distance  contracts  (contracts 
concluded  between  parties  in  different  locations  by  the  use  of  alternative  means  of 
communication such as the Internet) and unfair terms in consumer contracts. 

Approach to consumer vulnerability 

Consumer vulnerability within the context of the transposed UCPD  

In  line  with  the  UCPD  transposition,  vulnerability  is  determined  by  certain  physical  or 
mental  state  capacities  such  as  age,  physical  or  mental  disabilities  or  credulity  (Law 
363/2007).  Similarly,  an  average  consumer  is  defined  in  the  same  law  as  “a  person 
who  is  reasonably  well-informed,  attentive  and  prudent  according  to  prevalent  social, 
cultural and linguistic factors”.  

Consumer vulnerability in national consumer policy beyond the UCPD  

In  addition  to  the  concept  of  vulnerability  present  in  the  UCPD,  certain  occupational 
states such as unemployment, illiteracy, low income, health issues are also singled out 
in legislation (Law 123/2012, Law 192/2011 regarding social assistance).  

In  addition,  an  energy  vulnerable  consumer  is  defined  in  the  Law  of  energy  and  gas 
123/2012  as  “a  person  who  because  of  age,  health,  or  low  income  is  at  risk  of  being 
socially  excluded  and  who  because  of  this  risk  is  the  beneficiary  of  financial  social 
protection  measures  and  other  facilities.  The  specific  measures  of  social  protection  as 
well  as  the  eligibility  criteria  are  established  by  special  laws”  (definition  of  vulnerable 
consumers introduced in the Law of energy and gas 123/2012).   

Stakeholder assessment of the national approach to consumer vulnerability 

Despite  the  concept  of  vulnerability  being  present  in  legislation  beyond  the  UCPD 
transposition,  according  to  the  consulted  stakeholders  the  concept  of  consumer 
vulnerability  is  not  used  in  practice.  Similarly,  the  notion  of  an  average  consumer  is 
also difficult to interpret in practice. 

Specific legislative measures 

Most measures addressing vulnerable consumers are carried out in the context of the 
welfare  system  rather  than  through  the  consumer  protection  framework  and  are 
administered  by  the  Ministry  of  Labour  and  Social  protection.  An  example  of  such 
measures  includes,  in  the  energy  sector,  social  assistance  to  ensure  energy  heating 
access  during  the  winter  for  low-income  consumers.  The  interviewed  stakeholders 

523 

 
 
 
 
 
Annex 1│ Country fiches 

however highlighted that in practice this support is often difficult to obtain.  

Non-legislative measures 

While  the  National  Authority  for  Consumer  Protection  (ANPC)  administers  specific 
consumer  campaigns,  these  generally  do  not  focus  specifically  on  consumer 
vulnerability.  

Institutional structure and role of civil society 

Public bodies responsible for consumer policy 

Consumer protection in Romania is coordinated by the National Authority for Consumer 
Protection  (ANPC).  The  ANPC  coordinates  and  realises  the  strategy  and  policy  of  the 
Romanian  Government  with  regard  to  the  enforcement  of  Consumer  Protection  in  the 
country, preventing and fighting practices that prejudice consumers' life, health, safety 
or economic interests. Principal objectives of ANPC are to: 

  Ensure a high level of consumer protection through elaboration and promotion 

of legal acts and regulations and legislative initiatives;    

  Ensure  effective  enforcement  of  consumer  protection  rules,  regulations  and 

legislation; 

  Monitor and control of the quality of products and services; 

  Monitor,  protect  against  and  sanction  of  unfair,  fraudulent  or  aggressive 

practices; and 

  Provide consumers’ information, consulting and education.    

Supervision  and  enforcement  is  carried  out  by  12  regional  offices  of  ANPC.  ANPC 
cooperates, at central and local level, with other central and local public administration 
bodies.  These  include  the  National  Customs  Authority,  National  Authority  for 
Administration  and  Regulation  in  Communications  Field  (ANCOM),  the  Ministry  of 
Health,  the  Labour  Inspectorate,  the  Ministry  of  Tourism,  the  Ministry  of  Transports 
and Infrastructure, and the Ministry of Economy. 

Role of civil society 

In  addition  to  the  ANPC  there  are  a  number  of  other  bodies  operating  in  the  field  in 
Romania.  These  include  in  particular  the  national  consumer  organization  (APC 
Romania).  APC  implements  programmes  to  inform  and  educate  the  consumers, 
monitor  commercial  practices,  and  support  consumers  lodging  complaints.  It  however 
does  not  carry  out  activities  with  a  specific  vulnerability  focus.  Other  specific 
organisations  include  Romanian  Financial  Services  Users  and  Organisation  'Assistance 
for  consumers'.  In  addition  to  consumer  organisations,  there  are  also  other  bodies 
operating in the field, such as disability organisations.  

Incidence of consumer vulnerability  

Indicator of vulnerability 

Mean incidence  

Dimension  1 :  Heightened  risk  of  negative  outcomes 
or impacts on well-being 

Has experienced a problem and took no action 

Overpaid  for  services  due  to  being  unable  to  use  certain 
payment methods 

19% 

23% 

524 

 
 
 
 
 
Annex 1│ Country fiches 

Dimension 2 : Having characteristics that limit ability 
to maximise well-beings 

Feels vulnerable to 'a great extent' due to health problems 

Feels  vulnerable  to  'a  great  extent'  due  to  financial 
circumstances 

Feels  vulnerable  to  'a  great  extent'  due  to  employment 
situation 

Feels vulnerable to 'a great extent' due to age 

Feels  vulnerable  to  'a  great  extent'  due  to  belonging  to  a 
minority group 

Feels vulnerable to 'a great extent' due to personal issues 

Feels vulnerable to 'a great extent' due to other reasons 

Feels  vulnerable  to  'a  great  extent'  due  to  any  personal 
characteristic 

Dimension  3 :  Having  difficulty 
assimilating information 

in  obtaining  or 

Feels  'not  at  all'  informed  about  product  price,  quality, 
conditions,  etc  when  choosing  and  buying  goods  and 
services 

Compares  product  deals  by  using 
advertisements only - energy sector 

information 

from 

Compares  product  deals  by  using 
advertisements only - online sector  

information 

from 

Compares  product  deals  by  using 
advertisements only - finance sector 

information 

from 

Has  problems  comparing  deals  due  to  information-related 
factors - energy sector 

Has  problems  comparing  deals  due  to  information-related 
factors - online sector 

Has  problems  comparing  deals  due  to  information-related 
factors - finance sector 

20. Has an electricity supplier and has not switched due to 
information-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
information-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to 
information-related factors - financial sector 

Dimension  4 :  Inability  or  failure  to  buy,  choose  or 
access suitable products 

Does not compare product deals - energy sector 

Finds  it  very  difficult  to  compare  product  deals  -  energy 
sector 

525 

6% 

21% 

14% 

11% 

4% 

6% 

6% 

36% 

3% 

1% 

8% 

3% 

25% 

4% 

14% 

3% 

1% 

2% 

78% 

21% 

 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Does not compare product deals - online sector 

Finds  it  very  difficult  to  compare  product  deals  -  online 
sector 

Does not compare product deals - finance sector 

Finds  it  very  difficult  to  compare  product  deals  -  finance 
sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
energy sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
online sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
financial sector 

Has  problems  comparing  deals  due  to  market-related 
factors - energy sector 

Has  problems  comparing  deals  due  to  market-related 
factors - online sector 

Has  problems  comparing  deals  due  to  market-related 
factors - financial sector 

Has problems comparing deals due to access-related factors 
- energy sector 

Has problems comparing deals due to access-related factors 
- online sector 

Has problems comparing deals due to access-related factors 
- financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
personal factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
personal factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  personal 
factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
market-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
market-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  market-
related factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
access-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
access-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  access-
related factors - financial sector 

Has not switched due to termination costs 

30% 

2% 

66% 

18% 

37% 

12% 

44% 

26% 

10% 

29% 

44% 

5% 

3% 

22% 

4% 

14% 

10% 

3% 

10% 

36% 

7% 

5% 

27% 

526 

 
 
 
Annex 1│ Country fiches 

Has not switched due to bundling 

Excluded from e-commerce due to difficulty of the process 

Excluded  from  e-commerce  due  to  not  having  a  payment 
card 

Declined for a loan 

Dimension  5 :  Higher  susceptibility  to  marketing 
practices 

Vulnerable  to  a  great  extent  due  to  complexity  of  offers 
(own perception) 

Experiment choice under the marketing practice treatment - 
energy sector 

Experiment choice under the marketing practice treatment - 
online sector 

Experiment choice under the marketing practice treatment - 
finance sector 

Experiment choice under the marketing practice treatment - 
cross-cutting 

30% 

28% 

35% 

8% 

15% 

90% 

64% 

27% 

78% 

527 

 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Table 108: Country fiche - Slovenia 

Country fiche summary 

Approach  to  consumer  vulnerability:  There  appears  to  be  no  broader  strategic 
approach to consumer vulnerability in place. 

Measures  addressing  consumer  vulnerability:  There  is  evidence  of  a  range  of  sector-
specific measures addressing consumer vulnerability. 

National consumer protection framework 

Two  main  pieces  of  legislation  forming  part  of  the  Slovenian  consumer  protection 
framework  are  the  Consumer  Protection  Act  and  the  Consumer  Credit  Act356.  Other 
acts  involve  the  Consumer  Protection  against  Unfair  Commercial  Practices  Act,  which 
transposes  the  UCPD  (see  below),  as  well  as  the  Electronic  Communications  Act. 
Information activities for consumer NGOs are funded through the National Program for 
Consumer Protection. 

Approach to consumer vulnerability 

Consumer vulnerability within the context of the transposed UCPD  

The terms “vulnerability” as well as “average consumer” have been transposed directly 
into  Slovenian legislation  with  the  Law  on  the  Protection  of  Consumers  against  Unfair 
Commercial Practices (ZVPNPP).  

Consumer vulnerability in national consumer policy beyond the UCPD  

There  is  little  evidence  of  the  concept  of  consumer  vulnerability  being  in  use  in 
Slovenia beyond the transposition of the UCPD, although the CEER review of consumer 
vulnerability357  in  the  energy  sector  notes  that  the  concept  is  used  within  the  energy 
context.  

Stakeholder assessment of the national approach to consumer vulnerability 

Despite  the  use  in  the  energy  sector,  according  to  the  interviews  there  is  overall 
limited  use  of  the  terms  within  the  field  of  consumer  protection  in  Slovenia,  with  the 
concept  not  being  widely  known.  Similarly,  the  interviewees  argued  that  in  practice 
there is also limited use of the average consumer concept.  

Specific legislative measures 

There  are  selected  legislative  measures  in  place  in  Slovenia  that  take  consumer 
vulnerability  into  account.  These  include  limiting  the  ability  of  businesses  to  recollect 
incomes  for  unpaid  bills  under  a  specific  threshold.  In  the  energy  sector,  a  universal 
right  to  a  basic  service  is  also  in  place,  although,  according  to  the  interviews,  the 
general  experience  is  that  the  procedures  involved  are  quite  complex  and  there  are 
only a few cases (approx. 2-3 a year) where these provisions are used. In addition, in 
the  telecommunications  sector  provisions  exist  allowing  for  waiving  the  radio  and 
television  bills  for  specific  consumer  groups,  namely  socially  vulnerable  persons  and 
people with disabilities358.  

356 See http://www.ti.gov.si/en/areas_of_work/ 
357 CEER (2012). ‘Status Review of Customer and Retail Market Provisions from the 3rd Package 

as of 1 January 2012’ 

358 See http://www.disability-europe.net/content/aned/media/ANED%202012%20-
%20Task%203%20-%20Sl%20-%20Consumer%20Protection%20FINAL.doc 

528 

 
 
 
                                                 
Annex 1│ Country fiches 

Non-legislative measures 

With  regard  to  non-legislative  measures,  there  appears  to  be  few  measures  in  place 
focusing  on  vulnerability.  The  Market  Inspectorate’s  primary  focus  being  the  carrying 
out  and  reporting  on  market  surveillance,  with  some  of  the  specific  actions  being 
closely  linked  to  vulnerability,  such  as  the  case  of  “exhibition  sales”  to  older 
consumers359.  These  actions  however  serve  primarily  to  enforce  existing  legislation 
rather than raise awareness among consumers.  

Institutional structure and role of civil society 

Public bodies responsible for consumer policy 

Consumer  protection  in  Slovenia  falls  within  the  remit  of  the  Ministry  of  Economic 
Development  and  Technology,  and  the  Market  Inspectorate.  The  inspectorate  has  a 
responsibility to oversee the enforcement of Slovenian consumer protection legislation, 
carry  out  market  surveillance,  and  be  the  central  place  for  lodging  of  consumer 
complaints. The Inspectorate also has the competence to issue administrative decisions 
requesting changes in business practices, as well as impose criminal fines360. 
Role of civil society 

Slovene  Consumers’  Association  (SCA)  is  the  key  consumer  organisation  in  Slovenia. 
Other  organisations  include  the  International  Consumer  Research  Institute,  which 
publishes a consumer magazine VIP. A number of other consumer organisations with a 
more  regional  focus  are  also  present.    While  the  Slovene  Consumers’  Association 
provides  a  range  of  consumer  advice  and  information  services,  it  does  not  have  a 
specific focus on more vulnerable consumer groups. Nevertheless, specific activities are 
of  relevance  to  consumer  vulnerability,  such  as  information  about  “presentation”  and 
“exhibition” sales361. 

Incidence of consumer vulnerability  

Indicator of vulnerability 

Mean incidence  

Dimension  1 :  Heightened  risk  of  negative  outcomes 
or impacts on well-being 

Has experienced a problem and took no action 

Overpaid  for  services  due  to  being  unable  to  use  certain 
payment methods 

Dimension 2 : Having characteristics that limit ability 
to maximise well-beings 

9% 

16% 

Feels vulnerable to 'a great extent' due to health problems 

6% 

359 See 

http://www.ti.gov.si/si/storitve/s_cim_se_ukvarjamo/aktualna_porocila/2014_01_08_sejem
ska_prodaja_potrosnikom/ 

360 See http://www.ti.gov.si/en/areas_of_work/ 
361 See for instance http://www.zps.si/pravice/splet-katalogi-prodaja-na-domu/brezplacni-izlet-

v-zameno-za-predstavitev-izdelkov.html?Itemid=635 

529 

 
 
 
 
 
 
 
 
                                                 
Annex 1│ Country fiches 

Feels  vulnerable  to  'a  great  extent'  due  to  financial 
circumstances 

Feels  vulnerable  to  'a  great  extent'  due  to  employment 
situation 

Feels vulnerable to 'a great extent' due to age 

Feels  vulnerable  to  'a  great  extent'  due  to  belonging  to  a 
minority group 

Feels vulnerable to 'a great extent' due to personal issues 

Feels vulnerable to 'a great extent' due to other reasons 

Feels  vulnerable  to  'a  great  extent'  due  to  any  personal 
characteristic 

Dimension  3 :  Having  difficulty 
assimilating information 

in  obtaining  or 

Feels  'not  at  all'  informed  about  product  price,  quality, 
conditions,  etc  when  choosing  and  buying  goods  and 
services 

Compares  product  deals  by  using 
advertisements only - energy sector 

information 

from 

Compares  product  deals  by  using 
advertisements only - online sector  

information 

from 

Compares  product  deals  by  using 
advertisements only - finance sector 

information 

from 

Has  problems  comparing  deals  due  to  information-related 
factors - energy sector 

Has  problems  comparing  deals  due  to  information-related 
factors - online sector 

Has  problems  comparing  deals  due  to  information-related 
factors - finance sector 

20. Has an electricity supplier and has not switched due to 
information-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
information-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to 
information-related factors - financial sector 

Dimension  4 :  Inability  or  failure  to  buy,  choose  or 
access suitable products 

Does not compare product deals - energy sector 

Finds  it  very  difficult  to  compare  product  deals  -  energy 
sector 

Does not compare product deals - online sector 

Finds  it  very  difficult  to  compare  product  deals  -  online 
sector 

Does not compare product deals - finance sector 

18% 

14% 

3% 

1% 

3% 

3% 

27% 

2% 

6% 

7% 

35% 

12% 

18% 

2% 

47% 

530 

 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Finds  it  very  difficult  to  compare  product  deals  -  finance 
sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
energy sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
online sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
financial sector 

Has  problems  comparing  deals  due  to  market-related 
factors - energy sector 

12% 

19% 

Has  problems  comparing  deals  due  to  market-related 
factors - online sector 

27% 

Has  problems  comparing  deals  due  to  market-related 
factors - financial sector 

Has problems comparing deals due to access-related factors 
- energy sector 

Has problems comparing deals due to access-related factors 
- online sector 

5% 

Has problems comparing deals due to access-related factors 
- financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
personal factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
personal factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  personal 
factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
market-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
market-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  market-
related factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
access-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
access-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  access-
related factors - financial sector 

Has not switched due to termination costs 

Has not switched due to bundling 

Excluded from e-commerce due to difficulty of the process 

Excluded  from  e-commerce  due  to  not  having  a  payment 

531 

22% 

35% 

26% 

41% 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

card 

Declined for a loan 

Dimension  5 :  Higher  susceptibility  to  marketing 
practices 

Vulnerable  to  a  great  extent  due  to  complexity  of  offers 
(own perception) 

7% 

Experiment choice under the marketing practice treatment - 
energy sector 

Experiment choice under the marketing practice treatment - 
online sector 

Experiment choice under the marketing practice treatment - 
finance sector 

Experiment choice under the marketing practice treatment - 
cross-cutting 

532 

 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Table 109:  Country fiche - Slovakia 

Country fiche summary 

Approach  to  consumer  vulnerability:  There  is  no  evidence  of  a  broader  strategic 
approach to consumer vulnerability. 

Measures  addressing  consumer  vulnerability:  Only  a  limited  number  of  measures 
addressing consumer vulnerability have been identified. 

National consumer protection framework 

The Slovak consumer protection framework is based on a wide range of legal acts. One 
of the key documents is the Act no. 250 of 9 May 2007 on Consumer Protection, which 
transposes  for  instance  the  UCPD,  Directive  no.  8/27/EC,  Directive  no.  2005/29/EC, 
Directive  no.  98/6/EC,  Directive  no.  2009/22/EC,  Directive  2011/83/EU  into  Slovak 
legislation.  This  is  supported  by  a  range  of  specific  provisions,  such  as  the  Act  No. 
102/2014 Coll. on the protection of consumers in respect of distance and off-premises 
contracts. 

Approach to consumer vulnerability 

Consumer vulnerability within the context of the transposed UCPD  

The  term  “vulnerability”  is  present  within the  Slovak  Act  on  Consumer  Protection  due 
to  the  transposition  of  the  UCPD,  with  the  UCPD  terms  “mental  or  physical  infirmity, 
age  or  credulity”  being  used.  Similarly,  the  act  also  transposes  the  term  “average 
consumer”. 

Consumer vulnerability in national consumer policy beyond the UCPD  

There  appears  to  be  little  evidence  of  the  concept  of  consumer  vulnerability  being  in 
use  in  broader  national  consumer  policy.  While  the  Consumer  Policy  Strategy  for  the 
period  2014-2020362  makes  a  reference  to  “socially  disadvantaged  consumers”  and 
seniors, the terms is used in the context of the roles for consumer associations and is 
not elaborated on further within the strategy document.  

Stakeholder assessment of the national approach to consumer vulnerability 

The  overall  perception  of  the  Slovak  legislative  system  with  regard  to  consumers  and 
consumer vulnerability is that the existing legislation is appropriate but not effectively 
implemented. Inconsistent court judgments are another area highlighted as potentially 
problematic.   

Specific legislative measures  

No specific legislative measures addressing consumer vulnerability have been identified 
beyond  existing  EU  provisions.  According  to  the  interviewed  stakeholders,  there  are 
currently  efforts  in  place  to  develop  legislation  to  address  the  problems  arising in  the 
consumer  credit  industry,  although  this  is  still  in  progress  and  the  relevance  to 
vulnerability is not clear.  

Non-legislative measures 

362  See http://www.economy.gov.sk/strategie-spotrebitelskej-politiky-slovenskej-republiky-na-

roky-2014---2020/137628s 

533 

 
 
 
 
 
                                                 
Annex 1│ Country fiches 

There are a number of non-legislative measures of relevance to consumer vulnerability 
in  place.  These  include  primarily  education  measures  aimed  at  younger  consumers, 
such  as  steps  taken  to  assess  the  level  of  awareness  of  consumer  issues  among 
students  in  primary  and  secondary  schools,  which  is  to  serve  as  a  basis  for  further 
consumer  education  measures363.  Other  measures,  highlighted  by  stakeholders, 
include awareness raising campaigns focusing on the consumer loan market, although 
these do not have a specific focus on potentially vulnerable consumer groups.  

Institutional structure and role of civil society 

Public bodies responsible for consumer policy 

The Consumer Policy Department of the Slovak Ministry of the Economy has the overall 
responsibility  for  the  consumer  protection  system  in  the  country.  The  Ministry 
cooperates  with  other  relevant  Ministries  on  issues  falling  within  specific  thematic 
areas, with the enforcement being the responsibility of bodies such as the Slovak Trade 
Inspection  or  the  Regulatory  Office  for  Network  Industries,  Regulatory  Authority  for 
Electronic Communications and Postal Services, and the National Bank of Slovakia.  

Role of civil society 

The  consumer  organisations  operating  in  Slovakia  include  the  Association  of  Slovak 
Consumers  (ZSS),  as  well  a  range  of  other  national  and  regional  organisations.  The 
Association  of  Slovak  Consumers  does  not  have  explicit  work  streams  addressing 
consumer vulnerability, although it focuses on specific practices linked to vulnerability, 
such  as  doorstep  selling  of  energy  products364.  One  of  the  most  active  national-level 
association  is  SOS  Poprad,  which  focuses  on  consumers  vulnerability  by  providing 
information  materials  to  consumers  in  accessible  formats,  such  as  publishing 
information in Braille, as well as organizing seminars with a sign language interpreter 
and giving a legal and information support to seniors. 

There  are  also  a  number  of  regional  consumer  organisations  providing  a  range  of 
services to consumers on regional level. This includes working to increasing  consumer 
awareness and involvement in court proceedings. 

Incidence of consumer vulnerability  

Indicator of vulnerability 

 Mean incidence  

Dimension  1 :  Heightened  risk  of  negative  outcomes 
or impacts on well-being 

Has experienced a problem and took no action 

Overpaid  for  services  due  to  being  unable  to  use  certain 
payment methods 

Dimension 2 : Having characteristics that limit ability 
to maximise well-beings 

Feels vulnerable to 'a great extent' due to health problems 

Feels  vulnerable  to  'a  great  extent'  due  to  financial 

10% 

13% 

5% 

11% 

363 See http://www.economy.gov.sk/strategia-spotrebitelskej-politiky-na-roky-2007-2013-

5859/127544s 

364 See http://www.zss.sk/podomovy-predaj-energii 

534 

 
 
 
 
 
 
 
                                                 
Annex 1│ Country fiches 

circumstances 

Feels  vulnerable  to  'a  great  extent'  due  to  employment 
situation 

Feels vulnerable to 'a great extent' due to age 

Feels  vulnerable  to  'a  great  extent'  due  to  belonging  to  a 
minority group 

Feels vulnerable to 'a great extent' due to personal issues 

Feels vulnerable to 'a great extent' due to other reasons 

Feels  vulnerable  to  'a  great  extent'  due  to  any  personal 
characteristic 

Dimension  3 :  Having  difficulty 
assimilating information 

in  obtaining  or 

Feels  'not  at  all'  informed  about  product  price,  quality, 
conditions,  etc  when  choosing  and  buying  goods  and 
services 

Compares  product  deals  by  using 
advertisements only - energy sector 

information 

from 

Compares  product  deals  by  using 
advertisements only - online sector  

information 

from 

Compares  product  deals  by  using 
advertisements only - finance sector 

information 

from 

Has  problems  comparing  deals  due  to  information-related 
factors - energy sector 

Has  problems  comparing  deals  due  to  information-related 
factors - online sector 

Has  problems  comparing  deals  due  to  information-related 
factors - finance sector 

20. Has an electricity supplier and has not switched due to 
information-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
information-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to 
information-related factors - financial sector 

Dimension  4 :  Inability  or  failure  to  buy,  choose  or 
access suitable products 

Does not compare product deals - energy sector 

Finds  it  very  difficult  to  compare  product  deals  -  energy 
sector 

Does not compare product deals - online sector 

Finds  it  very  difficult  to  compare  product  deals  -  online 
sector 

Does not compare product deals - finance sector 

535 

10% 

3% 

2% 

4% 

3% 

21% 

3% 

3% 

3% 

3% 

21% 

7% 

12% 

55% 

11% 

30% 

2% 

45% 

 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Finds  it  very  difficult  to  compare  product  deals  -  finance 
sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
energy sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
online sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
financial sector 

Has  problems  comparing  deals  due  to  market-related 
factors - energy sector 

Has  problems  comparing  deals  due  to  market-related 
factors - online sector 

Has  problems  comparing  deals  due  to  market-related 
factors - financial sector 

Has problems comparing deals due to access-related factors 
- energy sector 

Has problems comparing deals due to access-related factors 
- online sector 

Has problems comparing deals due to access-related factors 
- financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
personal factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
personal factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  personal 
factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
market-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
market-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  market-
related factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
access-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
access-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  access-
related factors - financial sector 

Has not switched due to termination costs 

Has not switched due to bundling 

Excluded from e-commerce due to difficulty of the process 

Excluded  from  e-commerce  due  to  not  having  a  payment 
card 

10% 

42% 

18% 

27% 

40% 

17% 

36% 

11% 

4% 

1% 

17% 

26% 

28% 

18% 

536 

 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Declined for a loan 

Dimension  5 :  Higher  susceptibility  to  marketing 
practices 

Vulnerable  to  a  great  extent  due  to  complexity  of  offers 
(own perception) 

12% 

Experiment choice under the marketing practice treatment - 
energy sector 

Experiment choice under the marketing practice treatment - 
online sector 

Experiment choice under the marketing practice treatment - 
finance sector 

Experiment choice under the marketing practice treatment - 
cross-cutting 

537 

 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
Annex 1│ Country fiches 

Table 110:  Country fiche - Finland 

Country fiche summary 

Approach to consumer vulnerability: The concept of consumer vulnerability appears to 
have  been  in  place  within  national  consumer  policy  independently  of  the  UCPD 
transposition.  

Measures  addressing  consumer  vulnerability:  There  is  evidence  of  a  range  of  sector-
specific measures addressing consumer vulnerability. 

National consumer protection framework 

Significant  work  has  been  conducted  in  Finland  in  the  area  of  consumer  protection 
since  the  1990s,  following  the  1990’s  recession.  The  main  piece  of  legislation  in  this 
area  is  the  Consumer  Protection  Act,  which  was  introduced  in  1978,  and  was  latest 
amended  in  2008  through  the  transposition  of  the  UCPD.  A  number  of  other  sector 
specific legislation also relates to consumer protection. 

Approach to consumer vulnerability 

Consumer vulnerability within the context of the transposed UCPD  

There  is  no  specific  definition  of  vulnerable  consumers  in  the  Finnish  legislation. 
However,  the  concept  of  vulnerable  consumer  is  included  in  the  area  of  consumer 
protection. Chapter 2, section 3 of Act 561 (2008) amending the Consumer Protection 
Act  transposes  the  UCPD.  While  “vulnerability”  is  not  specifically  mentioned,  the 
concept  is  transposed  through  the  wording  “consumers  who  are  particularly 
impressionable due to their age, disability or credulity”. 

Consumer vulnerability in national consumer policy beyond the UCPD  

The  consumer  legislation  in  force  in  Finland  strives  to  address  the  legal  protection  of 
vulnerable consumer groups. The concept of essential services, which  was established 
in the  1990s,  as  a  consequence  of  the  recession,  makes  reference  to  both  temporary 
and  permanent  vulnerability,  while  regulation  regarding  marketing  was  revised  in 
2008,  introducing  a  ban  on  discrimination.  The  clause  also  prohibits  marketing 
targeted at children if it is against good practice.  

The Finnish concept of consumer used since the 1980s sees the consumer as someone 
who  is  not  very  rational  and  who  does  not  read  adverts  and  similar  information 
carefully.  According  to  the  interviewed  stakeholders,  the  concept  of  “average 
consumer”  is  rarely  used  since  that  would  create  confusion  due  to  the  very  well 
established existing consumer definition,  

Finally,  a  working  group  was  set  up  2007-2010  by  the  National  Research  and 
Development Centre for Welfare and Health with the aim of examining the situation of 
vulnerable  consumers  and in  particular  consumers  with  disabilities.  The  work  resulted 
in  two  reports:  ‘Vulnerability  in  legislation’  (2009)  and  ‘Structures,  vulnerability  and 
legislation’ (2009).  

Specific legislative measures  

A  number  of  specific  measures  have  been  identified.  In  the  financial  sector  a  debt 
collection  act  (March  2013)  addresses  inter  alia  the  charging  of  unreasonable  high 
collection  fees.  Under  this  new  Act,  the  maximum  amount  of  collection  fees  on 
consumer debts will be decreased in particular for smaller debts (total fee liability may 
be  no  higher  than  60  euros).  Fees  on  debts  of  no  more  than  1,000  euros  may  not 
exceed  120  euros.  This  measure  aims  to  protect  low-income  consumers  who  are 

538 

 
 
 
Annex 1│ Country fiches 

already  struggling  with  their  debts.  Furthermore,  under  the  new  Act,  debtors  may  be 
charged collection fees for no more than two demands for payment365.  
In  some  cases,  people  encountering  sudden  difficulties  (e.g.  illness,  unemployment) 
will be given more time to reimburse their debts. This has been used in contract terms 
since the 1980s and is known as social force majeure. Such rules are contained in e.g. 
the legislation on consumer credit and on interest on delayed payments.  

A  significant  reform  has  been  carried  out  with  regard  to  instant  credit,  with 
amendments  approved  by  the  Parliament  in  February  2013.  An  interest  ceiling  based 
on an annual percentage rate has been established. The amendments also prohibit the 
use of text messaging and other equivalent communication services for a surcharge in 
credit transactions.  

Under  the  Act  on  Credit  Institutions,  any  consumer  has  access  to  basic  banking 
services366.  This  is  defined  as  including  an  ordinary  deposit  account,  the  instruments 
needed to use the account and the opportunity to pay bills from the account. There has 
been  discussion  related  to  this  since  the  banking  industry  does  not  consider  online 
banking  services  to  fall  under  basic  banking  services.  A  bad  credit  record  may 
therefore  result  in  that  a  bank  denies  a  consumer  access  to  online  banking.  The 
Consumer  Agency  has  been  calling  attention  to  this,  highlighting  the  fact  that, 
according  to  the  preamble  to  the  government  proposal  regarding  basic  banking 
services, these services should include online banking services. 

With  regard  to  the  energy  sector,  the  concept  of  essential  services  is  important  in 
Finland.  These  services,  including  electricity  and  water  supply,  communication  and 
basic  banking  services,  must  be  guaranteed  for  any  consumer  at  a  reasonable  price, 
regardless  of  financial  status  or  place  of  residence.  According  to  the law,  if  heating is 
dependent on electricity, electricity supply must not  be interrupted due to unpaid bills 
between October and mid-April, before four months have passed from the due date of 
the bills. 

Non-legislative measures 

There  are  a  number  of  non-legislative  measures  addressing  consumer  vulnerability 
carried out by a wide range of organisations. For instance, the Finnish Direct Marketing 
Association  has  issued  guidelines  on  telemarketing  targeted  at  senior  citizens.  These 
guidelines  mention  e.g.  the  importance  of  using  a  clear  and  ordinary  language, 
establishing  whether  the  product  or  service  is  suitable  to  the  client,  and  of 
summarising at the end of the call what has been agreed.  

Consumer  associations  have  had  an  important  role  in  consumer  protection  in  e.g. 
drafting  contract  terms  in  collaboration  with  telecommunication  companies,  providing 
summaries in braille, or easy-read versions adapted to elderly people, as well as in the 
drafting of guidelines on issues such as contract terms.   

365 Finnish Competition and Consumer Authority, 

http://www2.kuluttajavirasto.fi/Page/26b5a41e-642e-4d78-a0fb-
bef206790a78.aspx?announcementId=8d2c4d36-24c2-46a0-a5b8-
3a9fae975b45&groupId=6da5f022-8c1c-4a2f-9c0e-37645ba8d18d 

366 Finnish Competition and Consumer Authority, http://www2.kuluttajavirasto.fi/en-

GB/archive2010/?announcementId=f267b54b-1604-4df2-9fa9-
e62b16745e13&groupId=9f7606f6-55dc-4ee7-9389-caabce8c1b65 

539 

 
 
 
 
 
                                                 
Annex 1│ Country fiches 

Municipalities have been given an informative role, especially in regards to consumers 
with special needs such as students or elderly people. 

Institutional structure and role of civil society 

Public bodies responsible for consumer policy 

The Competition and Consumer Authority is a new agency established in the beginning 
of  2013,  merging  the  Competition  authority  and  the  Consumer  Agency.  The  Finnish 
Competition  and  Consumer  Authority  aims  to  promote  competition  and  develop 
consumer  policies  and  consumer  protection.  The  authority  is  responsible  for  general 
consumer  policy  matters,  and  also  provides  consumer  information  and  education. 
Furthermore,  it  collaborates  with  local  authorities  and  municipalities  in  the  area  of 
consumer protection.  

The Consumer Ombudsman has the enforcement role. It is active in taking companies 
to  the  Market  Court  for  faulty  behaviour  on  the  market.  The  Market  Court  is  the 
dedicated court for competition and public procurement cases. Any case with a basis in 
the Consumer Protection Act is initiated by a petition from the Consumer Ombudsman 
to the Market Court.  

Other  sector-specific  authorities  are  also  involved  in  consumer  protection  e.g.  the 
Energy  Authority,  the  Ministry  of  Justice,  and  the  Finnish  Communications  Regulatory 
Authority.  The  Finnish  Consumer  Disputes  Board  works  to  resolve  disputes  between 
consumers and businesses regarding e.g. consumer goods and services. 

Role of civil society 

The two consumer associations in Finland, the Finnish Consumers’ Association and The 
Consumers merged in 2011 to become the Consumer Association of Finland.  

As  mentioned  above,  consumer  associations  have  had  an  important  role  in  consumer 
protection in e.g. drafting contract terms, providing summaries in braille, or easy-read 
versions adapted to elderly people, as well as in the drafting of guidelines.  

Incidence of consumer vulnerability  

Indicator of vulnerability 

Mean incidence  

Dimension  1 :  Heightened  risk  of  negative  outcomes 
or impacts on well-being 

Has experienced a problem and took no action 

Overpaid  for  services  due  to  being  unable  to  use  certain 
payment methods 

Dimension 2 : Having characteristics that limit ability 
to maximise well-beings 

Feels vulnerable to 'a great extent' due to health problems 

Feels  vulnerable  to  'a  great  extent'  due  to  financial 
circumstances 

Feels  vulnerable  to  'a  great  extent'  due  to  employment 
situation 

Feels vulnerable to 'a great extent' due to age 

Feels  vulnerable  to  'a  great  extent'  due  to  belonging  to  a 
minority group 

9% 

11% 

4% 

16% 

11% 

3% 

2% 

540 

 
 
 
 
 
 
Annex 1│ Country fiches 

Feels vulnerable to 'a great extent' due to personal issues 

Feels vulnerable to 'a great extent' due to other reasons 

Feels  vulnerable  to  'a  great  extent'  due  to  any  personal 
characteristic 

Dimension  3 :  Having  difficulty 
assimilating information 

in  obtaining  or 

Feels  'not  at  all'  informed  about  product  price,  quality, 
conditions,  etc  when  choosing  and  buying  goods  and 
services 

Compares  product  deals  by  using 
advertisements only - energy sector 

information 

from 

Compares  product  deals  by  using 
advertisements only - online sector  

information 

from 

Compares  product  deals  by  using 
advertisements only - finance sector 

information 

from 

Has  problems  comparing  deals  due  to  information-related 
factors - energy sector 

Has  problems  comparing  deals  due  to  information-related 
factors - online sector 

Has  problems  comparing  deals  due  to  information-related 
factors - finance sector 

20. Has an electricity supplier and has not switched due to 
information-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
information-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to 
information-related factors - financial sector 

Dimension  4 :  Inability  or  failure  to  buy,  choose  or 
access suitable products 

Does not compare product deals - energy sector 

Finds  it  very  difficult  to  compare  product  deals  -  energy 
sector 

Does not compare product deals - online sector 

Finds  it  very  difficult  to  compare  product  deals  -  online 
sector 

Does not compare product deals - finance sector 

Finds  it  very  difficult  to  compare  product  deals  -  finance 
sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
energy sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
online sector 

541 

3% 

3% 

22% 

2% 

4% 

4% 

3% 

11% 

8% 

13% 

45% 

12% 

36% 

7% 

55% 

9% 

34% 

21% 

 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Has  problems  comparing  deals  due  to  personal  factors  - 
financial sector 

Has  problems  comparing  deals  due  to  market-related 
factors - energy sector 

Has  problems  comparing  deals  due  to  market-related 
factors - online sector 

Has  problems  comparing  deals  due  to  market-related 
factors - financial sector 

Has problems comparing deals due to access-related factors 
- energy sector 

Has problems comparing deals due to access-related factors 
- online sector 

Has problems comparing deals due to access-related factors 
- financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
personal factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
personal factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  personal 
factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
market-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
market-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  market-
related factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
access-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
access-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  access-
related factors - financial sector 

Has not switched due to termination costs 

Has not switched due to bundling 

Excluded from e-commerce due to difficulty of the process 

Excluded  from  e-commerce  due  to  not  having  a  payment 
card 

Declined for a loan 

Dimension  5 :  Higher  susceptibility  to  marketing 
practices 

38% 

34% 

22% 

36% 

9% 

7% 

3% 

9% 

24% 

26% 

13% 

Vulnerable  to  a  great  extent  due  to  complexity  of  offers 
(own perception) 

6% 

Experiment choice under the marketing practice treatment - 

542 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

energy sector 

Experiment choice under the marketing practice treatment - 
online sector 

Experiment choice under the marketing practice treatment - 
finance sector 

Experiment choice under the marketing practice treatment - 
cross-cutting 

543 

 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Table 111: Country fiche - Sweden 

Country fiche summary 

Approach  to  consumer  vulnerability:  There  appears  to  be  no  broader  strategic 
approach to consumer vulnerability in place. 

Measures  addressing  consumer  vulnerability:  There  is  evidence  of  a  range  of  sector-
specific measures addressing consumer vulnerability. 

National consumer protection framework 

The  overall  objective  of  Swedish  consumer  policy  to  date  has  been  to  empower 
consumers to make active choices in the marketplace. The key legislative act relevant 
to consumer vulnerability is the Marketing Practices Act. 

Approach to consumer vulnerability 

Consumer vulnerability within the context of the transposed UCPD  

The Marketing Practices Act (2008:486), which transposes the UCPD, does not appear 
to  include  a  reference  to  vulnerable  consumers.  Similarly,  there  is  no  reference  or 
definition  of  average  consumer.  However,  recent  case  law  in  Sweden  has  included 
references to the average consumer367.  
Consumer vulnerability in national consumer policy beyond the UCPD  

The  term  of  consumer  vulnerability  is  not  widely  used  in  Sweden.  Instead  it  is  more 
common  to  speak  about  different  consumer  groups,  consumers  with  special  needs  or 
otherwise  safe/secure  consumers,  which  carries  a  more  positive  connotation  and 
highlights that every consumer has the right to feel secure in the marketplace. This is 
also in line with the objective of the Swedish consumer policy as set out above. There 
is  however  some  evidence  of  the  concept  of  consumer  vulnerability  being  defined  in 
the energy sector368.   
Stakeholder assessment of the national approach to consumer vulnerability 

Similarly to the general perception of consumer vulnerability in Sweden, the consulted 
stakeholders argue that every consumer can be vulnerable. Thus, by using the concept 
of  safe  or  secure  consumers  there  are  no  consumers  that  are  at  risk  of  being 
stigmatised and also highlights the dynamics of the concept of consumer vulnerability.  

Specific legislative measures 

There  are  a  number  of  legislative  measures  in  place  in  mainly  the  financial  sector 
aimed  at  protecting  vulnerable  consumers.  These  include  new  legislation  requiring 
companies  to  provide  short  informational  leaflets  on  investment  funds.  This  has  been 
implemented to facilitate comparison between different funds in regards to annual fees 
and risk levels. A proposal is also underway  regarding permits for businesses  offering 
consumer  credit,  with  the  objective  to  curb  payday  loans.  It  is  proposed  that  only 
limited  companies  and  economic  associations  should  be  given  permits  with  the 
condition that the business needs to be conducted in a fair way.  

367 See for example, the Consumer Ombudsman vs. Mercedes Benz.  
368 ACER (2013). Annual Report on the Results of Monitoring the Internal Electricity and Gas 

Markets in 2012. Available at: 
http://www.acer.europa.eu/Electricity/Market%20monitoring/Pages/Reports.aspx 

544 

 
 
 
                                                 
Annex 1│ Country fiches 

Non-legislative measures 

There  are  a  number  of  non-legislative  measures  in  place  in  the  key  sectors.  In  the 
energy  sector,  measures  include  non-exhaustively  an  industry  code  of  conduct 
regarding  selling  of  energy  services  agreed  between  the  Swedish  Consumer  Agency 
and  the  Industry  Association  for  Energy  Providers.  This  code  includes  guidelines  on 
selling  energy  services  through  doorstep,  telephone  or  selling  in  other  public  places. 
This  code  of  conduct  includes  what  information  should  be  provided  and  in  which 
format. In addition, specific rules are in place in regards to disconnection and payment 
for energy services, which is administered and provided by social welfare services. 

In  the  financial  sector,  the  Finance  Authority  receives  a  budget  yearly  to  increase 
financial  literacy  among  consumers  through  education.  The  trainings  are  held  in 
cooperation with union organisations, educational associations and other authorities. In 
addition,  the  government  has  established  requirements  on  basic  skills  in  personal 
finance in the high school curriculum. The government is also investing 60 million SEK 
in the coming 4 years on coordinated telephone and web-based consumer information 
to  help  consumers  get  easy  and  quick  service  on  rights  and  obligations  and  other 
information facilitating consumer choices. 

Institutional structure and role of civil society 

Public bodies responsible for consumer policy  

As of October 2014, consumer protection in Sweden falls within the competence of the 
Consumer  Policy  Division  of  the  Ministry  of  Finance  at  the  Government  Offices.  The 
division  is  responsible  for,  inter  alia,  marketing  legislation  and  product  safety  issues. 
The  Swedish  Consumer  Agency  (Konsumentverket)  is  a  governmental  agency 
operating  under  the  Ministry  of  Finance  and  is  tasked  with  safeguarding  consumer 
interests. The issues addressed by the Agency include: 

  Enforcement of consumer protection legislation, i.e. safety with regard to goods 
and services, company advertising and contract conditions, domestic finances  

  Consumer-related disabled and environmental issues;  

  Training of municipality consumer advisers as well as budget and debt advisers;  

  Monitoring consumer interests within the EU; and  

 

Information and education campaigns and information for consumers. 

The  Head  of  the  Consumer  Agency  is  at  the  same  time  the  Consumer  Ombudsman. 
The Consumer Ombudsman has the capacity to pursue legal action in the market court 
or  to  issue  an  information  or  prohibition  order.  In  addition  to  the  Swedish  Consumer 
Agency  there  are  a  number  of  other  agencies  tasked  with  implementing  the 
Government's consumer policy:  

  The National Board for Consumer Disputes; 

  The Swedish Estate Agents Inspectorate; 

  The Market Court; and 

  The Travel Guarantees Board. 

Consumer issues are also dealt with by other agencies, including:  

  The Financial Conduct Authority; 

  The Swedish Post and Telecom Authority; and 

  The Swedish Energy Market Inspectorate.  

545 

 
 
 
 
 
Annex 1│ Country fiches 

The  agencies  are  responsible  for  monitoring  compliance  with  specific  sector-relevant 
legislation and to provide information on the consumer protection available. Lastly, the 
Discrimination Ombudsman is also engaged in the protection of vulnerable consumers.  

Role of civil society 

The Swedish Consumers’ Association (Sveriges konsumenter) is the umbrella consumer 
organisation  in  Sweden.  It  currently  has  26  member  organisations,  some  of  which 
represent  groups  that  may  be  considered  vulnerable.  In  addition,  state  subsidies 
administered by the Consumer Agency are available to voluntary organisations working 
with consumer related issues.  Usually, a portion of the grants is given to organisations 
that work in the interests of potentially vulnerable consumer groups. 

Incidence of consumer vulnerability  

Indicator of vulnerability 

Mean incidence  

Dimension  1 :  Heightened  risk  of  negative  outcomes 
or impacts on well-being 

Has experienced a problem and took no action 

Overpaid  for  services  due  to  being  unable  to  use  certain 
payment methods 

Dimension 2 : Having characteristics that limit ability 
to maximise well-beings 

Feels vulnerable to 'a great extent' due to health problems 

Feels  vulnerable  to  'a  great  extent'  due  to  financial 
circumstances 

Feels  vulnerable  to  'a  great  extent'  due  to  employment 
situation 

Feels vulnerable to 'a great extent' due to age 

Feels  vulnerable  to  'a  great  extent'  due  to  belonging  to  a 
minority group 

Feels vulnerable to 'a great extent' due to personal issues 

Feels vulnerable to 'a great extent' due to other reasons 

6% 

8% 

3% 

8% 

6% 

3% 

2% 

3% 

3% 

Feels  vulnerable  to  'a  great  extent'  due  to  any  personal 
characteristic 

14% 

Dimension  3 :  Having  difficulty 
assimilating information 

in  obtaining  or 

Feels  'not  at  all'  informed  about  product  price,  quality, 
conditions,  etc  when  choosing  and  buying  goods  and 
services 

Compares  product  deals  by  using 
advertisements only - energy sector 

information 

from 

Compares  product  deals  by  using 
advertisements only - online sector  

information 

from 

Compares  product  deals  by  using 
advertisements only - finance sector 

information 

from 

1% 

1% 

1% 

1% 

546 

 
 
 
 
 
 
 
Annex 1│ Country fiches 

Has  problems  comparing  deals  due  to  information-related 
factors - energy sector 

Has  problems  comparing  deals  due  to  information-related 
factors - online sector 

Has  problems  comparing  deals  due  to  information-related 
factors - finance sector 

20. Has an electricity supplier and has not switched due to 
information-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
information-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to 
information-related factors - financial sector 

Dimension  4 :  Inability  or  failure  to  buy,  choose  or 
access suitable products 

Does not compare product deals - energy sector 

Finds  it  very  difficult  to  compare  product  deals  -  energy 
sector 

Does not compare product deals - online sector 

Finds  it  very  difficult  to  compare  product  deals  -  online 
sector 

Does not compare product deals - finance sector 

Finds  it  very  difficult  to  compare  product  deals  -  finance 
sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
energy sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
online sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
financial sector 

Has  problems  comparing  deals  due  to  market-related 
factors - energy sector 

Has  problems  comparing  deals  due  to  market-related 
factors - online sector 

Has  problems  comparing  deals  due  to  market-related 
factors - financial sector 

Has problems comparing deals due to access-related factors 
- energy sector 

Has problems comparing deals due to access-related factors 
- online sector 

Has problems comparing deals due to access-related factors 
- financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 

547 

11% 

7% 

9% 

50% 

13% 

53% 

6% 

58% 

8% 

35% 

24% 

35% 

31% 

26% 

33% 

10% 

16% 

1% 

 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

personal factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
personal factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  personal 
factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
market-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
market-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  market-
related factors - financial sector 

Has  an  electricity  supplier  and  has  not  switched  due  to 
access-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
access-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  access-
related factors - financial sector 

Has not switched due to termination costs 

Has not switched due to bundling 

Excluded from e-commerce due to difficulty of the process 

Excluded  from  e-commerce  due  to  not  having  a  payment 
card 

Declined for a loan 

Dimension  5 :  Higher  susceptibility  to  marketing 
practices 

6% 

14% 

21% 

8% 

Vulnerable  to  a  great  extent  due  to  complexity  of  offers 
(own perception) 

7% 

Experiment choice under the marketing practice treatment - 
energy sector 

Experiment choice under the marketing practice treatment - 
online sector 

Experiment choice under the marketing practice treatment - 
finance sector 

Experiment choice under the marketing practice treatment - 
cross-cutting 

548 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Table 112:  Country fiche – United Kingdom 

Country fiche summary 

Approach  to  consumer  vulnerability:  The  consumer  vulnerability  concept  is  well  known 
and  there  is  evidence  of  consumer  vulnerability  being  tackled  at  strategic  policy  level 
across a range of sectors.  

Measures addressing consumer vulnerability: There is evidence of a wide range of sector-
specific measures addressing consumer vulnerability. 

National consumer protection framework 

The  UK  consumer  policy focuses on ensuring that consumers have the  right information 
and are protected when buying goods and services. The  Consumer Rights Act, currently 
in  the  process  of  being  adopted, aims  to  clarify  and  simplify  consumer  rights.  In 
particular,  the  Act  aims  to  enhance  consumer  confidence  by  clarifying  standards  and 
clarifying  when  terms  and  conditions  can  be  considered  unfair.  In  addition,  it  seeks  to 
simplify enforcement and make it easier to deal with dishonest traders. Specifically, the 
Act will focus on: 

  What should happen when goods are faulty; 
  What should happen when digital content is faulty;  
  Unfair terms in a contract; 
  What happens when a business is acting in a way which isn’t competitive; 
  Written notice for routine inspections to be given by public enforcers, such as 

Trading Standards; 

  Greater flexibility for public enforcers to respond to breaches of consumer law, 

such as seeking redress for consumers who have suffered harm; and 

  How services should match up to what has been agreed, and what should happen 
when they do not or when they are not provided with reasonable care and skill369.  

Other  relevant  legislation  includes  the  Consumer  Contracts  Regulations  and  the 
Consumer Protection Regulations.  

Approach to consumer vulnerability 

Consumer vulnerability within the context of the transposed UCPD  

The  term  “vulnerable”  is  present  in  the  Consumer  Protection  from  Unfair  Trading 
Regulations 2008 through the transposition of the UCPD. The concept of vulnerability has 
been transposed in line with the Directive’s wording, using the terms mental or physical 
infirmity,  age,  and  credulity.  Similarly  the  “average  consumer”  term  has  also  been 
transposed into UK legislative framework.  

Consumer vulnerability in national consumer policy beyond the UCPD  

The  term  “vulnerable  consumer”  is  used  in  a  variety  of  policy  documents  in  the  UK 

369 See https://www.gov.uk/government/policies/providing-better-information-and-protection-

for-consumers/supporting-pages/consumer-bill-of-rights 

549 

 
 
 
 
 
                                                 
Annex 1│ Country fiches 

although  there  is  no  strict  definition  of  the  term.  For  example,  the  Consumer 
Empowerment  Strategy  in  2011370  contains  a  number  of  suggestions  for  additional 
support  for  vulnerable  consumers.  It  loosely  defines  those  consumers  who  have  never 
used a computer, those who are widowed, those on low incomes and certain age groups 
to be the most vulnerable in at least some respects. The energy regulator, Ofgem, states 
that “all consumers, in any market, can be vulnerable to detriment. The potential for, and 
impact of, vulnerability are a result of the interactions between individual circumstances 
and the operation of the market”371. Consumer Futures in turn use the term “consumers 
in vulnerable position” to indicate that vulnerability is linked to how the market operates, 
providing  the  following  definition:  “People  who  cannot  choose  or  access  essential 
products  and  services  which  are  suitable  for  their  needs  or  cannot  do  so  without 
disproportionate effort/cost/time”372. 
Stakeholder assessment of the national approach to consumer vulnerability 

According to all stakeholders consulted, consumer vulnerability is well integrated into the 
overall  UK  policy  framework.  There  are  also  specific  work  streams  on  consumer 
vulnerability being put in place as well, with for instance the Financial Conduct Authority 
(FCA) dedicating resources to research on consumer vulnerability in the financial sector.   

The difficulties with the concept relate to the definition and stakeholders converge on the 
notion  that  all  consumers  may  be  vulnerable  at  certain  points  in  time.  In  addition,  it  is 
recognised  that  consumer  vulnerability  is  complex  and  interrelated  concept  where  for 
example vulnerability in the financial sector such as lack of access to basic bank accounts 
may  spill  over  to  vulnerability in the  energy  sector  due  to  limited  means  of  paying  with 
direct debit.  

Specific legislative measures  

There are a wide variety of specific legislative measures in the UK that seek to mitigate 
consumer  vulnerability.  This  is  mainly  due  to  the  focus  on  consumer  vulnerability  as 
noted  above.  Below  follows  a  non-exhaustive  list  of  current  measures  in  the  three  key 
sectors. 

In the energy sector, there are several measures to help those consumers that may be at 
a  disadvantage.  To  this  end,  the  regulator  Ofgem  has  set  out  a  particular  strategy  to 
protect  vulnerable  consumers  in  the  energy  market.  One  example  of  a  legislative 
measure  includes  the  Energy  Company  Obligation  (ECO),  a  programme  designed  to 
reduce  Britain’s  energy  consumption  with  the  twin  aim  to  ease  fuel  poverty.  It  places  a 
legal obligation on energy companies to improve energy efficiency in households through 
the  establishment  of  three  different  targets.  One  of  these  targets  –  “the  affordable 
warmth obligation” – focuses on vulnerable consumers and housing in low-income areas. 
In addition, the Warm Home Discount Scheme is a 5-year programme introduced by the 
Government  in  April  2011  and  administered  by  energy  suppliers  to  provide  rebates  on 
the electricity bills of households that need it most in England, Scotland, and Wales. The 
Warm  Home  Discount  Scheme  (WHDS)  offers  eligible  customers  a  one-off  payment  on 
their electricity bill (currently £135). 

370 See 

https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/294798/bis-
11-749-better-choices-better-deals-consumers-powering-growth.pdf  

371 See https://www.ofgem.gov.uk/ofgem-publications/75550/consumer-vulnerability-

strategy.pdf  

372 See http://www.consumerfocus.org.uk/topic/tackling-consumer-vulnerability  

550 

 
 
 
                                                 
Annex 1│ Country fiches 

In  the  financial  sector  new  and  more  effective  regulations  and  sanctions  are  being 
introduced  to  tackle  consumer  vulnerability.  For  example,  the  government  has 
announced  a  package  of  measures  to  target  the  payday  loans  industry  which  includes 
rules on how loans are advertised, tightening the codes covering lenders and encouraging 
industry to improve compliance. In order to help consumers that are underserved in the 
financial  market,  the  UK  government  has  implemented  the  National  Employer  Savings 
Trust, which is a pension scheme enabling its members to build up a retirement pot. This 
is  believed  to  benefit  consumers  that  would  not  have  had  access  to  private  pension 
scheme otherwise. 

Non-legislative measures 

In  addition,  to  the  legislative  measures  described  above  there  is  also  a  range  of  non-
legislative measures in place in the UK. In the energy sector, the Extra help Unit run by 
Consumer Futures helps consumers to resolve conflicts with their providers. The industry 
association for energy suppliers, Energy UK, has established an industry code of conduct 
“the  Safety  Net”  agreeing  to  never  knowingly  disconnect  a  vulnerable  customer  at  any 
time of year, where for reasons of age, health, disability or severe financial insecurity.  

In  the  online  environment  sector  there  are  a  number  of  industry  standards  concerning 
children  and  the  internet.  For  example,  a  Code  of  Practice  agreed  in  2011  sets  out 
measures  to  help  parents  block  inappropriate  online  content373.  The  British  Standards 
Institute has published a new standard, which is designed to help businesses provide fair, 
flexible  service  that  are  accessible  to  all.  This standard  –  Inclusive  Service  Provision  BS 
18477  –  is  designed  to  raise  consumer  confidence  in  services  which are  offered  directly 
online  or  through  telephone  or  physically.  It  helps  organisation  to  improve  customer 
satisfaction  by  encouraging  the  use  of  fair,  ethical  and  inclusive  practices,  helping 
organisation  how  to  identify  vulnerable  consumers  and  how  to  treat  them  fairly  in 
compliance  with  law  as  well  as  helping  organisation  understand  what  consumers  are 
entitled to expect  from  them. The UK government is encouraging businesses to take up 
the standard.  

Institutional structure and role of civil society 

Public bodies responsible for consumer policy 

There are a variety of governmental and non-governmental institutions in the UK working 
with  issues  related  to  consumer  protection  and  consumer  vulnerability.  As  mentioned 
above,  the  main  governmental  agency  with  responsibilities  of  consumer  policy  is  the 
Department of Businesses, Skills and Innovation. In addition, the following organisations 
are active on issues related to consumer vulnerability: 

  The energy regulator Ofgem was established in 2000 through the Utilities Act with 
one  of  the  key  objectives  to  promote  the  protection  of  consumers  through  the 
promotion  and  oversight  of  the  competition  in  the  energy  market.  To  this  end, 
Ofgem is required to consider the interests of vulnerable consumers, defined non-
exhaustively  as  those  who  are  disabled,  chronically  sick,  of  pensionable  age  or 
residing  in  rural  areas.  Ofgem  has  a  particular  strategy  aiming  at  protecting 

373 See 

https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/251454/cod
e_of_practice.pdf  

551 

 
 
 
 
 
                                                 
Annex 1│ Country fiches 

vulnerable consumers in the energy market.  

  With  regard  to  telecommunications,  the  Communications  Act  2003  imposes  a 
general  duty  on  Ofcom  to  “further  the  interests  of  consumers”  in  the  relevant 
markets. In doing so, it has to consider, inter alia, the vulnerability of children and 
of others whose circumstances appear to put them in need of special protection.  

 

In April 2014, the regulation of consumer credit will be moved from the Office of 
Fair  Trading  (OFT)  to  the  Financial  Conduct  Authority  (FCA)  in  order  to  provide 
stronger protection in the financial services market.  

  Financial  Services  Consumer  Panel  is  an  independent  body  representing  the 
consumer  voice in financial markets.  One  of the key  objectives  of the  Panel is to 
advise and challenge the FCA from the earliest stages of its policy development to 
ensure that the consumer interest is taken into account. 

  Consumer  Futures  is  a  non-departmental  public  body  sponsored  by  the 
Department  of  Business,  Innovation  and  Skills,  which  aims  to  help  markets  work 
better for consumers, improve consumer protection and give greater clarity about 
where consumers need to turn to for help and advice. As of April 2014, Consumer 
Futures became part of the Citizens Advice service.  

 

In  regard  to  the  online  environment,  the  Competition  Markets  Authority  is 
responsible  for  consumer  protection  in  the  field  of  e-commerce.  In  addition,  the 
Information  Commissioner’s  Office  is  responsible  for  privacy  issues,  such  as 
information  gathered  by  online  operators.  The  Advertising  Standards  Authority  is 
the  UK’s  independent  regulator  of  advertising  across  all  media,  including  online 
advertisement.  

Role of civil society 

In  addition  to  the  public  bodies  set  out  above,  there  are  numerous  non-governmental 
organisations  working  on  consumer  related  issues  in  the  UK,  such  as  the  consumer 
organisation  ‘Which?’.  This  organisation  does  not  carry  out  any  activities  directed 
especially to vulnerable consumers, but instead considers all consumers to be potentially 
vulnerable in different markets and at different times.  

Incidence of consumer vulnerability  

Indicator of vulnerability 

Mean incidence  

Dimension  1 :  Heightened  risk  of  negative  outcomes  or 
impacts on well-being 

Has experienced a problem and took no action 

Overpaid  for  services  due  to  being  unable  to  use  certain 
payment methods 

Dimension  2 :  Having  characteristics  that  limit  ability  to 
maximise well-beings 

Feels vulnerable to 'a great extent' due to health problems 

Feels  vulnerable 
circumstances 

to 

'a  great  extent'  due 

to 

financial 

Feels vulnerable to 'a great extent' due to employment situation 

Feels vulnerable to 'a great extent' due to age 

Feels  vulnerable  to  'a  great  extent'  due  to  belonging  to  a 

6% 

9% 

3% 

9% 

8% 

5% 

2% 

552 

 
 
 
 
 
 
Annex 1│ Country fiches 

minority group 

Feels vulnerable to 'a great extent' due to personal issues 

Feels vulnerable to 'a great extent' due to other reasons 

Feels  vulnerable  to  'a  great  extent'  due  to  any  personal 
characteristic 

Dimension  3 :  Having  difficulty 
assimilating information 

in  obtaining  or 

Feels  'not  at  all'  informed  about  product  price,  quality, 
conditions, etc when choosing and buying goods and services 

Compares  product  deals  by  using 
advertisements only - energy sector 

information 

from 

Compares  product  deals  by  using 
advertisements only - online sector  

information 

from 

Compares  product  deals  by  using 
advertisements only - finance sector 

information 

from 

Has  problems  comparing  deals  due  to  information-related 
factors - energy sector 

Has  problems  comparing  deals  due  to  information-related 
factors - online sector 

Has  problems  comparing  deals  due  to  information-related 
factors - finance sector 

20.  Has  an  electricity  supplier  and  has  not  switched  due  to 
information-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
information-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  information-
related factors - financial sector 

Dimension 4 : Inability or failure to buy, choose or access 
suitable products 

Does not compare product deals - energy sector 

Finds it very difficult to compare product deals - energy sector 

Does not compare product deals - online sector 

Finds it very difficult to compare product deals - online sector 

Does not compare product deals - finance sector 

Finds it very difficult to compare product deals - finance sector 

Has problems comparing deals due to personal factors - energy 
sector 

Has  problems  comparing  deals  due  to  personal  factors  -  online 
sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
financial sector 

553 

4% 

2% 

17% 

2% 

3% 

2% 

2% 

11% 

6% 

7% 

1% 

0% 

1% 

38% 

10% 

33% 

3% 

60% 

8% 

24% 

13% 

33% 

 
 
 
 
 
 
 
Annex 1│ Country fiches 

Has  problems  comparing  deals  due  to  market-related  factors  - 
energy sector 

Has  problems  comparing  deals  due  to  market-related  factors  - 
online sector 

Has  problems  comparing  deals  due  to  market-related  factors  - 
financial sector 

Has  problems  comparing  deals  due  to  access-related  factors  - 
energy sector 

Has  problems  comparing  deals  due  to  access-related  factors  - 
online sector 

Has  problems  comparing  deals  due  to  access-related  factors  - 
financial sector 

Has an electricity supplier and has not switched due to personal 
factors - energy sector 

Has  an  internet  provider and  has  not  switched  due  to  personal 
factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  personal 
factors - financial sector 

Has an electricity supplier and has not switched due to market-
related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to  market-
related factors - online sector 

Has a bank account and has not switched due to market-related 
factors - financial sector 

Has an electricity supplier and has not  switched due  to access-
related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to  access-
related factors - online sector 

Has a bank account and has not switched due to access-related 
factors - financial sector 

Has not switched due to termination costs 

Has not switched due to bundling 

Excluded from e-commerce due to difficulty of the process 

Excluded from e-commerce due to not having a payment card 

Declined for a loan 

Dimension  5 :  Higher  susceptibility 
practices 

to  marketing 

Vulnerable  to  a  great  extent  due  to  complexity  of  offers  (own 
perception) 

Experiment  choice  under  the  marketing  practice  treatment  - 
energy sector 

Experiment  choice  under  the  marketing  practice  treatment  - 
online sector 

31% 

21% 

26% 

4% 

3% 

1% 

5% 

3% 

5% 

11% 

7% 

13% 

3% 

2% 

2% 

12% 

14% 

15% 

8% 

3% 

10% 

82% 

48% 

554 

 
 
 
 
Annex 1│ Country fiches 

Experiment  choice  under  the  marketing  practice  treatment  - 
finance sector 

Experiment  choice  under  the  marketing  practice  treatment  - 
cross-cutting 

26% 

60% 

555 

 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Table 113:  Country fiche - Iceland 

Country fiche summary 

Approach  to  consumer  vulnerability:  Evidence  suggests  that  national  consumer  policy 
takes a neutral approach and hence does not involve a specific approach to consumer 
vulnerability.  

Measures  addressing  consumer  vulnerability:  There  appears  to  be  only  a  limited 
number of measures addressing consumer vulnerability.  

National consumer protection framework 

Consumer  strategy  in  Iceland,  as  directed  by  the  Consumer  Agency,  aims  to  ensure 
correct  information  to  both  the  public  and  to  business  operators  on  rights  and 
obligations in consumer transactions with the view to ensure respect for legislation on 
safety and consumer rights.  

Approach to consumer vulnerability 

Consumer vulnerability within the context of the transposed UCPD 

The  UCPD  is  transposed  through  the  Act  No.  57/2005  on  the  surveillance  of  unfair 
business  practices  and  market  transparency.  The  term  “vulnerable  consumer”  is  not 
included in the transposition. Similarly, the term “average consumer” is not present in 
the  Act.  Only  Article  7  makes  a  particular  reference  to  children  and  young  adults  in 
relation to marketing practices.  

Consumer vulnerability in national consumer policy beyond the UCPD 

As  set  out  above,  the  term  consumer  vulnerability  is  generally  not  used  in  Iceland, 
neither is the concept of “average consumer”.  

Stakeholder assessment of the national approach to consumer vulnerability 

According  to  the  stakeholder  consulted,  vulnerability  is  generally  not  a  widely  used 
concept  in  Iceland.  Instead,  all  consumers  should  be  regarded  as  vulnerable 
throughout their life.  

Specific measures  

There  is  evidence  of  few  specific  measures  in  Iceland  relating  to  mitigating  consumer 
vulnerability. One example of specific measures in the financial sector includes recent 
legislation to curb the prevalence of payday loans. This involves limiting the amount of 
APR  to  50%  in  addition  to  the  interest  set  by  the  Central  Bank  in  order  to  limit  the 
excessive fees associated with these types of loans. However, there are indications of 
companies surpassing this limit by charging additional fees for fast tracking or instant 
approval of loans. According to the interviews, financial services regulation states that 
business  should  be  conducted  in  a  fair  way.  However,  what  is  fair  has  never  been 
established and thus is not effectively enforced.  

Non-legislative measures 

As  there  is  little  focus  on  vulnerable  consumers  in  the  legal  and  policy  framework, 
there is no evidence of relevant non-legislative measures in Iceland.   

Institutional structure and role of civil society 

Public bodies responsible for consumer policy 

Consumer  protection  in  Iceland  falls  within  the  competence  of  the  Ministry  of  the 

556 

 
 
 
 
Annex 1│ Country fiches 

Interior.  However,  it  is  the  Icelandic  Consumer  Agency,  a  government  agency 
operating  under  the  Ministry  of  Interior,  which  is  tasked  with  the  enforcement, 
supervision  and  policy  formulation  in  regards  to  consumer  legislation.  The  Consumer 
Agency  has  the  responsibility  to  enforce  various  legal  acts  and  can  use  different 
measures  such  as  fines,  product  recalls,  sales  bans,  sanctions  on  misleading 
advertisements or prohibiting the use of illegal standard contract terms.  

In  addition  to  the  Consumer  Agency,  a  Consumer  Spokesman  is  responsible  for 
safeguarding  the  interests  and  rights  of  consumers  and  for  enhancing  consumer 
protection.  

Role of civil society 

The  Consumer  Association  of  Iceland  has  around  9,000  members  and  works  to 
promote and protect the interests of consumers, while undertaking comparative testing 
of goods and services. In addition, the Association also engages in consumer education 
and  information.  As  pointed  out  above,  the  Association  does  not  have  any  particular 
focus on vulnerable consumers but considers all consumers as potentially vulnerable. 

Incidence of consumer vulnerability  

Indicator of vulnerability 

Mean incidence  

Dimension  1 :  Heightened  risk  of  negative  outcomes  or 
impacts on well-being 

Has experienced a problem and took no action 

Overpaid  for  services  due  to  being  unable  to  use  certain 
payment methods 

Dimension  2 :  Having  characteristics  that  limit  ability  to 
maximise well-beings 

Feels vulnerable to 'a great extent' due to health problems 

Feels  vulnerable 
circumstances 

to 

'a  great  extent'  due 

to 

financial 

Feels vulnerable to 'a great extent' due to employment situation 

Feels vulnerable to 'a great extent' due to age 

Feels  vulnerable  to  'a  great  extent'  due  to  belonging  to  a 
minority group 

Feels vulnerable to 'a great extent' due to personal issues 

Feels vulnerable to 'a great extent' due to other reasons 

Feels  vulnerable  to  'a  great  extent'  due  to  any  personal 
characteristic 

Dimension  3 :  Having  difficulty 
assimilating information 

in  obtaining  or 

Feels  'not  at  all'  informed  about  product  price,  quality, 
conditions, etc when choosing and buying goods and services 

Compares  product  deals  by  using 
advertisements only - energy sector 

information 

from 

557 

12% 

8% 

2% 

6% 

2% 

3% 

2% 

0% 

1% 

10% 

2% 

1% 

 
 
 
 
 
  
 
 
 
Annex 1│ Country fiches 

Compares  product  deals  by  using 
advertisements only - online sector  

information 

from 

Compares  product  deals  by  using 
advertisements only - finance sector 

information 

from 

Has  problems  comparing  deals  due  to  information-related 
factors - energy sector 

Has  problems  comparing  deals  due  to  information-related 
factors - online sector 

Has  problems  comparing  deals  due  to  information-related 
factors - finance sector 

20.  Has  an  electricity  supplier  and  has  not  switched  due  to 
information-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
information-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  information-
related factors - financial sector 

Dimension 4 : Inability or failure to buy, choose or access 
suitable products 

Does not compare product deals - energy sector 

Finds it very difficult to compare product deals - energy sector 

Does not compare product deals - online sector 

Finds it very difficult to compare product deals - online sector 

Does not compare product deals - finance sector 

Finds it very difficult to compare product deals - finance sector 

Has problems comparing deals due to personal factors - energy 
sector 

Has  problems  comparing  deals  due  to  personal  factors  -  online 
sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
financial sector 

Has  problems  comparing  deals  due  to  market-related  factors  - 
energy sector 

Has  problems  comparing  deals  due  to  market-related  factors  - 
online sector 

Has  problems  comparing  deals  due  to  market-related  factors  - 
financial sector 

Has  problems  comparing  deals  due  to  access-related  factors  - 
energy sector 

Has  problems  comparing  deals  due  to  access-related  factors  - 
online sector 

Has  problems  comparing  deals  due  to  access-related  factors  - 
financial sector 

Has an electricity supplier and has not switched due to personal 

3% 

1% 

32% 

15% 

20% 

91% 

40% 

48% 

14% 

71% 

17% 

51% 

25% 

36% 

23% 

37% 

41% 

27% 

2% 

5% 

558 

 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

factors - energy sector 

Has  an  internet  provider and  has  not  switched  due  to  personal 
factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  personal 
factors - financial sector 

Has an electricity supplier and has not switched due to market-
related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to  market-
related factors - online sector 

Has a bank account and has not switched due to market-related 
factors - financial sector 

Has an electricity supplier and has not  switched due to access-
related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to  access-
related factors - online sector 

Has a bank account and has not switched due to access-related 
factors - financial sector 

Has not switched due to termination costs 

Has not switched due to bundling 

Excluded from e-commerce due to difficulty of the process 

Excluded from e-commerce due to not having a payment card 

Declined for a loan 

Dimension  5 :  Higher  susceptibility 
practices 

to  marketing 

9% 

17% 

19% 

15% 

Vulnerable  to  a  great  extent  due  to  complexity  of  offers  (own 
perception) 

4% 

Experiment  choice  under  the  marketing  practice  treatment  - 
energy sector 

Experiment  choice  under  the  marketing  practice  treatment  - 
online sector 

Experiment  choice  under  the  marketing  practice  treatment  - 
finance sector 

Experiment  choice  under  the  marketing  practice  treatment  - 
cross-cutting 

559 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Table 114:  Country fiche - Norway 

Country fiche summary 

Approach  to  consumer  vulnerability:  While  the  concept  of  consumer  vulnerability 
appears  to  be  used  in  specific  sectors,  there  is  no  evidence  of  a  broader  strategic 
approach to tackling consumer vulnerability at national level.  

Measures addressing consumer vulnerability: There is evidence of a range of legislative 
and non-legislative measures addressing consumer vulnerability.  

National consumer protection framework 

According  to  the  Ministry  of  Children,  Equality  and  Social  Inclusion,  the  basis  of 
Norwegian  consumer  policy  is  founded  on  consumers’  right  to  be  well informed  about 
the  goods  and  services  they  purchase,  while  receiving  appropriate  information  about 
their rights and obligations. In addition, efficient systems for solving conflicts between 
buyers  and  sellers  must  be  ensured  and  laws and  regulation  must  consider  consumer 
interests in a good and balanced manner.   

Approach to consumer vulnerability 

Consumer vulnerability within the context of the transposed UCPD  

The new Marketing Control Act, which came into force on 1 June 2009 implements the 
EU  Unfair  Commercial  Practices  Directive  (2005/29/EC)374.  The  term  “vulnerable”  has 
been  transposed  in  line  with  the  Directive’s  wording,  using  the  terms  mental  or 
physical infirmity, age, and credulity. The Marketing Control Act does not mention the 
“average”  consumer.  However,  recent  case  law  in  the  financial  services  sector  has 
referred  to  “non-professional  investors”  (paragraph  125)  and  “small  savers  and 
inexperienced investors” (paragraph 128)375. 
Consumer vulnerability in national consumer policy beyond the UCPD  

The term “vulnerable consumers” is generally not used in Norwegian legislation beyond 
the  context  of  the  UCPD.  However,  the  Consumer  Ombudsman  is  placing  a  particular 
focus on vulnerable consumer groups (“Sårbare forbrukergrupper”). This is defined as 
those  who  may  be  more  vulnerable  due  to  age,  experience  and  lack  of  digital 
literacy376.  
Stakeholder assessment of consumer vulnerability  

According to the stakeholders, the concept of vulnerable consumers is not in wide use. 
However,  there  are  a  number  of  specific  measures  that  address  marketing  practices 
targeting children, young adults and seniors.  

Specific measures  

In  the  financial  sector,  the  Debt  Settlement  Act  gives  individuals  with  serious  debt 
problems the opportunity to regain control of their finances. A debt settlement scheme 
involves an agreement to pay as much as possible of the debt during a limited period 
(generally 5 years) and after this period the remaining debt is cancelled. The scheme 
covers all types of debt, including debt to public authorities, and can, as a general rule, 

374 See http://ec.europa.eu/justice/consumer-

marketing/files/ucpd_final_report_part_1_synthesis.pdf 

375 See HR-2013-00642-S, (Case no. 2011/1938), Civil Case, Appeal from Judgment 
376 See http://www.forbrukerombudet.no/2013/03/saarbare-forbrukargrupper-i-2014  

560 

 
 
 
                                                 
Annex 1│ Country fiches 

be  only  used  once  in  a  person's  lifetime.  The  measure  is  targeted  at  vulnerable 
individuals  who  are  permanently  unable  to  meet  their  liabilities377.   In  addition,  a 
proposal  is  underway  to  create  a  debt  register  (“gjeldsregister”)378.  The  register  will 
help  banks  and  other  loan  institutions  to  check  a  person’s  credit  history  in  order  to 
restrict  access  to  short-term  credit  and  make  it  more  difficult  for  individuals  to 
accumulate debt.  

In  the  energy  sector,  for  those  consumers  with  difficulty  paying  their  energy  bills 
specific rules are in place with regard to disconnection of energy supply. Whereas the 
consumer  will  not  lose  power,  the  contract  with  the  supplier  is  annulled  and  the 
consumer is moved to a supplier of last resort/default supplier.  

Non-legislative measures 

As  noted  above,  there  is  a  particular  focus  on  children  and  young  consumers  within 
Norwegian  consumer  policy  and  the  Consumer  Ombudsman  has  set  out  a  number  of 
industry  standards  to  protect  this  consumer  group.  For  example,  the  Consumer 
Ombudsman  has  issued  guidelines  for  industry  on  marketing  practices  and  on 
contracts379,  which  help  businesses  comply  with  legislation.  In  the  particular  area  of 
finance the Consumer Ombudsman has agreed with the financial services industry on a 
Model  Agreement  for  bankcards  used  by  children  under  the  age  of  13.  A  particular 
focus in Norway is also on children and the use of mobile in-app payments and industry 
standards  for  deployment  of  apps  and  payment  services  for  children  have  been 
developed.  

Institutional structure and role of civil society 

Public bodies responsible for consumer policy 

In Norway, formulation of consumer policy falls within the competences of the Ministry 
of  Children,  Equality  and  Social  Inclusion.  The  Consumer  Ombudsman  is  an 
independent  administrative  body  with  the  responsibility  for  supervising  commercial 
practices.  It  therefore  seeks  to  exert  influence  on  traders  to  comply  with  relevant 
regulations.  The  Consumer  Ombudsman  considers  cases  upon  complaints  from 
consumers  and  traders,  but  will  also,  at  own  initiative,  investigate  marketing 
measures.  The  Ombudsman  is  responsible  for  the  initial  review  of  cases  of  assumed 
violations.  If  voluntary  agreements  fail  to  be  reached  between  the  Consumer 
Ombudsman  and  traders,  the  Consumer  Ombudsman  may  submit  the  case  to  the 
Market Council, an administrative “court of law”. 

The  Market  Council  carries  out  supervision  of  the  Marketing  Control  Act,  as  well  as 
other parts of the regulatory framework, such as the advertising regulations in the Act 
on  Broadcasting.  The  Market  Council  has  the  authority  to  issue  decisions  banning 
unlawful marketing or contract terms and conditions in standard contracts. It also has 
the  authority  to  ban  unlawful  advertising  and  to  repeal  decisions  made  by  The 
Consumer Ombudsman as well as the Directorate of Health and The Norwegian Media 
Authority.  

377 See http://www.fininc.eu/knowledge-and-data/best-practices,en,48,96,3,70.html  
378 See http://www.forbrukerradet.no/annet/blogg/finans/en-fremtid-med-kreditt-og-

gjeldsregister  

379 See http://www.forbrukerombudet.no/id/11037367.0  

561 

 
 
 
 
 
                                                 
Annex 1│ Country fiches 

Role of civil society 

The  Norwegian  Consumer  Council  is  the  main  organisation  representing  consumer 
interests  in  Norway.  Although  the  organisation  is  publicly  funded,  the  Council  is  an 
independent  body  focused  on  empowering  consumers  in  the  marketplace.  The 
Consumer  Council  carries  out  a  range  of  activities  to  support  consumers  in  financial 
services,  energy  and  online  environment  sectors,  although  these  actions  do  not 
explicitly target vulnerable consumers.  

Incidence of consumer vulnerability  

Indicator of vulnerability 

Mean incidence  

Dimension  1 :  Heightened  risk  of  negative  outcomes  or 
impacts on well-being 

Has experienced a problem and took no action 

Overpaid  for  services  due  to  being  unable  to  use  certain 
payment methods 

Dimension  2 :  Having  characteristics  that  limit  ability  to 
maximise well-beings 

Feels vulnerable to 'a great extent' due to health problems 

Feels  vulnerable 
circumstances 

to 

'a  great  extent'  due 

to 

financial 

Feels vulnerable to 'a great extent' due to employment situation 

Feels vulnerable to 'a great extent' due to age 

Feels  vulnerable  to  'a  great  extent'  due  to  belonging  to  a 
minority group 

Feels vulnerable to 'a great extent' due to personal issues 

Feels vulnerable to 'a great extent' due to other reasons 

Feels  vulnerable  to  'a  great  extent'  due  to  any  personal 
characteristic 

Dimension  3 :  Having  difficulty 
assimilating information 

in  obtaining  or 

Feels  'not  at  all'  informed  about  product  price,  quality, 
conditions, etc when choosing and buying goods and services 

Compares  product  deals  by  using 
advertisements only - energy sector 

information 

from 

Compares  product  deals  by  using 
advertisements only - online sector  

information 

from 

Compares  product  deals  by  using 
advertisements only - finance sector 

information 

from 

Has  problems  comparing  deals  due  to  information-related 
factors - energy sector 

Has  problems  comparing  deals  due  to  information-related 
factors - online sector 

Has  problems  comparing  deals  due  to  information-related 

8% 

8% 

3% 

7% 

3% 

2% 

1% 

1% 

1% 

12% 

3% 

3% 

3% 

2% 

16% 

9% 

13% 

562 

 
 
 
 
 
 
 
Annex 1│ Country fiches 

factors - finance sector 

20.  Has  an  electricity  supplier  and  has  not  switched  due  to 
information-related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to 
information-related factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  information-
related factors - financial sector 

Dimension 4 : Inability or failure to buy, choose or access 
suitable products 

Does not compare product deals - energy sector 

Finds it very difficult to compare product deals - energy sector 

Does not compare product deals - online sector 

Finds it very difficult to compare product deals - online sector 

Does not compare product deals - finance sector 

Finds it very difficult to compare product deals - finance sector 

Has problems comparing deals due to personal factors - energy 
sector 

Has  problems  comparing  deals  due  to  personal  factors  -  online 
sector 

Has  problems  comparing  deals  due  to  personal  factors  - 
financial sector 

Has  problems  comparing  deals  due  to  market-related  factors  - 
energy sector 

Has  problems  comparing  deals  due  to  market-related  factors  - 
online sector 

Has  problems  comparing  deals  due  to  market-related  factors  - 
financial sector 

Has  problems  comparing  deals  due  to  access-related  factors  - 
energy sector 

Has  problems  comparing  deals  due  to  access-related  factors  - 
online sector 

Has  problems  comparing  deals  due  to  access-related  factors  - 
financial sector 

Has an electricity supplier and has not switched due to personal 
factors - energy sector 

Has  an  internet  provider and  has  not  switched  due  to  personal 
factors - online sector 

Has  a  bank  account  and  has  not  switched  due  to  personal 
factors - financial sector 

Has an electricity supplier and has not switched due to market-
related factors - energy sector 

563 

55% 

9% 

58% 

7% 

52% 

7% 

35% 

25% 

31% 

31% 

18% 

27% 

12% 

21% 

1% 

 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 1│ Country fiches 

Has  an  internet  provider  and  has  not  switched  due  to  market-
related factors - online sector 

Has a bank account and has not switched due to market-related 
factors - financial sector 

Has an electricity supplier and has not  switched  due  to access-
related factors - energy sector 

Has  an  internet  provider  and  has  not  switched  due  to  access-
related factors - online sector 

Has a bank account and has not switched due to access-related 
factors - financial sector 

Has not switched due to termination costs 

Has not switched due to bundling 

Excluded from e-commerce due to difficulty of the process 

Excluded from e-commerce due to not having a payment card 

Declined for a loan 

Dimension  5 :  Higher  susceptibility 
practices 

to  marketing 

7% 

22% 

20% 

6% 

Vulnerable  to  a  great  extent  due  to  complexity  of  offers  (own 
perception) 

5% 

Experiment  choice  under  the  marketing  practice  treatment  - 
energy sector 

Experiment  choice  under  the  marketing  practice  treatment  - 
online sector 

Experiment  choice  under  the  marketing  practice  treatment  - 
finance sector 

Experiment  choice  under  the  marketing  practice  treatment  - 
cross-cutting 

564 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 2│ Research methods used in the study 

Annex 2  Research methods used in the study 

A2.1  Pilot 

In  order  to  collect  preliminary  information  from  the  general  public  which  would  serve 
as support for finalising the questionnaire for the main survey, a pilot study in the UK 
has  been  implemented.  The  questionnaire  was  piloted  in  order  to  test  for  length  of 
questionnaire  and  any  difficulties  respondents  may  have  with  understanding  the 
behavioural experiments.  

The  pilot  was  run  in  the  UK  so  that  it  could  be  conducted  in  English.  Because  all 
materials  for  the  study  were  originally  designed  in  English,  running  the  pilot  in  an 
English  speaking  country  eased  testing  the  questionnaire  and  the  experiment,  and 
allowed to implement the necessary changes for the final version in a timely matter.  

In  total,  101  pilot  interviews  were  completed  between  12  and  19  September  2014, 
using the Ipsos online panel in the UK. To ensure that a range of respondents took part 
in  the  survey,  quotas  were  set  on  age,  gender,  education  level,  working  status  and 
urbanisation. Given the size of the pilot, there was no strict quota on region. 

The results of the pilot showed that the questionnaire and experiments together were 
too long. In the proposal it was budgeted that both the interview and the experiment 
would  each  last  10  minutes  (20  minutes  in  total).  The  pilot  data  showed  it  took  on 
average  12.6  minutes  to  complete  the  pilot  survey  questionnaire  and  5.6  minutes  for 
the experiment section. Given that it was very important that the survey questionnaire 
did not exceed 10 minutes (as this part of the questionnaire was fielded in all countries 
during the mainstage), it was decided to slightly shorten the questionnaire. 

A2.2  Translation 

After  the  changes  from  the  pilot  were  agreed  on  and  all  the  materials  for  the  survey 
were  signed-off,  the  questionnaire  and  experiment  were  translated  in  the  local 
language(s)  of  each  country.  Below  is  the  list  of  countries  with  their  corresponding 
language(s): 

565 

 
 
 
Annex 2│ Research methods used in the study 

Table 115:  Country and language 

Country 

Austria 
Belgium 
Bulgaria 
Cyprus 
Czech Republic 
Denmark 
Estonia 
Finland 
France 
Germany 
Greece 
Hungary 
Ireland 
Iceland 
Italy 
Latvia 
Lithuania 
Luxembourg 
Malta 
Netherlands 
Norway 
Poland 
Portugal 
Romania 
Slovakia 
Slovenia 
Spain 
Sweden 
UK 

Language 

German 
French, Dutch 
Bulgarian 
Greek 
Czech 
Danish 
Estonian/ Russian 
Finnish 
French 
German 
Greek 
Hungarian 
English 
Icelandic 
Italian 
Latvian/ Russian 
Lithuanian 
Luxembourgish/ French 
Maltese 
Dutch 
Norwegian 
Polish 
Portuguese 
Romanian 
Slovakian 
Slovenian  
Spanish 
Swedish 
English  

A2.3  Sample 

The  target  audience  of  the  consumer  survey  and  experiment  was  the  general 
population  aged  16  to  75  (although  older  respondents  were  not  excluded  from  the 
survey).  Quotas  were  set  on  gender,  age,  region,  urbanisation  level,  education  and 
working  status  (active/inactive),  to  ensure  that  the  sample  in  each  country  was 
representative  of  the  general  population.  In  each  of  the  big  or  middle-sized  countries 
at  least  1,000  respondents  were  interviewed.  For  the  seven  smallest  countries  – 
countries  that  individually  represent  less  than  0.5%  of  the  total  EU  population  –  at 
least  500  consumers  were  interviewed.  This  applied  to  Luxemburg,  Cyprus,  Malta, 
Slovenia, Estonia, Latvia and Iceland. For each country the best interviewing mode or a 
combination of interviewing modes was determined, as can be seen in the table below. 

566 

 
 
 
Annex 2│ Research methods used in the study 

Table 116:  Number of interviews conducted online, by telephone (CATI) and 

via in-home self-completion (CAPI) 

% of individuals 
with Broadband 
Internet access 
at home* 

Target 
total 
sample 
size 

Actual 
total 
sample 
size 

Online 

CATI 

CAPI 

Planned 

Realised 

Planned 

Realised 

Planned 

Realised 

NL 

LU 

SE 

DK (e) 

FI 

DE 

UK (e) 

IE 

FR 

AT 

BE 

MT 

EE 

SK 

SI 

CZ 

PL 

LV 

HU 

ES 

HR 

IT 

LT (e) 

CY 

PT (e) 

EL 

RO (e) 

BG 

IS 

NO 

94 

93 

92 

92 

87 

85 

83 

81 

80 

79 

78 

77 

75 

75 

74 

71 

70 

69 

69 

68 

66 

63 

62 

62 

61 

54 

54 

51 

95 

93 

1000 

500 

1000 

1000 

1000 

1000 

1000 

1000 

1000 

1000 

1000 

500 

500 

1000 

500 

1000 

1000 

500 

1000 

1000 

1000 

1000 

1000 

500 

1000 

1000 

1000 

1000 

500 

1000 

1019 

503 

1002 

1002 

1004 

1006 

1027 

1011 

1002 

1001 

1002 

524 

500 

1005 

502 

1000 

1009 

500 

1003 

1000 

1002 

1001 

1009 

500 

1012 

1004 

1001 

1000 

500 

1002 

940 

465 

920 

920 

870 

850 

830 

810 

800 

790 

780 

375 

750 

370 

710 

700 

345 

690 

680 

660 

630 

620 

610 

540 

540 

510 

475 

930 

959 

468 

921 

922 

874 

853 

830 

820 

800 

791 

780 

375 

752 

372 

710 

703 

345 

693 

680 

662 

631 

625 

621 

544 

541 

510 

475 

932 

60 

35 

80 

60 

35 

81 

130 

130 

190 

200 

210 

220 

500 

125 

250 

130 

290 

155 

310 

320 

340 

370 

191 

202 

210 

222 

524 

125 

253 

130 

290 

6 

155 

310 

320 

340 

370 

500 

500 

460 

460 

25 

70 

25 

70 

Total 
Note: ‘e’ signifies the experiments were run in the country. * Source: Eurostat 2013 

19110 

26653 

26500 

19189 

4970 

5009 

80 

80 

150 

170 

153 

197 

300 

300 

380 

384 

390 

391 

460 

490 

460 

490 

2420 

2455 

A2.4  Online panels 

The online part of the main stage fieldwork was conducted using Ipsos’ online panels. 
In some countries, Ipsos’ panels were supplemented with partner panels, either due to 
Ipsos  not  currently  having  a  panel  in  that  country  or  where  the  Ipsos  panel  was  too 
small  to  achieve  the  target  number  of  interviews.  The  management  of  the  online 
fieldwork  was  centralised  within  the  Ipsos  Interactive  Service  Bureau  (IIS).  All  the 
work  conducted  by  IIS  is  managed  centrally,  with  one  scripting,  data  collection  and 
data delivery process.  

All panel partners are chosen based on the availability and quality of their panels, and 
their  ability  to  implement  the  survey  according  to  the  quality  principles  and  guidance 
from  Ipsos.  All  selected  polling  institutes  are  well  known  for  the  quality  of  their 
network  and  are  involved  in  numerous  multilingual  and  multinational  surveys.  All  are 
ESOMAR members. The same script is used by all panel partners. The countries where 
we used external panel(s) were: 

567 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 2│ Research methods used in the study 

  Austria 

  Bulgaria 

  Croatia 

1 external panel 

1 external panel 

1 external panel 

  Czech Republic 

Ipsos Panel + 1 external panel 

  Denmark 

Ipsos Panel + 1 external panel 

  Estonia 

  Finland 

  Greece 

 

 

Iceland 

Ireland 

  Latvia  

  Lithuania 

1 external panel 

1 external panel 

1 external panel 

1 external panel 

1 external panel 

1 external panel 

1 external panel 

  Luxembourg   

1 external panel 

  Norway 

  Portugal 

  Slovakia 

  Slovenia 

Ipsos Panel + 1 external panel 

Ipsos Panel + 1 external panel 

1 external panel 

1 external panel 

A2.5  Offline interviews 

The  offline  part  of  the  main  stage  fieldwork  was  conducted  using  Ipsos’  regular 
network  partners  on  international  CATI  or  CAPI  surveys.  These  partners  all  have  a 
proven  track  record  of  delivering  high  quality  fieldwork,  combined  with  excellent  in-
country survey management. Each institute is member of ESOMAR. 

The  offline  part  of  the  study  has  been  managed  centrally  by  Ipsos’  International 
Coordination Centre (ICC) which is based in Brussels.  

The  table  below  shows  the  local institutes  and  details  of  the  offline  component  of  the 
survey. 

Table 117:  Country, details offline component and partner 

Country 

What? 

Partners 

Netherlands 
Luxembourg 
Sweden 
Denmark 
Finland 
Germany 
UK 
Ireland 
France 
Austria 
Belgium 
Malta 
Estonia 
Slovakia 
Slovenia 

60 CATIs 
35 CATIs 
80 CATIs 
80 CAPIs 
130 CATIs 
150 CAPIs 
170 CAPIs 
190 CATIs 
200 CATIs 
210 CATIs 
220 CATIs 
500 CATIs 
125 CATIs 
250 CATIs 
130 CATIs 

Ipsos 
TNS ILRES 
Ipsos 
DMA 
Taloustutkimus 
Ipsos 
Ipsos 
Ipsos 
Ipsos 
Spectra 
Ipsos 
Misco 
Turu-uuringute 
Ipsos 
Ipsos 

568 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 2│ Research methods used in the study 

Table 117:  Country, details offline component and partner 

Country 

What? 

Partners 

Czech Republic 
Poland 
Latvia 
Hungary 
Spain 
Croatia 
Italy 
Lithuania 
Cyprus 
Portugal 
Greece 
Romania 
Bulgaria 
Iceland 
Norway 

290 CATIs 
300 CAPIs 
155 CATIs 
310 CATIs 
320 CATIs 
340 CATIs 
370 CATIs 
380 CAPIs 
190 CATIs 
390 CAPIs 
460 CATIs 
460 CAPIs 
490 CAPIs 
25 CATIs 
70 CATIs 

Ipsos 
Ipsos 
SKDS 
Ipsos 
Ipsos 
Ipsos 
Ipsos 
RAIT 
Ipsos 
Ipsos 
Ipsos 
Ipsos 
Ipsos 
MMR 
Ipsos 

A2.6  Online panels 

The online part of the main stage fieldwork was conducted using Ipsos’ online panels. 
In some countries, Ipsos’ panels were supplemented with partner panels, either due to 
Ipsos  not  currently  having  a  panel  in  that  country  or  where  the  Ipsos  panel  was  too 
small  to  achieve  the  target  number  of  interviews.  The  management  of  the  online 
fieldwork  was  centralised  within  the  Ipsos  Interactive  Service  Bureau  (IIS).  All  the 
work  conducted  by  IIS  is  managed  centrally,  with  one  scripting,  data  collection  and 
data delivery process.  

All panel partners are chosen based on the availability and quality of their panels, and 
their  ability  to  implement  the  survey  according  to  the  quality  principles  and  guidance 
from  Ipsos.  All  selected  polling  institutes  are  well  known  for  the  quality  of  their 
network  and  are  involved  in  numerous  multilingual  and  multinational  surveys.  All  are 
ESOMAR members. The same script is used by all panel partners. The countries where 
we used external panel(s) were: 

  Austria 

  Bulgaria 

  Croatia 

1 external panel 

1 external panel 

1 external panel 

  Czech Republic 

Ipsos Panel + 1 external panel 

  Denmark 

Ipsos Panel + 1 external panel 

  Estonia 

  Finland 

  Greece 

 

 

Iceland 

Ireland 

  Latvia  

  Lithuania 

1 external panel 

1 external panel 

1 external panel 

1 external panel 

1 external panel 

1 external panel 

1 external panel 

569 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 2│ Research methods used in the study 

  Luxembourg   

1 external panel 

  Norway 

  Portugal 

  Slovakia 

  Slovenia 

Ipsos Panel + 1 external panel 

Ipsos Panel + 1 external panel 

1 external panel 

1 external panel 

A2.7  Fieldwork dates 

Fieldwork  was  completed  between  20  February  and  17  April  2015.  Countries  were 
launched in different batches. The estimated time needed to complete the fieldwork in 
each  country  varied  depending  on  the  complexity  of  the  applied  methodology,  the 
number  of  completes  to  be  achieved  in  the  country,  the  expected  response  rate  and 
the presence of the behavioural experiment in the survey.  

Table 118:  Fieldwork dates 

Country 

Start date 

End date 

Austria 
Belgium 
Bulgaria 
Croatia 
Cyprus 
Czech Republic 
Denmark 
Estonia 
Finland 
France 
Germany 
Greece 
Hungary 
Iceland 
Ireland 
Italy 
Latvia 
Lithuania 
Luxembourg  
Malta 
Netherlands 
Norway 
Poland 
Portugal 
Romania 
Slovakia 
Slovenia 
Spain 
Sweden 
UK 

5th March 
25th February 
25th February 
26th February 
6th March 
20th February 
18th March 
23rd February 
26th February 
27th February 
27th February 
20th February 
23rd February 
17th March 
26th February 
4th March 
5th March 
19th March 
11th March 
11th March 
25th February 
5th March 
20th February 
17th March 
17th March 
23rd February 
25th February 
20th February 
5th March 
6th March 

3rd April 
27th March 
22th March 
27th March 
18th March 
17th March 
17th April 
12th March 
18th March 
27th March 
19th March 
18th March 
30th March 
20th March 
30th March 
23th March 
25th March 
16th April 
20th March 
24th March 
13th March 
18th March 
29th March 
2nd April 
6th April 
2nd April 
22th March 
27th March 
29th March 
13th April 

A2.8  Interview length 

The table below shows the interview length per country. 

570 

 
 
 
 
 
 
 
 
Annex 2│ Research methods used in the study 

Table 119:  Average survey time 

Country 

Austria 
Belgium  
Bulgaria 
Croatia 
Cyprus 
Czech Republic 
Denmark (experiment) 
Estonia 
Finland 
France  
Germany 
Greece 
Hungary 
Iceland 
Ireland 
Italy 
Latvia 
Lithuania (experiment) 
Luxembourg  
Malta 
Netherlands  
Norway 
Poland 
Portugal (experiment) 
Romania (experiment) 
Slovakia 
Slovenia 
Spain 
Sweden  
United Kingdom (experiment) 

Average online 
interview length in 
minutes 

Average offline 
interview length in 
minutes 

13 
11 
14 
12 
- 
12 
25 
15 
12 
13 
12 
13 
12 
15 
12 
11 
15 
26 
13 
- 
11 
14 
13 
23 
24 
15 
13 
12 
12 
20 

18 
20 
15 
19 
20 
21 
45 
21 
20 
19 
22 
11 
21 
14 
18 
14 
19 
31 
20 
18 
23 
17 
21 
23 
36 
23 
17 
18 
19 
18 

A2.9  Weighting and outputs 

There are two sets of weights that have been applied to the data:  

3)  Within  each  country,  the  data  has  been  weighted  on  the  following 

variables: 

  Gender 

  Age – 6 classes 

  16 - 24 
  25 - 34 
  35 - 44 
  45 - 54 
  55 - 64 
  65+ 

571 

 
 
 
 
Annex 2│ Research methods used in the study 

  Urbanisation  level  (DegUrba  –for  all  countries  except  Iceland)  –  3 

classes: 

  Densely populated area 

Intermediate density area 

 
  Thinly populated area 

  Region (number of classes vary per country) 

  Working status – 2 classes: 

  Active 

 

Inactive 

  Education – 3 classes 

  Low 
  Middle 
  High 

4)  Across countries, the data has been weighted to ensure that each country is 

represented according to its population size in the EU-wide results. 

Two sets of tables were created:  

  One set with cross-tabulations for individual country results, and  

  One set with socio-demographic cross-tabulations at overall level. 

A  fully  labelled  SPSS  (Statistical  Package  for  the  Social  Sciences)  file  with  all  the 
variables of the survey and experiment was also provided to the contracting authority. 

A2.10  Strengths and weaknesses of the survey methods 

A2.10.1  Sample achieved by using a mixed method approach 

The  study  used  a  mixed  mode  approach  to  primary  data  collection.  A  combination  of 
online,  CAPI  and  CATI  was  used.  This  sub-section  discuses  the  strengths  and 
weaknesses of this mixed mode approach.   

A  fully  online  survey  cannot  alone  be  fully  representative  of  the  general  public.  In 
2013,  depending  on  the  country,  between  4%  and  42%  of  the  EU28  population  had 
never used the internet. Moreover, internet users can differ from non-internet users in 
many ways, including age, education and employment status (see  Table 120 to Table 
124 below). This means that an online survey can only focus on a part of the  general 
population. 

A  mixed  mode  approach  facilitates  the  engagement  of  parts  of  the  population  that 
presumably  could  not  have  been  reached  by  using  only  an  online  approach.  It  is  not 
possible  to  predict,  with  complete  certainty,  how  an  online-only  sample  will look.  The 
exact sample that could have been achieved using an online approach depends on the 
specific study and therefore cannot be compared to earlier research. Nevertheless, the 
Ipsos online panels do closely reflect Eurostat figures on the online population (defined 
as people using internet at least once a week). Therefore these Eurostat figures on the 
online  population in  EU  Member  States  can  be  used  to  predict  how  the  sample  of  the 
current study would have looked if respondents had only have been  contacted online. 
The focus is on the achieved quotas for age, education and employment status. Quotas 
were also placed on urbanisation rate, region and gender. 

572 

 
 
 
 
Annex 2│ Research methods used in the study 

Table 120 below illustrates how older age groups, people 55 to 64 and 65 to 74 years 
old,  were  reached  using  the  mixed  mode  approach.  The  first  column  shows  the 
proportion  of  the  55-64  and  65-74  age  groups  in  the  general  population  aged  16–74 
for  each  country  included  in  the  study  (based  on  Eurostat  data  from  2011).  These 
statistics were used as quotas for this study. The second column shows the proportion 
of  the  55-64  and  65-74  age  groups  in  the  online  population  aged  16-74  (based  on 
Eurostat data from 2011). This shows the sample that could have been expected if an 
online only interviewing approach was used. The third column shows the proportion of 
the  55-64  and  65-74  age  groups  in  the  final  data  for  this  study.  The  fourth  column 
shows the difference between the proportion of the 55-64 and 65-74 age groups in the 
final data (using the mixed mode approach) and the general population in percentage 
points.  A  negative  value  shows  the  extent  to  which  the  age  group  was  not  reached. 
That  is  a  negative  value  indicates  that  the  age  group  was  underrepresented  in  the 
sample  compared  to  the  general  population.  A  positive  percentage  indicates  that  this 
age group was  overrepresented compared to the general population. The fifth column 
shows  the  expected  level  of  over-  and  underrepresentation  compared  to  the  general 
population if the survey had been carried out by only online. 

When looking at the data in Table 120, it can be seen that the mixed mode approach 
more closely resembles the general population than could have been achieved from an 
online  only  survey.  In  particular,  calling  and  visiting  in-home  meant  people  65  years 
and  older  were  reached.  This  part  of  the  population  represents  only  1%  of  the  online 
population  in  countries  like  Croatia,  Greece,  Ireland,  Romania  and  Slovenia  (see 
column II). Only in Croatia was the 55-64 age group substantially underrepresented in 
the final sample, compared to the general population (-9% points, see column IV, 55-
64).  This  occurred  because  the  Croatian  CATI  respondents  that  participated  were 
generally older than 65, as can be seen by the overrepresentation of the Croatian 65-
74+ age group (see column IV, 65-74+).  

Table 120:  Final sample - age groups, by country (excl. CATI only countries) 

Age - gen.pop.1  Age - onl.pop.2 

Achieved3 
65-
74+ 

55-64  65-74  55-64  65-74  55-64 

16% 

17% 

14% 

18% 

18% 

17% 

16% 

19% 

18% 

16% 

16% 

18% 

14% 

17% 

13% 

12% 

31% 

12% 

11% 

13% 

12% 

13% 

11% 

15% 

13% 

12% 

9% 

11% 

14% 

10% 

9% 

11% 

16% 

11% 

17% 

14% 

13% 

6% 

12% 

6% 

14% 

10% 

5% 

5% 

2% 

1% 

4% 

9% 

3% 

7% 

6% 

7% 

1% 

3% 

1% 

3% 

15% 

21% 

14% 

9% 

18% 

18% 

20% 

18% 

19% 

17% 

18% 

18% 

14% 

19% 

11% 

12% 

31% 

19% 

11% 

13% 

7% 

13% 

16% 

15% 

14% 

12% 

12% 

20% 

AT 

BE 

BG* 

HR 

CZ 

DK* 

EE 

FI 

FR 

DE* 

EL 

HU 

IE 

IT 

573 

 Ach. /pop.4  Onl./gen.pop.5 

55-64 

65-
74+ 

55-64 

65-
74+ 

-1% 

-2% 

4% 

0% 

-9% 

0% 

1% 

4% 

-1% 

1% 

1% 

2% 

0% 

0% 

2% 

0% 

0% 

7% 

0% 

0% 

-5% 

0% 

5% 

0% 

1% 

0% 

3% 

6% 

-5% 

-3% 

-8% 

-7% 

-4% 

-29% 

-9% 

-11% 

-7% 

-1% 

-5% 

-2% 

-4% 

-3% 

-7% 

-4% 

-9% 

-6% 

-5% 

-8% 

-10% 

-12% 

-6% 

-8% 

-9% 

-8% 

-7% 

-11% 

 
 
 
  
  
Annex 2│ Research methods used in the study 

LV 

LT* 

LU 

NL 

PL* 

PT* 

RO* 

SK 

SI 

ES 

SE 

UK* 

15% 

14% 

14% 

18% 

18% 

16% 

15% 

16% 

18% 

15% 

17% 

16% 

13% 

12% 

15% 

12% 

9% 

13% 

11% 

9% 

12% 

11% 

14% 

12% 

9% 

8% 

11% 

17% 

9% 

8% 

5% 

9% 

9% 

8% 

16% 

13% 

2% 

2% 

9% 

7% 

2% 

3% 

1% 

2% 

1% 

3% 

9% 

7% 

17% 

11% 

19% 

20% 

18% 

16% 

15% 

17% 

17% 

11% 

18% 

21% 

13% 

12% 

19% 

12% 

9% 

14% 

11% 

7% 

13% 

12% 

13% 

9% 

2% 

-3% 

5% 

2% 

0% 

0% 

0% 

1% 

-1% 

-4% 

1% 

5% 

0% 

0% 

4% 

0% 

0% 

1% 

0% 

-6% 

-11% 

-6% 

-10% 

-3% 

-1% 

-9% 

-6% 

-5% 

-7% 

-8% 

-10% 

-10% 

-10% 

-2% 

-7% 

-7% 

1% 

1% 

-1% 

-3% 

-9% 

-11% 

-7% 

-1% 

-3% 

-8% 

-5% 

-5% 

NO 

17% 

11% 

16% 

7% 

19% 

7% 

2% 

-4% 

-1% 

-4% 

6% 

9% 

14% 

15% 

17% 

IS 
1) Proportion of 55-64 and 65-74 age groups in general population aged 16-74 - quota (Eurostat 2011). 
2) Proportion of 55-64 and 65-74 age groups in online population aged 16-74 (Eurostat 2011). 
3) Achieved proportion of interviews in age groups for this study. 
4) Difference achieved proportion of interviews and general population per age group, in % points. 
5) Expected results online only interviews: difference online and general population aged 16-74, in % points. 
*Countries using CAPI 
Source: Ipsos 

-1% 

0% 

2% 

9% 

-3% 

Another  demographic  group  that  is  difficult  to  reach  by  using  only  an  online  survey 
method is the economically inactive population.380 Table 121 illustrates how the mixed 
method  approach  facilitated  the  engagement  of  inactive  people.381  The  table  shows 
that  in  most  countries  the  proportion  of  inactive  respondents  interviewed  resembled 
quite accurately the proportion of inactive people in the general population aged 16-74 
(see column IV in the table above), while this would clearly have been much harder to 
achieve using an online only approach (see column V in the table above). An exception 
is  Ireland,  where  the  inactive  population  was  underrepresented  by  15  percentage 
points.  The  reason  for  this  is  that  the  inactivity  rate  in  the  general  population  is 
relatively  high  in  Ireland  compared  to  the  online  panel,  while  ‒  more  importantly  ‒ 
“active” respondents showed a high response rate, which led to some quotas filling up 

380  Defined  by  Eurostat  as  the  population  that  is  neither  employed  nor  unemployed: 

http://ec.europa.eu/eurostat/statistics-
explained/index.php/People_outside_the_labour_market 

381  The first column of the table shows the proportion of the economically inactive group in the 
general  population  aged  16-74  in  the  individual  countries.  The  second  column  shows  the 
proportion of the economically inactive population in the online population aged 16-74. The 
third column shows the proportion of the economically inactive group in the final data for this 
study. The fourth and fifth columns show the difference in % points between the proportion 
of economically inactive people in the general population and – respectively – the proportion 
of  economically  inactive  people  in  the  survey  for  this  study  and  the  proportion  of 
economically  inactive  people  in  the  online  population  aged  16-74  (the  latter  showing  the 
expected results of an online only survey). 

574 

 
 
 
  
  
  
  
  
  
  
 
 
 
 
 
                                                 
Annex 2│ Research methods used in the study 

quickly. This combination of factors caused some Irish quotas to conflict near the end 
of  fieldwork  and  made  it  impossible  to  achieve  the  set  active/inactive  quota.  In 
Denmark,  inactive  people  were  substantially  overrepresented  in  the  final  sample  (see 
column IV in the table above). This can be explained by the fact that the response rate 
of  the  Danish  inactive  population  for  the  online  survey  proved  higher  than  initially 
foreseen.  

Table 121:  Final sample – inactive population, by country (excl. CATI only 

countries) 

Gen.pop.1 

Onl.pop.2 

Achieved3 

 Ach./pop.4 

Onl./gen.pop.5 

Inactive 

Inactive 

Inactive 

Inactive 

Inactive 

35% 

41% 

38% 

57% 

37% 

30% 

32% 

34% 

37% 

33% 

48% 

44% 

49% 

45% 

35% 

36% 

37% 

30% 

39% 

32% 

41% 

37% 

43% 

34% 

29% 

32% 

29% 

43% 

22% 

29% 

12% 

23% 

22% 

23% 

16% 

24% 

26% 

21% 

20% 

26% 

37% 

20% 

15% 

13% 

30% 

25% 

18% 

12% 

13% 

19% 

17% 

15% 

23% 

22% 

23% 

17% 

38% 

44% 

39% 

51% 

35% 

42% 

31% 

41% 

42% 

31% 

50% 

46% 

34% 

48% 

36% 

43% 

41% 

36% 

37% 

27% 

38% 

36% 

43% 

41% 

31% 

32% 

34% 

43% 

3% 

3% 

1% 

-6% 

-2% 

12% 

-1% 

7% 

5% 

-2% 

2% 

2% 

-15% 

3% 

1% 

7% 

4% 

6% 

-2% 

-5% 

-3% 

-1% 

0% 

7% 

2% 

0% 

5% 

0% 

-13% 

-12% 

-26% 

-34% 

-15% 

-7% 

-16% 

-10% 

-11% 

-12% 

-28% 

-18% 

-12% 

-25% 

-20% 

-23% 

-7% 

-5% 

-21% 

-20% 

-28% 

-18% 

-26% 

-19% 

-6% 

-10% 

-6% 

-26% 

AT 

BE 

BG* 

HR 

CZ 

DK* 

EE 

FI 

FR 

DE* 

EL 

HU 

IE 

IT 

LV 

LT* 

LU 

NL 

PL* 

PT* 

RO* 

SK 

SI 

ES 

SE 

UK* 

NO 

IS 

575 

 
 
 
  
  
  
  
  
  
  
  
 
Annex 2│ Research methods used in the study 

1) Proportion of active/inactive groups in general population aged 16-74 (Eurostat 2011). 
2) Proportion of active/inactive groups in online population aged 16-74 (Eurostat 2011). 
3) Achieved proportion of interviews in active/inactive groups for this study. 
4) Difference achieved proportion of interviews and general population per age group, in % points. 
5) Expected results online only interviews: difference online and general population (aged 16-74), in % points. 
*Countries using CAPI 
Source: Ipsos 

A  third  demographic  group  that  is  difficult  to  reach  online  is  the  low  educated 
population.382  The  table  below  follows  the  same  structure  as  the  two  tables  above.383 
When  looking  at  the  results  it  is  possible  to  see  that  the  low  educated  group  was 
generally  underrepresented  in  the  study.  The  reason  for  this  is  that  low  educated 
people  are  generally  less  willing  to  participate  in  this  type  of  research  irrespective  of 
the  interviewing  approach  used.  This  explains  why  in  countries  with  a  relatively  low 
internet  penetration  where  respondents  were  contacted  offline,  low  educated  people 
can  still  be  significantly  underrepresented.  For  example  Portugal,  where  39%  of 
respondents  were  visited  by  interviewers  in  their  own  home  yet  low  educated  people 
were  underrepresented  by  almost  20  percentage  points.  It  should  be  added  that  an 
online  only  survey  would  certainly  not  have  achieved  the  figures  on  low  education  as 
shown for the online population (second column of the table). 

Table 122:  Sample outcomes – low education, by country (excl. CATI only 

countries) 

Gen.pop.1 

Onl.pop.2 

Achieved3 

 Ach. /pop.4  Onl./gen.pop.5 

Low edu. 

Low edu. 

Low edu. 

Low edu. 

Low edu. 

AT 

BE 

BG* 

HR 

CZ 

DK* 

EE 

FI 

FR 

20% 

36% 

19% 

30% 

15% 

33% 

20% 

27% 

35% 

14% 

27% 

3% 

16% 

13% 

30% 

17% 

22% 

26% 

15% 

16% 

18% 

17% 

19% 

24% 

11% 

19% 

25% 

-5% 

-20% 

-1% 

-13% 

4% 

-9% 

-9% 

-8% 

-10% 

-6% 

-9% 

-16% 

-14% 

-2% 

-3% 

-3% 

-5% 

-9% 

382 

Low  educated  means  here 

ISCED 

levels  0-2,  as  defined  by  Eurostat: 

http://ec.europa.eu/eurostat/statistics-
explained/index.php/International_Standard_Classification_of_Education_(ISCED) 

383 The first column of the table shows the proportion of the low educated group of people in the 
general  population  aged  16-74  in  the  individual  countries.  The  second  column  shows  the 
proportion  of  low  educated  people  in  the  online  population  aged  16-74.  The  third  column 
shows the proportion of low educated people in the survey for this study. The fourth and fifth 
columns show the difference in % points between the proportion of low educated people in 
the  general  population  and  –  respectively  –  the  proportion  of  low  educated  people  in  the 
survey for this study and the proportion of low educated people in the online population aged 
16-74 (the latter showing the expected results of an online only survey). 

576 

 
 
 
  
  
                                                 
Annex 2│ Research methods used in the study 

DE* 

EL 

HU 

IE 

IT 

LV 

LT* 

LU 

NL 

PL* 

PT* 

RO* 

SK 

SI 

ES 

SE 

UK* 

NO 

18% 

43% 

28% 

33% 

51% 

21% 

21% 

28% 

34% 

20% 

70% 

35% 

18% 

22% 

52% 

27% 

22% 

26% 

15% 

15% 

16% 

20% 

28% 

17% 

14% 

24% 

29% 

14% 

46% 

15% 

15% 

8% 

32% 

24% 

13% 

24% 

21% 

35% 

27% 

22% 

38% 

12% 

11% 

28% 

28% 

16% 

50% 

32% 

6% 

19% 

42% 

21% 

16% 

3% 

-8% 

-1% 

-11% 

-13% 

-9% 

-10% 

0% 

-6% 

-4% 

-20% 

-3% 

-12% 

-3% 

-10% 

-6% 

-6% 

-3% 

-28% 

-12% 

-13% 

-23% 

-4% 

-7% 

-4% 

-5% 

-6% 

-24% 

-20% 

-3% 

-14% 

-20% 

-3% 

-9% 

8% 

-18% 

-2% 

36% 

20% 

38% 

IS 
1) Proportion of low educated people in general population aged 16-74 (Eurostat 2011). 
2) Proportion of low educated people in online population aged 16-74 (Eurostat 2011). 
3) Achieved proportion of interviews with low educated people for this study. 
4) Difference achieved proportion of interviews with low educated people and proportion low educated people in 
general population, in % points. 
5) Expected results online only interviews: difference online and general population, in % points. 
* Countries using CAPI 
Source: Ipsos 

-18% 

-2% 

A2.10.2  Reaching minorities that do not speak the national language(s) 

The  number  of  quotas  set  for  this  study  was  high  (age,  gender,  education,  activity, 
urbanisation, and region), adding more quotas would have made reaching the required 
sample size difficult due to the use of interlocking quotas. For this reason, choices had 
to  be  made  regarding  which  quotas  were  set.  A  key  decision  agreed  with  the 
Commission  was  that  no  quota  was  put  on  the  proportion  of  immigrants  within  each 
country.  

A  key  reason  for  not  including  a  quota  on  the  proportion  of  immigrants  was  that 
reaching these minorities in surveys can be very challenging and can conflict with other 
quotas.  This  occurs,  for  example,  because  in  many  EU  Member  States  immigrant 
populations  are  concentrated  in  a  limited  number  of  highly  urbanised  geographical 
areas  and  have  a  different  –  relatively  young  –  age  profile.  When  searching  for 
minority groups, fieldwork is complicated further by language issues and relatively low 
participation in  online  samples,  two  issues  that  in  particular  affect  recent  immigrants. 
For  these  reasons,  studies  specifically  targeting  minorities  often  use  multilingual 
interviewers,  multilingual  research  material,  and  –  if  available  –  a  register  based 
sampling frame for face-to-face interviewing.  

577 

 
 
 
  
  
  
  
  
  
 
Annex 2│ Research methods used in the study 

During the development of the questionnaire, it was decided to include a question from 
Eurobarometer  342,  asking  respondents  if  their  mother  tongue  was  different  to  the 
national  language(s).384  It  was  anticipated  beforehand  that  the  final  sample  would 
include  a  low  proportion  of  respondents  that  answered  “yes”  to  this  question.  In  the 
study proposal it was estimated that in total 180 immigrants would be interviewed with 
the  mixed  mode  approach.  However,  when  comparing  the  present  study  to 
Eurobarometer  342  –  a  face-to-face  only  study  –  the  differences  are  minor.  In 
Eurobarometer 342, 8% of respondents at the EU27 level said that they had a mother 
tongue  other  than  the  official  language  of  their  country  of  residence,  while  for  the 
present study this figure was 7% at the EU28 level. These results differed little across 
modes.  The  proportion  of  respondents  that  indicated  they  had  a  mother  tongue 
different to the national language was 7% online, 8% in CAPI, and 5% in CATI.  

Table 123: Percentage of respondents with a mother 

tongue different from the official language(s) 
spoken in their country 

 Country 

Present 
survey1 

EB3422 

Difference 
present 
survey/EB342 
in percentage 
points 

AT 

BE 

BG 

CY 

CZ 

DE 

DK 

EE 

EL 

ES 

EU27/28* 

FI 

FR 
HR** 

HU 

IE 

IT 

LT 

LU 

LV 

MT 

7% 

7% 

7% 

10% 

4% 

8% 

8% 

10% 

5% 

10% 

7% 

4% 

5% 

12% 

1% 

20% 

5% 

11% 

18% 

27% 

4% 

11% 

10% 

11% 

2% 

5% 

12% 

6% 

18% 

2% 

5% 

8% 

2% 

7% 

13%* 

2% 

7% 

7% 

16% 

33% 

28% 

5% 

-4% 

-3% 

-4% 

8% 

-1% 

-4% 

2% 

-8% 

3% 

5% 

-1% 

2% 

-2% 

-1% 

-1% 

13% 

-2% 

-5% 

-15% 

-1% 

-1% 

384 See Special Eurobarometer 342 on Consumer empowerment (2011). QA49. “Is your mother 

tongue different from the official language(s) spoken in (OUR COUNTRY)?” 

578 

 
 
 
                                                 
Annex 2│ Research methods used in the study 

NL 

PL 

PT 

RO 

SE 

SI 

SK 

UK 

IS 

5% 

6% 

3% 

10% 

12% 

8% 

9% 

8% 

2% 

7% 

3% 

4% 

10% 

7% 

8% 

16% 

11% 

7% 

-2% 

3% 

-1% 

0% 

5% 

0% 

-7% 

-3% 

-5% 

4% 

7% 

NO 
3% 
1) Vulnerability study; Q11. “Is your mother tongue different from the official 
language(s) spoken in (country)? 
2) Special Eurobarometer 342 on Consumer empowerment (2011); QA49. “Is 
your mother tongue different from the official language(s) spoken in (OUR 
COUNTRY)?” 
* Eurobarometer 342 (2011) did not include Croatia, which joined the EU in 
2013. 
** Croatia was not part of Eurobarometer 342; the figure represents Eurostat 
data from 2014 on the foreign born population.  
Source: Ipsos 

A2.10.3  Mode effects 

In  a  mixed  mode  approach,  mode  effects  inevitably  play  a  role.  Results  from  self-
administered  and  interviewer-administered  surveys  are  not  always  the  same.  For 
example,  respondents  interviewed  in  person  or  by  telephone  tend  to  provide  more 
socially  desirable  responses  than  respondents  participating  in  a  self-administered 
interview. A 2015 a PEW Research Centre study on mode effects showed that this was 
particularly  the  case  for  questions  on  personal  issues  such  as  health  problems  or 
financial  troubles.385  The  PEW  study  did  conclude  however  that  interviewing  mode 
should  generally  have  a  limited  influence  on  outcomes:  When  60  identical  questions 
were  asked  to  similar  online  and  CATI  samples,  outcome  differences  ranged  in  size 
from 0 to 18 percentage points per question. 

For the current study it would be interesting to know to what extent mode effects did 
occur  and  if  the  differences  are  indeed  limited.  However,  researching  this  is 
complicated by the fact that the different modes in this study were not targeted at the 
same  people.  CATI  and  CAPI  methods  were used to contact demographic groups that 
cannot be reached online. This means that in particular older (55+), low educated and 
economically  inactive  people  were  called  or  visited,  while  the  online  sample  was 
targeted  to  the  younger,  highly  educated  and  economically  active  population.  The 
overlap  between  the  online  and  CATI/CAPI  sample  depended  on  the  country  and  was 
based on the internet penetration rate (which determined the proportion of people that 

385 See: “From telephone to the web: The challenge of mode of interview effects in public opinion 
Institute.  Link:  http://www.pewresearch.org/2015/05/13/from-

polls.”  Pew  Research 
telephone-to-the-web-the-challenge-of-mode-of-interview-effects-in-public-opinion-
polls/#fn-269745-2 

579 

 
 
 
 
 
 
 
 
                                                 
Annex 2│ Research methods used in the study 

were  called/visited,  see  the  methodology  section)  and  the  availability  of  older,  low 
skilled and/or economically inactive people in the online sample.386  

A closer look at the final unweighted data showed that the CAPI sample did not overlap 
much  with  the  online  sample  in  the  CAPI  countries.  For  CATI  the  story  was  slightly 
different:  in  6  countries  (BE,  FI,  FR,  SK,  SE  and  NL)  the  online  and  CATI  samples 
overlapped  substantially  for  the  55-64,  65-74  year  old  groups,  inactive  and  low  to 
medium  educated  group.  In  these  countries  at least  50  people  were  interviewed  both 
online  and  CATI  (i.e.  at  least  100  respondents  per  country)  in  these  specific  socio-
demographic  categories,  implying  that  a  meaningful  comparison  of  CATI/online 
responses  of  this  group  is  possible.  In  the  table  below  presents  an  overview  of  these 
samples.  It  should  also  be  stressed  that  the  CATI  and  online  samples  are  not  the 
same:  as  can  be  seen,  the  people  contacted  by  telephone  were  slightly  older,  lower 
educated  and  rural.  More  strict  filters  could  be  used  to  produce  more  comparable 
online  and  CATI  samples,  but  this  would  reduce  the  sample  size  substantially  and 
would not improve results.  

Table 124: Overlapping sample online/CATI 

Mode 

Base 

ONLINE  CATI 

How old are you? 

Base 

Mean 

Min 

Max 

998 

65,6 

55 

74 

Holds the education level (Education LFS) 

484 

63,7 

55 

74 

484 

39% 

61% 

514 

67,3 

55 

74 

514 

55% 

45% 

998 

47% 

53% 

- 

- 

- 

- 

998 

484 

514 

- 

- 

100% 

100% 

100% 

998 

49% 

51% 

484 

53% 

47% 

514 

45% 

55% 

998 

484 

514 

Base 

Low 

Medium 

High 

Active/Inactive 

Base 

Active 

Inactive 

Gender 

Base 

Woman 

Man 

Urbanisation 

Base 

386 People living in more or less urbanised zones were not targeted by a specific mode, but the 

final data shows that the urbanisation rate of the online sample was higher.  

580 

 
 
 
                                                 
Annex 2│ Research methods used in the study 

Densely populated area 

Intermediate density area 

Thinly populated area 

31% 

38% 

31% 

37% 

38% 

25% 

25% 

39% 

36% 

Final sample filtered on: age (55-64, 65-74 years old), education (low and 
medium) and activity (only inactive). 
Source: Ipsos 

Below the responses of the CATI and online respondents from the samples in the table 
above are compared for a number of questions that have been identified as important 
causes  of  vulnerability.  In  each  figure,  the  results  of  respondents  with  a  comparable 
profile  (the  respondents  aged  55-74,  with  a  low  or  medium  education,  economically 
inactive,  in  BE,  FI,  FR,  SK,  SE  and  NL)  are  compared  with  the  results  for  all 
respondents  in  the  same  countries.  The  variance  shows  the  difference  between  the 
CATI and online outcomes in percentage points.  

When looking at the figure below it can be observed that online respondents relatively 
often  indicated  that  they  had  difficulties  with  making  ends  meet.  Of  the  respondents 
with  a  similar  demographic  profile,  50%  of  online  respondents  said  they  had  financial 
difficulties,  compared  to  41%  of  CATI  respondents  from  the  similar  demographic 
group.  The  demographic  profile  of  the  respondents  seems  to  be  of  limited  influence. 
The  result  for  all  the  respondents  in  the  selected  countries  is  fairly  similar.  These 
results  are  in  line  with  the  literature  that  says  that  people  are  more  willing  to 
acknowledge  sensitive  personal  problems  online  compared  to  when  being  interviewed 
by an interviewer, although it should be stressed that the CATI/online difference is too 
small for any firm conclusions. 

581 

 
 
 
 
Annex 2│ Research methods used in the study 

Figure 40: Mode effects - how difficult is making ends meet 

Q10ab: “Thinking about your household’s financial situation, would you say that making ends meet every 
month is…?” 
*Respondents aged 55-74, education low-medium, inactive, in BE, FI, FR, SK, SE and NL. Base is 998 (484 
online, 514 CATI). 
**All respondents in in BE, FI, FR, SK, SE and NL. Base is 6034 (5086 online and 948 CATI). 
Source: Ipsos 

CATI respondents were relatively inclined to answer that  their friends or relatives had 
financial  difficulties  compared  to  online  respondents.  About  half  (49%)  of  CATI 
respondents  with  a  comparable  socio-demographic  profile  said  that  their  friends  and 
relatives found it difficult to make ends meet every month, compared to 40% of online 
respondents  from  the  same  socio-demographic  group  who  said  that  their  friends  and 
family  had  financial  difficulties.  Once  more,  it  should  be  noted  that  the  CATI/online 
differences  are  too  small  for  far-reaching  conclusions.  The  clearest  finding  is  that  the 
CATI/online difference is limited.  

582 

 
 
 
 
Annex 2│ Research methods used in the study 

Figure 41:  Mode effects – financial difficulties friends and relatives 

Q18_4. Most of my friends and relatives find it difficult to make ends meet every month: To what extent do 
you agree, or disagree, with the following statements? 
*Respondents aged 55-74, education low-medium, inactive, in BE, FI, FR, SK, SE and NL. Base is 998 (484 
online, 514 CATI). 
**All respondents in in BE, FI, FR, SK, SE and NL. Base is 6034 (5086 online and 948 CATI). 
Source: Ipsos 

CATI  respondents  were  more  disposed  to  indicating  that  they  had  problems  with 
reading their energy, telecommunication or banking contract terms and conditions due 
to  overly  small  print.  When  looking  at  the  respondents  with  the  same  socio-
demographic  characteristics,  this  difference  was  16  percentage  points,  for  all 
respondents  in  the  selected  countries  the  variance  was  19  percentage  points.  Within 
the  55-74  age  group,  age  is  not  a  key  factor;  compared  to  the  55-65  age  group,  the 
65-74  age  group  only  marginally  more  often  reported  to  have  had  problems  with 
reading  small  print.  A  more  likely  explanation  for  the  variance  in  the  CATI/online 
results may be that people with poor sight do not use internet and are hence not well 
represented in online panels. 

583 

 
 
 
 
Annex 2│ Research methods used in the study 

Figure 42: Mode effects – small print 

Q21_2. 'You were unable to read your energy, telecommunication or banking contract’s terms and conditions 
due to overly small print': Have you encountered any of the following situations in the past 12 months? 
*Respondents aged 55-74, education low-medium, inactive, in BE, FI, FR, SK, SE and NL. Base is 998 (484 
online, 541 CATI). 
**All respondents in in BE, FI, FR, SK, SE and NL. Base is 6034 (5086 online and 948 CATI). 
Source: Ipsos 

Social desirability effects do not seem to play an important role when it comes to 
knowing one’s bank contract. The variance between CATI and online respondents is 
small: 3 percentage points for all respondents in the selected countries and 5 
percentage points for the respondents with a comparable demographic profile. 

Figure 43: Mode effects – know contract conditions bank 

C2. To what extent would you say you know the contract conditions of this bank account? 
*Respondents aged 55-74, education low-medium, inactive, in BE, FI, FR, SK, SE and NL, who hold a bank 
account. Base is 966 (475 online, 491 CATI). 

584 

 
 
 
 
 
 
Annex 2│ Research methods used in the study 

**All respondents in in BE, FI, FR, SK, SE and NL, who hold a bank account. Base is 5876 (4980 online and 
896 CATI). 
Source: Ipsos 

The interview mode  seems to also have had little effect  on  responses to the question 
asking  if  respondents  read  their  most  recent  bank  communication.  The  variance 
between  mode  results  was  limited  to  2  percentage  points  for  all  respondents  in  the 
selected  countries  and  5  percentage  points  for  the  respondents  with  a  comparable 
socio-demographic profile. 

Figure 44:  Mode effects – read recent communication bank 

C8. Did you read the most recent communication (other than a statement) sent to you by your bank? 
*Respondents aged 55-74, education low-medium, inactive, in BE, FI, FR, SK, SE and NL, who hold a bank 
account. Base is 966 (475 online, 491 CATI). 
**All respondents in in BE, FI, FR, SK, SE and NL, who hold a bank account. Base is 5876 (4980 online and 
896 CATI). 
Source: Ipsos 

The  questions  testing  respondents’  cognitive  capabilities  were  not  identified  as  key 
indicators  of  vulnerability.  However,  these  questions  are  of  interest  in  this  section 
because  the  literature  mentions  cognitively  demanding  questions  as  questions  where 
significant mode effects can be expected. According to an experimental trial by Chang 
and  Krosnick  (2010),  face-to-face  interviews  place  higher  cognitive  demands  on 
respondents because they must remember the question and outcomes without looking 
at a screen for reference.387 It is likely that this effect is visible for CATI respondents in 
this  study.  In  the  question  asking  respondents  to  select  the  best  offer,  online 
respondents  clearly  outperformed  CATI  respondents.  When  filtering  on  the  same 
demographic  group  the  CATI/online  difference  is  23  percentage  points,  for  all 
respondents  in  the  selected  countries  the  comparable  difference  was  21  percentage 
points.  

387 See: Ariela Schachter, “Measurement Error in Panel Data: A Comparison of Face-to-Face and 

Internet Survey Samples”, Stanford University, Department of Sociology. Link: 

585 

 
 
 
 
                                                 
 
Annex 2│ Research methods used in the study 

Figure 45:  Mode effects – best offer 

Q23. Suppose that the exact same product is on sale in shop A and shop B. Which shop is cheaper if…? Shop 
A offers a TV set for [€440]. Shop B offers the exact same type of TV set at [€500], but with a discount of 
10%. 
*Respondents aged 55-74, education low-medium, inactive, in BE, FI, FR, SK, SE and NL. Base is 998 (484 
online, 514 CATI). 
**All respondents in BE, FI, FR, SK, SE and NL. Base is 6034 (5086 online and 948 CATI). 
Source: Ipsos 

The  question  asking  respondents  to  select  the  best  interest  rate  showed  only  a  very 
limited variance between CATI and online respondents. When focussing on respondents 
with  a  comparable  demographic  profile,  no  difference  was  observed.  Although 
cognitively  demanding,  this  question  does  not  require  respondents  to  remember 
complicated  response  options,  explaining  the  lack  of  mode  effects  in  this  question 
compared to the question on the best offer. 

http://web.stanford.edu/~mrosenfe/how_meet_public/Schachter_Measurement_Error_Worki
ng_Paper.pdf 

586 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                                                                                                                                     
Annex 2│ Research methods used in the study 

Figure 46:  Mode effects – best interest rate 

C10. Thinking now about savings or deposit accounts, which of the following would be the best interest rate?  
*Respondents aged 55-74, education low-medium, inactive, in BE, FI, FR, SK, SE and NL. Base is 998 (484 
online, 514 CATI). 
**All respondents in in BE, FI, FR, SK, SE and NL. Base is 6034 (5086 online and 948 CATI). 
Source: Ipsos 

A2.10.4  Mode effects compared to other studies 

Of course it would also be interesting to compare the results of the current study with 
comparable  earlier  studies  to  look  for  interview  mode  effects.  Completely  comparable 
studies are not available, but Eurobarometer 342 on Consumer Empowerment (a face-
to-face  only  study  for  which  fieldwork  was  carried  out  between  February  and  March 
2010)  did  contain  several  questions  which  were  very  similar  to  questions  used  in  the 
survey for the present study. 

In both the survey for the present study and in Eurobarometer 342 respondents were 
asked to select the best interest rates. The results are shown in the figure below. The 
results from the survey for the present study are split by mode. Looking at the results, 
it  can  be  observed  that  80%  of  the  Eurobarometer  respondents  provided  a  correct 
response compared to 64% of all respondents who provided the correct answer to the 
comparable question in the present study.  

Mode effects may play a role here because in the survey for the present study the CAPI 
respondents scored relatively high (65% provided the correct answer, compared to an 
average  of  64%  who  answered  correctly),  certainly  when  keeping  in  mind  that  the 
CAPI  sample  heavily  targeted  older,  low  educated  and  inactive  people.  The  relatively 
high  CAPI  and  Eurobarometer  (face-to-face)  scores  could  be  explained  by  the  role  of 
the interviewer. Studies have shown that survey results can be substantially influenced 

587 

 
 
 
 
Annex 2│ Research methods used in the study 

by  the  fact  that  interviewers  can  help  respondents  stay  focused  and  can  provide 
clarification or encouragement.388  

Figure 47:  Question on best interest rate, survey for the present study and 

Eurobarometer 342, by mode 

Based on: Survey question C10: “Thinking now about savings or deposit accounts, which of the following 
would be the best interest rate?”. Base: all respondents, EU28. 
Eurobarometern°342, Consumer empowerment, QA43: “Thinking now about savings or deposit accounts, 
which of the following would be the best interest rate?”. Base: all respondents, EU27. 
Source: Ipsos 

In  the  survey  for  the  present  study  and  in  Eurobarometer  342,  respondents  were 
asked  to  pick  the  best  offer  from  two  offers,  testing  their  cognitive  abilities  (see  also 
above,  for  the  more  precise  comparison  between  the  CATI  and  online  results  for  this 
question  in  this  study).  As  can  be  seen  in  the  figure  below,  the  results  for  these 
questions  were  fairly  similar  across  the  current  survey  and  the  Eurobarometer  study, 
with  about  four  fifths  of  respondents  providing  the  correct  answer.  In  the  survey  for 
the  present  study,  CATI  and  CAPI  respondents  scored  below  average,  as  could  be 
expected  when  taking  into  account  their  socio-demographic  profile.  Considering  their 
similar  socio-demographic  profile,  CAPI  respondents  scored  better  than  CATI 
respondents.  This  again  suggests  that  these  types  of  questions  are  more  difficult  to 
answer by phone. 

388 See: “From telephone to the web: The challenge of mode of interview effects in public opinion 
Institute.  Link:  http://www.pewresearch.org/2015/05/13/from-

polls.”  Pew  Research 
telephone-to-the-web-the-challenge-of-mode-of-interview-effects-in-public-opinion-
polls/#fn-269745-2 

588 

66%65%53%64%80%4% (CorrectAnswer)ONLINE Vulnerability studyCAPI Vulnerability studyCATI Vulnerability studyAVERAGE (EU28) Vulnerability studyEurobarometer 342 (face-to-face) 
 
 
 
                                                 
Annex 2│ Research methods used in the study 

Figure 48:  Question on best offer, survey for the present study and 

Eurobarometer 342, by mode 

Based on: Survey question Q23: “Suppose that the exact same product is on sale in shop A and shop B. 
Which shop is cheaper if…? Shop A offers a TV set for [€440]. Shop B offers the exact same type of TV set at 
[€500], but with a discount of 10%.”. Base: all respondents, EU28. 
Eurobarometern°342, Consumer empowerment, QA42: “The same flat-screen TV is on sale in both shop A 
and B. Which one is cheaper? – In shop A, the price is €500 but a discount of 10% is offered. In shop B, the 
price is €400.” Base: all respondents, EU27. 
Source: Ipsos 

A2.10.5  Survey methods - recommendations 

A  mixed-method  approach  was  chosen  for  this  study  in  order  to  include  people  that 
cannot be  reached  online, while keeping the budget within limits compared to a face-
to-face  only  method.  This  approach  largely  succeeded.  Older  age  groups  and 
economically inactive people were well represented in the final sample, much more so 
than  would  have  been  the  case  in  an  online  only  study.  People  with a  mother  tongue 
that is not an official language in their country of residence were well represented in all 
modes.  Convincing low  educated  people  to  participate  was  challenging.  However,  this 
is  an  effect  that  often  occurs  for  these  types  of  studies,  independent  of  the 
interviewing  mode  used.  The  underrepresentation  of  low  educated  people  in  the  final 
sample could for future studies possibly be reduced by taking more time for fieldwork, 
which is of course more costly as well.  

An inevitable ancillary effect of choosing a mixed method approach is that survey mode 
effects come into play. It is difficult to research to what extent mode effects influenced 
this  study:  measuring  mode  differences  was  not  an  aim  of  the  sample  design.  The 
CATI and online samples could be compared for only a small group (respondents aged 
55-74, education low-medium, inactive) in a small number of countries (BE, FI, FR, SK, 
SE  and  NL).  Therefore,  the  results  from  this  comparison  should  be  interpreted  with 
care.  Nonetheless,  the  CATI/online  mode  comparison  delivered  some  interesting 
results.  First  of  all,  the interview  mode  generally  seems  to  have  had  a  limited impact 
(the variance being below 10 percentage points). The exceptions are the questions on 
the  best  offer  and  the  small  print,  as  could  be  expected.  The  best  offer  question  is 
clearly  cognitively  challenging  and  the  literature  shows  that  these  kinds  of  questions 
are  relatively  hard  to  answer  by  phone.  The  outcomes  on  the  question  on  small  print 
can be explained by the assumption that people that cannot read small print probably 
are not represented well in online panels. 

589 

81%89%69%61%82%Correct answerEurobaromter 342 EU27 (face-to-face)ONLINE Vulnerability studyCAPI  Vulnerability studyCATI  Vulnerability studyAVERAGE (EU28)  Vulnerability study 
 
 
 
Annex 2│ Research methods used in the study 

The substantial difference in results between the question on the best interest rate in 
the  Eurobarometer  survey  and  the  current  survey  can  be  explained  by  the  fact  that 
respondents participating in a face-to-face interview get more stimulation (if not help) 
from  the  interviewer.  There  is  no  reason  to  believe  that  one  of  the  two  studies 
delivered  “better”  results,  but  the  difference  in  interviewing  modes  should  always  be 
kept in mind when comparing results. 

The  above  information  provides  an  indication  about  possible  mode  effects:  a  more 
thorough  study  would  be  needed  to  provide  more  in-depth  information.  This  falls 
outside the scope of this study and would require a sample designed so that all modes 
cover  all  socio-demographic  groups.  This  would,  for  example,  allow  research  into 
whether mode effects have a bigger impact on low educated respondents compared to 
high  educated  respondents  for  studies  like  the  present  one.  Additional  research  could 
also be used to look into the importance of the presence of an interviewer (in CAPI and 
CATI) or availability of on-screen information (in CAPI and online). 

A2.11   Biographical consumer interviews 

In  order  to  obtain  detailed  information  about  the  research  topic,  each  interview  was 
scheduled to last between one hour and one hour and a half. 

All respondents had previously participated in the quantitative survey. Recruitment was 
done based on their answers to the survey. The aim was to include people belonging to 
the following pre-defined typologies: 

  Typology I: feels vulnerable and has experienced a problem;  

  Typology II: feels vulnerable and has not experienced problem;  

  Typology III: does not feel vulnerable and has experienced problem.  

The  three  typologies  are  defined  below,  based  on  questions  Q16  and  Q21  from  the 
questionnaire which was used for the quantitative survey: 

  Typology I: Feels vulnerable & has experienced problem; Selected “To a great 
extent” or “To some extent” for at least one reason in Q16 AND selected "Yes" 
for at least one situation in Q21 

  Typology  II:  Feels  vulnerable  &  has  not  experienced  problem;  Selected  “To  a 
great extent” or “To some extent” for at least one reason in Q16 AND selected 
"No" for all situations in q21 

  Typology  III:  Does  not  feel  vulnerable  &  has  experienced  problem;  Selected 
“Hardly  at  all”  or  “Not  at  all”  for  all  reasons  in  Q16  AND  selected  "No"  for  all 
situations in q21 

The following questions were used in order to define each typology: 

Q16.  The  following  statements  are  about  disadvantages  that  consumers  may  have 
when dealing with retailers. To what extent do they apply to you personally? Possible 
answers: To a great extent/ To some extent/ Hardly at all/ Not at all 
You feel vulnerable or disadvantaged when choosing and buying goods or services… 

1.  Because of your health problems  
2.  Because of your poor financial circumstances 
3.  Because of your current employment situation 
4.  Because offers, terms or conditions are too complex  

590 

 
 
 
Annex 2│ Research methods used in the study 

5.  Because of your age  
6.  Because you belong to a minority group  
7.  Because of other personal issues [KEEP POSITION] 
8.  Because of other reasons [KEEP POSITION] 

Q21.  Have  you  encountered  any  of  the  following  situations  in  the  past  12  months? 
Possible answers: Yes/ No 

1: You had to pay more for your energy, telecommunication or banking services because 
you were not able to use certain payment methods such as online billing or direct debit 
2: You were unable to read your energy, telecommunication or banking contract’s terms 
and conditions due to overly small print 
3:  You  did  not  switch  your  energy,  telecommunication  or  banking  supplier  because  of 
additional costs for termination of the contract  (eg extra fees for returning the internet 
modem) 
4:  You  decided  not  to  purchase  a  product  or  service  online  because  you  found  it  too 
difficult to complete the purchase 
5:  You  were  unable  to  buy  a  product  or  service  online  because  you  didn't  have  a 
payment card allowing you to pay over the internet 
6.  You  decided  not  to  switch  provider  because  the  offer  was  bundled  or  because  your 
current product is part of a bundle 

In  each  country  we  aimed  to  recruit  at  least  two  respondents  form  Typology  I  and  II 
and  at  least  one  respondent  from  Typology  III  (and  to  conduct  nine  interviews  per 
country in total). 

The  number  of  in-depth  interviews  for  each  typology,  per  country,  is  included  in  the 
table below: 

Table 125:  Number of in-depth interviews 

Typology I: 
Feels 
vulnerable & 
has 
experienced 
problem 

Typology II: 
Feels 
vulnerable & 
has NOT 
experienced 
problem 

Typology III: 
DOESN'T feel 
vulnerable & 
has 
experienced 
problem 

Total 

Denmark 
Lithuania 
United Kingdom 
Romania 
Portugal 

2 
2 
4 
4 
4 

3 
5 
4 
3 
3 

4 
2 
2 
2 
2 

9 
9 
10 
9 
9 

A2.12   Discussion guide 

The  discussion  guide  used  for  conducting  the  in-depth  interviews  was  designed  by 
Ipsos, with input from the European Commission and London Economics. 

Each interview began with an introduction to the study, followed by a brief discussion 
about  respondents’  background,  with  the  aim  to  identify  possible  factors  which  may 
contribute to vulnerability, distrust or feeling disadvantaged.  

The moderator then provided an introduction to the following sections of the interview, 
all  the  while  setting  the  scene  for  more  specific,  sector-related  discussions.  The 

591 

 
 
 
  
 
 
Annex 2│ Research methods used in the study 

interview  continued  with  general  questions  about  the  situations  encountered  by  the 
respondent with retailers, possible solutions, attitudes and coping strategies.  

The  following  sections  focused  on  more  sector-specific  vulnerabilities,  exploring 
consumers’  experiences  when  dealing  with  the  energy  sector,  the  finance  sector  and 
the internet sector, and identifying difficulties and coping strategies when dealing with 
companies from these sectors.   

The interview ended with a short part related to vulnerabilities / feeling disadvantaged 
in regards to other sectors. Respondents were prompted to give examples and to talk 
about possible difficulties they may have experienced when dealing with retailers from 
other sectors. 

592 

 
 
 
Annex 3│ Indicators of vulnerability constructed from the survey data 

Annex 3  Indicators of vulnerability constructed from the survey data 

The indicators of vulnerability constructed from the survey data are presented in the following table. 

Table 126:  Indicator of vulnerability based on the survey 

#  Name 

Code 

Sector  Type 

Base 

non-
specific 

non-
specific 

non-
specific 

non-
specific 

non-
specific 

non-
specific 

non-
specific 

non-
specific 

Binary: 1 = "Yes", 0 = 
"No" 

Those who have you 
experienced a problem 

Binary: 1 = "Yes", 0 = 
"No" 

All 

Scale: 1 = "Not 
vulnerable" - 4 = 
"Very vulnerable" 

Scale: 1 = "Not at all" 
- 4 = "To a great 
extent" 

Scale: 1 = "Not at all" 
- 4 = "To a great 
extent" 

Scale: 1 = "Not at all" 
- 4 = "To a great 
extent" 

Scale: 1 = "Not at all" 
- 4 = "To a great 
extent" 

Scale: 1 = "Not at all" 
- 4 = "To a great 
extent" 

All 

All 

All 

All 

All 

All 

Comple
te 

Questio
ns 

Meaning 

Yes 

Yes 

Yes 

Yes 

Q22 

Q21.4 

Q16.1, 
Q16.2, 
Q16.3, 
Q16.5, 
Q16.6, 
Q16.7, 
Q16.8 
Q16.5 

Did not do anything when experienced a problem 
when buying or using goods or services in past 12 
months 
Overpaid for energy, telecommunication or banking 
services services due to being unable to use certain 
payment methods in past 12 months 

Extent feels vulnerable or disadvantaged when 
choosing and buying goods or services due to 
personal characteristics; maximum score across the 
following characteristics: health problems, financial 
circumstances, employment situation, age, 
belonging to a minority group, personal issues, or 
other reasons 
Extent feels vulnerable or disadvantaged when 
choosing and buying goods or services due to age 

Yes 

Q16.3 

Yes 

Q16.2 

Yes 

Q16.1 

Yes 

Q16.6 

Extent feels vulnerable or disadvantaged when 
choosing and buying goods or services due to 
employment situation 

Extent feels vulnerable or disadvantaged when 
choosing and buying goods or services due to 
financial circumstances 

Extent feels vulnerable or disadvantaged when 
choosing and buying goods or services due to 
health problems 

Extent feels vulnerable or disadvantaged when 
choosing and buying goods or services due to 
belonging to a minority group 

3_per 

non-

Scale: 1 = "Not at all" 

All 

Yes 

Q16.7 

Extent feels vulnerable or disadvantaged when 

Unassertive (took no 
action) when 
experienced a problem 
Overpaid for services 
due to being unable to 
use certain payment 
methods 
Perception of own 
vulnerability due to 
personal 
characteristics 

1 

2 

3 

3_age 

3_emp 

3_fin 

3_hea 

3_min 

Perception of own 
vulnerability due to 
personal 
characteristics 
Perception of own 
vulnerability due to 
personal 
characteristics 
Perception of own 
vulnerability due to 
personal 
characteristics 
Perception of own 
vulnerability due to 
personal 
characteristics 
Perception of own 
vulnerability due to 
personal 
characteristics 
Perception of own 

1 

2 

3 

3 

3 

3 

3 

3 

3 

593 

 
 
 
Annex 3│ Indicators of vulnerability constructed from the survey data 

Table 126:  Indicator of vulnerability based on the survey 

#  Name 

Code 

Sector  Type 

Base 

Comple
te 

Questio
ns 

Meaning 

3_oth 

specific 

non-
specific 

- 4 = "To a great 
extent" 

Scale: 1 = "Not at all" 
- 4 = "To a great 
extent" 

All 

choosing and buying goods or services due to 
personal issues 

Yes 

Q16.8 

Extent feels vulnerable or disadvantaged when 
choosing and buying goods or services due to other 
reasons 

3_b1_age 

non-
specific 

Binary: 1 = "Yes", 0 = 
"No" 

All 

Yes 

Q16.5 

3_b1_emp 

non-
specific 

Binary: 1 = "Yes", 0 = 
"No" 

All 

Yes 

Q16.3 

3_b1_fin 

non-
specific 

Binary: 1 = "Yes", 0 = 
"No" 

All 

Yes 

Q16.2 

3_b1_hea 

non-
specific 

Binary: 1 = "Yes", 0 = 
"No" 

All 

Yes 

Q16.1 

3_b1_min 

non-
specific 

Binary: 1 = "Yes", 0 = 
"No" 

All 

Yes 

Q16.6 

3_b1_per 

non-
specific 

Binary: 1 = "Yes", 0 = 
"No" 

All 

Yes 

Q16.7 

3_b1_oth 

non-
specific 

Binary: 1 = "Yes", 0 = 
"No" 

All 

Yes 

Q16.8 

3_b1_any 

non-
specific 

Binary: 1 = "Yes", 0 = 
"No" 

All 

Yes 

Yes 

Q16.1, 
Q16.2, 
Q16.3, 
Q16.5, 
Q16.6, 
Q16.7, 
Q16.8 
Q16.5 

Feels vulnerable or disadvantaged when choosing 
and buying goods or services to 'some extent' or 'a 
great extent' due to age 

Feels vulnerable or disadvantaged when choosing 
and buying goods or services to 'some extent' or 'a 
great extent' due to employment situation 

Feels vulnerable or disadvantaged when choosing 
and buying goods or services to 'some extent' or 'a 
great extent' due to financial circumstances 

Feels vulnerable or disadvantaged when choosing 
and buying goods or services to 'some extent' or 'a 
great extent' due to health problems 

Feels vulnerable or disadvantaged when choosing 
and buying goods or services to 'some extent' or 'a 
great extent' due to belonging to a minority group 

Feels vulnerable or disadvantaged when choosing 
and buying goods or services to 'some extent' or 'a 
great extent' due to personal issues 

Feels vulnerable or disadvantaged when choosing 
and buying goods or services to 'some extent' or 'a 
great extent' due to other reasons 

Feels vulnerable or disadvantaged when choosing 
and buying goods or services to 'some extent' or 'a 
great extent' due to any of the following personal 
characteristics: health problems, financial 
circumstances, employment situation, age, 
belonging to a minority group, personal issues, or 
other reasons 
Feels vulnerable or disadvantaged when choosing 
and buying goods or services to 'some extent' or 'a 

594 

3 

Perception of own 
vulnerability due to 

3_b2_age 

non-
specific 

Binary: 1 = "Yes", 0 = 
"No" 

All 

3 

3 

3 

3 

3 

3 

3 

3 

3 

vulnerability due to 
personal 
characteristics 
Perception of own 
vulnerability due to 
personal 
characteristics 
Perception of own 
vulnerability due to 
personal 
characteristics 
Perception of own 
vulnerability due to 
personal 
characteristics 
Perception of own 
vulnerability due to 
personal 
characteristics 
Perception of own 
vulnerability due to 
personal 
characteristics 
Perception of own 
vulnerability due to 
personal 
characteristics 
Perception of own 
vulnerability due to 
personal 
characteristics 
Perception of own 
vulnerability due to 
personal 
characteristics 
Perception of own 
vulnerability due to 
personal 
characteristics 

 
 
Annex 3│ Indicators of vulnerability constructed from the survey data 

Table 126:  Indicator of vulnerability based on the survey 

#  Name 

Code 

Sector  Type 

Base 

Comple
te 

Questio
ns 

Meaning 

great extent' due to age 

3_b2_emp 

non-
specific 

Binary: 1 = "Yes", 0 = 
"No" 

All 

Yes 

Q16.3 

3_b2_fin 

non-
specific 

Binary: 1 = "Yes", 0 = 
"No" 

All 

Yes 

Q16.2 

3_b2_hea 

non-
specific 

Binary: 1 = "Yes", 0 = 
"No" 

All 

Yes 

Q16.1 

3_b2_min 

non-
specific 

Binary: 1 = "Yes", 0 = 
"No" 

All 

Yes 

Q16.6 

3_b2_per 

non-
specific 

Binary: 1 = "Yes", 0 = 
"No" 

All 

Yes 

Q16.7 

3_b2_oth 

non-
specific 

Binary: 1 = "Yes", 0 = 
"No" 

All 

Yes 

Q16.8 

3_b2_any 

non-
specific 

Binary: 1 = "Yes", 0 = 
"No" 

All 

Yes 

3_comb1 

non-
specific 

Composite scale 

All 

Yes 

3_comb2 

non-
specific 

Composite scale 

All 

Yes 

Q16.1, 
Q16.2, 
Q16.3, 
Q16.5, 
Q16.6, 
Q16.7, 
Q16.8 
Q16.1, 
Q16.2, 
Q16.3, 
Q16.5, 
Q16.6, 
Q16.7, 
Q16.8 
Q16.1, 
Q16.2, 
Q16.3, 
Q16.5, 

Feels vulnerable or disadvantaged when choosing 
and buying goods or services to 'a great extent' due 
to employment situation 

Feels vulnerable or disadvantaged when choosing 
and buying goods or services to 'a great extent' due 
to financial circumstances 

Feels vulnerable or disadvantaged when choosing 
and buying goods or services to 'a great extent' due 
to health problems 

Feels vulnerable or disadvantaged when choosing 
and buying goods or services to 'a great extent' due 
to belonging to a minority group 

Feels vulnerable or disadvantaged when choosing 
and buying goods or services to 'a great extent' due 
to personal issues 

Feels vulnerable or disadvantaged when choosing 
and buying goods or services to 'a great extent' due 
to other reasons 

Feels vulnerable or disadvantaged when choosing 
and buying goods or services to 'a great extent' due 
to any of the following personal characteristics: 
health problems, financial circumstances, 
employment situation, age, belonging to a minority 
group, personal issues, or other reasons 

Sum of indicator_3_age, indicator_3_emp, 
indicator_3_fin, indicator_3_hea, indicator_3_min, 
indicator_3_per, and indicator_3_oth 

Sum of indicator_3_b1_age, indicator_3_b1_emp, 
indicator_3_b1_fin, indicator_3_b1_hea, 
indicator_3_b1_min, indicator_3_b1_per, and 
indicator_3_b1_oth 

personal 
characteristics 
Perception of own 
vulnerability due to 
personal 
characteristics 
Perception of own 
vulnerability due to 
personal 
characteristics 
Perception of own 
vulnerability due to 
personal 
characteristics 
Perception of own 
vulnerability due to 
personal 
characteristics 
Perception of own 
vulnerability due to 
personal 
characteristics 
Perception of own 
vulnerability due to 
personal 
characteristics 
Perception of own 
vulnerability due to 
personal 
characteristics 

Perception of own 
vulnerability due to 
personal 
characteristics 

Perception of own 
vulnerability due to 
personal 
characteristics 

3 

3 

3 

3 

3 

3 

3 

3 

3 

595 

 
 
Annex 3│ Indicators of vulnerability constructed from the survey data 

Table 126:  Indicator of vulnerability based on the survey 

#  Name 

Code 

Sector  Type 

Base 

Comple
te 

Composite scale 

All 

Yes 

3 

4 

4 

4 

5 

5 

5 

5 

5 

5 

5 

5 

5 

Perception of own 
vulnerability due to 
personal 
characteristics 

3_comb3 

non-
specific 

Does not feel informed 
about prices etc. 

4 

Does not feel informed 
about prices etc. 

4_b1 

Does not feel informed 
about prices etc. 

4_b2 

Gets information from 
few sources 

5_adv_en
e 

non-
specific 

non-
specific 

non-
specific 

energy 

Scale: 1 = "Very 
informed" - 4 = "Not 
at all informed" 
Binary: 1 = "Yes", 0 = 
"No" 

Binary: 1 = "Yes", 0 = 
"No" 

All 

All 

All 

Binary: 1 = "Yes", 0 = 
"No" 

Those who have 
compared deals 

Gets information from 
few sources 

5_adv_onl 

online 

Binary: 1 = "Yes", 0 = 
"No" 

Those who have 
compared deals 

Gets information from 
few sources 
Gets information from 
few sources 

5_adv_fin 

finance 

5_web_en
e 

energy 

Binary: 1 = "Yes", 0 = 
"No" 
Binary: 1 = "Yes", 0 = 
"No" 

Those who have 
compared deals 
Those who have 
compared deals 

Gets information from 
few sources 

5_web_onl 

online 

Binary: 1 = "Yes", 0 = 
"No" 

Those who have 
compared deals 

Gets information from 
few sources 
Gets information from 
few sources 

5_web_fin 

finance 

5_com_en
e 

energy 

Binary: 1 = "Yes", 0 = 
"No" 
Binary: 1 = "Yes", 0 = 
"No" 

Those who have 
compared deals 
Those who have 
compared deals 

Gets information from 
few sources 

5_com_onl 

online 

Binary: 1 = "Yes", 0 = 
"No" 

Those who have 
compared deals 

Gets information from 
few sources 

5_com_fin 

finance 

Binary: 1 = "Yes", 0 = 
"No" 

Those who have 
compared deals 

Yes 

Yes 

Yes 

No 

No 

No 

No 

No 

No 

No 

No 

No 

Questio
ns 
Q16.6, 
Q16.7, 
Q16.8 
Q16.1, 
Q16.2, 
Q16.3, 
Q16.5, 
Q16.6, 
Q16.7, 
Q16.8 
Q17.1 

Q17.1 

Q17.1 

A7 

B6 

C3 

A7 

B6 

C3 

A7 

B6 

C3 

Meaning 

Sum of indicator_3_b2_age, indicator_3_b2_emp, 
indicator_3_b2_fin, indicator_3_b2_hea, 
indicator_3_b2_min, indicator_3_b2_per, and 
indicator_3_b2_oth 

Does not feel informed about product about price, 
quality, conditions, etc. when when choosing and 
buying goods and services 
Feels 'not very' or 'not at all' informed about 
product about price, quality, conditions, etc. when 
when choosing and buying goods and services 
Feels 'not at all' informed about product about 
price, quality, conditions, etc. when when choosing 
and buying goods and services 
Gets information from advertisements ONLY when 
comparing deals offered  different gas and/or 
electricity providers 
Gets information from advertisements ONLY when 
comparing deals offered by different internet 
service providers 
Gets information from advertisements ONLY when 
comparing deals offered by different banks 
Gets information from searching on-line when 
comparing deals offered  different gas and/or 
electricity providers 
Gets information from searching on-line when 
comparing deals offered by different internet 
service providers 
Gets information from searching on-line when 
comparing deals offered by different banks 
Gets information from comparison websites when 
comparing deals offered  different gas and/or 
electricity providers 
Gets information from comparison websites when 
comparing deals offered by different internet 
service providers 
Gets information from comparison websites when 
comparing deals offered by different banks 

596 

 
 
Annex 3│ Indicators of vulnerability constructed from the survey data 

Table 126:  Indicator of vulnerability based on the survey 

#  Name 

Code 

Sector  Type 

Base 

Gets information from 
few sources 

5_org_ene 

energy 

Binary: 1 = "Yes", 0 = 
"No" 

Those who have 
compared deals 

Gets information from 
few sources 

5_org_onl 

online 

Binary: 1 = "Yes", 0 = 
"No" 

Those who have 
compared deals 

Gets information from 
few sources 

5_org_fin 

finance 

Binary: 1 = "Yes", 0 = 
"No" 

Those who have 
compared deals 

Gets information from 
few sources 

5_fri_ene 

energy 

Binary: 1 = "Yes", 0 = 
"No" 

Those who have 
compared deals 

Gets information from 
few sources 

5_fri_onl 

online 

Binary: 1 = "Yes", 0 = 
"No" 

Those who have 
compared deals 

Gets information from 
few sources 
Gets information from 
few sources 

5_fri_fin 

finance 

5_oth_ene 

energy 

Binary: 1 = "Yes", 0 = 
"No" 
Binary: 1 = "Yes", 0 = 
"No" 

Those who have 
compared deals 
Those who have 
compared deals 

Gets information from 
few sources 

5_oth_onl 

online 

Binary: 1 = "Yes", 0 = 
"No" 

Those who have 
compared deals 

5_oth_fin 

finance 

6_ene 

energy 

Binary: 1 = "Yes", 0 = 
"No" 
Binary: 1 = "Yes", 0 = 
"No" 

Those who have 
compared deals 
All 

5 

5 

5 

5 

5 

5 

5 

5 

5 

6 

6 

Gets information from 
few sources 
Has problems 
comparing deals due 
to information-related 
factors 

Has problems 
comparing deals due 
to information-related 
factors 

6_onl 

online 

Binary: 1 = "Yes", 0 = 
"No" 

All 

No 

B8a, B8b 

6 

Has problems 
comparing deals due 
to information-related 

6_fin 

finance 

Binary: 1 = "Yes", 0 = 
"No" 

All 

No 

C6, C6b 

597 

Comple
te 
No 

Questio
ns 
A7 

No 

No 

No 

No 

No 

No 

No 

No 

No 

B6 

C3 

A7 

B6 

C3 

A7 

B6 

C3 

A9a, A9b 

Meaning 

Gets information from national authorities and/or 
consumer organisations when comparing deals 
offered  different gas and/or electricity providers 
Gets information from national authorities and/or 
consumer organisations when comparing deals 
offered by different internet service providers 
Gets information from national authorities and/or 
consumer organisations when comparing deals 
offered by different banks 
Gets information from friends/family when 
comparing deals offered  different gas and/or 
electricity providers 
Gets information from friends/family when 
comparing deals offered by different internet 
service providers 
Gets information from friends/family when 
comparing deals offered by different banks 
Gets information from 'other' sources when 
comparing deals offered  different gas and/or 
electricity providers 
Gets information from 'other' sources when 
comparing deals offered by different internet 
service providers 
Gets information from 'other' sources when 
comparing deals offered by different banks 
Finds it difficult to compare deals in the energy 
sector due to one or more of the following reasons: 
I do not know where I can find this information; 
Suppliers do not provide enough information.  OR 
Never compares deals in the energy sector due to 
one or more of the following reasons: I do not 
know where I can find this information; Suppliers 
do not provide enough information 
Finds it difficult to compare deals in the finance 
sector due to one or more of the following reasons: 
I do not know where I can find this information; 
Suppliers do not provide enough information.  OR 
Never compares deals in the finance sector due to 
one or more of the following reasons: I do not 
know where I can find this information; Suppliers 
do not provide enough information 
Finds it difficult to compare deals in the online 
sector due to one or more of the following reasons: 
I do not know where I can find this information; 

 
 
Annex 3│ Indicators of vulnerability constructed from the survey data 

Table 126:  Indicator of vulnerability based on the survey 

#  Name 

Code 

Sector  Type 

Base 

Comple
te 

Questio
ns 

Meaning 

factors 

Has problems 
comparing deals due 
to information-related 
factors 
Has problems 
comparing deals due 
to information-related 
factors 
Has problems 
comparing deals due 
to information-related 
factors 
Has problems 
comparing deals due 
to information-related 
factors 
Has problems 
comparing deals due 
to information-related 
factors 
Has problems 
comparing deals due 
to information-related 
factors 
Has not switched due 
to information-related 
factors 

Has not switched due 
to information-related 
factors 

Has not switched due 
to information-related 
factors 

6 

6 

6 

6 

6 

6 

7 

7 

7 

6_1_ene 

energy 

Binary: 1 = "Yes", 0 = 
"No" 

All 

No 

A9a 

6_1_onl 

online 

Binary: 1 = "Yes", 0 = 
"No" 

All 

No 

B8a 

6_1_fin 

finance 

Binary: 1 = "Yes", 0 = 
"No" 

All 

No 

C6 

6_2_ene 

energy 

Binary: 1 = "Yes", 0 = 
"No" 

All 

No 

A9b 

6_2_onl 

online 

Binary: 1 = "Yes", 0 = 
"No" 

All 

No 

B8b 

6_2_fin 

finance 

Binary: 1 = "Yes", 0 = 
"No" 

All 

No 

C6b 

7_ene 

energy 

Binary: 1 = "Yes", 0 = 
"No" 

Those who have an 
energy supplier 

No 

A23 

7_onl 

online 

Binary: 1 = "Yes", 0 = 
"No" 

Those who have an 
internet connection 

No 

B15 

7_fin 

finance 

Binary: 1 = "Yes", 0 = 
"No" 

Those who have a bank 
account 

No 

C13 

8a  Has problems 

8a_ene 

energy 

comparing deals due 
to personal factors 

Binary: 1 = "Yes", 0 = 
"No" 

All 

No 

A9a, A9b 

Suppliers do not provide enough information.  OR 
Never compares deals in the online sector due to 
one or more of the following reasons: I do not 
know where I can find this information; Suppliers 
do not provide enough information 
Finds it difficult to compare deals in the energy 
sector due to one or more of the following reasons: 
I do not know where I can find this information; 
Suppliers do not provide enough information 
Finds it difficult to compare deals in the online 
sector due to one or more of the following reasons: 
I do not know where I can find this information; 
Suppliers do not provide enough information 
Finds it difficult to compare deals in the finance 
sector due to one or more of the following reasons: 
I do not know where I can find this information; 
Suppliers do not provide enough information 
Never compares deals in the energy sector due to 
one or more of the following reasons: I do not 
know where I can find this information; Suppliers 
do not provide enough information 
Never compares deals in the online sector due to 
one or more of the following reasons: I do not 
know where I can find this information; Suppliers 
do not provide enough information 
Never compares deals in the finance sector due to 
one or more of the following reasons: I do not 
know where I can find this information; Suppliers 
do not provide enough information 
Never switched electricity supplier in the last 5 
years for one of the following reasons: I am unsure 
about where to get information to help me make a 
good choice 
Never switched home Internet provider or Internet 
tariff scheme in the last 5 years for one of the 
following reasons: I am unsure about where to get 
information to help me make a good choice 
Never switched main bank account in the last 5 
years for one of the following reasons: I am unsure 
about where to get information to help me make a 
good choice 
Finds it difficult to compare deals in the energy 
sector due to one or more of the following reasons: 
I do not have enough time; I do not know how to 

598 

 
 
Annex 3│ Indicators of vulnerability constructed from the survey data 

Table 126:  Indicator of vulnerability based on the survey 

#  Name 

Code 

Sector  Type 

Base 

Comple
te 

Questio
ns 

Meaning 

8a  Has problems 

8a_onl 

online 

comparing deals due 
to personal factors 

Binary: 1 = "Yes", 0 = 
"No" 

All 

No 

B8a, B8b 

8a  Has problems 

8a_fin 

finance 

comparing deals due 
to personal factors 

Binary: 1 = "Yes", 0 = 
"No" 

All 

No 

C6, C6b 

8a  Has problems 

8a_1_ene 

energy 

comparing deals due 
to personal factors 

Binary: 1 = "Yes", 0 = 
"No" 

All 

No 

A9a 

8a  Has problems 

8a_1_onl 

online 

comparing deals due 
to personal factors 

Binary: 1 = "Yes", 0 = 
"No" 

All 

No 

B8a 

8a  Has problems 

8a_1_fin 

finance 

comparing deals due 

Binary: 1 = "Yes", 0 = 
"No" 

All 

No 

C6 

599 

compare; I am not interested; I am not financially 
minded; I am not technically minded (good at 
technical things).  OR Never compares deals in the 
energy sector due to one or more of the following 
reasons: I do not have enough time; I do not know 
how to compare; I am not interested; I am not 
financially minded; I am not technically minded 
(good at technical things) 
Finds it difficult to compare deals in the online 
sector due to one or more of the following reasons: 
I do not have enough time; I do not know how to 
compare; I am not interested; I am not financially 
minded; I am not technically minded (good at 
technical things).  OR Never compares deals in the 
online sector due to one or more of the following 
reasons: I do not have enough time; I do not know 
how to compare; I am not interested; I am not 
financially minded; I am not technically minded 
(good at technical things) 
Finds it difficult to compare deals in the finance 
sector due to one or more of the following reasons: 
I do not have enough time; I do not know how to 
compare; I am not interested; I am not financially 
minded; I am not technically minded (good at 
technical things).  OR Never compares deals in the 
finance sector due to one or more of the following 
reasons: I do not have enough time; I do not know 
how to compare; I'm not interested; I am not 
financially minded; I am not technically minded 
(good at technical things) 
Finds it difficult to compare deals in the energy 
sector due to one or more of the following reasons: 
I do not have enough time; I do not know how to 
compare; I am not interested; I am not financially 
minded; I am not technically minded (good at 
technical things) 
Finds it difficult to compare deals in the online 
sector due to one or more of the following reasons: 
I do not have enough time; I do not know how to 
compare; I am not interested; I am not financially 
minded; I am not technically minded (good at 
technical things) 
Finds it difficult to compare deals in the finance 
sector due to one or more of the following reasons: 

 
 
Annex 3│ Indicators of vulnerability constructed from the survey data 

Comple
te 

Questio
ns 

Meaning 

Table 126:  Indicator of vulnerability based on the survey 

#  Name 

Code 

Sector  Type 

Base 

to personal factors 

8a  Has problems 

8a_2_ene 

energy 

comparing deals due 
to personal factors 

Binary: 1 = "Yes", 0 = 
"No" 

All 

No 

A9b 

8a  Has problems 

8a_2_onl 

online 

comparing deals due 
to personal factors 

Binary: 1 = "Yes", 0 = 
"No" 

All 

No 

B8b 

8a  Has problems 

8a_2_fin 

finance 

comparing deals due 
to personal factors 

Binary: 1 = "Yes", 0 = 
"No" 

All 

No 

C6b 

8
b 

Has problems 
comparing deals due 
to market-related 
factors 

8b_ene 

energy 

Binary: 1 = "Yes", 0 = 
"No" 

All 

No 

A9a, A9b 

8
b 

Has problems 
comparing deals due 
to market-related 
factors 

8b_onl 

online 

Binary: 1 = "Yes", 0 = 
"No" 

All 

No 

B8a, B8b 

I do not have enough time; I do not know how to 
compare; I am not interested; I am not financially 
minded; I am not technically minded (good at 
technical things) 
Never compares deals in the energy sector due to 
one or more of the following reasons: I do not have 
enough time; I do not know how to compare; I am 
not interested; I am not financially minded; I am 
not technically minded (good at technical things) 
Never compares deals in the online sector due to 
one or more of the following reasons: I do not have 
enough time; I do not know how to compare; I am 
not interested; I am not financially minded; I am 
not technically minded (good at technical things) 
Never compares deals in the finance sector due to 
one or more of the following reasons: I do not have 
enough time; I do not know how to compare; I'm 
not interested; I am not financially minded; I am 
not technically minded (good at technical things) 
Finds it difficult to compare deals in the energy 
sector due to one or more of the following reasons: 
There are too many offers to choose from; Hard to 
compare like for like, prices are provided in 
different formats, deals include different offers 
(bundles/ packages of services); The wording used 
by suppliers is difficult to understand; Offers are 
time limited/ a better offer may come out next 
month.  OR Never compares deals in the energy 
sector due to one or more of the following reasons: 
There are too many offers to choose from; Hard to 
compare like for like, prices are provided in 
different formats, deals include different offers 
(bundles/ packages of services); The wording used 
by suppliers is difficult to understand; Offers are 
time limited/ a better offer may come out next 
month; Switching is a hassle 
Finds it difficult to compare deals in the online 
sector due to one or more of the following reasons: 
There are too many offers to choose from; Hard to 
compare like for like, prices are provided in 
different formats, deals include different offers 
(bundles/ packages of services); The wording used 
by suppliers is difficult to understand; Offers are 
time limited/ a better offer may come out next 

600 

 
 
Annex 3│ Indicators of vulnerability constructed from the survey data 

Table 126:  Indicator of vulnerability based on the survey 

#  Name 

Code 

Sector  Type 

Base 

Comple
te 

Questio
ns 

Meaning 

8
b 

Has problems 
comparing deals due 
to market-related 
factors 

8b_fin 

finance 

Binary: 1 = "Yes", 0 = 
"No" 

All 

No 

C6, C6b 

8b_1_ene 

energy 

Binary: 1 = "Yes", 0 = 
"No" 

All 

No 

A9a 

8b_1_onl 

online 

Binary: 1 = "Yes", 0 = 
"No" 

All 

No 

B8a 

Has problems 
comparing deals due 
to market-related 
factors 

Has problems 
comparing deals due 
to market-related 
factors 

8
b 

8
b 

601 

month.  OR Never compares deals in the online 
sector due to one or more of the following reasons: 
There are too many offers to choose from; Hard to 
compare like for like, prices are provided in 
different formats, deals include different offers 
(bundles/ packages of services); The wording used 
by suppliers is difficult to understand; Offers are 
time limited/ a better offer may come out next 
month; Switching is a hassle 
Finds it difficult to compare deals in the finance 
sector due to one or more of the following reasons: 
There are too many offers to choose from; Hard to 
compare like for like, prices are provided in 
different formats, deals include different offers 
(bundles/ packages of services); The wording used 
by banks is difficult to understand; Offers are time 
limited/ a better offer may come out next month.  
OR Never compares deals in the finance sector due 
to one or more of the following reasons: There are 
too many offers to choose from; Hard to compare 
like for like, prices are provided in different 
formats, deals include different offers (bundles/ 
packages of services); The wording used by banks 
is difficult to understand; Offers are time limited/ a 
better offer may come out next month; Switching is 
a hassle 
Finds it difficult to compare deals in the energy 
sector due to one or more of the following reasons: 
There are too many offers to choose from; Hard to 
compare like for like, prices are provided in 
different formats, deals include different offers 
(bundles/ packages of services); The wording used 
by suppliers is difficult to understand; Offers are 
time limited/ a better offer may come out next 
month 
Finds it difficult to compare deals in the online 
sector due to one or more of the following reasons: 
There are too many offers to choose from; Hard to 
compare like for like, prices are provided in 
different formats, deals include different offers 
(bundles/ packages of services); The wording used 
by suppliers is difficult to understand; Offers are 
time limited/ a better offer may come out next 
month 

 
 
Annex 3│ Indicators of vulnerability constructed from the survey data 

Table 126:  Indicator of vulnerability based on the survey 

#  Name 

Code 

Sector  Type 

Base 

8b_1_fin 

finance 

Binary: 1 = "Yes", 0 = 
"No" 

All 

Comple
te 
No 

Questio
ns 
C6 

8
b 

8
b 

8
b 

8
b 

Has problems 
comparing deals due 
to market-related 
factors 

Has problems 
comparing deals due 
to market-related 
factors 

Has problems 
comparing deals due 
to market-related 
factors 

Has problems 
comparing deals due 
to market-related 
factors 

8b_2_ene 

energy 

Binary: 1 = "Yes", 0 = 
"No" 

All 

No 

A9b 

8b_2_onl 

online 

Binary: 1 = "Yes", 0 = 
"No" 

All 

No 

B8b 

8b_2_fin 

finance 

Binary: 1 = "Yes", 0 = 
"No" 

All 

No 

C6b 

8c  Has problems 

8c_ene 

energy 

comparing deals due 
to access-related 
factors 

Binary: 1 = "Yes", 0 = 
"No" 

All 

No 

A9a, A9b 

Meaning 

Finds it difficult to compare deals in the finance 
sector due to one or more of the following reasons: 
There are too many offers to choose from; Hard to 
compare like for like, prices are provided in 
different formats, deals include different offers 
(bundles/ packages of services); The wording used 
by banks is difficult to understand; Offers are time 
limited/ a better offer may come out next month 
Never compares deals in the energy sector due to 
one or more of the following reasons: There are too 
many offers to choose from; Hard to compare like 
for like, prices are provided in different formats, 
deals include different offers (bundles/ packages of 
services); The wording used by suppliers is difficult 
to understand; Offers are time limited/ a better 
offer may come out next month; Switching is a 
hassle 
Never compares deals in the online sector due to 
one or more of the following reasons: There are too 
many offers to choose from; Hard to compare like 
for like, prices are provided in different formats, 
deals include different offers (bundles/ packages of 
services); The wording used by suppliers is difficult 
to understand; Offers are time limited/ a better 
offer may come out next month; Switching is a 
hassle 
Never compares deals in the finance sector due to 
one or more of the following reasons: There are too 
many offers to choose from; Hard to compare like 
for like, prices are provided in different formats, 
deals include different offers (bundles/ packages of 
services); The wording used by banks is difficult to 
understand; Offers are time limited/ a better offer 
may come out next month; Switching is a hassle 
Finds it difficult to compare deals in the energy 
sector due to one or more of the following reasons: 
There is only one provider in my area; I live in a 
housing cooperative where such services are 
collectively negotiated; I live in rented 
accommodation and my landlord does not allow me 
to switch.  OR Never compares deals in the energy 
sector due to one or more of the following reasons: 
There is only one provider in my area; I live in a 
housing cooperative where such services are 

602 

 
 
Annex 3│ Indicators of vulnerability constructed from the survey data 

Table 126:  Indicator of vulnerability based on the survey 

#  Name 

Code 

Sector  Type 

Base 

Comple
te 

Questio
ns 

Meaning 

8c  Has problems 

8c_onl 

online 

comparing deals due 
to access-related 
factors 

Binary: 1 = "Yes", 0 = 
"No" 

All 

No 

B8a, B8b 

8c  Has problems 

8c_fin 

finance 

comparing deals due 
to access-related 
factors 

8c  Has problems 

8c_1_ene 

energy 

comparing deals due 
to access-related 
factors 

Binary: 1 = "Yes", 0 = 
"No" 

All 

No 

C6, C6b 

Binary: 1 = "Yes", 0 = 
"No" 

All 

No 

A9a 

8c  Has problems 

8c_1_onl 

online 

comparing deals due 
to access-related 
factors 

Binary: 1 = "Yes", 0 = 
"No" 

All 

No 

B8a 

8c  Has problems 

8c_1_fin 

finance 

Binary: 1 = "Yes", 0 = 
"No" 

All 

No 

C6 

8c_2_ene 

energy 

Binary: 1 = "Yes", 0 = 
"No" 

All 

No 

A9b 

comparing deals due 
to access-related 
factors 
8c  Has problems 

comparing deals due 
to access-related 

603 

collectively negotiated; I live in rented 
accommodation and my landlord does not allow me 
to switch 
Finds it difficult to compare deals in the online 
sector due to one or more of the following reasons: 
There is only one provider in my area; I live in a 
housing cooperative where such services are 
collectively negotiated; I live in rented 
accommodation and my landlord does not allow me 
to switch.  OR Never compares deals in the online 
sector due to one or more of the following reasons: 
There is only one provider in my area; I live in a 
housing cooperative where such services are 
collectively negotiated; I live in rented 
accommodation and my landlord does not allow me 
to switch 
Finds it difficult to compare deals in the finance 
sector due to one or more of the following reasons: 
There is only one provider in my area.  OR Never 
compares deals in the finance sector due to one or 
more of the following reasons: There is only one 
provider in my area; I am currently in in a poor 
financial situation ( bankaccount  overdraft) 
Finds it difficult to compare deals in the energy 
sector due to one or more of the following reasons: 
There is only one provider in my area; I live in a 
housing cooperative where such services are 
collectively negotiated; I live in rented 
accommodation and my landlord does not allow me 
to switch 
Finds it difficult to compare deals in the online 
sector due to one or more of the following reasons: 
There is only one provider in my area; I live in a 
housing cooperative where such services are 
collectively negotiated; I live in rented 
accommodation and my landlord does not allow me 
to switch 
Finds it difficult to compare deals in the finance 
sector due to one or more of the following reasons: 
There is only one provider in my area 

Never compares deals in the energy sector due to 
one or more of the following reasons: There is only 
one provider in my area; I live in a housing 

 
 
Annex 3│ Indicators of vulnerability constructed from the survey data 

Table 126:  Indicator of vulnerability based on the survey 

#  Name 

Code 

Sector  Type 

Base 

Comple
te 

Questio
ns 

Meaning 

factors 

8c  Has problems 

8c_2_onl 

online 

comparing deals due 
to access-related 
factors 

8c  Has problems 

8c_2_fin 

finance 

comparing deals due 
to access-related 
factors 

Binary: 1 = "Yes", 0 = 
"No" 

All 

No 

B8b 

Binary: 1 = "Yes", 0 = 
"No" 

All 

No 

C6b 

9a  Has not switched due 

9a_ene 

energy 

to personal factors 

Binary: 1 = "Yes", 0 = 
"No" 

Those who have an 
energy supplier 

No 

A23 

9a  Has not switched due 

9a_onl 

online 

to personal factors 

Binary: 1 = "Yes", 0 = 
"No" 

Those who have an 
internet connection 

No 

B15 

9a  Has not switched due 

9a_fin 

finance 

to personal factors 

Binary: 1 = "Yes", 0 = 
"No" 

Those who have a bank 
account 

No 

C13 

9
b 

9
b 

9
b 

Has not switched due 
to market-related 
factors 

Has not switched due 
to market-related 
factors 

Has not switched due 
to market-related 
factors 

9b_ene 

energy 

Binary: 1 = "Yes", 0 = 
"No" 

Those who have an 
energy supplier 

No 

A23 

9b_onl 

online 

Binary: 1 = "Yes", 0 = 
"No" 

Those who have an 
internet connection 

No 

B15 

9b_fin 

finance 

Binary: 1 = "Yes", 0 = 
"No" 

Those who have a bank 
account 

No 

C13 

cooperative where such services are collectively 
negotiated; I live in rented accommodation and my 
landlord does not allow me to switch 
Never compares deals in the online sector due to 
one or more of the following reasons: There is only 
one provider in my area; I live in a housing 
cooperative where such services are collectively 
negotiated; I live in rented accommodation and my 
landlord does not allow me to switch 
Never compares deals in the finance sector due to 
one or more of the following reasons: There is only 
one provider in my area; I am currently in in a poor 
financial situation ( bankaccount  overdraft) 
Never switched electricity supplier in the last 5 
years for one of the following reasons: I wouldn't 
know how to switch even if I wanted to; I did not 
know it was possible to switch; I never thought 
about the issue 
Never switched home Internet provider or Internet 
tariff scheme in the last 5 years for one of the 
following reasons: I wouldn't know how to switch 
even if I wanted to; I did not know it was possible 
to switch; I never thought about the issue 
Never switched main bank account in the last 5 
years for one of the following reasons: I wouldn't 
know how to switch even if I wanted to; I did not 
know it was possible to switch; I never thought 
about the issue 
Never switched electricity supplier in the last 5 
years for one of the following reasons: I don't think 
there is any difference between the suppliers to 
make switching worthwhile; Switching is a hassle; 
It is difficult to compare the offers of different 
electricity providers 
Never switched home Internet provider or Internet 
tariff scheme in the last 5 years for one of the 
following reasons: I don't think there is any 
difference between the providers to make switching 
worthwhile; Switching is a hassle ; It is difficult to 
compare the offers of different Internet service 
providers 
Never switched main bank account in the last 5 
years for one of the following reasons: I don't think 
there is any difference between the providers to 

604 

 
 
Annex 3│ Indicators of vulnerability constructed from the survey data 

Table 126:  Indicator of vulnerability based on the survey 

#  Name 

Code 

Sector  Type 

Base 

Comple
te 

Questio
ns 

Meaning 

9c  Has not switched due 
to access-related 
factors 

9c_ene 

energy 

Binary: 1 = "Yes", 0 = 
"No" 

Those who have an 
energy supplier 

No 

A23 

9c  Has not switched due 
to access-related 
factors 

9c_onl 

online 

Binary: 1 = "Yes", 0 = 
"No" 

Those who have an 
internet connection 

No 

B15 

9c  Has not switched due 
to access-related 
factors 

9c_fin 

finance 

Binary: 1 = "Yes", 0 = 
"No" 

Those who have a bank 
account 

No 

C13 

9
d 

Has not switched due 
to termination costs 

9d_ter 

non-
specific 

Binary: 1 = "Yes", 0 = 
"No" 

All 

Yes 

Q21.3 

9e  Has not switched due 

9e_bun 

to bundling 

non-
specific 

Binary: 1 = "Yes", 0 = 
"No" 

10a 

online 

Binary: 1 = "Yes", 0 = 
"No" 

10b 

online 

10c 

online 

Binary: 1 = "Yes", 0 = 
"No" 

Binary: 1 = "Yes", 0 = 
"No" 

1
0 

1
0 

1
0 

Excluded from e-
commerce due to 
difficulty of the 
process 
Excluded from e-
commerce due to not 
having a payment card 
Excluded from e-
commerce due to 
difficulty of the 
process or not having 
a payment card 

605 

All 

All 

All 

All 

Yes 

Q21.6 

No 

Q21.4 

No 

No 

Q21.5 

Q21.4, 
Q21.5 

make switching worthwhile; Switching is a hassle ; 
It is difficult to compare the offers of different 
banks 
Never switched electricity supplier in the last 5 
years for one of the following reasons: I am in debt 
with my current supplier/s so don't think I can 
switch; I live in rented accommodation and don't 
think my landlord will allow me to switch; I live in a 
housing cooperative where such services are 
collectively negotiated; No other supplier is 
available in the area where I live 
Never switched home Internet provider or Internet 
tariff scheme in the last 5 years for one of the 
following reasons: I am in debt with my current 
provider/s so don't think I can switch; I live in 
rented accommodation and don't think my landlord 
will allow me to switch; I live in a housing 
cooperative where such services are collectively 
negotiated; No other provider is available in the 
area where I live 
Never switched main bank account in the last 5 
years for one of the following reasons: I am in debt 
with my current provider/s so don't think I can 
switch; No other provider is available in the area 
where I live 
Did not switch your energy, telecommunication or 
banking supplier because of additional costs for 
termination of the contract (eg extra fees for 
returning the internet modem) 
You decided not to switch provider because the 
offer was bundled or because your current product 
is part of a bundle 
Decided not to purchase a product or service online 
because you found it too difficult to complete the 
purchase 

Was unable to buy a product or service online 
because you didn't have a payment card allowing 
you to pay over the internet 
Decided not to purchase a product or service online 
because you found it too difficult to complete the 
purchase, AND/OR was unable to buy a product or 
service online because you didn't have a payment 
card allowing you to pay over the internet 

 
 
Annex 3│ Indicators of vulnerability constructed from the survey data 

Table 126:  Indicator of vulnerability based on the survey 

#  Name 

Code 

Sector  Type 

Base 

Declined a loan 

11_loan 

finance 

12_off 

12_off_b1 

non-
specific 

non-
specific 

Binary: 1 = "Yes", 0 = 
"No" 
Scale: 1 = "Not at all" 
- 4 = "To a great 
extent" 
Binary: 1 = "Yes", 0 = 
"No" 

All 

All 

All 

1
1 
1
2 

1
2 

1
2 

Perception of own 
vulnerability due to 
complexity of offers 
Perception of own 
vulnerability due to 
complexity of offers 

Perception of own 
vulnerability due to 
complexity of offers 

12_off_b2 

non-
specific 

Binary: 1 = "Yes", 0 = 
"No" 

All 

Yes 

Q16.4 

Comple
te 
No 

Questio
ns 
Q13b 

Yes 

Yes 

Q16.4 

Q16.4 

Meaning 

Tried to obtain an unsecured loan or credit in the 
last 5 years, but did not obtain it 
Extent feels vulnerable or disadvantaged when 
choosing and buying goods or services due to 
complexity of offers, terms or conditions 
Feels vulnerable or disadvantaged when choosing 
and buying goods or services to 'some extent' or 'a 
great extent' due to complexity of offers, terms or 
conditions 
Feels vulnerable or disadvantaged when choosing 
and buying goods or services to 'a great extent' due 
to complexity of offers, terms or conditions 

606 

 
 
 
 
Annex 4│ Incidence rate of vulnerability by sub-group 

Annex 4  Incidence rate of vulnerability by sub-group 

The tables in the present annex show for each country and the  EU Member States and Norway and Iceland the number of times across 
the 16 vulnerability indicators that a particular sub-group of a vulnerability driver shows a markedly higher incidence rate of vulnerability. 

Table 127:  Above average vulnerability by country and characteristic, number of vulnerability indicators  
Group  Characteristic 

AT  BE  BG
* 
2 
1 
- 
- 
1 
5 

5 
3 
- 
1 
4 
4 
3 
1 
- 
3 

HR
* 
1 
- 
- 
2 
3 
6 
6 
1 
- 
- 

CY
* 
2 
- 
- 
2 
4 
4 
7 
- 
- 
- 

5 
- 
- 
3 
2 
3 

CZ  DK  EE  FI  FR  DE  EL
* 
2 
- 
- 
3 
2 
4 
5 
- 
- 
- 

2 
1 
1 
- 
8 
8 
5 
3 
- 
2 

4 
2 
- 
1 
2 
2 
3 
2 
- 
1 

2 
2 
2 
4 
4 
7 
4 
- 
1 
- 

3 
- 
- 
- 
2 
6 

6 
2 
- 
1 
- 
5 

1 
1 
- 

2 
- 
1 

- 
- 
- 

4 
- 
- 
1 
6 
4 

1 
2 
1 
5 
2 
1 

IS  IE  IT
* 
1 
- 
- 
2 
5 
5 
4 
1 
- 
- 

1 
- 
- 

- 
1 
2 

LV
* 
3 
- 
- 
- 
1 
1 
- 
1 
3  11 
5 
1 
7 
5 
- 
- 
- 
- 
1 
- 

LT  LU  MT
* 
- 
- 
3 
2 
1 
3 
7 
- 
- 
- 

5 
- 
3 
1 
- 
3 
1 
1 
- 
3 

NO
* 
7 
6 
3 
1 
- 
1 
3 
1 
- 
- 

PL
* 
- 
2 
- 
- 
4 
4 

PT  RO  SK  SI
* 
2 
- 
1 
- 
5 
7 

2 
1 
2 
1 
7 
7 

2 
- 
- 
2 
4 
8 

2 
- 
- 

- 
1 
- 

2 
- 
- 

7 
- 
- 
- 
2 
8 
7 
- 
- 
- 

ES
* 
- 
- 
- 
1 
2 
6 
4 
- 
- 
- 

SE  NL  UK  EU2
8 
1 
- 
- 
- 
1 
8 
10 
- 
- 
- 

3 
- 
- 
- 
2 
8 
4 
- 
- 
- 

5 
2 
- 
- 
2 
2 
6 
- 
- 
- 

1 
- 
- 
- 
5 
9 
7 
1 
- 
- 

- 
2 
6 
3 
- 
- 
- 
2 
8 
- 
- 
- 

1 
- 
1 
1 
- 
- 
- 
3 
5 
- 
- 
- 

- 
2 
4 
- 
- 
- 
3 
5 
4 
- 
- 
- 

- 
- 
4 
- 
- 
- 
1 
1 
3 
- 
- 
- 

1 
- 
5 
- 
1 
1 
6 
2 
7 
- 
- 
- 

- 
3 
1 
- 
- 
- 
3 
3 
3 
- 
- 
- 

- 
1 
1 
- 
1 
- 
5 
2 
2 
- 
- 
- 

- 
3 
2 
- 
2 
2 
6 
7 
9 
- 
- 
- 

- 
3 
5 
2 
- 
- 
1 
5 
5 
- 
1 
- 

- 

2 
- 
2 
- 
3 
6 
4 
- 
- 
- 

1 
- 
6 
2 
- 
- 
1 
7 
8 
- 
- 
- 

- 
- 
3 
2 
- 
- 
2 
4 
2 
- 
- 
- 

- 
- 
3 
2 
- 
- 
- 
2 
6 
- 
1 
- 

4 
- 
7 
1 
2 
2 
2 
5 
8 
- 
2 
- 

2 
- 
2 
2 
- 
- 
1 
5 
4 
1 
- 
- 

- 
1 
5 
- 
2 
- 
5 
3 
1 
- 
- 
- 

- 
1 
3 
- 
3 
- 
3 
7 
8 
- 
- 
- 

- 
2 
2 
- 
- 
2 
- 
4 

- 
- 
4 
- 
- 
- 
1 
3 

- 
- 
2 
5 
6 
9 
2 
2 
- 
- 
1 
1 
1 
2 
4 
5 
1  10  10  6 
- 
4 
- 
4 
- 
- 

- 
- 
- 

- 
- 
- 

- 
- 
- 
2 
1 
1 
4 
- 
6 
- 
- 
- 
1 
- 
- 
- 
- 
- 
1 
2 
2 
5  10  6 
7 
3 
- 
- 
- 
- 
- 
- 

2 
2 
6 
- 
- 
- 
3 
3 
4  10 
- 
- 
- 
1 
- 
- 

4 

4 

5 

3 

6 

5 

6 

3 

4 

4 

- 

7 

5 

- 

3 

1 

5 

5 

4 

5 

3 

3 

- 

1 

3 

1 

2 

2 

- 

2 
- 
2 

7 
- 
3 

7 
- 
- 

2 
- 
1 

3 
- 
6 

7 
- 
2 

5 
- 
8 

7 
- 
1 

7  10  5 
- 
- 
- 
1 
- 
1 

4 
- 
2 

9 
- 
- 

6 
- 
- 

4 
- 
5 

7 
- 
4 

6 
- 
3 

3 
- 
1 

3 
- 
4 

2 
- 
4 

- 
4 

8 
- 
4 

8 
- 
1 

5 
- 
5 

4 
- 
4 

7 
- 
1 

9 
- 
- 

6 
- 
1 

- 
2 

- 
1 
- 

- 
- 
3 
3 
1 
- 
2 
3 
5 
- 
- 
- 

7 
- 
1 
- 
4 
6 
2 
- 
- 
- 

- 
6 
3 
- 
1 
- 
2 
5 
3 
1 
1 
- 

- 
1 
- 

- 
2 
6 
1 
1 
- 
1 
7 
7 
1 
- 
- 

Age 

16 - 24 
25 - 34 
35 - 44 
45 - 54 
55 – 64 
65 – 74 
75+ 

Gender  Female 

- 
1 
3 
- 
1 
1 
1 
6 
6 
- 
1 
- 

Male 
Densely populated 
Intermediate 
density 
Thinly populated 
1 
2 
3 
4 
5 
6 or more 
Low 
Medium 
High 
No 
Yes, but no 
difficulty 
Yes, and causes 
diff’ty 
7 
employed full-time 
- 
employed part-time  3 

4 

Populat
ion 
density 
of area 
Househ
old size 

Educati
on level 

Differe
nt 
mother 
tongue 

Occupa
tional 

607 

HU
* 
1 
- 
- 
- 
7 
2 

- 
- 
- 

- 
- 
3 
- 
- 
- 
1 
4 
3 
- 
- 
- 

EEA 

1 
- 
- 
- 
1 
8 
10 
- 
- 
- 

- 
- 
1 
- 
- 
- 
2 
2 
8 
- 
- 
- 

1 

7 
- 
- 

- 
- 
1 
- 
- 
- 
2 
2 
8 
- 
- 
- 

1 

7 
- 
- 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 127:  Above average vulnerability by country and characteristic, number of vulnerability indicators  
Group  Characteristic 

AT  BE  BG
* 

HR
* 

CY
* 

CZ  DK  EE  FI  FR  DE  EL
* 

HU
* 

IS  IE  IT
* 

LV
* 

LT  LU  MT
* 

NO
* 

PL
* 

PT  RO  SK  SI
* 

ES
* 

SE  NL  UK  EU2
8 

status  self-employed full-

4 

3 

2 

4 

1 

- 

- 

3 

1 

5 

- 

1 

- 

2 

6 

2 

7 

7 

3 

4 

5  11  4 

- 

6 

5  10 

- 

2 

2 

6 

4 

- 

- 

- 

3 

1 

3 

4 

3 

5 

1 

3 

1 

4 

3 

2 

5 

7 

5  10  2 

2 

3 

6 

- 

- 

2 

4 

2 

- 

- 

5 

5 

3 

4 

6 

6 

7  11  5 

6 

5 

2 

5 

3 

3 

4 

1 

5 

6 

5 

8 

4 

4 

3 

6 

3 

3 

5 

6  12 

3 

6 

8 

4 

7 

6 

9 

4  11  7 

4 

6 

4 

4 

7 

3 

8 

7  10  2 

4  10  4 

4 

5  10  6 

4  10  7 

4 

6 

3 

4 

8 

6  11  9  11  9  11  3 

6 

6 

4  10  6 

9 

7 

4 

5 

6 

7 

5 

8 

7 

7 

7 

9 

7 

7 

time 
self-employed part-
time 
unemployed but 
looking  
unemployed not 
looking 
long-term sick / 
disabled 
housewife / 
homemaker 
retired 
in full time 
education 
studying, part time 
job 
very easy 
fairly easy 
fairly difficult 

5 
6 

2 
5 

- 
5 

6 

5 

4 

- 
- 
- 
2 

- 
- 
- 
- 

6 
2 

- 

- 
- 

1 
7 
4 
- 

3 
7 

- 

- 
- 
- 
- 

6 
- 
6 
- 
- 

3 
6 
1 
- 
1 
- 
2 

2 
1 
- 
- 

- 
2 
- 
- 

4 
- 
4 
- 
2 

1 
6 
2 
- 
1 
- 
7 

6 
1 
2 
- 

Difficult
y to 
make 
ends 
meet 
Person
al 
situatio
n 

very difficult 
12  7 
married 
- 
- 
remarried 
2 
5 
living with a partner  - 
- 
single 
4 
5 
divorced or 
separated 
widowed 
other 
0 
1 
2 
3 

1 
9 
11  6 
4 
7 
- 
- 
3 
4 
- 
- 
2 
3 

4 or more 

…do not buy online 
…buy on credit 

2 
8 
- 
4 

7 
8 
- 
2 

Numbe
r of 
depend
ent 
childre
n 
Single parent 
Friends 
or 

4 
8 

3 
5 

1 
6 

3 
2 

- 
3 

2 
7 

5 
5 

3 
3 

7 
2 

3 
6 

2 
5 

4 
5 

1 
8 

2 
1 

5 
3 

7 
3 

5  10  5 
6 
8 
4 

3 
6 

4 
5 

5 
6 

4 
7 

- 
8 

4 
8 

4 

1 

2 

4 

6 

8 

2 

4 

4 

7 

2 

1 

5  10  1  11 

- 

2 

1 

6 

1 

5 

5 

4 

6 

- 
3 
- 
1 

- 
1 
- 
5 

- 
1 
- 
4 

- 
- 
- 
2 

- 
2 
- 
- 

4  10  7  11  5 
- 
- 
- 
7 
5 
6 
- 
2 
- 
4 
1 
5 

- 
4 
- 
1 

- 
2 
- 
3 

2 
2 
3 
3 
6 
6 
-  10  4 
1 
1 
- 
- 
- 
- 
- 
1 
- 
- 
7 
4 

3 
1 
- 
- 

6 
3 
2 
3 

5 
4 
1 
4 

1 
6 
6 
- 
1 
3 
3 

3 
- 
- 
4 

6 
8 
9 
- 
3 
2 
2 

3 
5 
3 
2 

8 
6 
9 
- 
- 
- 
1 

3 
7 
- 
2 

- 
1 
- 
2 

6 
- 
2 
- 
3 

4 
8 
9 
- 
- 
3 
2 

2 
7 
2 
4 

- 
2 
1 
- 

4 
- 
2 
1 
1 

- 
6 
2 
- 
- 
- 
5 

1 
- 
1 
- 

- 
7 
- 
- 

4 
- 
1 
- 
- 

4 
2 
3 
- 
- 
- 
4 

5 
5 
2 
- 

- 
1 
- 
- 

- 
1 
- 
- 

- 
1 
1 
- 

9  11  6 
- 
- 
1 
2 
9 
5 
5 
2 
1 
- 
2 
3 

- 
1 
- 
- 

3 
- 
2 
- 
4 

- 
2 
2 
1 

6 
- 
2 
1 
2 

- 
- 
4 

7 
- 
3 
2 
2 

- 
7 
1 
- 
3 
1 
2 

5 
2 
- 
3 

6 
6 
5 
- 
- 
- 
4 

1 
6 
5 
- 

1 
4 
7 
- 
1 
- 
2 

2 
2 
3 
- 

4 
5 
1 
3  10  9 
5 
5 
1 
- 
1 
- 
4 
2 
- 
2 
3 
- 
3 
1 
1 

5 
4 
2 
- 

4 
2 
- 
- 

5 
9 
1 
3 

- 
- 

- 
1 
4 
- 

5 
5 
2 
- 
3 
2 
4 

2 
4 
2 
- 

- 
- 
- 
- 
- 
- 
10  2 

10  4 
- 
- 
6 
2 
- 
- 
- 
9 

- 
- 
1 
1 

6 
2 
- 
2 
- 

- 
3 
- 
- 

- 
- 
- 
- 

- 
- 
- 
- 

6  11  4 
1 
- 
- 
3 
8 
- 
- 
1 
- 
1 
2 
- 

5 
4 
7 
- 
3 
1 
3 

8 
7 
- 
1 

3 

1 

- 
- 
1 
4  11  9  10  9 
4  10  5 
2 
1 
- 
- 
1 
- 
- 
1 
1 
1 
- 
1 
- 
- 
- 
8 
6 

1 
1 
- 
6 
7 

4 
2 
3 
- 

2 
2 
1 
- 

4 
6 
- 
1 

5 
3 
- 
- 

4 
2 
- 
- 

- 
1 
- 
- 

6 
- 
4 
1 
2 

3 
6 
2 
- 
- 
- 
3 

5 
2 
2 
- 

- 
- 
- 
4 

7 
- 
7 
- 
3 

3 
2 
7 
- 
3 
1 
1 

5 
2 
- 
2 

- 
1 
- 
1 

- 
- 
- 
4 

5  11 
- 
- 
2 
5 
- 
- 
1 
1 

6 
3 
9 
7  13  10 
5 
4  11 
- 
- 
- 
- 
- 
2 
- 
3 
2 
- 
1 
2 

7 
3 
- 
4 

4 
8 
4 
- 

6 
4 
2 
- 

8 

6 

4 
6 

2 

- 
- 
- 
- 

6 
- 
1 
- 
- 

EEA 

- 

- 

3 

8 

6 

5 
6 

2 

- 
- 
- 
- 

6 
- 
1 
- 
- 

3 
10 
5 
- 
- 
- 
- 

6 
4 
2 
- 

608 

 
 
 
 
 
 
 
 
 
 
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 127:  Above average vulnerability by country and characteristic, number of vulnerability indicators  
Group  Characteristic 

AT  BE  BG
* 

HR
* 

CY
* 

CZ  DK  EE  FI  FR  DE  EL
* 

HU
* 

IS  IE  IT
* 

LV
* 

LT  LU  MT
* 

NO
* 

PL
* 

PT  RO  SK  SI
* 

ES
* 

SE  NL  UK  EU2
8 

2 
6 
11  7 
6 
7 
- 
- 

3 
8 
8 
1 

1 
8 
7 
- 

- 
8 
1 
- 

- 
6 
3 
2 

2 
- 
- 
14  10  6 
3 
10  8 
- 
- 
- 

- 
6 
3 
- 

- 
4 
3 
- 

- 
8 
4 
- 

- 
5 
- 
- 

- 
2 
2 
- 

3 
7 
3 
- 

1 
9 
6 
- 

4 
8 
4 
- 

3 

6 

- 
2 
4 
4  10  9  13  4 
2  12  3 
7 
- 
- 
- 
- 

7 
- 

- 
9 

- 
8 

- 
8 

- 
- 
5 
5 
8  10  11  10  10  4 
- 
- 

- 
7 

1 

- 

- 

- 

- 

- 

- 

- 

2 

- 
- 
7  14  9  15  15  8  14  7 
2  14  9  14  14  8  13  3 
- 
- 

- 

- 

- 

- 

- 

- 

- 

- 
6 
1 
- 

1 
6 
3 
- 

- 
7 
4 
- 

1 
2 
3 
1 

2 
6 
4 
- 

2 
8 
9 
- 

- 
6 
8 
- 

1 
5 
3 
- 

1 
9 
4 
- 

1 
- 
1 
8  14  6 
5  12  6 
- 
- 
- 

1 
5 
- 
- 

- 
9 
2 
- 

- 
7 
5 
- 

2 
8 
1 
- 

1 
5 
5 
2 

4 
5 
1 
- 

1 
9 
5 
1 

1  10  2 
1  12  7 
5 
8 
- 
- 
- 
1 

3 
- 
- 
5  11  5 
7 
6 
1 
2 
- 
1 

- 
9 
3 
- 

- 
9 
7 
2 

2 
2 
- 
8  10  5 
4 
3 
2 
- 
- 
- 

- 
- 
- 
9  11  6 
5 
3 
3 
- 
- 
- 

- 
8 
2 
- 

- 
9 
8 
- 

5 
7 
7 
- 

2 
6 
3 
7  10  11 
7 
5 
9 
- 
- 
- 

- 
- 
1 
6 
6 
6 
8  11  6 
- 
- 
- 

- 
6 
8 
- 

- 

3 

2 
- 
1 
8  10  4  10  7 
1  15  6 
6 
- 
- 
- 
- 

9 
- 

- 
- 
- 
9  11  9 
8 
- 

- 
5 
3  12  4 
- 
- 
- 

1 

3 

1 
- 
- 
9 
8  13  13  12 
5  13  7  13  12 
- 
- 
- 

- 

- 

1 

- 

- 

- 

- 

- 

- 

- 

- 

- 

2 

- 

- 

2 

- 

- 

- 

1 

2 

2 

- 

- 

- 

- 

- 

1 

- 

- 

- 

- 

- 

6 

5 

4 

1 

1 

9 

4 

7 

7 

5 

5 

4 

3 

1 

5 

8 

7 

3 

- 

3 

7 

4 

8 

7 

6 

6 

3 

3 

5  10 

6 

7 

9 

2 

5 

1 

5 

6 

7 

8 

8 

6 

3 

6 

3 

7 

5 

4 

8 

8 

2  11  5 

6 

8 

5 

6 

5 

6 

6  12 

8 

6 

5 

7 

8 

5 

9 

4 

7 

4 

6 

6 

4 

6 

5  12  7 

3  10  8 

7 

7 

6 

9 

8  13  7 

8 

6 

7 

9 

11  8 
- 
- 

4 
1 
2 
4 
- 

3 
1 
4 

- 
1 
5 
4 
- 

2 
- 
4 

4 
1 

1 
- 
1 
1 
- 

1 
1 
- 

6 
- 

1 
- 
2 
3 
- 

1 
- 
3 

4 
1 

1 
- 
2 
- 
- 

- 
- 
- 

9 
- 

3 
- 
5 
7 
1 

1 
- 
2 

7 
- 

1 
- 
1 
4 
- 

1 
4 
4 

8 
- 

3 
- 
4 
4 
- 

1 
3 
3 

7 
- 

- 
- 
3 
6 
- 

- 
- 
5 

6  13  3 
1 
- 
- 

5  10  10  2 
- 
1 

1 

- 

1 
- 
1 
3 
- 

1 
- 
2 

- 
3 
6 
5 
- 

3 
- 
6 

1 
- 
3 
2 
- 

- 
- 
1 

1 
- 
- 
2 
- 

- 
- 
1 

- 
2 
2 
- 
- 

1 
- 
1 

- 
2 
7 
7 
- 

- 
5 
8 

1 
- 
3 
3 
- 

2 
- 
2 

3 
- 

1 
- 
2 
5 
- 

- 
- 
4 

6 
3 

1 
2 
1 
3 
1 

2 
2 
4 

8 
1 

4 
3 
- 
3 
2 

- 
2 
2 

3  12  5 
2 
- 
2 

3 
1 
2 
- 
2 

- 
- 
2 

1 
1 
2 
6 
- 

2 
- 
- 

- 
- 
2 
4 
1 

- 
- 
2 

5 
- 

2 
2 
3 
5 
- 

3 
5 
6 

4 
- 

- 
- 
2 
2 
- 

1 
- 
- 

9 
- 

- 
- 
2 
8 
- 

- 
1 
4 

4 
- 

- 
- 
3 
5 
- 

- 
- 
4 

5 
- 

2 
- 
3 
4 
- 

1 
- 
3 

5  12  11 
- 
- 
5 

1 
1 
3 
4 
3 

- 
- 
- 

1 
3 
2 
5 
1 

- 
- 
2 

2 
- 
7 
5 
- 

- 
1 
5 

9 

9 
- 

- 
- 
4 
6 
- 

- 
- 
3 

family  …can’t make ends 

Knows 
energy 
contrac
t 
conditio
n 
Knows 
internet 
contrac
t 
conditio
n 
Knows 
bank 
contrac
t 
conditio
n 
No 
respons
e 

meet 
Not at all 
Not very 
Fair amount 

Completely 
Not at all 
Not very 
Fair amount 

Completely 
Not at all 
Not very 
Fair amount 

Completely 
Energy sector 
contract 
Online sector 
contract 
Finance sector 
contract 

Unable read Ts&Cs due 
small print 
Compar
e deals 
from 
energy 
supplie
rs 

From time to time 
When need to 
renew 
Sporadically 
Only the first time 
Never 
From time to time 
When need to 
renew 
Sporadically 
Only the first time 

Compar
e deals 
from 
internet 
provide

609 

EEA 

2 
11 
7 
- 

- 
6 
8 
- 

- 
12 
12 
- 

- 

6 

8 

9 

9 
- 

- 
- 
4 
6 
- 

- 
- 
3 

 
 
 
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 127:  Above average vulnerability by country and characteristic, number of vulnerability indicators  
Group  Characteristic 

AT  BE  BG
* 
2 
2 
- 

4 
4 
- 

5 
4 
- 

HR
* 
1 
6 
1 

CY
* 
- 
1 
- 

CZ  DK  EE  FI  FR  DE  EL
* 
4 
3 
- 

7 
4 
1 

6 
5 
1 

4 
2 
- 

7 
5 
- 

4 
5 
- 

4 
6 
- 

HU
* 
2 
4 
- 

IS  IE  IT
* 
4 
4 
- 

7 
5 
- 

4 
1 
4 

LV
* 
4 
4 
- 

LT  LU  MT
* 
1 
2 
- 

4 
2 
3 

3 
7 
- 

NO
* 
5 
4 
- 

PL
* 
3 
4 
- 

PT  RO  SK  SI
* 
3 
4 
- 

2 
6 
- 

4 
5 
- 

9 
4 
- 

ES
* 
5 
4 
- 

SE  NL  UK  EU2
8 
5 
7 
- 

5 
3 
5  10 
- 
3 

4 
7 
- 

rs  

Compar
e deals 
from 
banks 

Read 
last bill 
from 
energy 
supplie
r 

Read 
last 
commu
nication  
internet 
provide
r  
Read 
last 
commu
nication  
from 
bank 

Readin
g  
commu
nication 
energy 
supplie
r 
Readin
g  
commu
nication 

Never 
No response 
From time to time 
When need to 
renew 
Sporadically 
Only the first time 
Never 
In detail 
Glanced or skim 
read 
Looked at total 
price 
Not at all 
Don’t know 
In detail 
Glanced or skim 
read 
Saw what it was 
Not at all 

Don’t know 
In detail 
Glanced or skim 
read 
Saw what it was 
Not at all 
Don’t know 
Very easy 
Easy 
Difficult 
Very difficult 

No response 
Very easy 
Easy 
Difficult 
Very difficult 

3 
- 
3 
6 
- 

- 
- 
- 
6 
- 

- 
1 
- 
7 
- 

1 
2 
- 
4 
- 

- 
- 
1 
1 
- 

1 
1 
3 
5 
- 

1 
2 
2 
4 
- 

3 
2 
2 
4 
- 

- 
- 
2 
6 
- 

2 
- 
- 
3 
- 

4 
1 
2 
7 
- 

1 
1 
1 
4 
- 

1 
- 
- 
5 
3 

1 
1 
4 
- 
1 
- 
1 
3 
- 
1  10  4 
- 
1 
4 

2 
- 
1 
4 
- 

- 
3 
3 
5 
1 

3 
- 
- 
4 
- 

3 
- 
1 
1 
1 

1 
2 
4 
7 
1 

- 
- 
3 
5 
- 

2 
- 
1 
6 
- 

2 
- 
2 
5 
- 

- 
- 
4 
8 
- 

1 
- 
3 
6 
- 

1 
- 
3 
5 
- 

1 
- 
2 
5 
- 

2 
- 
1 
3 
- 

2 
- 
1 
6 
- 

- 

- 

- 

- 

- 

2 

- 

- 

- 

- 

1 

- 

- 

- 

- 

- 

2 

- 

- 

2 

- 

- 

- 

- 

- 

- 

- 

- 

- 

1 

10  8 
10  5 
2 
8 
- 
- 

- 
4 
6 
- 

1 
- 
- 
6 
- 
2 
6  10  2 

7 
- 

4 
- 

- 
- 

- 
- 
2 
2 
8 
- 
4 
10  8 
4 
10  5 
- 
2 
- 
- 
- 
- 
6 
1 
6 
15  13  6 

5 
5 
8 
- 
- 
- 
1 
- 
- 
11  7 
3 
11  16  7 

1 
7 
3 
- 

- 
3 
4 

4 
- 

- 
- 
8 
2 
- 
- 
2 
6 

4 
- 
- 
5 
9 

1 
1 
5 
1 

- 
- 
4 

3 
- 

5 
6 
7 
- 

- 
7 
9 

6 
- 

6 
9 
5 
- 

- 
5 
5 

4 
- 

5 
3 
3 
- 

9 
7 
8 
2 

- 
5  13 
6 
4 
7 
5  10  6 
- 
- 
- 

- 
- 
- 
4  10  1 
6 
8 
3 

1 
- 

5 
- 

7 
- 

- 
8 
8 

6 
1 

- 
3 
4 

4 
- 

- 

- 

- 
6 

- 
- 
- 
- 
- 
2  11  2 
2  10  10  7 
8  12  12  7  10  5 
7 
1 
4 
5  10  6 
8  12  3 
- 
- 
- 
- 
- 
2 
- 
- 
- 
- 
- 
- 
- 
- 
4 
6  14  6  15  2 
5 
6 
7  10  10  15  14  9  15  7 

7 
- 
- 

7 
- 
- 

4 
1 
2 

6 
- 
- 

5 
1 
- 
- 
- 
- 
3  12  6  10  11  5  10  4 
- 
7 

-  12  11  9 

5 
1 
- 

4 
- 
- 

7 
- 
- 

4 

6 

- 
4 
4 
- 

- 
2 
4 

6 
- 

- 
1 
3 
6 
1 
- 
- 
4 

4 
- 
- 
3 
- 

5 
- 
6 
3 

- 
5 
2 

2 
8 
9 
- 

- 
- 
6 

3 
4 
6 
- 

- 
2 
3 

2  10  8 
- 
- 
2 

- 
- 
- 
4 
1 
1 
4  13  6 
5 
8 
1 
- 
- 
1 
- 
- 
- 
8 
1 
7 
7  15  5 

4 
6 
- 
1 
- 
- 
- 
- 
2 
3  11  6 
9  15  9 

1 
4 
6 
- 

1 
1 
4 

5 
- 

1 
1 
6 
4 
- 
- 
4 
9 

6 
- 
- 
5 
- 

3 
- 
4 
1 

1 
3 
- 

5 
8 
9 
3 

- 
8 
8 

2 
7 
8 
- 

2 
2 
5 

8 
- 

1 
7 
6 
2 

- 
2 
7 

4 
- 

5 
8 
2  10  7 
- 
1 
- 

8 
2 
1 
7  12  6 
3 
- 

- 
2 
5 

2 
5 
7 

8 
- 

1 
- 
2 

2 
- 

- 
5 
7 

5 
- 

7  11  3 
- 
- 
- 

- 
8 

- 
8 

- 
- 
- 
- 
5 
5 
3 
2 
7 
2  10  2  11  6 
7 
3  10  2 
8 
- 
- 
1 
- 
2 
- 
1 
- 
- 
- 
5 
5 
5  12  2 
6  10  5  12  8  14  10  5 

- 
3 
5 
5 
- 
- 
5 

1 
7 
6 
9 
- 
- 
4 

6 
3 
- 
7 

4 
4 
6 
- 

- 
2 
2 

6 
- 

- 
2 
3 
3 
2 
- 
1 
9 

4 
8  10  5 
-  10  4 
5 
6 
1 
- 
- 
- 
- 
3 
1 
1 
3 
- 
- 
- 
2 
- 
- 
- 
- 
- 
3  12  6  10  5 
7 
8 
3 
6 
7 
7 
3 
6 
-  11  7 

9 

9 

1 
5 
5 
- 

- 
4 
4 

6 
- 

2 
9 
5 
2 

- 
- 
7 

- 
- 

5 
6 
7 
1 

8 
7 
5 
- 

- 
- 
6  10 
5 
8 

5 
- 

4 
- 

- 
9 
9 
7 
2 
- 
7 

- 
- 
- 
- 
4  10 
4 
3 
7  12  10 
6 
7 
7  10 
9 
- 
1 
- 
2 
- 
- 
- 
- 
- 
6 
9  12 
7  12  11  16  15 

4 
5 
2 
- 
- 
- 
3 
9 
8  14 

6 
- 
- 
9 
- 

8 
1 
- 
7 
5 

7 
- 
- 
8 
13 

1 
- 
- 
6 
- 

- 

6 
7 
6 
- 

- 
4 
6 

5 
- 

EEA 

5 
7 
- 

1 
- 
- 
6 
- 

- 

6 
7 
6 
- 

- 
4 
6 

5 
- 

- 
4 
10 
8 
- 
- 
6 
15 

7 
- 
- 
8 
13 

610 

 
 
 
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 127:  Above average vulnerability by country and characteristic, number of vulnerability indicators  
Group  Characteristic 

AT  BE  BG
* 

HR
* 

CY
* 

CZ  DK  EE  FI  FR  DE  EL
* 

HU
* 

IS  IE  IT
* 

LV
* 

LT  LU  MT
* 

NO
* 

PL
* 

PT  RO  SK  SI
* 

ES
* 

SE  NL  UK  EU2
8 

3 
4 
6 
- 
1 
- 
- 
- 
- 
10  7 
2 
14  14  7 

5 
8 
3 
- 
- 
- 
- 
- 
- 
1  13  7 
2 

6 
- 
- 

9 
- 
- 

5 
4 
5 
- 
- 
1 
- 
- 
- 
7  10  7  15  1 
5  11  16  4 

2 
- 
1 
6 

5 
4 
- 
- 
- 
- 
2 
1 
4  11  12  5 

8 
- 
- 
5 

1 
- 
- 

4 
- 
- 
2 
4  13  3 

6 
1 
7 
- 
- 
1 
1 
- 
1 
6  10  3  12  2 
4 

7 
1 
- 

7 

8  11  7 

7 
- 
- 
8 

6 
6 
3 
- 
- 
- 
- 
- 
- 
5  12  2 
6 

6 
- 
1 

4 
- 
- 

8 
5 
- 
1 
- 
- 
1  11  12  11 
5 

7 
- 
- 
8 
7  13  10  14 

3  15  10  3 

6 
5 
- 
- 
3 

9 
3 
- 
- 
2 

4 
2  11  8 
1 
5 
- 
- 
- 
- 
- 
- 
2 
- 
8 
4 
2 
8 
5 
3 
6  10  10  7 
4 
1 
- 
- 
- 
- 
1 
- 
- 
- 
7 
- 
7 
3 
- 
- 
6 
3 
2 
1 
6 
- 
- 
- 
- 
- 
- 
1 
1 
- 

3 
- 
1 
- 
1 
- 
1 

- 
- 
2 
3 
5 
- 
1 

9 
7 
- 
- 
1 
8 
- 
- 
5 
2 
4 
- 
1 

5 
- 
- 
2 
9 
3 
- 
2 
- 
3 
3 
- 
- 

3 
- 
- 
1 
8 
4 
- 
- 
5 
- 
2 
- 
- 

1  11  3 
4 
7 
4 
- 
- 
- 
- 
1 
- 
1  12  5 
6 
6 
3 
- 
- 
3 
- 
- 
- 
5 
5 
1 
1 
- 
2 
5 
6 
2 
- 
- 
- 
2 
- 
1 

5 
- 
- 
3 
4 
4 
- 
- 
1 
4 
6 
- 
- 

8 
6 
- 
2 
1 
3 
1 
- 
6 
6 
1 
- 
- 

6 
- 
- 
3 
3 
- 
1 
2 
8 
2 
1 
- 
1 

2  11  5 
4 
3 
- 
- 
- 
3 
- 
- 
2 
7 
1 
5 
5 
6 
- 
- 
1 
- 
- 
- 
- 
4 
6 
4 
2 
- 
2 
3 
- 
1 
- 
- 
- 
- 
1 
- 

7 
2 
- 
- 
1 
3 
- 
1 
7 
6 
3 
- 
- 

4  11  5 
4 
1 
- 
- 
- 
- 
1 
9 
2 
7 
5 
4 
5  10  7 
- 
- 
- 
- 
- 
- 
5 
2 
7 
4 
6 
4 
4 
4 
5 
- 
- 
- 
- 
- 
1 

4 
7 
- 
- 
5 
7 
- 
- 
5 
2 
5 
- 
- 

7 
8 
- 
- 
2 
5 
- 
- 
3 
1 
1 
- 
2 

8  12 
6 
9 
- 
- 
- 
- 
2  10 
9  10 
- 
- 
- 
- 
7 
4 
- 
1 
6  10 
- 
- 
- 
- 

2 

4 

8 

8 

3 

5 

3 

4 

5 

3 

6 

7 

4 

5 

5 

4 

5 

9 

4 

3 

4 

6 

4 

4 

4 

7 

3 

- 

- 
- 
- 

- 

- 
- 
- 

- 

- 
- 
- 

2 

- 
- 
- 

- 

- 
- 
- 

- 

- 
- 
- 

- 

- 
- 
- 

- 

- 
- 
- 

- 

- 
- 
- 

- 

- 
- 
- 

- 

- 
- 
- 

- 

- 
- 
- 

- 

- 
- 
- 

- 

- 
- 
- 

9 

- 
- 
4 

- 

- 
- 
- 

- 

- 
- 
- 

- 

- 
- 
- 

- 

- 
- 
- 

8 

9 

- 
- 
2 

- 
- 
- 

4 

1  10  6  15  8 

9  12  4 

3 

1 

7 

8 

2 

4 

6 

4  11  7 

4 

5 

-  11 

9 

- 

- 
- 
- 

- 

- 
- 
- 

- 

- 
- 
- 

- 
- 
- 

- 
- 
5 

6 

6 

7  13  4 

- 

- 

- 

1 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

1 

- 

- 

- 

3 

- 

- 

- 

- 

- 

- 

- 

1 

1 

- 

- 

6 

- 

3 

1 

4 

3 

3 

1 

4 

2 

1 

6 

4 

2 

4 

4 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

1 

- 

- 

- 

- 

- 

2 

3 

2 

3  10  4 

2 

6 

5 

4 

- 

- 

- 

- 

- 
- 
- 

- 

- 

- 

6 

5 

6 

8 

7  12  4  13  5 

6 

7 

5 

5 

5 

7 

9 

5  13  8 

6 

6 

9 

5 

6 

7 

9 

9 

10 

10 

EEA 

7 
- 
- 
8 
14 

9 
6 
- 
- 
4 
9 
- 
- 
4 
- 
4 
- 
- 

5 

9 

- 
- 
2 

8 

- 

- 

4 

9 
6 
- 
- 
4 
9 
- 
- 
4 
- 
4 
- 
- 

5 

- 
- 
2 

8 

- 

- 

5 
- 
- 
3 
8 

5 
- 
- 
- 
4 
6 
- 
- 
2 
4 
3 
- 
- 

5 
- 
- 
3 
4 
4 
- 
- 
2 
6 
5 
- 
- 

No response 
Very easy 
Easy 
Difficult 
Very difficult 

internet 
provide
r 
Readin
g  
commu
nication 
from 
bank  
Careful 
dealing 
with 
people 
Most 
people 
can be 
trusted 
Credulity test: Incorrect 
Most 
ads 
report 
objecti
ve facts 
Willing
ness to 
take 
risks 

No response 
Trust 1: Very low 
Trust 1: Low 
Trust 1: High 
Trust 1: Very high 
Trust 2: Very low 
Trust 2: Low 
Trust 2: High 
Trust 2: Very high 

10  5 
6 
8 
- 
- 
- 
- 
2 
3 
7 
5 
- 
- 
- 
- 
8 
4 
- 
2 
Credulity: Very low  2 
2 
Credulity: Low 
- 
- 
Credulity: High 
- 
2 

- 

- 

- 

- 
- 
- 

- 
- 
- 

- 
- 
- 

Credulity: Very high  6  12  5 
Risk taking: Not at 
all 
Risk taking: Not 
very 
Risk taking: Fairly 
Risk taking: Very 
Impulsiveness 1: V. 
high 
Impulsiveness 1: 
High 
Impulsiveness 1: 
Low 
Impulsiveness 1: V. 
low 
Impulsiveness 2: V. 
high 

9  11  3 

14  7 

5 

5 

5 

3 

- 

- 

- 

- 

- 

- 

Hard 
time to 
break 
habits 

Good 
resist 

611 

 
 
 
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 127:  Above average vulnerability by country and characteristic, number of vulnerability indicators  
Group  Characteristic 

AT  BE  BG
* 

HR
* 

CY
* 

CZ  DK  EE  FI  FR  DE  EL
* 

HU
* 

IS  IE  IT
* 

LV
* 

LT  LU  MT
* 

NO
* 

PL
* 

PT  RO  SK  SI
* 

ES
* 

SE  NL  UK  EU2
8 

tempta
tion 

Strong 
self-
discipli
ne 

Impulsi
ve in 
decisio
ns 

Impulsiveness 2: 
High 
Impulsiveness 2: 
Low 
Impulsiveness 2: V. 
low 
Impulsiveness 3: V. 
high 
Impulsiveness 3: 
High 
Impulsiveness 3: 
Low 
Impulsiveness 3: V. 
low 
Impulsiveness 4: V. 
high 
Impulsiveness 4: 
High 
Impulsiveness 4: 
Low 
Impulsiveness 4: V. 
low 

Answer incorrect to 1st 
comp test 
Answer incorrect to 2nd 
comp test 
Incorrect answer meaning of 
kWh 
Incorrect answer meaning of 
Mbps 
Did not indentify best 
interest rate  
Infrequ
ent use 
of 
internet 

Search 
Compare prices 
Banking 
Purchases 
Selling 
Social 
Email 

3 

1 

- 

- 

- 

- 

2 

2 

2 

1 

3 

3 

- 

- 

- 

- 

- 

4 

- 

- 

- 

3 

1 

3 

6 

2 

- 

3 

- 

- 

- 

- 

- 

- 

3 

1 

- 

- 

- 

3 

3 

- 

- 

- 

- 

- 

- 

1 

3 

- 

- 

6 

6 

3 

2 

2 

1 

- 

- 

- 

4 

- 

- 

- 

- 

2 

- 

- 

- 

5 

1 

1 

3 

3 

4 

- 

- 

- 

- 

- 

1 

3 

- 

- 

- 

- 

- 

- 

4 

2 

4 

4 

3 

12  4 

7 

7 

2 

8  10  5  11  7 

9 

7 

6 

5 

9 

6 

5 

4 

6 

2 

9 

8  10  8 

5 

4 

7  10  10  12 

9 

- 

- 

1 

- 

- 

- 

- 

4 

2 

1 

- 

- 

- 

- 

5 

1 

- 

- 

- 

10  8 

3 

3 

1 

1 

- 

- 

- 

- 

5 

1 

7 

- 

- 

- 

2 

3 

1 

5 

4 

4 

1 

1 

1 

1 

1 

5 

3 

1 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

4 

- 

- 

- 

- 

2 

5 

- 

- 

- 

- 

1 

3 

3 

6 

- 

- 

- 

2 

5 

- 

- 

1 

3 

- 

- 

- 

- 

- 

- 

1 

- 

8 

1 

4 

1 

1 

1 

3 

6 

2 

5 

5 

1 

8 

7 

6 

7 

7 

5 

1 

4 

6 

9 

4 

4  11  7 

1 

9 

6  10  5 

4 

5 

7 

4 

3 

7 

5 

1 

- 

1 

- 

2 

4 

- 

- 

1 

9 

2 

- 

- 

3 

- 

- 

- 

- 

2 

- 

- 

- 

1 

1 

- 

- 

- 

- 

- 

- 

3 

1 

1 

1 

- 

- 

- 

- 

- 

- 

4 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

- 

1 

1 

- 

- 

2 

5 

1 

- 

3 

3 

3 

5 

2 

2 

4 

5 

2 

5 

8 

5 

3 

1 

5 

7 

5 

3 

7 

3 

1 

6 

5 

6 

7 

1 

8 

6 

5 

1 

3 

3 

6 

4 

6 

1  11  4 

5 

8 

8 

7 

4 

7 

6 

9 

- 

- 

1 

6 

- 

- 

5 

7 

7 

3 

7 

4 

1 

5 

8 

6 

7 

2 

6 

5 

1 

2 

7 

4 

6 

7 

2 

- 

6 

4 

6 

6 

8 

4 

3 

7 

3  10 

7 

1 

2 

7 

6 

1 

- 

4 

5 

7 

1 

2 

2 

3 

4 

3 

2 

2 

3 

1 

4 

9 

- 

5 

1 

- 

5 

1 

8 

3 

- 

3 

5 

5 

6 

1 

3 

4 

6 

5 

2 

7 

5 

4 

1 

6 

3 

- 

5 

1 

4 

6 

3 

7 

9 

7 

5 

3 

5 

3 

6 

2 
4 
8 
7 
7 
- 
6 
7 

2 
5 
4 
6 
2 
- 
1 
5 

2 
2 
2 
1 
2 
1 
2 
2 

4 
8 
8 
6 
4 
1 
6 
8 

- 
5 
3 
1 
1 
- 
- 
3 

6 
4 
5 
4 
5 
2 
8 
7 

3 
5 
6 
4 
3 
- 
3 
7 

- 
9 
7 
8 
7 
- 
6 
6 

- 
7 
6 
9 
3 
- 
3 
6 

3 
4 
- 
5  10  4 
5  10  5 
4 
8 
2 
5 
5 
1 
- 
2 
- 
5 
3 
1 
3 
7 
5 

1 
5 
5 
2 
1 
- 
4 
6 

2 
3 
- 
3 
6 
5 
2  10  6 
4 
6 
6 
4 
8 
1 
2 
1 
- 
6 
4 
2 
6 
7 
2 

1 
6 
6 
4 
2 
- 
6 
5 

- 
8 
9 
9 
8 
- 
7 
7 

3 
5 
3 
8 
2 
- 
5 
5 

2 
7 
2 
5 
8 
5 
2 
5 
5 
4  13  5 
3 
5 
1 
1 
- 
- 
5 
4 
3 
5 
5 
5 

5 
7 
7 
6 
4 
1 
6 
5 

5 
5 
3 
7  10  7 
6 
9 
7 
5 
8 
2 
4 
3 
1 
2 
- 
1 
7 
4 
3 
9  10  4 

5 
7 
8 
6 
3 
- 
1 

7 
2 
2 
8  11 
6 
7 
7 
6 
9 
8 
4 
8 
2 
2 
1 
- 
- 
5 
7 
2 
5  13  6  10 

- 

6 

4 
9 
8 
8 
6 
- 
5 
9 

EEA 

- 

- 

3 

9 

- 

- 

1 

6 

- 

- 

5 

7 

8 

- 

6 

4 
9 
8 
8 
5 
- 
5 
9 

612 

 
 
 
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 127:  Above average vulnerability by country and characteristic, number of vulnerability indicators  
Group  Characteristic 

Uses 
internet 
at least 
once a 
month 
(out of 
7 
activitie
s) 
Note: Out of 16 vulnerability indicators. *out of 10 vulnerability indicators. 

CY
* 
5 
5 
4 
3  10  3 
1 
3 
- 
- 
- 

CZ  DK  EE  FI  FR  DE  EL
* 
3 
6 
5 
5 
6 
8 
8  12  4 
6 
8 
4 
2 
8 
2 
- 
- 
- 
- 
- 
- 

6 
4 
6  11  3 
8 
6 
5 
4 
7 
3 
3 
- 
- 
- 
- 
- 

AT  BE  BG
* 
2 
3 
- 
1 
2 
- 
- 

HR
* 
8 
2 
7 
3 
- 
- 
- 

0 
1 
2 
3 
4 
5 
6 

9 
3 
8 
8 
2 
- 
- 

6 
6 
8 
5 
7 
2 
1 

6 
5 
6 
7 
6 
- 
- 

7 

1 

1 

4 

1 

1 

1 

3 

1 

1 

- 

- 

HU
* 
6 
2 
6 
4  11  8 
1 
- 
- 

IS  IE  IT
* 
4 
5 
2 
- 
7 
7 
3  10  4 
5 
2  10  5 
- 
2 
1 
- 
- 
- 

LV
* 
5 
4 
2 
4 
5 
- 
- 

LT  LU  MT
* 
5 
6 
1 
- 
2 
1 
- 

3 
6 
5 
4 
6 
- 
1 

7 
4 
9 
6 
5 
1 
2 

NO
* 
7 
- 
3 
6 
5 
1 
- 

PT  RO  SK  SI
PL
* 
* 
7  11  4 
5 
4 
1 
5 
8 
1 
- 
7 
5 
5  11  8 
3 
4 
3 
4 
4 
2 
4 
- 
2 
3 
- 
1 
- 
1 
3 
- 
- 
- 
1 
- 

ES
* 
6 
3 
5 
4 
5 
- 
- 

SE  NL  UK  EU2
8 
9 
6  10  11 
8 
7  10 
2 
7  11  12 
9 
6 
9 
7 
7 
4 
2 
6 
6 
- 
1 
- 
1 
- 
- 
- 
- 

1 

- 

1 

1 

1 

1 

2 

1 

2 

1 

2 

2 

1 

- 

- 

1 

4 

- 

2 

1 

EEA 

9 
8 
12 
6 
4 
- 
- 

1 

613 

 
 
 
The next series of tables shows for each of the sets of vulnerability drivers, the countries for which a particular sub-group was identified 
as having a significantly higher incidence rate of vulnerability 

Personal characteristics 

Annex 4│ Incidence rate of vulnerability by sub-group 

Table 128:  Above average vulnerability in countries due to personal and demographic (age) 
D  Indicator 

16 - 24 

25 - 34 

BG, LT, LU 

AT,  EE, 
NO 

45 - 54 

55 – 64 

65-74 

75+ 

IE, MT 

EE, FI, DE, SI, NL, UK (6)  

CY,  CZ,  EL,  LV,  NO, 
SE (6)  

35 - 
44 

1  2. Overpaid for services due to 
being unable to use certain 
payment methods 
2  4. Perception of own 

vulnerability due to personal 
characteristics 
In this section referred to as 
‘Feels vulnerable to 'a great 
extent' due to employment 
situation’ 

2  5. Perception of own 

vulnerability due to personal 
characteristics 
In this section referred to as 
‘Feels vulnerable to 'a great 
extent' due to financial 
circumstances’ 

2  10. Perception of own 

vulnerability due to personal 
characteristics 
In this section referred to as 
‘Feels vulnerable to 'a great 
extent' due to any personal 
characteristic’ 

3  15. Has problems comparing 

deals due to information-related 
factors- energy sector* 
3  17. Has problems comparing 

AT,  BE,  DK,  EE,  FR, 
IE, LU, SI (8)  

AT,  DE, 
IS, 
PL 
(4)  

CZ,  LU, 
MT,  NO 
(4)  

FI, IS 

HU, LT, NL 

UK 

HR, SK, SE 

AT, BE, DK, DE, LU, 
SK (6)  

CZ,  DK, 
EE, 
IS, 
NO,  PL, 
SE (7)  

LV,  LU, 
MT, 
NO,  SI 
(5)  

FI,  DE,  LU, 
ES (4)  

CY, EE, HU, LT, NL (5)  

BG, HR, EE, RO, SI, UK (6)   LV, LT, MT, SK, SE (5)  

AT,  DK,  IS,  IE,  LU, 
SK (6)  

AT, DE 

LU, NO 

FI 

CY, HU, LT 

BE,  BG,  HR,  EE,  RO,  SK, 
SI, NL, UK (9)  

HR,  IT,  LT,  MT,  SK, 
SE (6) EEA, EU28 

FI, DE, LT, NO (4)  

DK 

EE,  IS, 
PT 

EE, IS 

BE, IE, SE, UK (4)  

CZ, FR, SK, SE, NL (5)  

UK (1) EEA, EU28 

deals due to information-related 
factors – financial sector* 

LU 

CZ 

EE, FR, SK 

614 

 
 
 
 
  
  
  
  
  
  
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 128:  Above average vulnerability in countries due to personal and demographic (age) 
D  Indicator 

25 - 34 

45 - 54 

55 – 64 

65-74 

35 - 
44 

4  21. Does not compare product 

deals - energy sector 

4  22. Finds it very difficult to 

compare product deals - energy 
sector 

4  23. Does not compare product 

deals - finance sector 

16 - 24 
AT,  BE,  CZ,  DK, EE, 
FI,  DE,  IT,  NO,  PT, 
SK,  SI,  SE,  NL,  UK 
(15) EEA, EU28 

NO, SE 

EL 

BE, FR, DE, NO, SK, 
SE, NL (7)  

EE 

BE,  CY,  FI,  FR,  HU, 
NO,  PT,  SK,  SE,  NL 
(10)  

4  24. Finds it very difficult to 

compare product deals - finance 
sector 

4  25. Has problems comparing 

BE, SE 

deals due to personal factors – 
energy sector* 

AT,  BE,  CZ,  FI,  DE, 
IE, NO, SK, SE (9)  

4  26. Has problems comparing 

deals due to personal factors – 
on-line sector 

BG, HR, CY, DE, EL, 
LT, NO, RO (8)  

NO 

NO 

HR,  CZ,  EL,  IE,  IT,  LT, 
PL, PT, SI (9)  

AT, HR, CZ, HU, IE, IT, 
LV, LT, PT, RO, SI, ES, 
UK (13)  

CY, MT 

BG, HR, CZ, EE, EL, IE, 
IT,  LT,  PL,  PT,  RO,  SI 
(12)  

AT, CZ, DK, HU, IE, IT, 
LT, RO, SI (9)  

AT, HR, CY, CZ, DE, EL, IE, 
IT,  LT,  PL, PT,  RO,  SI, ES, 
UK (15) EEA, EU28 
AT,  BE,  BG,  HR,  CZ,  FI, 
FR, DE, EL, IE, IT, LU, NO, 
PL, PT, RO, SK, SI, ES, NL, 
UK (21) EEA, EU28 
AT,  BE,  BG,  HR,  CY,  CZ, 
EE,  FI,  DE,  EL,  HU,  IE,  IT, 
LV, LT, MT, PL, PT, RO, SK, 
SI,  ES,  UK  (23)  EEA, 
EU28 
AT,  BG,  HR,  CZ,  EE,  DE, 
EL,  IE,  IT,  LT,  MT,  PL,  PT, 
RO,  SK,  SI,  ES,  NL,  UK 
(19) EEA, EU28 

75+ 
AT,  BE,  HR,  CY,  CZ, 
DK, EE, EL, IT, LV, LT, 
MT,  SK,  ES,  SE,  NL, 
UK (17) EEA, EU28 
AT,  HR,  CY,  CZ,  DK, 
EE,  EL,  LV,  LT,  MT, 
SK,  ES,  NL,  UK  (14) 
EEA, EU28 

AT,  HR,  CY,  CZ,  DK, 
EE, EL, IT, LV, LT, MT, 
NO,  SK,  ES,  SE,  NL, 
UK (17) EEA, EU28 
BE,  HR,  CY,  CZ,  EE, 
EL,  IT,  LT,  MT,  NO, 
SK,  ES,  NL,  UK  (14) 
EEA, EU28 

LT, PT 

PT, RO, UK (3) EEA, EU28  UK (1) EEA, EU28 

HR,  EE,  EL, 
IS,  IT,  LT, 
MT, PT (8)  

AT, CY, EE, FI, FR, HU, 
LV, LT, PL, PT, SK (11)  

BE,  CY,  DK,  IS,  LU,  MT, 
SK, ES (8) EEA, EU28 

CY,  MT 
EU28 

(2)  EEA, 

4  27. Has problems comparing 

deals due to personal factors – 
finance sector* 

4  28. Has problems comparing 
deals due to market-related 
factors – energy sector* 
4  29. Has problems comparing 
deals due to market-related 
factors – on-line sector 

4  30. Has problems comparing 
deals due to market-related 
factors – finance sector* 

FI, IE, NO 

NO 

BE, CZ, PT, RO (4)  

BE,  LT,  PT,  RO  (4)  EEA, 
EU28 

LT, UK (2) EEA, EU28 

BE,  LT, 
PT 

EE, IS 

EL, RO 

BG 

MT 

AT,  HR,  CY, 
DK,  EE,  EL, 
IT, 
IE, 
IS, 
NO, RO (11)  

CZ, IS, SE, UK (4)  
AT,  BE,  CY,  CZ,  DK, 
EE,  FI,  FR,  HU,  IS,  IT, 
LV,  LT,  PL,  PT,  SK,  SI, 
(19)  EEA, 
ES,  UK 
EU28 

CZ, FR, SK, NL (4)  

BE,  CY,  CZ,  DK,  EE,  FR, 
HU, IT, LT, LU, PT, ES, SE, 
NL (14) EEA, EU28 

CY, LU (2) EEA, EU28 

SK 

PT 

RO 

BE, LT, UK 

CZ, FR, NL 

Note: * -= Following 11 countries not covered: BG, HR, CY, EL, HU, IT, LV, MT, PL, SI, ES. 
Source: London Economics 

615 

 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
Table 129:  Above average vulnerability in countries due to personal and demographic drivers 

Annex 4│ Incidence rate of vulnerability by sub-group 

(household size, population density) 

D  Indicator 

Household size 

1 

2 

3 

4 

5 

SI 

DK,  IS,  MT, 
PT, UK (5)  

AT 

SI 

AT,  BE,  BG, 
DK,  LT,  PT, 
SK,  NL,  UK 
(9)  

LU 

DE,  IS,  MT, 
NO, PL (5)  

AT 

BG,  HR,  DK, 
LT,  MT,  PT, 
SI,  NL,  UK 
(9)  

LU 

DE, IS 

FR,  LT,  MT, 
PT, SE (5)  

CZ,  EE,  FI, 
FR,  MT,  PL, 
UK (7)  

CZ,  FI,  FR, 
DE,  EL,  IS, 
IE,  IT,  MT, 
ES 
(10) 
EEA, EU28 

CZ,  EE,  FI, 
FR,  DE,  IS, 
MT,  PL,  UK 
(9) 
EEA, 
EU28 

Population density of area 
Intermedia
te density 
area 

Densely 
populated 
area 

Thinly 
populated 
area 

BE, FI 

AT 

LU 

BE, DE 

AT, LU 

IE, LU 

BE, DE, NO 

AT, LU 

BE, CZ, DE 

6 or more 
AT,  BG,  HR, 
CY,  CZ,  DE, 
EL,  HU,  IS, 
IT,  LT,  LU, 
ES (13)  

AT,  BE,  BG, 
CY,  DK,  EE, 
FI,  HU,  IS, 
IE,  MT,  NO, 
SE (13)  
AT,  BG,  HR, 
CY,  EL,  HU, 
IE,  IT,  LV, 
MT,  PL,  RO, 
SK,  ES,  SE 
(15) 
EEA, 
EU28 

AT,  BE,  BG, 
FI,  EL,  HU, 
IS,  IE,  MT, 
NO,  PL,  SE 
(12) 
EEA, 
EU28 

1  2. Overpaid for services 

due to being unable to 
use certain payment 
methods 

2  4. Perception of own 

vulnerability due to 
personal characteristics 
In this section referred to 
as ‘Feels vulnerable to 'a 
great extent' due to 
employment situation’ 

2  5. Perception of own 

vulnerability due to 
personal characteristics 
In this section referred to 
as ‘Feels vulnerable to 'a 
great extent' due to 
financial circumstances’ 

2  10. Perception of own 
vulnerability due to 
personal characteristics 
In this section referred to 
as ‘Feels vulnerable to 'a 
great extent' due to any 
personal characteristic’ 

3  15. Has problems 

comparing deals due to 
information-related 
factors- energy sector* 

3  17. Has problems 

IE, NL, UK 

IE 

DE, LT 

EE,  IS,  LT, 
NO,  SE,  UK 
(6)  

AT,  DE,  IS, 
LT,  LU,  RO, 
SE (7)  

CZ 

LT 

AT, UK 

comparing deals due to 
information-related 
factors – financial sector* 

IE 

DE 

FR,  LT,  LU, 
SE (4)  

LT, LU 

616 

 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 129:  Above average vulnerability in countries due to personal and demographic drivers 

(household size, population density) 

D  Indicator 

Household size 

2 

3 

4 

5 

6 or more 

1 
AT,  BE,  BG, 
HR,  CZ,  EE, 
FI, DE, IE, IT, 
LV,  LT,  NO, 
PL,  PT,  SI, 
ES, UK (18)  
HR,  CZ,  EE, 
EL,  HU,  IE, 
LT,  PL,  PT, 
RO,  SK,  SI, 
ES, UK (14)  
AT,  BG,  HR, 
CY,  CZ,  EE, 
EL,  HU,  IE, 
IT,  LT,  MT, 
NO,  PL,  PT, 
SK,  SI,  ES 
(18) 
EEA, 
EU28 
BG,  HR,  EE, 
EL, IE, IT, LT, 
MT,  PT,  RO, 
SK,  SI,  ES 
(13)  

4  21. Does not compare 
product deals - energy 
sector 

4  22. Finds it very difficult 

to compare product deals 
- energy sector 

4  23. Does not compare 
product deals - finance 
sector 

4  24. Finds it very difficult 

to compare product deals 
- finance sector 

4  25. Has problems 

comparing deals due to 
personal factors – energy 
sector* 

4  26. Has problems 

HR,  EL,  IE, 
IT,  PL,  PT,  SI 
(7)  

NL 

BE, EE, DE 

EL, LV, SI 

FR 

BE, DE, NO 

BG, CY, PL 

AT 

PL 

NO 

LU, SK, NL 

Population density of area 
Intermedia
te density 
area 

Densely 
populated 
area 

Thinly 
populated 
area 

BE,  CZ,  DE, 
LV,  NO,  SI, 
ES,  SE,  NL, 
UK (10)  

BE,  BG,  CZ, 
EE,  NO,  SI, 
SE,  NL,  UK 
(9)  

FI,  FR, 
IT, 
LU,  NO,  SI, 
ES, NL (8)  

BE,  BG,  CZ, 
DE,  EL,  NL 
(6)  

AT,  DE,  IE, 
NO, SE (5)  

CZ, DK, FI 

EE 

PT,  SI,  ES, 
NL (4)  

UK 

LT 

BE,  EL,  PT, 
RO, NL (5)  

HR,  CZ,  RO, 
SI (4)  

EL 

LT 

BE, PT 

EE, PT 

BE 

EE 

comparing deals due to 
personal factors – on-line 
sector 

DK,  FR,  EL, 
HU,  LV,  LU, 
PT, ES (8)  

HR,  LV,  LT, 
PT (4)  

BG, EE 

DE, RO 

BG,  HU,  IT, 
MT, ES (5)  

CZ,  DE,  IE, 
LT,  PL,  PT, 
SI, ES (8)  

IE 

UK 

BG,  FI,  MT, 
PT (4)  

4  27. Has problems 

comparing deals due to 
personal factors – finance 
sector* 

DE,  IS,  LT, 
NO (4)  

NO 

DK, FR 

IS, 
IE,  LU, 
PT,  RO,  NL 
(6)  

617 

 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Table 129:  Above average vulnerability in countries due to personal and demographic drivers 

Annex 4│ Incidence rate of vulnerability by sub-group 

(household size, population density) 

D  Indicator 

Household size 

1 

2 

3 

4 

5 

6 or more 

Population density of area 
Intermedia
te density 
area 

Densely 
populated 
area 

Thinly 
populated 
area 

4  28. Has problems 

comparing deals due to 
market-related factors – 
energy sector* 
4  29. Has problems 

comparing deals due to 
market-related factors – 
on-line sector 
4  30. Has problems 

comparing deals due to 
market-related factors – 
finance sector* 

NL 

BE,  BG,  HR, 
CZ,  EL,  LV, 
LU,  MT,  ES 
(9)  

LT 

LT 

EE 

HR, IT 

LT, MT 

EE, RO 

NO, PT, SI 

PT 

DE 

DE,  IS,  NO, 
RO, SE (5)  

BG,  EL,  IE, 
IT,  LT,  PT, 
SK,  SI,  SE 
(9)  

PT,  SK,  NL, 
UK (4)  

IE 

CY, LT 

BG,  HR,  FI, 
FR,  EL,  PT, 
SE (7)  

UK 

Note: * -= Following 11 countries not covered: BG, HR, CY, EL, HU, IT, LV, MT, PL, SI, ES. 
Source: London Economics 

618 

 
 
 
  
  
  
  
  
  
  
  
  
 
 
 
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 130:  Above average vulnerability in countries due to personal and demographic (gender, 

education, mother tongue) 
Gender 

D  Indicator 

Education level 

different mother tongue than country of residence 

Female 

Male 

Low 

Medium 

High 

no 

DE, SK, UK 

BG, LT, SK, UK (4)  

AT 

BG,  HR,  CY,  CZ,  FR, 
IS,  IE,  LV,  LT,  LU, 
MT,  NO,  PL,  RO,  SK, 
SE,  NL,  UK 
(18) 
EEA, EU28 

BE 

BG,  HR,  CZ,  EE,  FR, 
HU,  IS,  IE,  LV,  LT, 
LU,  RO,  SK,  SI,  SE, 
NL,  UK  (17)  EEA, 
EU28 

yes, but it does 
not cause 
difficulty 
AT,  BE,  CZ,  DK, 
EE, FI, FR, DE, HU, 
IT, LT, LU, MT, NO, 
PL, ES, SE, NL (18) 
EEA, EU28 

yes, and it 
causes me 
difficulty as 
consumer 
AT,  BE,  CZ,  DK, 
EE, FR, DE, HU, IE, 
IT, LU, NO, PL, SK, 
SE,  UK  (16)  EEA, 
EU28 

AT, BG, DK, EE, FI, 
HU, LU, MT (8)  

AT,  BG,  HR,  CZ, 
DK, EE, FI, FR, DE, 
EL,  HU,  IT,  LT,  LU, 
RO, SE (16)  

AT,  BE,  CY,  CZ, 
DK, EE, FR, HU, IS, 
IT, LV, LU, PL (13)  

AT,  BG,  HR,  DK, 
DE,  EL,  HU,  IE,  IT, 
LU,  SE,  UK  (12) 
EEA, EU28 

AT,  BE,  CZ,  DK, 
EE,  HU,  IS,  IT,  LT, 
LU, PL, SE (12)  

AT,  BG,  HR,  CZ, 
DK, EE, FI, FR, DE, 
EL,  HU,  IE,  IT,  LT, 
LU, NO, SK, SI, SE, 
UK 
EEA, 
EU28 

(20) 

EE 

AT, DE, NO, SK (4)  

LU, PT 

BE,  LT,  PT, 
NL (4)  

DE, LT 

AT, BE, DE, RO (4) 
EEA, EU28 

PT 

LU 

AT, FI, RO, NL (4)  

1  2. Overpaid for services 

due to being unable to 
use certain payment 
methods 

2  4. Perception of own 

vulnerability due to 
personal characteristics 
In this section referred to 
as ‘Feels vulnerable to 'a 
great extent' due to 
employment situation’ 

2  5. Perception of own 

vulnerability due to 
personal characteristics 
In this section referred to 
as ‘Feels vulnerable to 'a 
great extent' due to 
financial circumstances’ 

2  10. Perception of own 
vulnerability due to 
personal characteristics 
In this section referred to 
as ‘Feels vulnerable to 'a 
great extent' due to any 
personal characteristic’ 

3  15. Has problems 

comparing deals due to 
information-related 
factors- energy sector* 

3  17. Has problems 

comparing deals due to 
information-related 
factors – financial sector* 

619 

DE, IS 

 
 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Table 130:  Above average vulnerability in countries due to personal and demographic (gender, 

education, mother tongue) 
Gender 

D  Indicator 

Education level 

different mother tongue than country of residence 

Annex 4│ Incidence rate of vulnerability by sub-group 

Female 

Male 

AT,  HR,  CZ, 
DK,  FI, 
IT, 
NO, SI (8)  

DE, IE 

LU 

PL 

4  21. Does not compare 
product deals - energy 
sector 

4  22. Finds it very difficult 

to compare product deals 
- energy sector 

4  23. Does not compare 
product deals - finance 
sector 

4  24. Finds it very difficult 

to compare product deals 
- finance sector 

4  25. Has problems 

comparing deals due to 
personal factors – energy 
sector* 

4  26. Has problems 

comparing deals due to 
personal factors – on-line 
sector 

CZ,  DK,  FI, 
PT, SI (5)  

4  27. Has problems 

comparing deals due to 
personal factors – finance 
sector* 

Low 
AT,  BE,  BG,  HR,  DK, 
EE,  FI,  DE,  EL,  IE, 
IT,  LV,  NO,  PL,  PT, 
RO,  SK,  SI,  ES,  SE, 
NL,  UK  (22)  EEA, 
EU28 
AT,  BE,  BG,  HR,  CZ, 
DK,  EE,  FI,  DE,  EL, 
HU,  IE,  LV,  LT,  LU, 
NO,  PL,  RO,  SK,  SI, 
SE,  UK  (22)  EEA, 
EU28 
AT,  BE,  BG,  HR,  CY, 
CZ,  DK,  EE,  FI,  DE, 
EL,  HU,  IS,  IE,  IT, 
LV,  LT,  LU,  NO,  PL, 
RO,  SK,  SI,  ES,  SE, 
NL,  UK  (27)  EEA, 
EU28 
AT,  BG,  HR,  CY,  EE, 
DE,  IE,  LV,  LT,  PL, 
RO,  SK,  SI,  SE,  UK 
(15) EEA, EU28 

DE,  NO,  RO,  SK,  UK 
(5) EEA, EU28 

AT,  HR,  CY,  DE,  EL, 
IS,  LT,  NO,  RO,  SI, 
ES, SE (12)  

EE,  DE,  IE,  LT,  NO, 
RO,  UK  (7)  EEA, 
EU28 

Medium 

High 

no 

BG 

yes, but it does 
not cause 
difficulty 

yes, and it 
causes me 
difficulty as 
consumer 

HR, DK, EE, FI, DE, 
SI (6)  

BG, HR, CZ, HU, LT 
(5)  

BG,  HR,  DK,  FI, 
DE, LV, LU, SK, SI, 
SE,  NL,  UK  (12) 
EEA, EU28 

BG, HR, DK, EE, FI, 
FR,  DE,  EL,  IE,  LV, 
LT,  RO,  SK,  SI,  ES 
(15) EEA, EU28 

BG,  HR,  CY,  HU, 
MT (5)  

BG, CZ, EE, IS, LT, 
MT, NO, SK, NL (9)  

BG, HR, FI, DE, EL, 
IE,  IT,  LT,  MT,  RO, 
SK, SI, NL, UK (14)  
BG,  HR,  DK,  EE, 
FR, DE, LV, LT, MT, 
PL, RO, SI, ES (13) 
EEA, EU28 

FR, DE, NO, PT (4)  

AT,  IE,  LT,  SK,  SE, 
NL (6)  

PT 

BE, HR, IS, NO, PT, 
SI (6)  

CY, FR, IE, NO, ES, 
NL (6)  

NO 

FI,  DE,  IE,  LT,  SK, 
SE, NL (7)  

620 

 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 130:  Above average vulnerability in countries due to personal and demographic (gender, 

education, mother tongue) 
Gender 

D  Indicator 

Education level 

different mother tongue than country of residence 

Female 

Male 

Low 

Medium 

High 

no 

yes, but it does 
not cause 
difficulty 

yes, and it 
causes me 
difficulty as 
consumer 

4  28. Has problems 

comparing deals due to 
market-related factors – 
energy sector* 
4  29. Has problems 

comparing deals due to 
market-related factors – 
on-line sector 
4  30. Has problems 

comparing deals due to 
market-related factors – 
finance sector* 

IE 

PT 

LT, PT 

IE,  LU,  RO,  SK,  NL 
(5)  

CZ, PT, UK 

BG, RO 

HR, CY, EL, RO (4)  

EL, LV 

HR,  CY,  CZ,  FR, 
HU, PT, SI (7)  

CY, FR, IT, LV, MT, 
RO, ES, NL (8)  

EE 

PT 

PT 

IS 

NL, UK 

Note: * -= Following 11 countries not covered: BG, HR, CY, EL, HU, IT, LV, MT, PL, SI, ES. 
Source: London Economics 

621 

 
 
 
  
  
  
  
  
  
  
  
  
 
 
Situational drivers 

Annex 4│ Incidence rate of vulnerability by sub-group 

Table 131:  Above average vulnerability in countries due to situational drivers (occupation) 
D  Indicator 

retired 

employ
ed full-
time 

employe
d part-
time 

self-
employe
d full-
time 

self-
employe
d part-
time 

unemplo
yed but 
looking 
for a job 

unemplo
yed and 
not 
looking 
for a job 

long-
term sick 
or 
disabled 

housewif
e / 
homema
ker 

pupil / 
student / 
in full 
time 
educatio
n 

studying 
in 
combinat
ion with 
a part-
time job 

1 

2 

2 

2. Overpaid for 
services due to being 
unable to use certain 
payment methods 

4. Perception of own 
vulnerability due to 
personal 
characteristics 
In this section 
referred to as ‘Feels 
vulnerable to 'a great 
extent' due to 
employment 
situation’ 

5. Perception of own 
vulnerability due to 
personal 
characteristics 
In this section 
referred to as ‘Feels 
vulnerable to 'a great 
extent' due to 
financial 
circumstances’ 

BG,  EE,  EL, 
HU,  LU,  SI 
(6)  

AT,  HR,  IT, 
PL,  PT,  RO, 
SI (7)  

BE, BG, HR, 
CY,  CZ,  FI, 
HU, 
MT, 
NO,  PL,  SK 
(11)  

NO,  PL,  SI, 
ES (4)  

AT 

CY,  FI,  FR, 
IT,  LU,  MT, 
NO (7)  

EE,  FR,  IS, 
LV,  LU,  MT, 
PL, ES (8)  

FI 

BE,  BG,  CY, 
DK,  FR,  DE, 
IT,  MT,  NO, 
UK (10)  

BE,  HR,  CY, 
DK,  EE,  FR, 
IS,  LT,  LU 
(9)  
BE, BG, HR, 
CY, CZ, DK, 
EE,  FI,  FR, 
DE,  EL,  HU, 
IS,  IE,  IT, 
LV,  LT,  LU, 
MT, NO, PL, 
PT,  RO,  SK, 
SI,  ES,  SE, 
NL,  UK (29) 
EEA, EU28 

AT,  BE,  BG, 
HR,  CY,  CZ, 
DK,  EE,  FI, 
FR, DE, HU, 
IE,  IT,  LT, 
LU, MT, NO, 
PL,  PT,  SK, 
SI,  ES,  SE, 
NL,  UK (26) 
EEA, EU28 

AT,  HR,  CZ, 
EE,  HU,  LV, 
MT, 
NO, 
SK,  SI,  ES, 
SE (12)  

AT,  BE,  HR, 
CY, CZ, DK, 
EE,  FI,  EL, 
HU,  IT,  LT, 
NO, PT, RO, 
SK,  SI,  SE 
(18)  EEA, 
EU28 

AT,  BE,  HR, 
CY, CZ, DK, 
EE,  FI,  IT, 
LV,  LT,  NO, 
PT,  RO,  SI, 
ES,  SE,  NL 
(18)  EEA, 
EU28 

HR,  CZ,  FR, 
IE,  IT,  LV, 
LT,  RO,  SI, 
SE (10)  

BE,  BG,  CY, 
CZ,  DK,  EE, 
FI,  FR,  DE, 
EL,  HU,  IS, 
IE,  IT,  LV, 
LU, MT, NO, 
PL,  PT,  SK, 
SI,  ES,  SE, 
NL,  UK (26) 
EEA, EU28 
AT,  BE,  BG, 
HR,  CY,  CZ, 
DK,  EE,  FI, 
FR, DE, HU, 
IS,  IE,  IT, 
LV,  LT,  MT, 
NO,  PL,  PT, 
RO,  SK,  SI, 
ES,  SE,  NL, 
UK 
(28) 
EEA, EU28 

AT,  DK,  EE, 
IS,  SK,  SE 
(6)  

DE,  IS,  MT, 
NO,  NL,  UK 
(6)  

BG, LU 

BE,  DK,  EE, 
IT,  LV,  RO, 
SK,  ES,  SE 
(9)  

CY,  IS,  IE, 
NO,  PL,  PT, 
RO,  SK,  SE 
(9) 
EEA, 
EU28 

CY,  CZ,  FI, 
DE,  IE,  LV, 
MT, NO, PL, 
PT,  RO,  SK, 
SE, UK (14) 
EEA, EU28 

SK, UK 

AT,  BG,  CZ, 
FR,  IE,  LU, 
NO, ES (8)  

AT,  BE,  BG, 
EE,  IT,  MT, 
ES (7)  

BG,  LT,  SK, 
SE (4)  

AT,  BE,  BG, 
FR,  DE,  IE, 
LU,  SK,  ES, 
NL (10)  

BE,  FR,  IT, 
ES (4) EEA, 
EU28 

622 

 
 
 
 
  
  
  
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 131:  Above average vulnerability in countries due to situational drivers (occupation) 
D  Indicator 

retired 

employ
ed full-
time 

employe
d part-
time 

self-
employe
d full-
time 

self-
employe
d part-
time 

unemplo
yed but 
looking 
for a job 

unemplo
yed and 
not 
looking 
for a job 

long-
term sick 
or 
disabled 

housewif
e / 
homema
ker 

pupil / 
student / 
in full 
time 
educatio
n 

studying 
in 
combinat
ion with 
a part-
time job 

AT,  BE,  BG, 
HR,  CY,  CZ, 
DK,  EE,  FI, 
FR,  DE,  EL, 
IS,  IE,  IT, 
LT,  LU,  MT, 
NO,  PL,  PT, 
SK,  SI,  ES, 
SE,  NL,  UK 
(27)  EEA, 
EU28 

AT,  BE,  HR, 
CZ,  DK,  FI, 
IT,  LV,  LT, 
NO, PT, RO, 
SK,  SI,  SE 
(15)  EEA, 
EU28 

AT,  BE,  BG, 
HR,  CY,  CZ, 
DK,  EE,  FI, 
FR,  EL,  HU, 
IS,  IE,  IT, 
LV,  LT,  MT, 
NO,  PL,  PT, 
RO, SK, ES, 
SE,  NL,  UK 
(27)  EEA, 
EU28 

CY,  CZ,  FI, 
HU,  IS,  IE, 
LV, MT, NO, 
PL,  PT,  RO, 
(13) 
SE 
EEA 

BG,  HR,  LT, 
RO, SK, SE, 
NL (7)  

AT,  BG,  FR, 
IS,  IE,  LU, 
NO,  ES,  NL 
(9)  

AT,  BE,  EE, 
IT,  MT,  NO, 
ES (7)  

EE,  FR,  NO, 
UK (4)  

AT,  CZ,  EE, 
DE,  IS,  SK, 
NL (7)  

CZ,  DK,  EE, 
FI,  FR,  NL 
(6)  

BE, IS, PT 

EE,  DE,  IE, 
NL (4)  

AT,  FI,  DE, 
LT,  LU,  NO, 
SK (7)  

CZ,  EE,  FI, 
IE,  NO,  UK 
(6)  

CZ,  DK,  EE, 
PL, ES (5)  

BE, LU, PL 

BE,  EE,  LT, 
PT, SE (5)  

EE,  FR,  LU, 
RO, SE (5)  

CZ,  EE,  LT, 
PT (4)  

AT,  EE,  IE, 
LU, UK (5)  

CY,  FI,  FR, 
DE,  HU,  IT, 
NO, 
MT, 
SK, UK (10)  

AT,  BE,  CZ, 
DK,  FR,  DE, 
IE,  LU,  RO, 
SK, NL (11)  

AT,  BE,  CZ, 
DE,  IS,  LT, 
LU,  PT,  SE 
(9)  

AT, CZ, NO, 
SI (4)  

FR, NL 

BE, FI, MT 

NO, NL, UK 

IS, LU 

CZ,  DK,  FI, 
IE, RO (5)  

BG, CY, CZ, 
EE,  FI,  FR, 
DE,  IE,  IT, 
LT,  NO,  PL, 
PT,  SK,  ES, 
NL,  UK (17) 
EEA, EU28 

AT,  DK,  EE, 
FI,  HU,  IT, 
NO, SK, SE, 
UK (10)  

HR,  FR,  EL, 
IE,  LV,  LU, 
MT, UK (8)  

HR,  EL,  IT, 
LV,  PL,  PT, 
SI,  ES,  UK 
(9) 
EEA, 
EU28 

DE, LT 
AT,  BE,  HR, 
CY, CZ, DK, 
EE,  DE,  EL, 
IE,  IT,  LV, 
LT,  PL,  PT, 
SI,  ES,  UK 
(18)  EEA, 
EU28 
AT, BG, HR, 
CZ,  DK,  EE, 
FI,  DE,  EL, 
HU,  IE,  IT, 
LV,  LT,  PL, 
PT,  RO,  SK, 
SI,  ES,  NL, 
UK 
(22) 
EEA, EU28 

LU, NO 
AT,  BE,  CZ, 
DK,  FI,  DE, 
HU,  IE,  IT, 
LV,  LU,  NO, 
PT,  SK,  SI, 
ES,  SE,  NL, 
UK 
(19) 
EEA, EU28 

AT,  DE,  PT, 
SE (4)  

AT,  BE,  BG, 
HR,  CY,  CZ, 
DK,  EE,  FI, 
DE,  HU,  LV, 
NO, 
MT, 
SK,  SI,  SE, 
NL, UK (19)  

BE,  FR,  DE, 
MT, 
NO, 
SK,  SE,  UK 
(8)  

AT,  BE,  BG, 
CY,  NO,  PT, 
SK, UK (8)  

CY,  FI,  LU, 
NO,  PL,  ES 
(6)  

HR,  IS,  IE, 
NO, SK (5)  

AT,  DE,  LT, 
NO, NL (5)  

CZ,  DK,  PL, 
SE, UK (5)  

BE,  CY,  CZ, 
DK,  EE,  FR, 
IE,  IT,  LV, 
LT, MT,  NO, 
SK,  SE,  NL 
(15)  EEA, 
EU28 

HR,  CY,  CZ, 
DK,  FI,  EL, 
HU,  IE,  LV, 
LU, MT, NO, 
PL,  PT,  RO, 
SI,  ES,  SE, 
NL (19)  

AT,  HR,  EL, 
HU,  LU,  PT, 
RO, ES (8)  

2 

3 

3 

4 

4 

10. Perception of 
own vulnerability due 
to personal 
characteristics 
In this section 
referred to as ‘Feels 
vulnerable to 'a great 
extent' due to any 
personal 
characteristic’ 

15. Has problems 
comparing deals due 
to information-
related factors- 
energy sector* 
17. Has problems 
comparing deals due 
to information-
related factors – 
financial sector* 
21. Does not 
compare product 
deals - energy sector 

22. Finds it very 
difficult to compare 
product deals - 
energy sector 

623 

 
 
 
  
  
  
  
  
  
Table 131:  Above average vulnerability in countries due to situational drivers (occupation) 
D  Indicator 

retired 

employ
ed full-
time 

employe
d part-
time 

self-
employe
d full-
time 

self-
employe
d part-
time 

unemplo
yed but 
looking 
for a job 

unemplo
yed and 
not 
looking 
for a job 

long-
term sick 
or 
disabled 

housewif
e / 
homema
ker 

Annex 4│ Incidence rate of vulnerability by sub-group 

AT, BG, HR, 
CY,  CZ,  EE, 
FR,  EL,  HU, 
IE,  LV,  LU, 
PL,  RO,  SK, 
SI,  ES,  SE 
(18)  EEA, 
EU28 

HR,  CY,  CZ, 
FI,  EL,  HU, 
IS,  IE,  IT, 
LV,  PL,  RO, 
SI,  SE,  UK 
(15)  

AT, HR, DK, 
EL,  HU,  IS, 
IT,  LV,  MT, 
NO, PT, RO, 
SI,  ES  (14) 
EEA, EU28 

CY,  DK,  EL, 
MT,  SI,  ES 
(6)  

AT,  BE,  BG, 
HR, CZ, DK, 
EE,  DE,  EL, 
HU,  IE,  IT, 
LV,  LT,  LU, 
MT, NO, PL, 
PT,  RO,  SK, 
SI,  ES,  SE, 
UK 
(25) 
EEA, EU28 
AT, BG, HR, 
CZ,  EE,  DE, 
HU,  IE,  IT, 
LT,  PL,  PT, 
RO,  SK,  SI, 
ES,  SE,  NL, 
UK 
(19) 
EEA, EU28 

AT,  EE,  FR, 
PT, UK (5)  

FI, NO, PT 

EE,  PT,  RO, 
UK (4)  

IS,  LV,  RO, 
SI (4)  

IE, NO 

AT, CZ, IE 

AT,  DK,  FI, 
FR, DE, HU, 
MT, 
NO, 
RO,  SK,  UK 
(11)  

BG, LV, NO, 
RO (4)  

CY, FR, IS 

HR,  CZ,  FR, 
NO (4)  

EE,  DE,  LU, 
UK (4)  

BE, CZ, RO, 
SK (4)  

PT, SE, UK 

CZ,  FI,  IS, 
IE, LT (5)  

AT, LU, UK 

BG, EE, HU, 
LV,  SI,  UK 
(6)  

BG,  HR,  IE, 
MT,  PL,  SI, 
SE (7)  

AT,  BE,  HR, 
CZ,  DK,  FI, 
DE,  IT,  LU, 
NO,  PL,  SE, 
NL (13)  

CY,  CZ,  EE, 
LT,  MT,  PT, 
RO (7)  

AT, CZ, RO 

IE 

AT,  CZ,  DK, 
DE, IE (5)  

CZ, EE, NO, 
SK, UK (5)  

BE,  BG,  CY, 
CZ,  EE,  FI, 
DE,  EL,  HU, 
IS,  IE,  IT, 
LV,  LT,  MT, 
PL,  SK,  SI, 
SE,  NL,  UK 
(21)  EEA, 
EU28 

BE, BG, HR, 
CZ,  FI,  FR, 
IS,  IT,  LT, 
NO, PL,  RO, 
SI,  SE  (14) 
EEA, EU28 
BE,  EE,  FI, 
FR,  IS,  LT, 
NO,  SE,  UK 
(9) 
EEA, 
EU28 

AT, BG, HR, 
CY,  EE,  DE, 
EL,  HU,  IS, 
LV,  LT,  LU, 
PL,  RO,  SK, 
ES, NL (17)  

BE,  EE,  DE, 
IS,  NO,  SE, 
UK (7)  

AT,  HR,  EE, 
FR,  LT,  NO, 
SK,  SI,  ES 
(9)  
DK,  EE,  FR, 
DE,  IE,  SK, 
SE (7) EEA, 
EU28 

AT,  BE,  HR, 
CZ,  DK,  FR, 
EL,  IE,  LV, 
MT,  PT,  ES 
(12)  EEA, 
EU28 

CY,  CZ,  DE, 
PT,  SI,  ES 
(6)  

DE,  HU,  IS, 
LT,  NO,  UK 
(6)  

BE,  IS,  NO, 
PT (4)  

BE,  CZ,  LT, 
PT, RO (5)  

EE,  DE,  IS, 
IE,  NO,  UK 
(6)  

AT,  FR,  IE, 
NO, PT, RO, 
SE, UK (8)  

pupil / 
student / 
in full 
time 
educatio
n 

studying 
in 
combinat
ion with 
a part-
time job 

BE,  CZ,  FI, 
FR,  DE,  EL, 
HU,  IE,  IT, 
LT,  NO,  SK, 
ES,  SE,  UK 
(15)  EEA, 
EU28 

AT,  BG,  CY, 
IE,  LV,  MT, 
NO,  PT,  SK, 
SI,  SE,  UK 
(12)  

BE,  FR,  NO, 
PT, SE (5)  
AT,  CZ,  EE, 
FI,  DE,  IE, 
LT,  LU,  NO, 
SK,  SE,  NL, 
UK (13)  

BE, BG, HU, 
MT,  PT,  SE 
(6)  

AT,  BE,  IE, 
LT,  NO,  UK 
(6)  
AT, BG, HR, 
CY,  CZ,  FI, 
FR,  HU,  IE, 
LV,  LT,  MT, 
NO,  PL,  PT, 
SI,  SE,  UK 
(18)  

CZ,  EE,  IS, 
LT (4)  

BE,  FR,  NO, 
PT,  RO,  SE, 
NL (7)  

CZ,  DE,  IE, 
LU, NO, RO, 
SK, NL (8)  

EE, UK 

EE,  DE,  SK, 
SE, NL (5)  

CZ,  DK,  EE, 
FI,  LT,  NL 
(6)  

AT,  IS,  NO, 
UK (4)  

CZ,  FR,  IS, 
SE, NL (5)  

LU 

EE,  DE,  IE, 
LT, PT (5)  

624 

4 

23. Does not 
compare product 
deals - finance sector 

4 

4 

4 

4 

4 

24. Finds it very 
difficult to compare 
product deals - 
finance sector 

25. Has problems 
comparing deals due 
to personal factors – 
energy sector* 

26. Has problems 
comparing deals due 
to personal factors – 
on-line sector 

27. Has problems 
comparing deals due 
to personal factors – 
finance sector* 
28. Has problems 
comparing deals due 
to market-related 
factors – energy 
sector* 

 
 
 
  
  
  
  
  
  
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 131:  Above average vulnerability in countries due to situational drivers (occupation) 
D  Indicator 

retired 

employ
ed full-
time 

employe
d part-
time 

self-
employe
d full-
time 

self-
employe
d part-
time 

unemplo
yed but 
looking 
for a job 

unemplo
yed and 
not 
looking 
for a job 

long-
term sick 
or 
disabled 

housewif
e / 
homema
ker 

29. Has problems 
comparing deals due 
to market-related 
factors – on-line 
sector 

AT,  DK,  EE, 
IS,  LV,  LT 
(6)  

AT, BG, HR, 
FR,  EL,  IT, 
PL,  RO,  SI, 
ES (10)  

BG, HR, CY, 
CZ,  DE,  IE, 
LT,  LU,  NO, 
RO, SK (11)  

BE,  CY,  HU, 
NL (4)  

HR,  EL,  IT, 
LV,  MT,  SK 
(6)  

AT,  CZ,  EE, 
FR,  IT,  LV, 
LT,  NO,  PL, 
SK,  ES,  SE, 
NL,  UK (14) 
EEA, EU28 

AT,  CY,  CZ, 
EL,  LU,  MT, 
PL (7)  

AT,  BE,  HR, 
CY, CZ, DK, 
FI,  FR,  EL, 
IT,  LV,  NO, 
PT,  SI,  ES, 
SE,  NL,  UK 
(18)  EEA, 
EU28 

pupil / 
student / 
in full 
time 
educatio
n 

studying 
in 
combinat
ion with 
a part-
time job 

EL,  HU,  LT, 
LU, RO (5)  

BG, HU,  PT, 
ES, UK (5)  

30. Has problems 
comparing deals due 
to market-related 
factors – finance 
sector* 

AT,  BE,  CZ, 
DE,  LT,  LU, 
NO,  PT,  SK 
(9)  
Note: * -= Following 11 countries not covered: BG, HR, CY, EL, HU, IT, LV, MT, PL, SI, ES. 
Source: London Economics 

BE,  EE,  IE, 
RO, NL (5)  

EE, NL 

LT, NO 

DK,  EE,  FI, 
LT, RO (5)  

LT, UK 

SE, NL 

IS 

BE,  FR,  DE, 
IE,  LT,  PT 
(6) EU28 

4 

4 

625 

 
 
 
  
  
  
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 132:  Above average vulnerability in countries due to situational drivers (financial situation, 

personal situation) 

D  Indicator 

difficulty to make ends meet 

married 

1  2. Overpaid for 
services due to 
being unable to 
use certain 
payment methods 

2  4. Perception of 

own vulnerability 
due to personal 
characteristics 
In this section 
referred to as 
‘Feels vulnerable 
to 'a great extent' 
due to 
employment 
situation’ 

very 
easy 

fairly 
easy 

fairly 
difficult 

HU, RO 

NO 

CZ 

AT,  DK, 
LU,  NO, 
SE (5)  

very 
difficult 
HR, 
AT, 
EE, 
DK, 
EL, 
FR, 
HU, 
IS, 
IE, LT, LU, 
SI, ES, UK 
(14)  
AT, 
BE, 
BG,  HR, 
CZ,  DK, 
EE, FI, FR, 
DE,  HU, 
IS,  IE,  IT, 
LV, 
LT, 
LU,  NO, 
PT, 
PL, 
SK, 
RO, 
ES, 
SI, 
NL, 
SE, 
UK 
(27) 
EEA, 
EU28 

remarrie
d 

not 
married 
living 
with a 
partner 

single 

divorced 
or 
separate
d 

widowed  other 

BE, 
AT, 
BG, 
CY, 
EL, IT, PL, 
SK, 
ES, 
UK (10)  

EL, IT, LT, 
ES (4)  

EE, LT 

HR 

LU, 
FR, 
NO, 
SK, 
SI, NL (6)  

BG, 
CZ, 
EE, LU, SK 
(5)  

HR, 
CY, 
PL, SK, NL 
(5)  

EE, IT 

AT, 
BE, 
CZ,  DK, 
FR, 
LU, 
NO (7)  

BE,  MT, 
NO, 
ES, 
SE, NL (6)  

LT, 
LU, 
PT, UK (4)  

CZ,  DK, 
EE,  IE,  IT, 
SK, 
NO, 
SE, 
SI, 
NL, 
UK 
(11)  

626 

 
 
 
 
  
  
  
  
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 132:  Above average vulnerability in countries due to situational drivers (financial situation, 

personal situation) 

D  Indicator 

difficulty to make ends meet 

married 

very 
easy 

fairly 
easy 

fairly 
difficult 

CZ, 
AT, 
EE, 
DK, 
FI, 
DE, 
LU,  NO, 
PL, SE, UK 
(11)  

CZ, HU 

CZ, HU 

EE, 
DK, 
LU, 
FI, 
NO, 
PL, 
SE, UK (8)  

very 
difficult 
AT, 
BE, 
BG,  HR, 
CZ,  DK, 
EE, FI, FR, 
EL, 
DE, 
HU, 
IS, 
IE,  IT,  LV, 
LU, 
LT, 
PL, 
NO, 
RO, 
PT, 
SI, 
SK, 
SE, 
ES, 
UK 
NL, 
(28)  EEA, 
EU28 
AT, 
BE, 
BG,  HR, 
CZ,  DK, 
EE, FI, FR, 
EL, 
DE, 
HU, 
IS, 
IE,  IT,  LV, 
LU, 
LT, 
PL, 
NO, 
RO, 
PT, 
SI, 
SK, 
SE, 
ES, 
UK 
NL, 
(28)  EEA, 
EU28 

2  5. Perception of 

own vulnerability 
due to personal 
characteristics 
In this section 
referred to as 
‘Feels vulnerable 
to 'a great extent' 
due to financial 
circumstances’ 

2  10. Perception of 
own vulnerability 
due to personal 
characteristics 
In this section 
referred to as 
‘Feels vulnerable 
to 'a great extent' 
due to any 
personal 
characteristic’ 

627 

remarrie
d 

not 
married 
living 
with a 
partner 

single 

divorced 
or 
separate
d 

widowed  other 

AT, 
HR, 
CY, FI, EL, 
IE,  PL,  SK 
(8)  

AT, 
BE, 
CZ,  DK, 
FR, 
DE, 
SK (7)  

IS, IT, LU, 
MT (4)  

BG, 
CZ, 
DE, 
IE, 
ES, 
NL, 
(13) 

BE, 
CY, 
FR, 
HU, 
MT, 
SE, 
UK 
EEA, 
EU28 

BG,  HR, 
EL, 
EE, 
LU, 
LT, 
RO, 
PT, 
SI, SE, UK 
(11)  

BE,  FI, IE, 
LU, 
IT, 
PL, 
NO, 
ES, 
SK, 
SE, 
NL 
(11)  

HR, 
CY, 
CZ, FI, IE, 
LV, 
PL, 
ES, NL (9)   IT, LU, MT 

CZ, 
AT, 
FR, 
DK, 
IS, LU, NO 
(7)  

BE, 
HR, 
FR,  MT, 
ES, 
SE, 
NL, UK (8)  

BG,  HR, 
EE, 
DK, 
DE, 
EL, 
LT,  MT, 
PT, 
PL, 
SI, 
RO, 
UK 
SE, 
(14)  EEA, 
EU28 

BE,  DK, 
FI,  IS,  IE, 
IT, 
LU, 
MT,  NO, 
SE, 
SK, 
NL, 
UK 
(13)  

 
 
 
  
  
  
  
Table 132:  Above average vulnerability in countries due to situational drivers (financial situation, 

Annex 4│ Incidence rate of vulnerability by sub-group 

personal situation) 

D  Indicator 

difficulty to make ends meet 

married 

very 
easy 

fairly 
easy 

fairly 
difficult 

RO 

LT 

DE, 
UK 

NL, 

very 
difficult 
AT, EE, FI, 
IS, LT, PT, 
SK, 
RO, 
UK 
(9) 
EEA, 
EU28 

remarrie
d 

not 
married 
living 
with a 
partner 

single 

divorced 
or 
separate
d 

widowed  other 

FR, IS, IE, 
LU,  NO, 
SK, SE (7)  

NO 

FR, 
DE, 
LU, UK (4)  

3  15. Has problems 

comparing deals 
due to 
information-
related factors- 
energy sector* 
3  17. Has problems 

comparing deals 
due to 
information-
related factors – 
financial sector* 

4  21. Does not 

compare product 
deals - energy 
sector 

AT,  DK, 
FI,  IE,  LT, 
NL, UK (7)     

NO 

DE, 

FR, 
IE, SE (4)   PT 

BE 

EL, 
EE, 
HU,  LV, 
NL (5)  

AT, 
BE, 
DK, IE, IT, 
NO, 
PL, 
SK, ES (9)  

NO 

HR, 
CZ, 
IE, 
SK 

AT, 
CY, 
FR, 
MT, 
(8)  

CY,  MT, 
SK 

AT, 
CZ, 
DE, 
NO, 
ES, 
NL, 
(12)  

BE, 
FI, 
IE, 
SI, 
SE, 
UK 

IE, 
SK, 

BE, 
LV, 
UK (5)  

AT, 
FI, 
SK, 
DE, 
NL, UK (6) 
EEA, 
EU28 

AT, EE, FI, 
FR, LT, UK 
(6)  

AT, 
BE, 
IS, LT (4)  
BE, 
AT, 
BG,  HR, 
CZ, 
CY, 
EE, 
DK, 
FI, 
DE, 
EL,  IE,  IT, 
LU, 
LT, 
PL, 
NO, 
RO, 
PT, 
ES, 
SI, 
UK 
NL, 
(23)  EEA, 
EU28 

FR,  NO, 
UK 

AT, 
BE, 
CZ,  DK, 
FI, 
FR, 
DE,  HU, 
IT, 
MT, 
NO,  RO, 
SI, 
SK, 
NL, 
SE, 
(17) 
UK 
EEA, 
EU28 

628 

 
 
 
  
  
  
  
  
  
  
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 132:  Above average vulnerability in countries due to situational drivers (financial situation, 

personal situation) 

D  Indicator 

difficulty to make ends meet 

married 

very 
easy 

fairly 
easy 

fairly 
difficult 

very 
difficult 

remarrie
d 

not 
married 
living 
with a 
partner 

single 

divorced 
or 
separate
d 

widowed  other 

4  22. Finds it very 
difficult to 
compare product 
deals - energy 
sector 

IE 

DK, LU 

4  23. Does not 

compare product 
deals - finance 
sector 

BE, 
AT, 
FI, 
DK, 
FR, IS, IE, 
LU,  NO, 
SE, 
SK, 
UK 
(12) 
EEA, 
EU28 

SI 

CY, 
CZ, 
FI,  IS,  LV, 
SK, 
NO, 
SE (8)  

CY, MT 

AT, 
HR, 
CY, FI, FR, 
LU, 
IE, 
PT, 
MT, 
UK (10)  

FR, 
NO, 
(4)  

LV, 
SE 

IT 

DK, FI, IE, 
LU,  NO, 
RO, 
ES, 
SE, UK (9)     

DK,  NO, 
UK 

BG, 
CY, 
FR, IE, SI, 
ES, 
SE, 
UK (8)  

CY 

CZ, 
LV, 
SK, 

BE, 
IS, 
NO, 
SE (7)  

CY, EE, FI, 
DE, 
FR, 
IE, 
HU, 
LT,  MT, 
NO,  NL, 
UK 
(12) 
EEA, 
EU28 

BG, 
AT, 
CZ, 
HR, 
EE, 
DK, 
FR, 
FI, 
EL, 
DE, 
HU, 
IS, 
IE,  IT,  LV, 
LU, 
LT, 
RO, 
PT, 
SI, 
SK, 
NL, 
ES, 
UK 
(24) 
EEA, 
EU28 
BG, 
AT, 
HR, 
CY, 
CZ,  DK, 
FI, 
EE, 
DE, 
EL, 
IE,  IT,  LV, 
LT, 
LU, 
MT,  NO, 
PT, 
PL, 
SK, 
RO, 
ES, 
SI, 
NL, 
UK 
(25)  EEA, 
EU28 

FI, 
CZ, 
DE, 
FR, 
HU, IE, IT, 
LU, 
LT, 
SK, 
NO, 
SE, 
UK 
(13)  EEA, 
EU28 

EE, 
FR, 
IT, 
SK, 
ES, 
UK 

CZ, 
FI, 
DE, 
LV, 
SI, 
SE, 
(12)  

629 

 
 
 
  
  
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 132:  Above average vulnerability in countries due to situational drivers (financial situation, 

personal situation) 

D  Indicator 

difficulty to make ends meet 

married 

very 
easy 

fairly 
easy 

fairly 
difficult 

very 
difficult 

remarrie
d 

not 
married 
living 
with a 
partner 

single 

divorced 
or 
separate
d 

widowed  other 

4  24. Finds it very 
difficult to 
compare product 
deals - finance 
sector 

4  25. Has problems 

comparing deals 
due to personal 
factors – energy 
sector* 

4  26. Has problems 

comparing deals 
due to personal 
factors – on-line 
sector 

4  27. Has problems 

comparing deals 
due to personal 
factors – finance 
sector* 

HU 

FR, DE 

DK, 
FI, 
DE, IS, IE, 
LU,  NO, 
SE, NL (9)  

AT, 
EE, 
NO, 
(6)  

BE, 
FI, 
SK 

BG,  DK, 
FR,  HU, 
IS,  RO, 
ES (7)  

EL, IT 

EE 

BE, 
IT, 
SK, 

AT, 
HR, 
RO, 
UK (7)  

PT 

AT,  DK, 
NO, 
IE, 
SK, SE (6)  

NO, SE 

CZ, 
IE, 
ES, NL (4)  

FI,  IE,  LV, 
UK (4)  

FR 

EE, IE 

DE, 

AT, 
NO 

BE, DK 

AT, 
BG, 
HR,  DK, 
FI, 
EE, 
IT, 
HU, 
LU, 
PL, 
SK, SI, SE 
(13)  

EE, 
DE, 
(5) 

CZ, 
FR, 
SE 
EEA, 
EU28 

LV, 
NO, 

BG, 
LT, 
ES (5)  

BE,  DK, 
HU, 
FI, 
LV, 
LU, 
PT, RO (8)  

CY, IE, IT 

FR, 
LU, 
NO,  NL, 
UK (5)  

IS, IE, NO 

AT, 
BG, 
CZ,  DK, 
FR, 
EE, 
DE, 
EL, 
IS, IT, LV, 
LU, 
LT, 
PT, 
MT, 
SK, 
RO, 
ES, 
SI, 
NL, 
UK 
(21)  EEA, 
EU28 
BE, 
AT, 
FI,  IS,  IE, 
PT, 
LT, 
RO, 
UK 
(9)  EEA, 
EU28 
AT, 
BE, 
HR,  DK, 
FI, IS, LU, 
PL, 
MT, 
SK, 
PT, 
ES, 
SI, 
NL, 
UK 
(15)  EEA, 
EU28 
CZ, 
AT, 
FR, 
FI, 
LT, 
DE, 
RO, 
PT, 
SK, 
UK 
(10)  EEA, 
EU28 

BE, 
FR, 
DE,  NO, 
SE, UK (6)  
CZ, 
AT, 
FR, 
FI, 
IE, 
DE, 
SK, 
UK 
(8)  EEA, 
EU28 
BG, 
AT, 
CZ, 
HR, 
DK, 
EE, 
FI, FR, EL, 
IT, 
HU, 
LT, 
RO, 
SK, SI, UK 
(16)  EEA, 
EU28 

EE, 
FR, 
SK, 
(7) 

CZ, 
FI, 
DE, 
UK 
EEA, 
EU28 

630 

 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 132:  Above average vulnerability in countries due to situational drivers (financial situation, 

personal situation) 

D  Indicator 

difficulty to make ends meet 

married 

4  28. Has problems 

comparing deals 
due to market-
related factors – 
energy sector* 
4  29. Has problems 

comparing deals 
due to market-
related factors – 
on-line sector 

4  30. Has problems 

comparing deals 
due to market-
related factors – 
finance sector* 

very 
easy 

fairly 
easy 

fairly 
difficult 

very 
difficult 

LT 

LT 

LT 

EE, 
PT, SK (4)   IS 

IS, 

BG,  EL, 
LT 
HU, 
(4)  

EE, NO 

PT 

NO, PT 

BG, 
AT, 
CZ, 
HR, 
FI, 
DE, 
EL,  IS,  IE, 
PT, 
IT, 
UK 
SK, 
(13)  

DE, 
FI, 
NO, 
UK 
(4)  EEA, 
EU28 

CY, PT 

single 

remarrie
d 

not 
married 
living 
with a 
partner 

EE, 
BE, 
IS,  IE,  LT, 
SK, NL (7)  

BG, 
AT, 
CZ, 
HR, 
DK, 
EE, 
FR, IS, PL, 
SE 
SI, 
(11)  

EE, IS, IE, 
LT, LU, NL 
(6)  

PT 

BG, EL, LV 

divorced 
or 
separate
d 

BE, 
NO, 
UK (5)  

FR, 
PT, 

CZ, 
BE, 
FI, 
FR, 
DE,  HU, 
IE,  IT,  LV, 
NO, 
NL 
(11)  EEA, 
EU28 

FI, LU 

widowed  other 

AT, FI, FR, 
IE, 
NO, 
SK (6)  
AT, 
BE, 
CY, 
CZ, 
HU, 
FR, 
IS, IE, LU, 
PT, 
MT, 
SI, 
SK, 
ES, 
UK 
(15)  EEA, 
EU28 

DE, 
RO, 
UK 

BE, 
IS, 
SK, 
(6)  

DE, 
AT, 
LT, LU, RO 
(5)  

CZ, 
AT, 
EL, 
DE, 
PL, 
LT, 
RO, SI (8)  

CZ, 
AT, 
DE, PT (4)  

Note: * -= Following 11 countries not covered: BG, HR, CY, EL, HU, IT, LV, MT, PL, SI, ES. 
Source: London Economics 

631 

 
 
 
  
  
  
  
  
  
  
 
 
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 133:  Above average vulnerability in countries due to situational drivers (dependent children, 

single parent, friends) 

D  Indicator 

Number of dependent children 

0 

1 

2 

3 

4 or more 

Single 
parent 

Friends do 
not buy 
online 

Friends buy 
on credit 

IT,  LU,  MT, 
NL (4)  

DE,  NO,  SE, 
UK (4)  

BG,  HR,  CZ, 
FR,  HU,  PL, 
PT, ES (8)  

HR,  HU,  IS, 
IT,  LV,  RO, 
SK,  ES,  SE 
(9)  

AT,  FR,  HU, 
LU (4)  

AT,  CZ,  DK, 
EE,  FI,  DE, 
SE, NL (8)  

Friends 
can’t make 
ends meet 

AT,  CZ,  DK, 
FI,  DE,  LU, 
NO,  SE,  NL 
(9)  

1  2. Overpaid for services 

due to being unable to 
use certain payment 
methods 

2  4. Perception of own 

vulnerability due to 
personal characteristics 
In this section referred to 
as ‘Feels vulnerable to 'a 
great extent' due to 
employment situation’ 

2  5. Perception of own 

vulnerability due to 
personal characteristics 
In this section referred to 
as ‘Feels vulnerable to 'a 
great extent' due to 
financial circumstances’ 

2  10. Perception of own 
vulnerability due to 
personal characteristics 
In this section referred to 
as ‘Feels vulnerable to 'a 
great extent' due to any 
personal characteristic’ 

3  15. Has problems 

comparing deals due to 
information-related 
factors- energy sector* 

3  17. Has problems 

comparing deals due to 
information-related 
factors – financial sector* 

EE,  FI,  MT, 
UK (4)  

AT,  BE,  BG, 
CZ,  EE,  IT, 
NO, PL (8)  

DK,  FI,  HU, 
IS,  NO,  PL, 
SK  (7)  EEA, 
EU28 

AT,  BE,  FR, 
HU,  IE,  LU, 
MT, NO (8)  

AT, SE 

AT,  BE,  FI, 
IS,  MT,  PL, 
SE (7)  

IS,  LU,  PT, 
RO, UK (5)  

BG,  HR,  CY, 
CZ,  EE,  EL, 
HU,  IE,  LV, 
MT,  NO,  PL, 
NL (13)  

BG,  HR,  CZ, 
DK,  FR,  HU, 
LV, PL, SI, SE 
(10) 
EEA, 
EU28 

AT,  BE,  BG, 
FR,  HU,  IE, 
IT,  LU,  MT, 
PL,  RO,  SE, 
NL,  UK  (14) 
EEA, EU28 

UK 

AT,  BE,  FI, 
IS, SE (5)  

FI, PT 

BG,  CZ,  EE, 
FI,  EL,  HU, 
IS, IE, MT, PL 
(10)  

BE,  BG,  CY, 
CZ,  HU,  IS, 
NO,  PL,  SI, 
ES,  SE,  UK 
EEA, 
(12) 
EU28 

AT,  BE,  CZ, 
FR,  HU,  IS, 
IE,  IT,  RO, 
ES,  UK  (11) 
EEA, EU28 

AT,  DK,  EE, 
DE,  LU,  NL 
(6)  

AT,  BE,  CZ, 
DK,  EE,  FI, 
FR,  DE,  IS, 
LU,  NO,  NL 
(12)  

AT,  BE,  CZ, 
DK,  EE,  FR, 
DE,  IS,  LU, 
SE, NL (11)  

AT,  DK,  DE, 
LU,  NO,  SE, 
UK (7)  
AT,  BE,  CZ, 
DK,  EE,  FI, 
FR,  DE,  IS, 
IE, IT, LV, LT, 
LU,  MT,  NO, 
PL,  SK,  SI, 
SE,  NL,  UK 
(22) 
EEA, 
EU28 

AT,  BE,  CZ, 
DK,  EE,  FI, 
FR,  DE,  IE, 
LT,  LU,  NO, 
PL,  SK,  SE, 
NL,  UK  (17) 
EEA, EU28 

CZ 

LT, LU, NO 

LT, LU, PT 

PT, SE 

IS, NO, SK 

AT,  DE,  LT, 
LU, NO (5)  

LT 

PT 

FR,  LT,  LU, 
RO,  SE,  UK 
(6) 
EEA, 
EU28 

FI, NO 

AT, DE 

NO 

632 

 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 133:  Above average vulnerability in countries due to situational drivers (dependent children, 

single parent, friends) 

D  Indicator 

Number of dependent children 

Single 
parent 

0 

1 

2 

3 

4 or more 

PT, SI 

EL, RO, ES 

BE,  BG,  CZ, 
EE,  DE,  LV, 
LU,  NO,  PT, 
ES, NL (11)  

AT,  BE,  DK, 
DE,  LV,  NO, 
PT,  SK,  SE, 
NL,  UK  (11) 
EEA, EU28 

LU 

PL 

AT 

BE,  BG,  CY, 
CZ,  FI,  HU, 
RO (7)  

BE,  BG,  EE, 
LV,  MT,  ES, 
SE (7)  

BE,  BG,  DK, 
FI,  FR,  EL, 
HU,  IT,  LU, 
NO,  RO,  SK, 
SI,  NL  (14) 
EEA, EU28 

AT,  LU,  RO, 
UK (4)  

BE,  FR,  LV, 
LU,  MT,  PT, 
UK (7)  
AT,  BE,  CY, 
CZ,  DK,  FI, 
FR,  DE,  IS, 
IE,  LV,  LU, 
MT,  NO,  RO, 
SK,  UK  (17) 
EEA, EU28 

LU, NL 

NL 

BG,  CY,  CZ, 
DE,  LU,  PT, 
RO, ES (8)  

BE,  BG,  CY, 
LV,  ES,  UK 
(6)  

BE,  CZ,  FI, 
FR,  LV,  LU, 
NL (7)  

IE 

DE 

NL 

LU, NO 

AT,  BE,  DK, 
FI,  DE, 
IE, 
NO, UK (8)  

UK 

Friends do 
not buy 
online 

DK,  FI,  DE, 
IE, IT, PL, PT, 
ES,  UK  (9) 
EEA, EU28 
FI,  DE,  EL, 
HU,  IE,  IT, 
LV,  LU,  PL, 
UK  (10)  EEA, 
EU28 

BG,  FI,  HU, 
IT,  MT, 
IE, 
PL, ES (8)  

Friends buy 
on credit 

Friends 
can’t make 
ends meet 

NO, UK 

IS 

AT,  DE,  NO, 
SI, SE (5)  

NO 

NO, UK 

EE 

NO, UK 

comparing deals due to 
personal factors – on-line 
sector 

LT 

BE,  BG,  HR, 
EE,  NO,  RO 
(6)  

DE, MT, NL 

CY,  CZ,  DE, 
IT, 
IE, 
EL, 
MT,  PL,  PT 
(9)  

BE,  CZ,  LT, 
PL, SI, NL (6)  

HR,  DE,  LU, 
NO,  PL,  RO, 
SI, ES (8)  

IE, MT 

FI 

IS, RO 

CZ,  DK,  EE, 
DE,  NO,  NL 
(6)  

DK, LU, UK 

IS, NO 

LT, PT 

LU, NO, RO 

AT,  LT,  NO, 
PT, NL (5)  

DE 

CZ 

RO 

EE 

4  21. Does not compare 
product deals - energy 
sector 

4  22. Finds it very difficult 

to compare product deals 
- energy sector 

4  23. Does not compare 
product deals - finance 
sector 

4  24. Finds it very difficult 

to compare product deals 
- finance sector 

4  25. Has problems 

comparing deals due to 
personal factors – energy 
sector* 

4  26. Has problems 

4  27. Has problems 

comparing deals due to 
personal factors – finance 
sector* 

4  28. Has problems 

comparing deals due to 
market-related factors – 
energy sector* 

633 

 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Table 133:  Above average vulnerability in countries due to situational drivers (dependent children, 

single parent, friends) 

D  Indicator 

Number of dependent children 

0 

1 

2 

3 

4 or more 

Single 
parent 

Friends do 
not buy 
online 

Friends buy 
on credit 

Friends 
can’t make 
ends meet 

Annex 4│ Incidence rate of vulnerability by sub-group 

4  29. Has problems 

comparing deals due to 
market-related factors – 
on-line sector 
4  30. Has problems 

DK 

LT, MT 

CZ,  EE,  PT, 
SI (4)  

AT,  BG,  EL, 
IE,  MT,  PL, 
PT, RO (8)  

comparing deals due to 
market-related factors – 
finance sector* 

LT, PT, RO 
Note: * -= Following 11 countries not covered: BG, HR, CY, EL, HU, IT, LV, MT, PL, SI, ES. 
Source: London Economics 

EE, PT 

AT,  CY,  IE, 
LT,  LU,  MT, 
NL (7)  
BE,  CZ,  DK, 
FI,  IS,  RO, 
SK,  NL,  UK 
(9) 
EEA, 
EU28 

HU,  LT,  RO, 
SK,  SI,  UK 
(6)  

BG, CZ, LV 

FI,  DE, 
RO (4)  

IE, 

634 

 
 
 
  
  
  
  
  
  
  
Annex 4│ Incidence rate of vulnerability by sub-group 

Behavioural drivers 

Table 134:  Above average vulnerability in countries due to behavioural drivers (trust 1, 2) 
D  Indicator 

"You need to be very careful in dealing with people" 

"I believe most people can be trusted" 

1  2. Overpaid for services 

due to being unable to 
use certain payment 
methods 

2  4. Perception of own 

vulnerability due to 
personal characteristics 
In this section referred to 
as ‘Feels vulnerable to 'a 
great extent' due to 
employment situation’ 

2  5. Perception of own 

vulnerability due to 
personal characteristics 
In this section referred to 
as ‘Feels vulnerable to 'a 
great extent' due to 
financial circumstances’ 

2  10. Perception of own 
vulnerability due to 
personal characteristics 
In this section referred to 
as ‘Feels vulnerable to 'a 
great extent' due to any 
personal characteristic’ 

3  15. Has problems 

comparing deals due to 
information-related 
factors- energy sector* 

3  17. Has problems 

Trust 1: Very low 

Trust 1: 
Low 

Trust 1: 
High 

Trust 1: Very 
high 

Trust 2: Very low 

Trust 2: 
Low 

Trust 2: 
High 

DE, SE, NL 

BG,  CY, 
CZ, 
FR, 
SK (5)  

AT,  CY,  CZ,  EE, 
HU, IE, NO, SK, UK 
(9)  

AT, NL 

FI 

FR 

AT,  BE,  DK,  EE,  DE, 
IS,  PL,  SE,  NL,  UK 
(10) EEA, EU28 

AT,  BE,  DK,  EE,  DE, 
IS,  IE,  IT,  LT,  NO, 
PL,  PT,  ES,  SE,  NL, 
UK (16) EEA, EU28 

AT,  BE,  DK,  EE,  FI, 
FR,  DE,  IS,  IE,  IT, 
LT,  NO,  PL,  ES,  SE, 
NL,  UK  (17)  EEA, 
EU28 

CZ, SK 

BG, EL, HU, PL, SI, 
UK (6)  

BE, HR, DK, EE, FI, FR, 
DE, IS, IT, NO, PL, RO, 
SK,  NL,  UK  (15)  EEA, 
EU28 

IS 

CY,  CZ, 
SK 

CZ, DK, EL, HU, IE, 
LV, PL, SI, UK (9)  

CZ, SK 

BG, CY, EL, HU, IE, 
LU, RO, SI, UK (9)  

AT,  BE,  BG,  HR,  CZ, 
DK,  EE,  FI,  FR,  DE, 
HU, IS, IT, LV, LT, NO, 
PL, PT, RO, SK, SI, ES, 
SE,  NL 
(24)  EEA, 
EU28 

BE,  BG,  HR,  CZ,  DK, 
EE, FI, FR, DE, HU, IS, 
IE,  IT,  LV,  LT,  NO,  PL, 
SK, SI, ES, SE, NL, UK 
(23) EEA, EU28 

IS, NO 

LU 

IS 

LU 

AT,  BE,  IE,  NL,  UK 
(5) EEA, EU28 

LT 

RO, SK, SE 

BE, EE, IE, RO, SK, SE, 
NL (7) EEA, EU28 

FI 

comparing deals due to 
information-related 
factors – financial sector*  NL 

IE, LU, RO 

DE, NL 

Trust 2: Very 
high 
BE,  BG,  CY,  FR, 
DE,  HU,  IE,  IT, 
LU,  NO,  PL,  PT, 
NL, UK (14) EEA, 
EU28 

BE,  CZ,  FR,  DE, 
IE,  LT,  LU,  PL, 
SE,  NL,  UK  (11) 
EEA, EU28 

CZ,  FR,  DE,  HU, 
IS,  IT,  LT,  LU, 
PL,  RO,  ES,  SE, 
NL, UK (14) EEA, 
EU28 

AT,  CY,  CZ,  FR, 
DE,  HU,  IE,  IT, 
LU,  PL,  PT,  RO, 
ES,  SE,  NL,  UK 
(16) EEA, EU28 

CZ,  LU,  NO,  PT 
(4)  

FR,  LT,  LU,  UK 
(4)  

635 

 
 
 
  
  
  
  
  
  
  
  
  
  
  
Table 134:  Above average vulnerability in countries due to behavioural drivers (trust 1, 2) 
D  Indicator 

"You need to be very careful in dealing with people" 

"I believe most people can be trusted" 

Annex 4│ Incidence rate of vulnerability by sub-group 

Trust 1: Very low 

Trust 1: 
Low 

Trust 1: 
High 

Trust 2: 
Low 

Trust 2: 
High 

Trust 2: Very 
high 

4  21. Does not compare 
product deals - energy 
sector 

4  22. Finds it very difficult 

to compare product deals 
- energy sector 

4  23. Does not compare 
product deals - finance 
sector 

4  24. Finds it very difficult 

to compare product deals 
- finance sector 

4  25. Has problems 

comparing deals due to 
personal factors – energy 
sector* 

4  26. Has problems 

comparing deals due to 
personal factors – on-line 
sector 

4  27. Has problems 

AT,  BE,  IE,  SI,  ES, 
SE (6) EEA, EU28 

AT,  BE,  EE,  FI,  DE, 
IE,  IT,  NO,  PL,  SK, 
SI,  ES,  SE,  NL,  UK 
(15) EEA, EU28 

AT,  DK,  IE,  IT,  LT, 
SI, ES (7)  

MT 

AT,  EE,  FI,  DE,  IE, 
LT,  ES,  SE,  NL,  UK 
(10)  

DK,  LT,  PT,  SI,  ES 
(5)  

MT 

comparing deals due to 
personal factors – finance 
sector* 

SE 

Trust 1: Very 
high 
BE,  HR,  CZ,  DK, 
EE,  EL,  HU,  IE,  IT, 
MT,  PL,  RO,  ES, 
NL,  UK  (15)  EEA, 
EU28 
BG, HR, CZ, EE, FI, 
FR,  EL,  HU,  IE,  IT, 
PL, PT, RO, SI, ES, 
UK 
EEA, 
EU28 
HR,  CY,  CZ,  DE, 
EL,  HU,  IS,  IE,  IT, 
LV,  LU,  MT,  PL,  SI, 
ES,  NL,  UK  (17) 
EEA, EU28 
BG, HR, CY, CZ, FI, 
FR,  EL,  HU,  IE,  IT, 
LV, MT, PL, RO, ES 
(15) EEA, EU28 

(16) 

Trust 2: Very low 

AT, BE, CZ, DK, EE, FI, 
IE, IT, LV, NO, SK, SI, 
ES,  UK 
(14)  EEA, 
EU28 
AT,  BE,  HR,  CZ,  DK, 
EE, FI, FR, DE, EL, HU, 
IE,  IT,  PL,  SK,  SI,  ES, 
SE,  NL,  UK  (20)  EEA, 
EU28 

HR, EE, FI, FR, EL, HU, 
IE, IT, NO, SK, SI, ES, 
UK (13) EEA, EU28 
DK, EE, FI, FR, DE, IE, 
LV, PL, SK, SI, ES, SE, 
NL,  UK 
(14)  EEA, 
EU28 

AT,  CZ,  FR,  IE,  RO 
(5)  

FI,  NO,  UK  (3)  EEA, 
EU28 

LU, 

IE, 
MT 

FR 

CZ, 
LU, 
MT,  RO, 
SK (5)  

LV, 
SK 

CZ, 
LU, 
(4)  

SK 

BG,  CY, 
EL, 
CZ, 
HU, 
LV 
(6)  

CY,  EL,  IT,  LT,  MT, 
PL, SK, ES, UK (9)  

BG, CY, EE, FI, FR, EL, 
LT, PT, RO, SK, SI, ES, 
UK (13)  

LU 

EL 

FI 

PT 

AT,  BE,  HR,  IE, 
PT, SI, ES (7)  

BE,  BG,  CZ,  FI, 
DE,  IE,  LT,  LU, 
MT,  NO,  PT,  SK, 
SI, ES (14)  

AT,  EE,  HU,  LT, 
MT,  NO,  RO,  SI 
(8)  

BE,  CZ,  EE,  HU, 
NO,  SK,  SI,  UK 
(8)  

BE, PT 

AT,  BE,  HR,  IT, 
LV,  LT,  MT,  NO, 
PT, RO, ES (11)  

CZ 

BE, SK 

AT, SK, UK 

CZ, RO 

4  28. Has problems 

comparing deals due to 
market-related factors – 
energy sector* 
4  29. Has problems 

comparing deals due to 
market-related factors – 
on-line sector 

LT 

IE, SE, UK 

RO 

BE,  LU,  RO,  UK 
(4)  

DE, LT, NL 

MT 

BG,  CY, 
EL, 
LV, 
RO,  SK, 
SI (7)  

AT,  EL,  IE,  LV,  MT 
(5)  

BG, HR, EE, FI, DE, PT, 
NL, UK (8)  

LT 

EL 

CY,  FR,  IT,  MT, 
RO, SI (6)  

636 

 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 134:  Above average vulnerability in countries due to behavioural drivers (trust 1, 2) 
D  Indicator 

"You need to be very careful in dealing with people" 

"I believe most people can be trusted" 

Trust 1: Very low 

Trust 1: 
Low 

Trust 1: 
High 

Trust 1: Very 
high 

Trust 2: Very low 

Trust 2: 
Low 

Trust 2: 
High 

Trust 2: Very 
high 

4  30. Has problems 

comparing deals due to 
market-related factors – 
finance sector* 

IS 

SK 

CZ, IE, UK 

BE, DE 

FR 

Note: * -= Following 11 countries not covered: BG, HR, CY, EL, HU, IT, LV, MT, PL, SI, ES. 
Source: London Economics 

637 

 
 
 
  
  
  
 
 
 
 
 
 
 
 
 
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 135:  Above average vulnerability in countries due to behavioural drivers (credulity, risk) 
D  Indicator 

Incorrect answer 
to credulity test 

"Most advertisements report objective fact, I trust 
most of the information provided in advertisements" 

Willingness to take risks 

1  2. Overpaid for services 

due to being unable to 
use certain payment 
methods 

2  4. Perception of own 

vulnerability due to 
personal characteristics 
In this section referred to 
as ‘Feels vulnerable to 'a 
great extent' due to 
employment situation’ 

2  5. Perception of own 

vulnerability due to 
personal characteristics 
In this section referred to 
as ‘Feels vulnerable to 'a 
great extent' due to 
financial circumstances’ 

2  10. Perception of own 
vulnerability due to 
personal characteristics 
In this section referred to 
as ‘Feels vulnerable to 'a 
great extent' due to any 
personal characteristic’ 

Credulity: 
Very low  

Credulity: 
Low  

Credulity: 
High 

SK 

DK, EE, IT 

BG, IS, LT 

BG, UK 

AT, DE 

BG,  CY,  CZ,  LT,  RO, 
SK, SE (7)  

BG,  HR,  CZ, 
DK,  IS,  MT, 
PT,  RO,  NL, 
UK (10)  

SE 

BG,  CZ,  LT,  PL,  SK 
(5)  

BG,  HR,  IS, 
RO,  NL,  UK 
(6)  

AT, FI, SE 

Credulity: 
Very high 
AT,  BE,  BG, 
CY,  CZ,  DK, 
EE,  FI,  FR, 
DE,  EL,  IE, 
LT,  LU,  PL, 
RO,  SK,  ES, 
SE,  NL,  UK 
(21) 
EEA, 
EU28 

AT,  BE,  DK, 
FI,  FR,  DE, 
IS,  IT,  LV, 
LT,  LU,  NO, 
PL,  SK,  SI, 
SE,  NL  (17) 
EEA, EU28 
AT,  BE,  BG, 
CY,  DK,  EE, 
FI,  DE,  HU, 
IS,  IT,  LV, 
LT,  LU,  NO, 
PL,  SK,  SI, 
ES,  SE,  NL, 
UK (22) EEA, 
EU28 
AT,  BE,  BG, 
CZ,  DK,  EE, 
FI,  DE,  EL, 
HU,  IE,  IT, 
LV,  LT,  LU, 
MT,  NO,  PL, 
RO,  SK,  SI, 
ES,  SE,  UK 
(24) 
EEA, 
EU28 

Risk 
taking: Not 
at all 

Risk 
taking: Not 
very  

Risk 
taking: 
Fairly  

Risk 
taking: 
Very 

LT,  UK  (2) 
EEA, EU28 

LT,  PT,  RO, 
UK  (4)  EEA, 
EU28 

DK,  LT,  PT, 
RO,  UK  (5) 
EEA, EU28 

PT,  UK  (2) 
EEA, EU28 

UK 

UK 

UK  (1)  EEA, 
EU28 

638 

 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 135:  Above average vulnerability in countries due to behavioural drivers (credulity, risk) 
D  Indicator 

Incorrect answer 
to credulity test 

"Most advertisements report objective fact, I trust 
most of the information provided in advertisements" 

Willingness to take risks 

Credulity: 
Very low  

Credulity: 
Low  

Credulity: 
High 

Credulity: 
Very high 

Risk 
taking: Not 
at all 

Risk 
taking: Not 
very  

Risk 
taking: 
Fairly  

Risk 
taking: 
Very 

3  15. Has problems 

comparing deals due to 
information-related 
factors- energy sector* 

3  17. Has problems 

comparing deals due to 
information-related 
factors – financial sector* 

4  21. Does not compare 
product deals - energy 
sector 

4  22. Finds it very difficult 

to compare product deals 
- energy sector 

4  23. Does not compare 
product deals - finance 
sector 

4  24. Finds it very difficult 

to compare product deals 
- finance sector 

AT, DE 

HR,  CZ,  EE,  LV,  PT, 
SK, SI, ES, UK (9)  

AT,  BG,  HR,  CZ,  EE, 
DE, EL, IT, LT, PT, SI 
(11)  

BG,  HR,  CZ,  EE,  LV, 
LU,  MT,  PT,  SK,  SI, 
ES (11)  

BG,  CZ,  EL,  IS,  LT, 
PL, RO, SK, SI (9)  

BE,  CZ,  EE, 
DE,  IE,  NL, 
UK (7)  

NL, UK 
BE,  CZ,  DE, 
EL, 
IT, 
IE, 
LV,  PL,  PT, 
SK,  SI,  ES, 
UK (13) EEA, 
EU28 
AT,  BG,  HR, 
CZ,  EE,  FI, 
FR,  EL,  HU, 
IE, 
IT,  LV, 
LU,  PL,  SK, 
SI,  ES,  NL, 
UK (19) EEA, 
EU28 
BG,  CZ,  EE, 
DE,  EL,  IE, 
IT,  LV,  LT, 
PT,  RO,  SK, 
SI,  ES  (14) 
EEA, EU28 
AT,  CZ,  EE, 
FR,  HU,  IE, 
IT,  LV,  PL, 
RO,  SK,  ES, 
SE,  NL,  UK 
(15) 
EEA, 
EU28 

639 

LT 

PT 

LT 

BE,  CZ,  IS, 
IE (4)  

UK 

NL 

AT,  DK,  FI, 
IE, LV, SI, NL 
(7)  

PT,  UK  (2) 
EEA, EU28 

BE,  CZ,  DK, 
FR, 
IT, 
IE, 
MT,  NO,  PT, 
SK (10) EEA, 
EU28 

LT,  PT,  RO, 
UK  (4)  EEA, 
EU28 

LU 

HR,  DK,  HU, 
MT,  NO,  NL 
(6)  

LT,  PT,  RO, 
UK  (4)  EEA, 
EU28 

BE,  BG,  CY, 
DK,  EL,  IS, 
MT,  NO,  ES, 
NL (10)  

LT,  RO,  UK 
(3) 
EEA, 
EU28 

 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 135:  Above average vulnerability in countries due to behavioural drivers (credulity, risk) 
D  Indicator 

Incorrect answer 
to credulity test 

"Most advertisements report objective fact, I trust 
most of the information provided in advertisements" 

Willingness to take risks 

Credulity: 
Very low  

Credulity: 
Low  

Credulity: 
High 

Credulity: 
Very high 

Risk 
taking: Not 
at all 

Risk 
taking: Not 
very  

Risk 
taking: 
Fairly  

Risk 
taking: 
Very 

4  25. Has problems 

comparing deals due to 
personal factors – energy 
sector* 

PT 

DE 

HR,  CY,  EL,  LV,  LT, 
LU, MT, PT, RO (9)  

LV, SI 

CZ,  IS,  IT, 
NO, RO (5)  

RO 

UK 

CY, LV, PT, RO (4)  

IE, IT, LV, ES 
(4)  

BE,  CZ,  IS, 
IE, PT (5)  

AT,  BG,  HR, 
CY,  CZ,  EL, 
LV,  LT,  NO, 
RO (10)  

LT,  PT,  RO, 
UK  (4)  EEA, 
EU28 

DK,  LT,  PT, 
RO, UK (5)  

BE,  CZ,  NO, 
SK (4)  

LT,  PT,  RO, 
UK  (4)  EEA, 
EU28 

BE,  CZ,  IS, 
NO (4)  

BE,  EL,  IE, 
MT,  PT,  RO 
(6)  

RO 

4  26. Has problems 

comparing deals due to 
personal factors – on-line 
sector 

4  27. Has problems 

comparing deals due to 
personal factors – finance 
sector* 

4  28. Has problems 

comparing deals due to 
market-related factors – 
energy sector* 
4  29. Has problems 

comparing deals due to 
market-related factors – 
on-line sector 
4  30. Has problems 

comparing deals due to 
market-related factors – 
finance sector* 

Note: * -= Following 11 countries not covered: BG, HR, CY, EL, HU, IT, LV, MT, PL, SI, ES. 
Source: London Economics 

EE, UK 

BE 

LT 

UK 

LT 

640 

 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
 
 
 
 
 
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 136:  Above average vulnerability in countries due to behavioural drivers (impulsivity 1, 2) 
D  Indicator 

"I have a hard time breaking bad habits" 

“I’m good at resisting temptation” 

1  2. Overpaid for services 

due to being unable to 
use certain payment 
methods 

2  4. Perception of own 

vulnerability due to 
personal characteristics 
In this section referred to 
as ‘Feels vulnerable to 'a 
great extent' due to 
employment situation’ 

2  5. Perception of own 

Impulsiveness 1: 
Very high 

AT,  CZ,  DK,  EE,  FR, 
DE,  HU,  IE,  IT,  PL, 
SI, ES, NL (13) EEA, 
EU28 

AT,  BE,  CZ,  DK,  EE, 
FI,  FR,  DE,  IE,  IT, 
LU,  NO,  PL,  RO,  SI, 
ES,  SE,  NL,  UK  (19) 
EEA, EU28 

vulnerability due to 
personal characteristics 
In this section referred to 
as ‘Feels vulnerable to 'a 
great extent' due to 
financial circumstances’ 

AT,  BE,  BG,  HR,  CY, 
CZ,  DK,  EE,  FI,  FR, 
DE,  EL,  IE,  IT,  LU, 
MT,  PL,  PT,  RO,  SI, 
ES,  SE,  NL,  UK  (24) 
EEA, EU28 

2  10. Perception of own 
vulnerability due to 
personal characteristics 
In this section referred to 
as ‘Feels vulnerable to 'a 
great extent' due to any 
personal characteristic’ 

3  15. Has problems 

comparing deals due to 
information-related 
factors- energy sector* 

3  17. Has problems 

comparing deals due to 
information-related 
factors – financial sector* 

4  21. Does not compare 
product deals - energy 
sector 

AT,  BE,  BG,  HR,  CZ, 
DK,  EE,  FI,  FR,  DE, 
EL,  HU,  IE,  IT,  LU, 
MT,  NO,  PL,  PT,  RO, 
SI,  ES,  SE,  NL,  UK 
(25) EEA, EU28 

BE,  EE,  DE,  NO,  NL 
(5)  

BE,  EE,  FR,  LT,  NO, 
NL (6)  

BE,  HR,  CZ,  EE,  DE, 
IE,  IT,  LU,  NO,  SI, 
NL (11) EEA, EU28 

641 

Impulsive
ness 1: 
High 

Impulsi
veness 
1: Low  

Impulsiveness 
1: Very low 

Impulsiveness 2: 
Very high 

Impulsive
ness 2: 
High 

Impulsi
veness 
2: Low  

Impulsiveness 
2: Very low 

ES 

IS 

IS 

IS 

AT,  CY,  DK,  FI,  DE, 
SK, SI, NL (8)  

LU 

IS 

BE, HR, NL 

AT, CY, DK, FI, FR, DE, 
HU,  IS,  MT,  NO,  SK, 
(13)  EEA, 
ES,  UK 
EU28 

CZ 

BE,  BG,  CZ,  EE, 
NO, PL, SK, NL, UK 
(9)  

AT,  BE,  BG,  CY,  CZ, 
DK,  FI,  DE,  IS,  IE,  IT, 
LU, NO, PL, RO, SK, SE 
(17) EEA, EU28 

LU, NO 

BE, BG, HR, EE, LT, 
PL, SK (7)  

AT,  BE,  BG,  HR,  CY, 
CZ,  DK,  EE,  FI,  DE, 
HU, IS, IE, IT, LU, NO, 
PL, RO, SK, ES, SE, UK 
(22) EEA, EU28 

NO 

PL, SE 

BE,  BG,  CZ,  DE, 
IS,  IE,  PL,  RO, 
ES,  SE,  NL,  UK 
(12) EEA, EU28 

BG,  EE,  DE,  IS, 
IE,  LV,  LT,  PL, 
ES,  NL,  UK  (11) 
EEA, EU28 

SK 

SK 

AT, BE, HR, EL, IE, 
IT,  LV,  PT,  SK,  SI, 
NL,  UK  (12)  EEA, 
EU28 

AT, CZ, FI, DE, LU, RO 
(6)  

EE,  DE,  LT, 
PT, SE (5)  

AT, IS, NL 

AT,  EE,  FI,  IS,  LT,  NO 
(6)  
AT,  BE,  HR,  CZ,  FI, 
DE, IE, IT, LU, MT, NO, 
PL, PT, SK, SI, ES, SE, 
NL,  UK 
(19)  EEA, 
EU28 

FR, IE 

AT,  BE,  HR, 
CY,  EE,  DE, 
NO,  SK,  SI, 
NL (10)  

BE, IE, IT, PT, UK 
(5)  

 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 136:  Above average vulnerability in countries due to behavioural drivers (impulsivity 1, 2) 
D  Indicator 

"I have a hard time breaking bad habits" 

“I’m good at resisting temptation” 

Impulsive
ness 1: 
High 

Impulsi
veness 
1: Low  

Impulsiveness 1: 
Very high 

AT,  BE,  BG,  CZ,  DK, 
EE,  FI,  FR,  DE,  IE, 
IT,  LT,  LU,  NO,  PL, 
ES, SE, NL (18) EEA, 
EU28 

AT,  CZ,  DK,  DE,  HU, 
IS,  IE,  MT,  NO,  SE 
(10)  

4  22. Finds it very difficult 

to compare product deals 
- energy sector 

4  23. Does not compare 
product deals - finance 
sector 

4  24. Finds it very difficult 

to compare product deals 
- finance sector 

CZ,  EE,  FI,  DE,  LU, 
NO, PL, NL, UK (9)  

4  25. Has problems 

comparing deals due to 
personal factors – energy 
sector* 

AT,  BE,  EE,  FR,  DE, 
NO,  NL  (7)  EEA, 
EU28 

4  26. Has problems 

comparing deals due to 
personal factors – on-line 
sector 

4  27. Has problems 

comparing deals due to 
personal factors – finance 
sector* 

4  28. Has problems 

comparing deals due to 
market-related factors – 
energy sector* 
4  29. Has problems 

AT,  BE,  HR,  CZ,  EE, 
FR,  DE,  EL,  IT,  LV, 
LT,  NO,  PL,  ES,  NL 
(15) EEA, EU28 

AT,  CZ,  EE,  FI,  FR, 
DE, NO, SE (8)  

BE,  EE,  FI,  RO,  SE, 
NL (6)  

comparing deals due to 
market-related factors – 
on-line sector 

BE,  EE,  EL,  IT,  LV, 
LT,  MT,  PT,  RO,  SI, 
NL (11)  

EL 

CY, LT 

Impulsiveness 
1: Very low 
BG, HR, CZ, EE, FI, 
FR,  EL,  HU,  IS,  IE, 
IT,  LT,  LU,  PL,  PT, 
RO, SK, SI, ES, NL, 
EEA, 
UK 
EU28 
AT,  BE,  BG,  HR, 
EE,  FI,  FR,  DE,  EL, 
HU, IE, IT, LV, NO, 
RO,  SK,  SI,  ES 
(18) EEA, EU28 

(21) 

BG, HR, CZ, FI, FR, 
EL,  IE,  IT,  LT,  LU, 
RO, SK, SI, NL, UK 
(15) EEA, EU28 

Impulsiveness 2: 
Very high 
AT,  BE,  BG,  HR,  CZ, 
DK, EE, FI, FR, DE, EL, 
HU,  IS,  IE,  IT,  LV,  LT, 
LU,  MT,  NO,  PL,  PT, 
SK, SI, ES, SE, NL, UK 
(28) EEA, EU28 
AT,  BE,  BG,  HR,  DK, 
EE, FI, EL, HU, LV, LT, 
LU,  MT,  NO,  PL,  PT, 
RO, SK, SI, SE, NL, UK 
(22) EEA, EU28 
BG,  HR,  CZ,  DK,  EE, 
FI, FR, DE, EL, HU, IS, 
IE, IT, LV, LT, LU, NO, 
PL, PT, SK, SI, ES, SE, 
(25)  EEA, 
NL,  UK 
EU28 

Impulsive
ness 2: 
High 

Impulsi
veness 
2: Low  

Impulsiveness 
2: Very low 

LU 

CZ,  EE,  DE,  EL, 
IE,  IT,  LV,  PL, 
SK,  ES,  NL,  UK 
(12) EEA, EU28 

CY,  DE,  IS, 
SI (4)  

CZ,  EE,  IE,  IT, 
SK, ES (6)  

AT,  CZ,  IE,  PL, 
SK (5)  

IE, SK, UK 

AT, DK, IE, LT, LU, 
SK, NL (7)  

LU 

AT,  DE,  LT,  LU,  NO, 
RO,  NL,  UK  (8)  EEA, 
EU28 
AT,  BE,  HR,  CY,  DK, 
EE,  FR,  DE,  EL,  HU, 
LV,  MT,  NO,  PL,  ES, 
NL,  UK 
(17)  EEA, 
EU28 

AT,  CZ, DE, 
NL (4)  

FI,  FR,  EL, 
IS,  PT,  SK, 
SI, NL (8)  

AT, FI, LT, LU, NO, PT, 
SK,  SE,  NL,  UK  (10) 
EEA, EU28 

DE,  IS,  NO, 
SK (4)  

AT, EE, FI, DE, NO, RO 
(6)  

DE, IE 

DE, EL, LV, LT, NO, PL, 
PT, NL (8)  

AT,  CY,  CZ, 
FR,  EL,  SI 
(6)  

LT, LU 

IS 

IS 

642 

 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 136:  Above average vulnerability in countries due to behavioural drivers (impulsivity 1, 2) 
D  Indicator 

"I have a hard time breaking bad habits" 

“I’m good at resisting temptation” 

Impulsiveness 1: 
Very high 

Impulsive
ness 1: 
High 

Impulsi
veness 
1: Low  

Impulsiveness 
1: Very low 

Impulsiveness 2: 
Very high 

Impulsive
ness 2: 
High 

Impulsi
veness 
2: Low  

Impulsiveness 
2: Very low 

4  30. Has problems 

comparing deals due to 
market-related factors – 
finance sector* 

EE, FI, PT 

LT 

AT, BE, DE, IS (4)  

EE, IE 

Note: * -= Following 11 countries not covered: BG, HR, CY, EL, HU, IT, LV, MT, PL, SI, ES. 
Source: London Economics 

Table 137:  Above average vulnerability in countries due to behavioural drivers (impulsivity 3, 4) 
D  Indicator 

“People would say that I have very strong self-discipline”

“I’m impulsive in the purchase decisions I take” 

Impulsiveness 1: 
Very high 

Impulsive
ness 1: 
High 

Impulsi
veness 
1: Low  

Impulsiveness 
1: Very low 

BG,  DK,  FR,  IE,  RO, 
SK, ES, NL, UK (9)  

LT 

PL 

HR,  CY,  DK,  HU,  LV, 
LT,  NO,  PL,  PT,  RO, 
SE,  NL,  UK 
(13) 
EEA, EU28 

AT, IS, NO 

BE, CZ, LT, LU, PL, 
SI (6)  

Impulsiveness 2: 
Very high 
AT,  BE,  BG,  CZ,  DK, 
EE, FR, DE, HU, IE, IT, 
LV, LT, NO, PL, PT, RO, 
SK,  SI,  ES,  SE,  UK 
(22) EEA, EU28 

AT,  BE,  HR,  CZ,  DK, 
EE,  FI,  FR, DE,  LT,  PL, 
PT,  SK,  SI,  SE  (15) 
EEA, EU28 

Impulsive
ness 2: 
High 

Impulsi
veness 
2: Low  

Impulsiveness 
2: Very low 

BE, CZ, DK, 
FR,  EL,  IS, 
PT (7)  

AT, CZ, LU 

BG, MT, UK 

AT,  BG,  HR,  DK,  FI, 
DE,  EL,  HU,  IT,  LV, 
LT,  MT,  NO,  PL,  PT, 
SK,  ES,  SE,  NL,  UK 
(20) EEA, EU28 

BE,  DK,  FR, 
LU,  NO,  SI, 
UK (7)  

BE, BG, CZ, EE, IS, 
IE,  LT,  PL,  SI,  ES, 
UK (11)  

AT,  BE,  BG,  CZ,  DK, 
EE,  FI,  FR,  DE,  IS,  IE, 
LV,  LT,  NO,  PL,  RO, 
SK,  SI,  ES,  SE,  UK 
(21) EEA, EU28 

AT,  BG,  CY,  EL, 
LT,  MT,  NO,  PL, 
RO,  SK,  SI,  NL, 
UK 
(13)  EEA, 
EU28 

IS 

1  2. Overpaid for services 

due to being unable to 
use certain payment 
methods 

2  4. Perception of own 

vulnerability due to 
personal characteristics 
In this section referred to 
as ‘Feels vulnerable to 'a 
great extent' due to 
employment situation’ 

2  5. Perception of own 

vulnerability due to 
personal characteristics 
In this section referred to 
as ‘Feels vulnerable to 'a 
great extent' due to 
financial circumstances’ 

643 

 
 
 
  
  
  
  
 
 
 
 
 
 
 
  
  
  
  
  
  
  
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 137:  Above average vulnerability in countries due to behavioural drivers (impulsivity 3, 4) 
D  Indicator 

“People would say that I have very strong self-discipline”

“I’m impulsive in the purchase decisions I take” 

Impulsiveness 1: 
Very high 

Impulsive
ness 1: 
High 

Impulsi
veness 
1: Low  

Impulsiveness 
1: Very low 

Impulsiveness 2: 
Very high 

Impulsive
ness 2: 
High 

Impulsi
veness 
2: Low  

Impulsiveness 
2: Very low 

2  10. Perception of own 
vulnerability due to 
personal characteristics 
In this section referred to 
as ‘Feels vulnerable to 'a 
great extent' due to any 
personal characteristic’ 

3  15. Has problems 

comparing deals due to 
information-related 
factors- energy sector* 

3  17. Has problems 

comparing deals due to 
information-related 
factors – financial sector* 

4  21. Does not compare 
product deals - energy 
sector 

4  22. Finds it very difficult 

to compare product deals 
- energy sector 

4  23. Does not compare 
product deals - finance 
sector 

4  24. Finds it very difficult 

to compare product deals 
- finance sector 

AT,  BE,  BG,  HR,  CY, 
CZ,  DK,  EE,  FI,  DE, 
HU,  IT,  LV,  LT,  LU, 
NO,  PL,  SK,  ES,  SE, 
NL,  UK  (22)  EEA, 
EU28 

FR, NO 

BE,  CZ,  IS,  IE,  LT, 
PL, SI, ES (8)  

AT,  BE,  BG,  HR,  CZ, 
DK, EE, FI, FR, DE, IE, 
IT, LV, LT, NO, PL, PT, 
RO,  SI,  ES,  SE,  UK 
(22) EEA, EU28 

CZ 

AT, EE, RO, SE (4)  

DK 

AT, FI, LU 

FR, LT, PT 

IE 

BE,  EE,  DE, 
HU,  PL,  SK, 
SI, UK (8)  

AT,  FR,  NO, 
PT,  SI,  NL 
(6)  

AT,  CY,  DK, 
FI,  DE,  HU, 
PL,  SI,  UK 
(9)  

AT,  DK,  EE,  FR,  NO, 
RO, UK (7)  
AT,  BE,  CZ,  DK,  FI, 
FR,  DE,  EL,  IE,  IT, 
PT,  SI,  ES,  SE,  NL, 
UK (16) EEA, EU28 
AT,  BE,  BG,  HR,  CZ, 
DK,  EE,  FI,  FR,  DE, 
EL,  HU,  IS,  IE,  IT, 
LT,  LU,  NO,  PL,  PT, 
RO,  SK,  SI,  ES,  SE, 
NL,  UK  (27)  EEA, 
EU28 
AT,  BE,  BG,  HR,  CZ, 
DK,  FI,  FR,  DE,  EL, 
HU,  IS,  IE,  IT,  LV, 
NO,  PL,  PT,  RO,  ES, 
(23) 
SE,  NL,  UK 
EEA, EU28 
BG,  HR,  DK,  EE,  FI, 
FR,  DE,  EL,  HU,  IS, 
IE,  IT,  LU,  NO,  PL, 
PT,  RO,  SE,  NL,  UK 
(20) EEA, EU28 

FR, IS, IE, LT (4)  

BE,  CZ, EE,  IE,  LV, 
UK (6)  

BE,  HR,  CZ,  IE,  NO, 
PT, SI, ES (8)  

NL, UK 

BE, CZ, EE, FI, DE, 
IE,  IT,  LV,  SK,  ES, 
EEA, 
UK 
EU28 

(11) 

CZ,  FI,  DE,  IS,  IE,  IT, 
LT, LU, NO, PL, PT, ES, 
(14)  EEA, 
NL,  UK 
EU28 

CZ,  EE,  IE,  IT,  LV, 
SK, ES (7)  

CZ, DK, FI, HU, IS, IE, 
LT,  LU,  NO,  PT,  ES 
(11)  

CZ, LV, LT, SK, ES, 
NL (6)  

AT,  DK,  EE,  FI,  IE,  IT, 
LU,  PT,  ES,  UK  (10) 
EEA, EU28 

BG,  EE,  LT,  NO, 
PL,  SK,  NL,  UK 
(8)  

CZ,  EE,  SK,  NL 
(4)  

LU, SK 
AT,  CZ,  EE,  FI, 
IT,  PL,  PT,  RO, 
SK,  SI,  UK  (11) 
EEA, EU28 

AT,  BE,  BG,  CZ, 
EE, FI, EL, IE, IT, 
LT,  NO,  PL,  PT, 
RO,  SK,  SI,  ES, 
SE,  NL,  UK  (20) 
EEA, EU28 

BG,  CZ,  EE,  EL, 
HU,  IE,  IT,  LV, 
RO,  SK,  SI,  ES, 
(13)  EEA, 
UK 
EU28 
AT,  BG,  EE,  DE, 
EL,  IE,  LV,  LT, 
LU,  NO,  PL,  RO, 
SK,  SI,  NL  (15) 
EEA, EU28 

644 

 
 
 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 137:  Above average vulnerability in countries due to behavioural drivers (impulsivity 3, 4) 
D  Indicator 

“People would say that I have very strong self-discipline”

“I’m impulsive in the purchase decisions I take” 

Impulsiveness 1: 
Very high 

Impulsive
ness 1: 
High 

Impulsi
veness 
1: Low  

Impulsiveness 
1: Very low 

Impulsiveness 2: 
Very high 

Impulsive
ness 2: 
High 

Impulsi
veness 
2: Low  

Impulsiveness 
2: Very low 

4  25. Has problems 

comparing deals due to 
personal factors – energy 
sector* 

4  26. Has problems 

comparing deals due to 
personal factors – on-line 
sector 

4  27. Has problems 

AT,  DK,  FI,  DE,  IE, 
LU,  NO,  PT,  SE,  UK 
(10) EEA, EU28 

AT,  BG,  FI,  FR,  EL, 
IS,  IE,  LV,  PL,  PT, 
SK,  SI,  ES,  NL,  UK 
(15) EEA, EU28 

DE, UK 

FR, HU, MT, 
PL,  SI,  UK 
(6)  

AT, BE, EE, FR, LT, LU, 
PT, NL (8)  

UK 

CZ,  EL,  LT,  RO,  ES 
(5)  

AT,  BE,  DK,  EL,  HU, 
IS, LV, LT, LU, NO, PL, 
PT, RO, NL, UK (15)  

CZ,  FR,  LT, 
PT (4)  

EE 

AT,  BG,  EL,  LT, 
ES (5)  

comparing deals due to 
personal factors – finance 
sector* 

AT,  CZ,  FI,  IS,  LU, 
NO,  PT,  SE,  NL,  UK 
(10)  

AT, DE, SK 

IE 

AT,  FR,  IS,  IE,  LT,  LU, 
NO, UK (8)  

CZ 

EE, DE 

4  28. Has problems 

comparing deals due to 
market-related factors – 
energy sector* 
4  29. Has problems 

comparing deals due to 
market-related factors – 
on-line sector 
4  30. Has problems 

comparing deals due to 
market-related factors – 
finance sector* 

AT, CZ, FI, IE (4)  

EE 

LT 

IS 

NO 

PT 

LT 

AT,  HR,  CZ,  FI,  DE, 
EL, IE, MT, PL, PT, SI 
(11)  

BG, EE, HU, 
NL (4)  

LV 

AT,  HU,  LT,  MT,  PT, 
RO, SK (7)  

EL, LT 

CY 

CY, EE, LV 

CZ, DE, LU, RO (4)  

FR, NO 

BE, CZ 

LU 

EE 

Note: * -= Following 11 countries not covered: BG, HR, CY, EL, HU, IT, LV, MT, PL, SI, ES. 
Source: London Economics 

645 

 
 
 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
 
 
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 138:  Above average vulnerability in countries due to behavioural drivers (knowledge tests) 
D  Indicator 

1  2. Overpaid for services due to being unable to use 

certain payment methods 

2  4. Perception of own vulnerability due to personal 

characteristics 
In this section referred to as ‘Feels vulnerable to 'a 
great extent' due to employment situation’ 

2  5. Perception of own vulnerability due to personal 

characteristics 
In this section referred to as ‘Feels vulnerable to 'a 
great extent' due to financial circumstances’ 

2  10. Perception of own vulnerability due to personal 

characteristics 
In this section referred to as ‘Feels vulnerable to 'a 
great extent' due to any personal characteristic’ 

Answered incorrectly 
first computational test 
AT,  CZ,  FI,  DE,  NO,  SI,  SE, 
NL, UK (9)  

Answered 
incorrectly 
second 
computational 
test 
DK,  FI,  DE,  IS,  IE, 
SE, NL, UK (8)  

Incorrect 
answer to 
meaning of 
kWh 
FI,  LT,  NO,  SE 
(4)  

Incorrect answer 
to meaning of 
Mbps 

Did not 
indentify best 
interest rate for 
a savings 
account 

FI 

SE, UK 

AT, DK, DE,  LU,  RO,  SK,  SE 
(7)  

BG,  DK,  DE,  LT,  PL, 
SK, SE, UK (8)  

BG, SI, SE 

BE, BG, DE, LU, NO, RO, SK, 
SI, SE (9) EEA, EU28 

BE,  BG,  DK,  LT,  LU, 
RO,  SK,  SE,  UK  (9) 
EEA, EU28 

BG,  DE, 
MT, SE (5)  

IS, 

HR,  LT,  MT,  RO,  SK, 
SE (6)  

AT, BE, BG, DK, DE, HU, IS, 
LT,  LU,  NO,  RO,  SK,  SI,  ES, 
SE (15) EEA, EU28 

AT,  BG,  HR,  CZ,  DK, 
LT,  LU,  NO,  PL,  RO, 
SK,  ES,  SE,  UK  (14) 
EEA, EU28 

BG,  HR,  DK, 
IS,  LT,  MT,  SE 
(7)  

HR,  HU,  LT,  MT,  RO, 
SK, SE (7)  

AT, DE, LU, NL (4)  

BE,  CZ,  DE,  LU, 
NO,  SK,  SI,  ES, 
UK (9) EEA, EU28 
AT,  BE,  BG,  CZ, 
DK,  DE,  IE,  LU, 
NO,  SK,  SI,  SE, 
EEA, 
UK 
EU28 

(13) 

3  15. Has problems comparing deals due to 

information-related factors- energy sector* 

FI, DE, NO 

3  17. Has problems comparing deals due to 

information-related factors – financial sector* 

NO 

4  21. Does not compare product deals - energy sector 

4  22. Finds it very difficult to compare product deals - 

energy sector 

4  23. Does not compare product deals - finance sector 

AT,  BE,  HR,  CZ,  DK,  EE,  FI, 
DE,  EL,  IE,  IT,  LV,  NO,  PL, 
PT, SI, SE, NL, UK (19) EEA, 
EU28 

AT,  BG,  HR, CZ, DK, EE,  FI, 
DE,  EL,  HU,  IE,  IT,  LV,  LT, 
LU,  NO,  PT,  RO,  SK, SI,  ES, 
SE, NL, UK (24) EEA, EU28 

BG,  HR,  CZ,  DK,  EE,  FI,  EL, 
HU,  IE,  IT,  LV,  LT,  MT,  NO, 
PL,  PT,  RO,  SK,  SI,  ES,  UK 
(21) EEA, EU28 

BE, FI, NO 

NO 

DE, NO 

AT,  HR,  CZ,  DK,  EE, 
FI,  DE,  IE,  IT,  LV, 
NO,  PT,  SK,  SI,  SE, 
NL,  UK  (17)  EEA, 
EU28 
AT,  BE,  BG,  HR,  CZ, 
DK,  EE,  FI,  FR,  DE, 
EL, IS, IE, IT, LV, LT, 
NO,  PT,  RO,  SK,  SI, 
ES,  SE,  NL,  UK  (25) 
EEA, EU28 
AT,  BE,  BG,  HR,  CZ, 
DK,  EE,  FI,  DE,  EL, 
HU,  IE,  IT,  LV,  NO, 
PL,  PT,  RO,  SK,  SI, 
(23) 
ES,  SE,  UK 
EEA, EU28 

LT, NO 

HR, DK, EE, FI, 
IE,  IT,  NO,  PT, 
SI, SE, NL (11)  

AT,  BE,  BG, 
HR, EE, FI, DE, 
EL,  HU, 
IE, 
NO, PT, SI, SE, 
NL (15)  

BG,  HR,  DK, 
EE,  FI,  IS,  IT, 
LU,  MT,  NO, 
PT, SI, ES, SE, 
NL (15)  

AT,  BE,  HR,  CZ,  DK, 
EE,  FI, DE,  EL,  IE,  IT, 
NO,  PL,  PT,  RO,  SI, 
ES,  SE,  NL,  UK  (20) 
EEA, EU28 
AT,  BE,  BG,  HR,  CZ, 
DK,  EE,  FI,  FR,  DE, 
EL,  HU,  IE,  LT,  NO, 
PT,  RO,  SK,  SI,  ES, 
NL,  UK  (22)  EEA, 
EU28 
AT,  BE,  BG,  HR,  CZ, 
DK,  EE,  DE,  EL,  HU, 
IE, IT, LT, MT, NO, PL, 
PT,  RO,  SK,  SI,  ES, 
SE,  NL,  UK  (24)  EEA, 
EU28 

HR,  CZ,  IE,  IT, 
NO,  PT,  SI,  SE, 
NL,  UK  (10)  EEA, 
EU28 

CZ,  DK,  EL,  NO, 
PT, SK, SI, SE, UK 
(9)  

HR,  CZ,  DK,  EL, 
HU,  IT,  LV,  MT, 
NO,  PL,  PT,  RO, 
SK, SI, ES, SE, UK 
(17) EEA, EU28 

646 

 
 
 
 
  
  
  
  
  
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 138:  Above average vulnerability in countries due to behavioural drivers (knowledge tests) 
D  Indicator 

Answered incorrectly 
first computational test 
BG,  CZ,  EE,  FI,  DE,  EL,  HU, 
LV,  LT,  LU,  NO,  PL,  RO,  SK, 
SI,  SE,  NL,  UK  (18)  EEA, 
EU28 

Answered 
incorrectly 
second 
computational 
test 
AT,  BG,  CZ,  DK,  EE, 
FI,  DE,  EL,  IE,  LV, 
LT,  PL,  RO,  SK,  SI, 
UK (16) EEA, EU28 

Incorrect 
answer to 
meaning of 
kWh 

BG,  HR,  EE, 
LV, SI (5)  

LU, NO, PT, RO, NL, UK (6)  

AT,  FI,  NO,  PT,  RO, 
UK (6) EEA 

FI,  IE,  NO,  SE 
(4)  

AT,  BG,  EE,  FI,  FR, 
EL,  IE,  IT,  LV,  LT, 
NO, PT (12)  
IE,  UK 
EU28 

(2)  EEA, 

BG,  HR,  DK, 
EE, FR, EL, HU, 
LV, MT, NO, PT 
(11)  

FI, IS, NO 

Incorrect answer 
to meaning of 
Mbps 

BG,  HR,  EE,  DE,  EL, 
IE,  LT,  RO,  SK,  SI 
(10)  
BE,  DE,  IE,  NO,  PT, 
RO,  UK 
(7)  EEA, 
EU28 
BE,  BG,  DK,  EE,  FI, 
FR,  DE,  EL,  HU,  IS, 
IT,  LU,  MT,  NO,  PL, 
PT,  RO,  SK,  SI,  SE, 
UK (21) EEA, EU28 
DE,  PT,  RO,  UK  (4) 
EEA, EU28 

Did not 
indentify best 
interest rate for 
a savings 
account 

BG,  CZ,  MT,  SK 
(4)  

IE 

HR,  EL,  NO,  PL, 
PT, RO (6)  

UK 

CY,  EE,  EL,  LV,  LT, 
PT, SK (7)  

CY, HU, PT, RO 
(4)  

BG, CY, EE, FI, IE, PT, 
SK (7)  

HR, PT, RO 

4  24. Finds it very difficult to compare product deals - 

finance sector 

4  25. Has problems comparing deals due to personal 

factors – energy sector* 

4  26. Has problems comparing deals due to personal 

factors – on-line sector 

BG,  CY,  CZ,  EE,  FI,  FR,  EL, 
HU, LV, NO, PL, SK, ES, NL, 
UK (15) EEA, EU28 

4  27. Has problems comparing deals due to personal 

factors – finance sector* 

EE, RO, SK, UK (4)  

4  28. Has problems comparing deals due to market-

related factors – energy sector* 

4  29. Has problems comparing deals due to market-

related factors – on-line sector 

BG, EL, LV, PT, UK (5)  

4  30. Has problems comparing deals due to market-

related factors – finance sector* 

Note: * -= Following 11 countries not covered: BG, HR, CY, EL, HU, IT, LV, MT, PL, SI, ES. 
Source: London Economics 

647 

 
 
 
  
  
  
  
  
  
  
  
  
  
Annex 4│ Incidence rate of vulnerability by sub-group 

Market-related and experience drivers 

Table 139:  Above average vulnerability in countries due to market-related and experience drivers 

(T&Cs, knowledge, compares deals - energy) 

D  Indicator 

1  2. Overpaid for 
services due to 
being unable to 
use certain 
payment methods 

2  4. Perception of 

own vulnerability 
due to personal 
characteristics 
In this section 
referred to as 
‘Feels vulnerable 
to 'a great extent' 
due to 
employment 
situation’ 

2  5. Perception of 

own vulnerability 
due to personal 
characteristics 
In this section 
referred to as 
‘Feels vulnerable 
to 'a great extent' 
due to financial 
circumstances’ 

Unable to read 
Ts&Cs due to 
small print 

AT,  BE,  BG,  HR, 
CY, CZ, DK, EE, FI, 
FR, DE, EL, HU, IS, 
IE,  IT,  LV,  LT,  LU, 
MT,  NO,  PL,  PT, 
RO, SK, SI, ES, SE, 
NL,  UK  (30)  EEA, 
EU28 

Knows energy contract conditions 

Not at all 

Not very 

Fair 
amount 

Comple
tely 

Time 
to 
time 

Compare deals from energy supplier 
When 
need to 
renew 

Only the 
first time 

Spor
adica
lly 

Never 

EL, 
HU, IS, 
LT,  LU, 
MT,  SE 
(7)  

BG,  HR, 
CZ,  DK, 
EL, 
FR, 
IT, 
HU, 
LU, 
ES 
(10)  

IT, 

IS, 
MT 

MT, 
SE 

BG, CY, UK 

AT,  HR,  DK,  EE, 
DE,  HU,  IS,  LU, 
NO, SE, UK (11)  

BE,  FR,  DE,  NO,  PT, 
ES,  SE,  UK  (8)  EEA, 
EU28 

CZ, EE, SE 

IS 

AT,  HU, 
LU,  MT 
(4)  

PL, SE 

AT, MT 

IS 

IS, UK 

NO, SK 

AT,  BE,  BG,  HR, 
CY, DK, FI, FR, DE, 
HU,  IS,  IE,  LV,  LU, 
PL, PT, ES, SE, NL, 
UK 
EEA, 
EU28 

(20) 

AT,  BE,  BG,  DK,  FI, 
FR,  DE,  LT,  NO,  PL, 
PT,  RO,  SK,  ES  (14) 
EEA, EU28 

BE,  EE,  IS, 
NO, SE (5)  

AT,  MT, 
SE 

LT,  PL, 
SE 

AT,  CZ, 
EE,  MT, 
ES, 
SE, 
UK (7)  

DE, 
IS, 
LU, 
NO, 
NL (5)  

BE, CZ, IE, SK (4)  

648 

 
 
 
  
  
  
  
  
  
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 139:  Above average vulnerability in countries due to market-related and experience drivers 

(T&Cs, knowledge, compares deals - energy) 

D  Indicator 

Unable to read 
Ts&Cs due to 
small print 

Knows energy contract conditions 

Not at all 

Not very 

Fair 
amount 

Comple
tely 

Time 
to 
time 

Compare deals from energy supplier 
When 
need to 
renew 

Only the 
first time 

Spor
adica
lly 

Never 

AT,  BE,  BG,  HR, 
CY, CZ, DK, EE, FI, 
FR, DE, HU, IS, IE, 
LT, LU, NO, PL, PT, 
SK, ES, SE, NL, UK 
(24) EEA, EU28 

AT,  BG,  HR,  DK,  FI, 
FR,  DE,  LT,  NO,  PL, 
PT, SK, ES (13) EEA, 
EU28 

AT, BE, CZ, DK, FI, 
DE,  IS,  IE,  LT,  LU, 
NO,  SK,  NL,  UK 
(14) EEA, EU28 

AT,  FI,  DE,  IE,  NO, 
SK,  UK  (7)  EEA, 
EU28 

EE, DE, NO 

BE,  DK,  FI, 
DE,  IE,  NO, 
PT,  RO,  UK 
(9) 
EEA, 
EU28 

AT,  BE,  DE,  IS,  IE, 
LU,  NO,  SK,  SE, 
NL,  UK  (11)  EEA, 
EU28 

AT, NO 

AT,  BE,  BG,  HR,  CY, 
CZ,  DK,  EE,  FI,  FR, 
DE,  EL,  HU,  IE,  IT, 
LV,  LU,  NO,  PL,  PT, 
RO,  SK,  SI,  ES,  SE, 
NL,  UK  (27)  EEA, 
EU28 

AT,  BE,  HR, 
DK,  EE,  FI, 
FR,  DE,  EL, 
HU,  IE,  IT, 
NO,  PL,  PT, 
RO,  SK,  SI, 
ES,  SE,  NL, 
UK  (22)  EEA, 
EU28 

IE, SI 

2  10. Perception of 
own vulnerability 
due to personal 
characteristics 
In this section 
referred to as 
‘Feels vulnerable 
to 'a great extent' 
due to any 
personal 
characteristic’ 
3  15. Has problems 

comparing deals 
due to 
information-
related factors- 
energy sector* 
3  17. Has problems 

comparing deals 
due to 
information-
related factors – 
financial sector* 

4  21. Does not 

compare product 
deals - energy 
sector 

649 

AT,  BE, 
CZ 

LT, SE 

CY, 
EE, 
LU, 

AT, 
CZ, 
LV, 
NL (7)  

LU 

IE, SK, SI 

AT, 
BE, 
DE,  IE 
(4)  

CZ,  DK,  EE, 
FR,  DE,  IE, 
SK,  SE,  UK 
(9) 
EEA, 
EU28 

AT,  FI,  DE,  NO, 
SK (5) EEA, EU28 

EE, 
PT, 

AT, 
LU, 
UK (5)  

LT, 
LU, 
PT, NL 
(4)  

LT 

SE 

 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Table 139:  Above average vulnerability in countries due to market-related and experience drivers 

(T&Cs, knowledge, compares deals - energy) 

Annex 4│ Incidence rate of vulnerability by sub-group 

D  Indicator 

Unable to read 
Ts&Cs due to 
small print 

Knows energy contract conditions 

4  22. Finds it very 
difficult to 
compare product 
deals - energy 
sector 

4  23. Does not 

compare product 
deals - finance 
sector 

4  24. Finds it very 
difficult to 
compare product 
deals - finance 
sector 

4  25. Has problems 

comparing deals 
due to personal 
factors – energy 
sector* 

4  26. Has problems 

comparing deals 
due to personal 
factors – on-line 
sector 

AT,  BE,  HR,  CZ, 
DK, EE, FI, FR, DE, 
IE, LV, NO, PL, SK, 
SI, ES, NL, UK (18) 
EEA, EU28 

AT,  HR,  CZ,  DK, 
FR, DE, IS, IE, NO, 
SK, SI, NL, UK (13) 
EEA, EU28 

DE, LT, NO, NL (4)  

Not at all 
AT,  BE,  BG,  HR,  CY, 
CZ,  DK,  EE,  FI,  FR, 
DE,  EL,  HU,  IE,  IT, 
LV,  LT,  LU,  NO,  PL, 
PT,  RO,  SK,  SI,  ES, 
SE,  NL,  UK 
(28) 
EEA, EU28 

AT,  BE,  BG,  HR,  CY, 
CZ,  DK,  EE,  FI,  FR, 
DE,  EL,  HU,  IS,  IE, 
IT,  LV,  LT,  LU,  MT, 
NO,  PL,  PT,  RO,  SK, 
SI,  ES,  SE,  NL,  UK 
(30) EEA, EU28 
AT,  BE,  BG,  HR,  CZ, 
DK,  EE,  FI,  FR,  DE, 
EL,  HU,  IE,  IT,  LV, 
LT,  LU,  NO,  PL,  PT, 
RO,  SI,  ES,  SE,  NL, 
UK (26) EEA, EU28 

AT,  BE,  CZ,  DK,  FI, 
DE,  IE,  LT,  NO,  PT, 
RO,  SK,  SE,  NL,  UK 
(15) EEA, EU28 

Not very 

Fair 
amount 

Comple
tely 

Time 
to 
time 

DE, PL 

AT,  DK,  DE, 
EL, IE, IT, PL, 
SK,  SI,  NL, 
UK  (11)  EEA, 
EU28 

DE, NL 
AT,  BE,  DK, 
EE,  FI,  FR, 
DE,  IE,  NO, 
SK,  SE,  NL 
EEA, 
(12) 
EU28 

Compare deals from energy supplier 
When 
need to 
renew 

Only the 
first time 

Spor
adica
lly 

Never 
AT,  BE,  HR,  CZ, 
DK,  EE,  FI,  FR, 
DE, EL, HU, IE, IT, 
LV,  LT,  LU,  NO, 
PL,  PT,  RO,  SK, 
SI, ES, SE, NL, UK 
(26) EEA, EU28 
AT,  BE,  BG,  HR, 
CZ,  DK,  EE,  FI, 
FR,  DE,  EL,  HU, 
IE,  IT,  LV,  LT,  LU, 
NO,  PL,  PT,  RO, 
SK,  SI,  ES,  SE, 
NL,  UK  (27)  EEA, 
EU28 

IE 

AT,  BE,  CZ, 
EE,  FI,  DE, 
EL, IE, IT, LV, 
NO,  PT,  SK, 
SI,  ES,  SE, 
NL,  UK  (18) 
EEA, EU28 

MT 

EE, DE 

CZ, EE, FI, IE, LV, 
PL, PT, SI, ES, SE, 
NL,  UK  (12)  EEA, 
EU28 

AT,  BG,  HR,  CY,  CZ, 
DK,  FI,  FR,  DE,  HU, 
IE,  IT,  LV,  NO,  PL, 
PT,  RO,  SK,  SI,  ES, 
NL,  UK  (22)  EEA, 
EU28 

AT,  BE,  HR, 
FI,  DE,  EL, 
IE,  NO,  PL, 
SI,  ES,  SE, 
NL  (13)  EEA, 
EU28 

AT, CZ, EE, DE, EL, 
MT, SK, NL (8)  

IS, LT 

BG 

BE,  HR,  CY, 
CZ,  FI,  DE, 
EL, IE, IT, LV, 
MT,  PL,  PT, 
RO,  SI,  ES, 
NL,  UK  (18) 
EEA, EU28 

AT,  BE,  HR,  CZ, 
DK,  EE,  FI,  FR, 
DE, IE, IT, LV, LT, 
LU,  NO,  PL,  PT, 
SK, SI, ES, NL, UK 
(22) EEA, EU28 

650 

 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 139:  Above average vulnerability in countries due to market-related and experience drivers 

(T&Cs, knowledge, compares deals - energy) 

D  Indicator 

Unable to read 
Ts&Cs due to 
small print 

Knows energy contract conditions 

Not at all 

Not very 

Fair 
amount 

Comple
tely 

Time 
to 
time 

Compare deals from energy supplier 
When 
need to 
renew 

Only the 
first time 

Spor
adica
lly 

Never 

4  27. Has problems 

comparing deals 
due to personal 
factors – finance 
sector* 

4  28. Has problems 

comparing deals 
due to market-
related factors – 
energy sector* 

4  29. Has problems 

comparing deals 
due to market-
related factors – 
on-line sector 

4  30. Has problems 

comparing deals 
due to market-
related factors – 
finance sector* 

EE, DE, NO 

AT,  CZ,  FI,  FR,  DE, 
IS,  IE,  LT,  LU,  NO, 
RO,  SK,  SE,  NL,  UK 
(15) EEA, EU28 

AT, CZ, EE, FI, DE, 
IS,  IE,  LT,  NO,  PT, 
RO,  NL,  UK  (13) 
EEA, EU28 

DE 

AT,  BE,  BG,  CY, 
CZ, EE, FI, FR, DE, 
EL,  HU,  IS,  IE,  IT, 
LT, LU, MT, NO, PL, 
RO, SK, ES, NL, UK 
(24) EEA, EU28 

BG,  CY,  HU,  LT,  SK, 
UK (6)  

BE, CZ, IS, LU, NO, 
PT, RO, SK, NL, UK 
(10)  

DE 

AT,  FI,  DE, 
IE, SE, NL (6)  
AT,  BE,  CZ, 
EE,  FI,  DE, 
IS, 
IE,  LT, 
NO,  PT,  SE, 
NL,  UK  (14) 
EEA, EU28 
AT,  BG,  HR, 
CZ,  EE,  FI, 
DE,  IS,  IE, 
IT,  LU,  NO, 
PL,  SI,  NL, 
UK  (16)  EEA, 
EU28 

DE, IE, NL 

AT,  BE,  CZ, 
FI,  DE, 
IE, 
PT,  SE,  UK 
(9)  

CZ,  DK,  FI,  DE, 
IE,  NO,  PT,  SK, 
SE,  NL,  UK  (11) 
EEA, EU28 

IE 

MT 

CY, MT 

DE, LT 

BE,  HR,  CZ, 
EE,  DE,  EL, 
IS, 
IT, 
IE, 
MT,  PL,  RO, 
SI,  ES,  UK 
(15) 
EEA, 
EU28 

CZ, LV 

LU, 
PT 

LT, 
NO, 
(4)  

PT, NL  BE, IE, NO 

Note:  *  -= Following  11  countries  not covered:  BG,  HR, CY,  EL,  HU, 
the indicator  
Source: London Economics 

IT, LV, MT, PL, SI, ES. Frequency to compare deals from energy supplier is used to construct 

651 

 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 140:  Above average vulnerability in countries due to market-related and experience drivers (read 

last bill - energy) 

D  Indicator 

Read last bill from energy supplier 

In 
detai
l 

Glanced 
or skim 
read 

Looked at 
total 
price 

Not at all 

Don’t know 

No response 
to 
knowledge / 
read 

Reading last bill from energy supplier 

Very 
easy 

Eas
y 

Difficult 

Very difficult  No response 

1  2. Overpaid for 
services due to 
being unable to 
use certain 
payment methods 

2  4. Perception of 

IS 

FR,  DE,  NL, 
UK (4)  

CY,  CZ,  EE, 
LU, SI (5)  

IT, 
AT,  CZ, 
RO, SK, NL (6)  

HU, IS, 
ES 

FI,  FR,  DE, 
IS,  LV,  NL, 
UK (7)  

BG,  DK,  EE,  FI, 
DE,  EL,  IE,  LV, 
LT,  MT,  RO,  SI, 
UK (13)  

own vulnerability 
due to personal 
characteristics 
In this section 
referred to as 
‘Feels vulnerable 
to 'a great extent' 
due to 
employment 
situation’ 

2  5. Perception of 

own vulnerability 
due to personal 
characteristics 
In this section 
referred to as 
‘Feels vulnerable 
to 'a great extent' 
due to financial 
circumstances’ 

HU, 
NO 

AT, DE, IS 

FR, LU 

IT, LU, PT, NL 
(4)  

CZ,  DE, 
IS, 
RO, SI, UK (6)  

BE, LU, 
NO,  SE 
(4)  

DE,  IS,  NO, 
NL, UK (5)  

AT,  CY,  CZ,  EE, 
FI,  FR,  DE,  IE, 
LU,  NO,  PL,  PT, 
UK 
(13)  EEA, 
EU28 

LU 

HU, 
IS 

MT 

AT,  BE,  DK, 
FI,  LU,  NO, 
SI,  SE,  UK 
(9)  

AT,  HR,  DK, 
FR,  NO,  PL, 
RO (7)  

CY,  DK,  EL, 
IS, IE, IT, LV, 
LT,  MT,  PT 
(10)  

BE,  BG,  CZ, 
EE,  FI,  FR,  IT, 
MT,  NO,  PT, 
RO, SI, ES, UK 
(14) 
EEA, 
EU28 

BE, 
CZ,  LT, 
LU,  SI, 
UK (6)  

BE,  CY, DK, 
EE,  FI,  DE, 
IS,  IE,  LU, 
MT, NO, SE, 
NL, UK (14)  

AT,  BE,  BG,  HR, 
CY,  CZ,  EE,  FI, 
FR,  IE,  LV,  LU, 
NO,  PL,  PT,  SI, 
ES,  SE,  NL,  UK 
(20) EEA, EU28 

DK,  FR,  NO, 
RO (4)  

652 

 
 
 
  
  
  
  
  
  
  
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 140:  Above average vulnerability in countries due to market-related and experience drivers (read 

last bill - energy) 

D  Indicator 

Read last bill from energy supplier 

In 
detai
l 

Glanced 
or skim 
read 

Looked at 
total 
price 

Not at all 

Don’t know 

No response 
to 
knowledge / 
read 

Reading last bill from energy supplier 

Very 
easy 

Eas
y 

Difficult 

Very difficult  No response 

2  10. Perception of 
own vulnerability 
due to personal 
characteristics 
In this section 
referred to as 
‘Feels vulnerable 
to 'a great extent' 
due to any 
personal 
characteristic’ 
3  15. Has problems 

comparing deals 
due to 
information-
related factors- 
energy sector* 

3  17. Has problems 

comparing deals 
due to 
information-
related factors – 
financial sector* 

4  21. Does not 

compare product 
deals - energy 
sector 

653 

HU 

MT 

AT,  BE,  DE, 
IS, NO (5)  

AT,  HR,  DK, 
FR,  NO,  PL, 
RO (7)  

AT,  CZ,  DK, 
DE,  EL,  IS, 
IE, 
IT,  LT, 
MT, PT (11)  

CZ,  FI,  FR, 
HU, 
IT,  LV, 
NO,  RO,  SK, 
SI,  UK 
(11) 
EEA, EU28 

CZ, 
LU,  SI, 
SE (4)  

IE 

AT,  BE,  DK, 
EE,  FI,  DE, 
PT,  UK  (8) 
EEA, EU28 

AT,  CZ,  FI, 
LT,  RO,  SE 
(6)  

AT,  FI,  DE, 
IE,  LU,  NO 
(6)  

CZ, DE 

AT,  BE,  HR, 
CZ,  DK,  EE, 
FI,  FR,  DE, 
HU,  IE,  IT, 
LV,  LT,  LU, 
NO,  PL,  PT, 
RO,  SK,  SI, 
ES,  SE,  NL, 
UK (25) EEA, 
EU28 

LU, NO 
AT,  BE,  BG, 
HR,  CY,  CZ, 
DK,  FI,  FR, 
DE,  EL,  HU, 
IE, IT, LV, LT, 
NO,  PL,  PT, 
RO,  SK,  SI, 
ES,  SE,  NL, 
UK  (26)  EEA, 
EU28 

NO, UK 

AT,  BE,  HR, 
CZ, DK, EE, FI, 
FR, DE, EL, IE, 
IT, LV, LT, NO, 
PL, PT, SK, SI, 
ES, SE, NL, UK 
(23) 
EEA, 
EU28 

AT, CZ, DK, 
EE,  FI,  FR, 
DE,  IT,  LV, 
PL,  PT,  UK 
(12)  EEA, 
EU28 

AT,  BE,  BG,  HR, 
CY,  CZ,  EE,  FI, 
FR,  DE,  EL,  IE, 
LV,  LT,  NO,  PL, 
PT,  SI,  ES,  SE, 
NL, UK (22) EEA, 
EU28 

DK,  NO,  RO, 
UK (4)  

AT,  BE,  CZ,  DK, 
EE,  FI,  FR,  DE, 
IS,  IE,  LU,  NO, 
PT,  SK,  NL,  UK 
(16) EEA, EU28 

AT, DE, NO 

BE,  FI,  DE, 
IS,  LU,  MT, 
NO, NL (8)  
AT,  BE,  CZ, 
DK,  EE,  FI, 
FR,  DE,  IS, 
IE,  LT,  LU, 
NO, PT, RO, 
SK,  SE,  NL, 
UK 
(19) 
EEA, EU28 

AT,  EE,  FI, 
DE,  LT,  NO, 
SK,  SE,  NL, 
UK 
(10) 
EEA, EU28 

AT,  BE,  DK,  EE, 
FI,  FR,  DE,  IS, 
IE,  LU,  NO,  PT, 
RO,  SE,  UK  (15) 
EEA, EU28 

AT,  BE,  HR, 
CZ,  DK,  EE, 
FI, FR, DE, EL, 
HU, IE, IT, LV, 
LT,  NO,  PL, 
PT,  RO,  SK, 
SI,  ES,  SE, 
NL,  UK  (25) 
EEA, EU28 

AT,  BE,  CZ,  DK, 
EE,  DE,  EL,  IE, 
IT,  LV,  LU,  NO, 
PL,  RO,  SK,  SI, 
NL, UK (18) EEA, 
EU28 

 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Table 140:  Above average vulnerability in countries due to market-related and experience drivers (read 

Annex 4│ Incidence rate of vulnerability by sub-group 

last bill - energy) 

D  Indicator 

Read last bill from energy supplier 

4  22. Finds it very 
difficult to 
compare product 
deals - energy 
sector 

4  23. Does not 

compare product 
deals - finance 
sector 

4  24. Finds it very 
difficult to 
compare product 
deals - finance 
sector 

4  25. Has problems 

comparing deals 
due to personal 
factors – energy 
sector* 

In 
detai
l 

Glanced 
or skim 
read 

Looked at 
total 
price 

AT, DK, DE, 
IS,  IT,  NO 
(6)  

AT,  BE,  CZ, 
DK,  FI,  FR, 
DE,  IT,  MT, 
NO,  PL,  PT, 
RO,  SI,  ES, 
UK 
(16) 
EEA, EU28 

AT,  EE,  FI, 
DE,  NO,  PL 
(6)  

AT,  BE,  CZ, 
EE,  FI,  FR, 
DE,  IE,  PT, 
SE,  NL,  UK 
(12)  EEA, 
EU28 

Not at all 
AT,  BE,  BG, 
HR,  CZ,  DK, 
EE,  FI,  FR, 
DE,  EL,  HU, 
IE, 
IT,  LV, 
LT,  LU,  NO, 
PL,  PT,  RO, 
SK,  SI,  ES, 
SE,  NL,  UK 
EEA, 
(27) 
EU28 
AT,  BE,  BG, 
HR,  CY,  CZ, 
DK,  EE,  FI, 
FR,  DE,  EL, 
HU,  IE,  IT, 
LV,  LT,  LU, 
NO,  PL,  PT, 
RO,  SK,  SI, 
ES,  SE,  NL, 
UK (28) EEA, 
EU28 

AT,  BE,  HR, 
DK,  DE,  IE, 
LT,  LU,  PL, 
RO,  SI,  ES, 
SE,  NL,  UK 
(15) 
EEA, 
EU28 
AT,  BE,  CZ, 
DK,  FI,  DE, 
IE,  LU,  NO, 
PT,  RO,  SK, 
SE,  NL,  UK 
(15) 
EEA, 
EU28 

Don’t know 

AT,  BG,  CZ, 
DK,  EE,  FI, 
FR,  DE,  EL, 
HU,  IS,  IE, 
LV,  LT,  LU, 
NO,  PL,  PT, 
RO,  SI,  ES, 
SE,  NL,  UK 
EEA, 
(24) 
EU28 

BG,  HR,  CY, 
CZ,  DK,  FI, 
FR,  DE,  EL, 
HU,  IS,  IE, 
IT,  LV,  LT, 
MT,  NO,  PT, 
RO,  SK,  SI, 
ES,  SE,  NL 
(24) 
EEA, 
EU28 
AT,  BG,  CZ, 
FI,  FR,  DE, 
EL, 
IS,  LV, 
LT,  LU,  NO, 
PT,  RO,  SI, 
ES,  SE,  UK 
(18) 
EEA, 
EU28 

AT,  BE,  CZ, 
FI,  DE,  LT, 
NO,  PT,  RO, 
SK,  SE,  NL, 
UK  (13)  EEA, 
EU28 

No response 
to 
knowledge / 
read 

AT,  BE,  CZ, 
EE, FI, DE, EL, 
HU, 
IT,  LV, 
MT,  NO,  PL, 
PT, SK, SI, SE, 
NL,  UK  (19) 
EEA, EU28 

AT,  BE,  BG, 
DK, EE, FI, FR, 
EL,  IE,  IT,  LV, 
LT, PL, PT, SK, 
SI,  SE,  NL, UK 
(19) 
EEA, 
EU28 

BE,  BG,  CZ, 
IT, LV, NO, PL, 
PT, NL (9)  

AT, DK, EE, FI, 
IE,  PT, 
DE, 
RO,  UK 
(9) 
EEA, EU28 

Reading last bill from energy supplier 

Very 
easy 

Eas
y 

Difficult 

Very difficult  No response 

AT,  BE,  BG,  HR, 
CY,  CZ,  DK,  EE, 
FI,  FR,  DE,  EL, 
HU,  IS,  IE,  IT, 
LV,  LT,  LU,  MT, 
NO,  PL,  PT,  RO, 
SK,  SI,  ES,  SE, 
NL, UK (30) EEA, 
EU28 

FI,  DE,  EL, 
LU, NO, RO, 
UK (7)  

ES 

CY,  FI,  DE, 
MT (4)  

FI,  DE,  IE, 
LV,  NO,  UK 
(6)  

AT,  BE,  CZ,  EE, 
DE,  IE,  IT,  LV, 
MT,  PL,  PT,  RO, 
SI,  ES,  SE,  UK 
(16) EEA, EU28 
AT,  BE,  BG,  HR, 
CY,  CZ,  DK,  EE, 
FI,  FR,  DE,  EL, 
HU,  IS,  IE,  IT, 
LV,  LT,  MT,  PL, 
PT,  RO,  SK,  SI, 
ES,  SE,  NL,  UK 
(28) EEA, EU28 

CZ,  FI,  FR, 
DE,  IS,  LT, 
NO,  SE,  UK 
(9) 
EEA, 
EU28 

AT,  BE,  CZ,  EE, 
FI,  DE,  IE,  NO, 
PT,  RO,  SE,  NL, 
UK 
(13)  EEA, 
EU28 

AT,  BE,  BG, 
HR,  CZ,  DK, 
EE, FI, FR, DE, 
EL, HU, IE, IT, 
LV,  LT,  LU, 
NO,  PL,  PT, 
RO,  SK,  SI, 
ES, SE, NL, UK 
EEA, 
(27) 
EU28 

AT,  BE,  BG, 
HR,  CY,  CZ, 
DK, EE, FI, FR, 
DE,  EL,  HU, 
IE,  IT,  LV,  LT, 
LU,  NO,  PL, 
PT,  RO,  SK, 
SI,  ES,  SE, 
NL,  UK  (28) 
EEA, EU28 

AT,  BE,  BG, 
CZ,  DE,  EL, 
LV,  LT,  LU, 
NO,  PL,  PT, 
RO,  SI,  ES, 
NL,  UK  (17) 
EEA, EU28 

AT,  BE,  CZ, 
DK, FI, DE, IE, 
LT,  NO,  PT, 
RO,  SK,  SE, 
NL,  UK  (15) 
EEA, EU28 

654 

 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 140:  Above average vulnerability in countries due to market-related and experience drivers (read 

last bill - energy) 

D  Indicator 

Read last bill from energy supplier 

No response 
to 
knowledge / 
read 

Reading last bill from energy supplier 

Very 
easy 

Eas
y 

Difficult 

Very difficult  No response 

AT,  BG,  DK, 
EE,  FR,  EL,  IE, 
IT, LV, LT, MT, 
PT, RO, ES, UK 
(15)  

LT 

AT,  HR,  CY, 
CZ,  FI,  DE, 
IE,  LV,  LU, 
MT,  PL,  UK 
(12)  

AT,  BE,  HR,  CY, 
DK,  EE,  FI,  FR, 
DE,  EL,  HU,  IE, 
LV,  LT,  LU,  MT, 
NO,  PT,  SI,  ES, 
SE,  NL,  UK  (23) 
EEA, EU28 

AT,  BG,  HR, 
DK,  EE,  DE, 
EL, HU, IE, IT, 
LV,  NO,  PT, 
RO,  SK,  ES, 
UK  (17)  EEA, 
EU28 

Don’t know 
AT,  BG,  HR, 
CY,  FI,  FR, 
DE,  HU,  IE, 
IT,  LT,  MT, 
NO,  RO,  SI, 
ES,  NL,  UK 
(18) 
EEA, 
EU28 

AT,  EE,  FI, 
DE, 
IE, 
IS, 
NO,  PT,  NL 
(9)  

CZ,  EE,  FI,  IE, 
NO,  PT,  RO, 
SK, UK (9)  

AT,  BE,  EE,  FI, 
DE,  IE,  PT,  SE, 
NL, UK (10) EEA, 
EU28 

AT, CZ, FI, IE, 
LU,  NO,  PT, 
RO,  NL,  UK 
(10) 
EEA, 
EU28 

AT,  BE,  CZ,  DK, 
EE,  FI,  DE,  IS, 
IE,  LT,  LU,  NO, 
PT,  RO,  SK,  SE, 
NL, UK (18) EEA, 
EU28 

AT,  BG,  HR,  CY, 
DK,  FI,  DE,  EL, 
HU,  IS,  IE,  IT, 
LV,  LU,  NO,  PL, 
PT,  RO,  SI,  ES, 
SE,  NL,  UK  (23) 
EEA, EU28 

BG,  LV,  LT, 
PT, RO (5)  

FI, DE, NO 
AT,  BE,  CZ, 
DK,  EE,  FI, 
FR,  DE,  IS, 
IE,  LT,  LU, 
NO, PT, RO, 
SK,  SE,  NL, 
UK 
(19) 
EEA, EU28 
AT,  BE,  BG, 
HR, CY, CZ, 
DK,  EE,  FI, 
FR,  DE,  EL, 
IE,  IT,  LV, 
LT, MT, NO, 
PL,  PT,  RO, 
SK,  SI,  SE, 
NL, UK (26) 
EEA, EU28 

BG,  EL,  IE, 
LT,  PL,  PT, 
RO (7)  

BG,  IE,  LV, 
PT, RO (5)  

CY, HU, LV 

MT 

Not at all 
AT,  BG,  HR, 
DK,  FI,  DE, 
EL,  HU,  IE, 
IT,  LV,  NO, 
PL,  PT,  RO, 
SK,  ES,  SE, 
UK (19) EEA, 
EU28 
AT,  CZ,  DK, 
FI,  DE,  LT, 
LU,  NO,  PT, 
RO,  SK,  SE, 
NL,  UK  (14) 
EEA, EU28 

IE, RO, SE 

4  26. Has problems 

comparing deals 
due to personal 
factors – on-line 
sector 

4  27. Has problems 

comparing deals 
due to personal 
factors – finance 
sector* 

4  28. Has problems 

comparing deals 
due to market-
related factors – 
energy sector* 

4  29. Has problems 

comparing deals 
due to market-
related factors – 
on-line sector 

In 
detai
l 

Glanced 
or skim 
read 

Looked at 
total 
price 

LT 

LV, 

CZ, 
UK 

IS 

DE 

MT 

CZ, LV 

BE,  DK,  FI, 
DE,  IE,  MT, 
PL,  PT,  SI, 
NL (10)  

AT,  BE,  CZ, 
FR,  DE,  PT 
(6) 
EEA, 
EU28 

BE,  EE,  FI, 
DE,  IS,  LT, 
PT,  SK,  NL, 
UK 
(10) 
EEA, EU28 

HR, CY, CZ, 
FI,  IS,  LT, 
MT,  PL,  PT, 
SK,  SI,  NL, 
UK (13)  

655 

 
 
 
  
  
  
  
  
  
  
  
  
  
  
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 140:  Above average vulnerability in countries due to market-related and experience drivers (read 

last bill - energy) 

D  Indicator 

Read last bill from energy supplier 

In 
detai
l 

Glanced 
or skim 
read 

Looked at 
total 
price 

Not at all 

Don’t know 

No response 
to 
knowledge / 
read 

4  30. Has problems 

comparing deals 
due to market-
related factors – 
finance sector* 

Note: * -= Following 11 countries not covered: BG, HR, CY, EL, HU, IT, LV, MT, PL, SI, ES. 
Source: London Economics 

IS 

DE 

DE 

Reading last bill from energy supplier 

Very 
easy 

Eas
y 

Difficult 
AT,  BE,  CZ, 
DK,  EE,  FI, 
FR,  DE,  IS, 
IE,  LU,  NO, 
PT,  RO,  SK, 
SE,  NL,  UK 
(18)  EEA, 
EU28 

Very difficult  No response 

AT,  BE,  DK,  EE, 
FI,  FR,  DE,  IS, 
LU,  NO,  PT,  RO, 
SE, UK (14) EEA, 
EU28 

Table 141:  Above average vulnerability in countries due to market-related and experience drivers 

(knows contract, compares deals - online) 

D  Indicator 

Knows internet contract conditions 

Not at all 

Not very 

Fair 
amoun
t 

Compl
etely 

No response 

Compares deals from internet providers 

Time 
to 
time 

When 
need to 
renew 

Spor
adica
lly 

Only the 
first time 

Never 

1  2. Overpaid for 
services due to 
being unable to 
use certain 
payment methods 

2  4. Perception of 

own vulnerability 
due to personal 
characteristics 
In this section 
referred to as 
‘Feels vulnerable 
to 'a great extent' 
due to 
employment 
situation’ 

BG,  HR,  EL,  LU,  PL, 
PT (6)  

HR,  IT,  LT,  ES 
(4)  

BG 

LU 

DK,  FI,  DE,  IS,  IE, 
NO, SE, NL, UK (9)   MT 

BE,  BG, 
HR, 
EE, 
FR,  NO, 
ES (7)  

BG, 
PT 

SI 

BE,  EE,  FR,  IT,  LV, 
LU, SI, ES (8)  

DK, EE, FR, MT, 
SI, ES, NL (7)  

AT 

IS,  LU, 
NL 

UK 

CZ 

AT,  DE, 
NO 

DK, IE 

BE,  HR,  LU, 
MT, SI (5)  

CZ,  IS,  LV,  LT,  SK, 
ES (6)  

656 

 
 
 
  
  
  
  
  
  
 
 
  
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 141:  Above average vulnerability in countries due to market-related and experience drivers 

(knows contract, compares deals - online) 

D  Indicator 

Knows internet contract conditions 

Not at all 

Not very 

Fair 
amoun
t 

Compl
etely 

No response 

Compares deals from internet providers 

Time 
to 
time 

When 
need to 
renew 

Spor
adica
lly 

Only the 
first time 

Never 

2  5. Perception of 

own vulnerability 
due to personal 
characteristics 
In this section 
referred to as 
‘Feels vulnerable 
to 'a great extent' 
due to financial 
circumstances’ 
2  10. Perception of 
own vulnerability 
due to personal 
characteristics 
In this section 
referred to as 
‘Feels vulnerable 
to 'a great extent' 
due to any 
personal 
characteristic’ 
3  15. Has problems 

comparing deals 
due to 
information-
related factors- 
energy sector* 
3  17. Has problems 

comparing deals 
due to 
information-
related factors – 
financial sector* 

657 

AT,  BE,  CZ,  EE,  FR, 
IT,  LV,  MT,  NO,  RO, 
SK, SI, ES (13)  

BG, DK, FI, FR, 
IS, PL, ES (7)  

DE,  LU, 
PL 

AT,  FI, 
IE,  MT, 
SE,  NL, 
UK (7)  

HR,  EE,  LT,  SE,  UK 
(5)  

LU,  PL, 
SE,  NL 
(4)  

AT,  DE, 
IT 

DK, IE 

BE,  IS,  IE, 
LV,  LU,  ES 
(6)  

CZ,  FR,  EL,  LT,  SK, 
ES (6)  

BE,  CZ,  FR,  IS,  IT, 
LV,  LU,  MT,  NO,  SK, 
SI, ES (12)  

AT,  FI,  IE,  LT,  LU, 
NO, RO, SK (8)  

FR,  IS,  PL,  ES 
(4)  

AT, BE, CZ, DK, 
EE,  FI,  DE,  IE, 
LT,  LU,  PT,  RO, 
SE,  NL,  UK 
(15) 
EEA, 
EU28 

AT,  FI, 
LU,  NL 
(4)  

BE,  HR,  EE,  HU,  LT, 
NO,  SK,  SE,  NL,  UK 
(10) EEA, EU28 

LU, 
MT, SE 

AT, 
BE, 
DE, IT (4)  

AT, 
DK 

BE, LV 

EL, IS, SK, SI (4)  

LT 

LT 

LT 

CZ,  DK, 
LT, 
RO 
(4)  

DK, 
EE, 
IE, LT, 
PT (5)  

AT,  DK,  FI, 
IE,  LT,  PT 
(6)  

DE,  IE,  NO,  PT,  RO 
(5)  

NO, RO 

PT, UK 

PT 

SE 

PT 

LU, PT 

LT 

 
 
 
  
  
  
  
  
Table 141:  Above average vulnerability in countries due to market-related and experience drivers 

(knows contract, compares deals - online) 

D  Indicator 

Knows internet contract conditions 

Compares deals from internet providers 

Annex 4│ Incidence rate of vulnerability by sub-group 

Not at all 

Not very 

Fair 
amoun
t 

Compl
etely 

No response 

Time 
to 
time 

When 
need to 
renew 

Spor
adica
lly 

4  21. Does not 

compare product 
deals - energy 
sector 

4  22. Finds it very 
difficult to 
compare product 
deals - energy 
sector 

4  23. Does not 

compare product 
deals - finance 
sector 

4  24. Finds it very 
difficult to 
compare product 
deals - finance 
sector 

4  25. Has problems 

AT,  BE,  HR,  CZ,  DK, 
EE,  FI,  FR,  DE,  HU, 
IE,  IT,  LV,  LU,  NO, 
PL,  PT,  RO,  SK,  SI, 
ES,  SE,  NL,  UK  (24) 
EEA, EU28 
AT,  BE,  BG,  CZ,  DK, 
EE,  FI,  FR,  DE,  HU, 
IE,  IT,  LV,  LU,  NO, 
PL,  PT,  RO,  SK,  ES, 
SE, NL, UK (23) EEA, 
EU28 

AT,  BE,  BG,  CY,  CZ, 
DK,  EE,  FI,  FR,  DE, 
EL,  HU,  IS,  IE,  IT, 
LV,  LU,  MT,  NO,  PT, 
SK,  SI,  ES,  SE,  NL 
(25) EEA, EU28 

BE,  BG,  CZ,  FI,  FR, 
DE,  EL,  HU,  IS,  IT, 
LV,  LU,  MT,  NO,  PL, 
PT,  SI,  ES,  SE,  NL 
(20) EEA, EU28 

comparing deals 
due to personal 
factors – energy 
sector* 

AT,  BE,  CZ,  DK,  EE, 
FI,  DE,  IE,  LT,  NO, 
RO,  SK,  SE,  NL,  UK 
(15) EEA, EU28 

AT, BE, CZ, DK, 
FI,  FR,  DE,  EL, 
HU,  IE,  IT,  LV, 
NO,  PL,  SI,  ES, 
SE,  NL 
(18) 
EEA, EU28 

EE,  FI,  DE,  LU, 
PL, NL (6)  

AT,  DK,  FI,  FR, 
DE, EL, NO, PL, 
PT,  SI,  ES,  NL, 
UK  (13)  EEA, 
EU28 

EE, FI 

AT, BE, CZ, DK, 
EE,  FI,  FR,  DE, 
IE, NO, SK, SE, 
NL  (13)  EEA, 
EU28 

AT, BE, HR, CZ, DK, 
EE,  FI,  DE,  EL,  IE, 
IT,  LV,  LT,  PL,  PT, 
RO,  SI,  ES,  SE,  NL, 
UK (21) EEA, EU28 
AT, BE, HR, CZ, DK, 
EE,  FI,  FR,  DE,  EL, 
HU,  IE,  IT,  LV,  LT, 
NO, PL, PT, RO, SK, 
SI,  ES,  SE,  NL,  UK 
(25) EEA, EU28 

AT, BE, BG, HR, CY, 
CZ,  DK,  EE,  FI,  FR, 
DE,  EL,  HU,  IE,  IT, 
LV,  LT,  LU,  MT,  NO, 
PL,  PT,  RO,  SK,  SI, 
ES,  SE,  UK  (28) 
EEA, EU28 
AT, BG, HR, CZ, DK, 
EE,  FI,  DE,  HU,  IE, 
IT,  LV,  LT,  LU,  MT, 
PL,  RO,  SK,  SI,  ES, 
SE,  NL,  UK  (23) 
EEA, EU28 

Only the 
first time 
HR, CZ, DK, 
EE,  FI,  FR, 
DE,  HU,  IE, 
IT,  LV,  PL, 
SK,  SI,  ES, 
NL, UK (17) 
EEA, EU28 

DE 
AT, HR, CZ, 
DK,  EE,  FI, 
FR,  DE,  EL, 
IE,  IT,  LV, 
MT,  PL,  PT, 
SK,  SI,  ES, 
NL, UK (20) 
EEA, EU28 

Never 

AT, BE, HR, CZ, DK, 
EE,  FI,  FR,  DE,  EL, 
HU,  IE,  IT,  LV,  LT, 
LU,  NO,  PL,  PT,  RO, 
SK,  SI,  ES,  SE,  NL, 
UK (26) EEA, EU28 

AT, BE, BG, CZ, DK, 
EE,  FI,  FR,  DE,  IE, 
IT,  LV,  LU,  NO,  PL, 
SK, ES, SE, NL (19) 
EEA, EU28 

AT, BE, BG, CZ, DK, 
EE,  FI,  FR,  DE,  EL, 
HU,  IS,  IE,  IT,  LV, 
LU,  MT,  NO,  PL,  PT, 
SK,  SI,  ES,  SE,  NL, 
UK (26) EEA, EU28 

PT 

FI, IS, IT 

PT, RO, UK (3) EEA, 
EU28 

IE, LT 

AT,  DE,  IE, 
UK (4)  

AT,  BE,  CZ, DK,  EE, 
FI,  DE,  IE,  NO,  SK, 
SE,  NL,  UK  (13) 
EEA, EU28 

658 

 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 141:  Above average vulnerability in countries due to market-related and experience drivers 

(knows contract, compares deals - online) 

D  Indicator 

Knows internet contract conditions 

Compares deals from internet providers 

Time 
to 
time 

When 
need to 
renew 

Spor
adica
lly 

Only the 
first time 

Never 

Fair 
amoun
t 

Compl
etely 

No response 

LT 

LT,  PT,  RO,  UK  (4) 
EEA, EU28 

AT,  EE,  FI, 
DE,  IE,  PT, 
SK, UK (8)  

AT,  CZ,  FI,  DE,  IE, 
LU, SK, NL (8) EEA, 
EU28 

IE 

LT 

IS, LT, PT 

EE, PT 

BE,  DK,  FI, 
DE,  IE,  LT, 
PT,  SK,  UK 
(9) 
EEA, 
EU28 

DE, IE, SK 

BG,  MT, 
PL 

EE, IS, PT 

AT, EE, FR, DE, 
IE,  LT,  PT,  SE, 
NL,  UK 
(10) 
EEA, EU28 

PT 

EE, 
LU, 
PT, 
SK, 
UK 
(5)  

PT 

IE, LT, PT 

PT 

Not at all 

AT,  BG,  HR,  CY,  CZ, 
DK,  EE,  FI,  FR,  DE, 
EL,  HU,  IS,  IE,  IT, 
LV,  LT,  MT,  NO,  PL, 
PT,  RO,  SK,  ES,  SE, 
NL,  UK  (27)  EEA, 
EU28 

AT,  CZ,  EE,  IS,  LU, 
NO, PT, SK (8)  

FI,  LT,  RO,  SK,  UK 
(5)  

BG,  HR,  DE,  EL,  HU, 
IT,  LV,  LT,  PT,  RO, 
SK, ES, UK (13)  

Not very 
AT,  BE,  BG, 
HR,  CY,  CZ, 
DK,  EE,  FI,  FR, 
DE,  EL,  HU,  IS, 
IE,  IT,  LU,  NO, 
PL, PT, RO, SK, 
SI,  ES,  SE,  NL, 
UK  (27)  EEA, 
EU28 

BE,  CZ,  EE,  FI, 
FR,  DE,  IE,  LU, 
NO,  SE,  NL 
(11)  

AT,  BE,  EE,  FI, 
DE,  IE,  LT,  PT, 
SE,  NL 
(10) 
EEA, EU28 
AT,  BE,  BG, 
HR,  CY,  CZ, 
DK,  EE,  FI,  FR, 
DE,  EL,  HU,  IS, 
IE,  IT,  LV,  LT, 
LU, NO, PL, PT, 
RO, SK, SI, ES, 
SE,  NL,  UK 
(29) 
EEA, 
EU28 

4  26. Has problems 

comparing deals 
due to personal 
factors – on-line 
sector 

4  27. Has problems 

comparing deals 
due to personal 
factors – finance 
sector* 

4  28. Has problems 

comparing deals 
due to market-
related factors – 
energy sector* 
4  29. Has problems 

comparing deals 
due to market-
related factors – 
on-line sector 

4  30. Has problems 

comparing deals 
due to market-
related factors – 
finance sector* 

659 

 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
Note:  *  -=  Following  11  countries  not  covered:  BG,  HR,  CY,  EL,  HU,  IT,  LV,  MT,  PL,  SI,  ES.  Frequency  to  compare  deals  from  online  provider  is  used  to  construct  the 
indicator  
Source: London Economics 

Table 142:  Above average vulnerability in countries due to market-related and experience drivers(read 

Annex 4│ Incidence rate of vulnerability by sub-group 

last communication - online) 

D  Indicator 

Read last communication from internet provider 

Not at all 

Don’t know 

No response 
to 
knowledge 
/ read 

Reading last communication from internet provider 

Very 
easy 

Eas
y 

Difficult 

Very difficult  No response 

In 
detai
l 

Glanced 
or skim 
read 

CY, 
IS, PT  BG 

Saw what 
it was 

AT, HR, CZ, 
FI,  EL,  HU, 
MT,  ES,  UK 
(9)  

LT, MT, PT 

DK, FI, DE, IS, 
NO,  SE,  NL, 
UK (8)  

HU, IS, 
ES 

FI,  FR,  DE, 
IS,  LV,  NL, 
UK (7)  

BG,  DK,  EE,  FI, 
DE,  EL,  IE,  LV, 
LT,  MT,  RO,  SI, 
UK (13)  

LU, 
NO 

AT,  DK,  EE, 
FI,  DE,  IS 
(6)  

BE, CZ, NL 

HR,  HU,  IT, 
MT, SI (5)  

UK 

BE, LU, 
NO,  SE 
(4)  

DE,  IS,  NO, 
NL, UK (5)  

AT,  CY,  CZ,  EE, 
FI,  FR,  DE,  IE, 
LU,  NO,  PL,  PT, 
(13)  EEA, 
UK 
EU28 

LU 

FI,  IS, 
NO, 
SE, 
NL (5)   MT 

CZ,  DK,  FI, 
DE,  LV,  LU, 
MT, NO, ES, 
UK (10)  

BE, FR, PL 

HR,  HU,  IT, 
LU,  MT,  NO, 
PL, SK, SI (9)  

HR,  EE,  LT, 
NO, UK (5)  

BE, 
CZ,  LT, 
LU,  SI, 
UK (6)  

BE,  CY, DK, 
EE,  FI,  DE, 
IS,  IE,  LU, 
MT, NO, SE, 
NL, UK (14)  

AT,  BE,  BG,  HR, 
CY,  CZ,  EE,  FI, 
FR,  IE,  LV,  LU, 
NO,  PL,  PT,  SI, 
ES,  SE,  NL,  UK 
(20) EEA, EU28 

DK,  FR,  NO, 
RO (4)  

660 

1  2. Overpaid for 
services due to 
being unable to 
use certain 
payment methods 

2  4. Perception of 

own vulnerability 
due to personal 
characteristics 
In this section 
referred to as 
‘Feels vulnerable 
to 'a great extent' 
due to 
employment 
situation’ 

2  5. Perception of 

own vulnerability 
due to personal 
characteristics 
In this section 
referred to as 
‘Feels vulnerable 
to 'a great extent' 
due to financial 
circumstances’ 

 
 
 
 
  
  
  
  
  
  
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 142:  Above average vulnerability in countries due to market-related and experience drivers(read 

last communication - online) 

D  Indicator 

Read last communication from internet provider 

In 
detai
l 

Glanced 
or skim 
read 

Saw what 
it was 

Not at all 

Don’t know 

No response 
to 
knowledge 
/ read 

Reading last communication from internet provider 

Very 
easy 

Eas
y 

Difficult 

Very difficult  No response 

FI, 
LU, 
NO, 
SE (4)  

AT,  DK,  FI, 
DE, IS (5)  

BE, CZ, IS 

IT,  LU, 
AT, 
MT,  NO,  SI 
(6)  

BE,  HR,  EE, 
HU,  LT,  NO, 
SK,  SE,  NL, 
UK  (10)  EEA, 
EU28 

CZ, 
LU,  SI, 
SE (4)  

FI,  DE,  IS, 
LT,  PT,  SK, 
NL,  UK  (8) 
EEA, EU28 

CZ,  DK,  LT, 
LU,  NO,  SK, 
SE (7)  

PT, 

LT, 
RO 

IE,  LT, 

FR, 
NO, PT (5)  

BE, FR, LU 

SK, UK 
CZ,  EE,  FI, 
HU (4) 

PT 
AT,  BE,  HR, 
CZ,  DK,  EE, 
FI,  FR,  DE, 
EL,  HU,  IE, 
IT,  LV,  LU, 
NO,  PL,  SK, 
SI,  ES,  SE, 
NL,  UK  (23) 
EEA, EU28 

LT, NO, UK 
AT,  CY,  CZ, 
DK,  EE,  FI, 
FR,  DE,  EL, 
HU, 
IT, 
IE, 
LV,  MT,  NO, 
PL,  PT,  RO, 
SK,  SI,  ES, 
NL,  UK  (24) 
EEA, EU28 

BE, NO 
AT,  BE,  HR, 
CZ,  DK,  EE, 
FI,  DE,  EL,  IE, 
IT,  LV,  LT,  PL, 
PT,  RO,  SI, 
ES, SE, NL, UK 
(21) 
EEA, 
EU28 

BG 

EE 
IT,  LU, 
MT, NO 
(4) 

DK,  NO,  RO, 
UK (4)  

AT, DE, NO 

BE,  FI,  DE, 
IS,  LU,  MT, 
NO, NL (8)  
AT,  BE,  CZ, 
DK,  EE,  FI, 
FR,  DE,  IS, 
IE,  LT,  LU, 
NO, PT, RO, 
SK,  SE,  NL, 
UK 
(19) 
EEA, EU28 

AT, BG, HR, 
CY,  EE,  FR, 
EL,  IT,  MT, 
SK,  ES,  SE, 
UK 
(13) 
EEA, EU28 
AT, CZ, DK, 
DE,  IE,  IT, 
LV (7) 

AT,  BE,  BG,  HR, 
CY,  CZ,  EE,  FI, 
FR,  DE,  EL,  IE, 
LV,  LT,  NO,  PL, 
PT,  SI,  ES,  SE, 
NL,  UK  (22)  EEA, 
EU28 

AT,  BE,  CZ,  DK, 
EE,  FI,  FR,  DE, 
IS,  IE,  LU,  NO, 
PT,  SK,  NL,  UK 
(16) EEA, EU28 

(17) 

BE,  BG,  HR,  CY, 
CZ,  DK,  FI,  IS, 
IE,  IT,  LU,  MT, 
NO,  PL,  RO,  SK, 
SI 
EEA, 
EU28 
BE,  BG,  HR,  CY, 
CZ,  FI,  FR,  IE, 
IT,  LU,  MT,  PT, 
SI,  ES,  SE  (15) 
EEA, EU28 

2  10. Perception of 
own vulnerability 
due to personal 
characteristics 
In this section 
referred to as 
‘Feels vulnerable 
to 'a great extent' 
due to any 
personal 
characteristic’ 
3  15. Has problems 

comparing deals 
due to 
information-
related factors- 
energy sector* 

3  17. Has problems 

comparing deals 
due to 
information-
related factors – 
financial sector* 

4  21. Does not 

compare product 
deals - energy 
sector 

661 

 
 
 
  
  
  
  
  
  
  
  
 
 
 
 
Table 142:  Above average vulnerability in countries due to market-related and experience drivers(read 

Annex 4│ Incidence rate of vulnerability by sub-group 

last communication - online) 

D  Indicator 

Read last communication from internet provider 

In 
detai
l 

Glanced 
or skim 
read 

Saw what 
it was 

Not at all 

Don’t know 

4  22. Finds it very 
difficult to 
compare product 
deals - energy 
sector 

4  23. Does not 

compare product 
deals - finance 
sector 

4  24. Finds it very 
difficult to 
compare product 
deals - finance 
sector 

AT, BE, CY, 
EE, FI, FR, 
DE, LU, NO, 
PL, SK, ES, 
SE, NL (14) 
EEA, EU28 

BE, CZ, FI, 
FR, EL, IE, 
IT, LV, LU, 
MT, NO, ES 
(12) EEA, 
EU28 

AT,  BE,  HR, 
CY,  CZ,  DK, 
FI,  FR,  DE, 
EL,  HU,  IE, 
IT,  LV,  LT, 
LU,  NO,  PT, 
SK,  ES,  SE, 
NL,  UK  (23) 
EEA, EU28 

BE,  CY,  CZ, 
DK,  FI,  FR, 
DE,  HU,  IS, 
IE, IT, LV, LT, 
LU,  MT,  NO, 
PL,  PT,  SK, 
SI,  ES,  NL, 
UK  (23)  EEA, 
EU28 

DE, UK 

CZ,  IS,  NO, 
PT (4)  

DK, NO 

BE, FI, LU, 
NL (4)  

BE, CZ, FR, 
IE, NO, PT, 
ES, NL (8)  

No response 
to 
knowledge 
/ read 

AT, BE, HR, 
CZ, DK, EE, 
FI, FR, DE, 
EL, HU, IE, 
IT, LV, LT, 
NO, PL, PT, 
RO, SK, SI, 
ES, NL, UK 
(24) EEA, 
EU28 
AT,  BE,  BG, 
HR,  CY,  CZ, 
DK, EE, FI, FR, 
DE,  EL,  HU, 
IE,  IT,  LV,  LT, 
LU,  MT,  NO, 
PL,  PT,  RO, 
SK,  SI,  ES, 
SE,  UK  (28) 
EEA, EU28 
AT, BG, CZ, 
DK, EE, DE, 
HU, IE, IT, 
LV, LT, LU, 
MT, PL, RO, 
SK, SI, ES, 
SE, NL, UK 
(21) EEA, 
EU28 

Reading last communication from internet provider 

Very 
easy 

Eas
y 

Difficult 

Very difficult  No response 

AT,  CZ,  EE, 
FI,  DE,  HU, 
IE,  IT,  LV, 
PL,  PT,  RO, 
SK,  SI,  SE, 
(16) 
NL 
EEA, EU28 

AT,  BE,  BG,  HR, 
CY,  CZ,  FR,  IE, 
IT,  LU,  MT,  PT, 
ES, SE (14) EEA, 
EU28 

FI, 
NO, 
SI, SE, 
UK (5)  

IS 

AT,  CY,  CZ, 
EE,  FI,  DE, 
IE,  IT,  LV, 
RO,  SI,  SE, 
UK (13)  
AT, BE, 
CZ, DK, 
EE, FI, DE, 
IE, LT, LU, 
NO, PT, 
RO, SK, 
NL, UK 
(16) EEA, 
EU28 

AT,  BE,  BG,  HR, 
CY,  CZ,  FR,  IE, 
IT,  LU,  MT,  PT, 
RO,  SK,  SI,  ES, 
SE 
(17)  EEA, 
EU28 

IS 

AT,  BE,  DK,  FI, 
FR,  DE,  IS,  IE, 
LU,  NO,  PT,  RO, 
SE 
(13)  EEA, 
EU28 

SK 

NO, SE 

LT 

EE 

662 

 
 
 
  
  
  
  
  
  
  
  
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 142:  Above average vulnerability in countries due to market-related and experience drivers(read 

last communication - online) 

D  Indicator 

Read last communication from internet provider 

In 
detai
l 

Glanced 
or skim 
read 

Saw what 
it was 

Not at all 

Don’t know 

No response 
to 
knowledge 
/ read 

Reading last communication from internet provider 

Very 
easy 

Eas
y 

Difficult 

Very difficult  No response 

BE, CZ, 
EE, LU, 
NO, SK, NL 
(7)  

AT, HR, FI, 
FR, DE, EL, 
IS, IT, NO, 
PL, SK, SI, 
ES, NL, UK 
(15) EEA, 
EU28 

AT,  BE,  CZ, 
DK,  FI,  FR, 
DE,  IE,  NO, 
PT,  SK,  SE, 
NL,  UK  (14) 
EEA, EU28 
AT, BE, BG, 
HR, CY, CZ, 
DK, EE, FI, 
FR, DE, EL, 
HU, IS, IE, 
IT, LV, LT, 
LU, NO, PL, 
PT, RO, SK, 
SI, ES, SE, 
NL, UK (29) 
EEA, EU28 

AT,  CZ,  DK, 
FI,  DE, 
IE, 
LT,  NO,  SK, 
NL (10)  

AT, EE, FI, 
FR, IE, LU, 
NO, PT, RO, 
UK (10) EEA, 
EU28 

AT, HR, CY, 
CZ, DK, FI, 
FR, DE, EL, 
HU, IS, IE, 
IT, LV, LT, 
NO, PT, RO, 
SI, ES, NL, 
UK (22) EEA, 
EU28 

AT, DK, FI, FR, 
HU,  IS,  IE,  IT, 
LU,  NO,  PL, 
PT,  RO,  SI, 
ES,  UK  (16) 
EEA, EU28 

AT, CZ, DK, 
NO,  NL,  UK 
(6)  

AT,  BE,  CZ, 
FI,  DE, 
IE, 
LU,  NO,  PT, 
SE,  NL  (11) 
EEA, EU28 

AT,  DE,  IE, 
NO, SK (5)  

DE, 
IE,  LT, 
NO,  PT,  RO, 
UK  (7)  EEA, 
EU28 

AT, BE, 
CZ, EE, FI, 
DE, IS, IE, 
LT, LU, 
NO, PT, 
RO, SK, 
SE, UK 
(16) EEA, 
EU28 

EE, 
PT 

AT,  BE,  FI,  FR, 
IS,  IE,  LU,  NO, 
PT,  RO,  SK,  SE 
(12) EEA, EU28 

AT,  BE,  HR, 
CZ,  DK,  EE, 
FI, FR, DE, EL, 
IE,  IT,  LV,  LT, 
NO,  PL,  PT, 
SK,  SI,  ES, 
SE,  NL,  UK 
EEA, 
(23) 
EU28 
AT,  BE,  HR, 
CZ,  DK,  EE, 
FI, FR, DE, EL, 
HU, IE, IT, LV, 
LT,  NO,  PL, 
PT, SK, SI, ES, 
SE,  NL,  UK 
(24) 
EEA, 
EU28 

BG, LT 

BE,  HR,  CY,  DE, 
IE,  IT,  MT,  RO, 
SK, ES, SE (11)  

AT,  BE,  BG,  HR, 
CY,  DK,  FI,  FR, 
DE,  IT,  LU,  MT, 
NO,  PT,  RO,  ES, 
SE, UK (18) EEA, 
EU28 

HR,  FI,  NO, 
PT,  SK,  NL 
(6)  

4  25. Has problems 

comparing deals 
due to personal 
factors – energy 
sector* 

4  26. Has problems 

comparing deals 
due to personal 
factors – on-line 
sector 

4  27. Has problems 

comparing deals 
due to personal 
factors – finance 
sector* 

663 

 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 142:  Above average vulnerability in countries due to market-related and experience drivers(read 

last communication - online) 

D  Indicator 

Read last communication from internet provider 

In 
detai
l 

Glanced 
or skim 
read 

Saw what 
it was 

Not at all 

Don’t know 

No response 
to 
knowledge 
/ read 

Reading last communication from internet provider 

Very 
easy 

Eas
y 

Difficult 

Very difficult  No response 

4  28. Has problems 

comparing deals 
due to market-
related factors – 
energy sector* 

4  29. Has problems 

comparing deals 
due to market-
related factors – 
on-line sector 

4  30. Has problems 

comparing deals 
due to market-
related factors – 
finance sector* 

IE, LT 

AT, BE, FI, FR, 
LU, NL (6)  

PT 

BE, CY, DE, IE, 
IT, SK, SI, ES, 
SE (9) EEA, 
EU28 

IS 

LT 

MT 

LV 

BE,  FI,  DE, 
LT,  NO,  PT, 
NL,  UK  (8) 
EEA, EU28  DE, LT 
AT, HR, 
EE, FI, DE, 
EL, IT, MT, 
NO, PL, PT, 
SK, SI, ES, 
NL, UK 
(16) EEA, 
EU28 

BG, HR, CY, 
CZ, FI, DE, 
EL, HU, IE, 
LV, LT, PL, 
PT, RO, UK 
(15)  

AT, HR, FR, 
DE, EL, HU, 
IE, IT, LV, 
LT, PT, ES 
(12) EEA, 
EU28 

BE, CZ, FI, 
FR, HU, LT, 
LU, NO, RO, 
SK, SI, UK 
(12)  

HR, CZ, FI, 
DE, EL, IE, 
LT, PT, SI, 
SE, NL 
(11)  

AT, BE, BG, HR, 
DK, FI, FR, DE, 
IS, IE, IT, LU, 
SK, SI, ES, SE 
(16) EEA, EU28 

AT,  CZ,  FI, 
IE,  PT,  NL 
(6)  

AT, BE, FI, IE, SK 
(5)  

Note: * -= Following 11 countries not covered: BG, HR, CY, EL, HU, IT, LV, MT, PL, SI, ES. 
Source: London Economics 

PT 

CZ,  FI,  PT, 
UK (4)  

NO, PT, SK 

AT, PT 

BE, FR, IS, LU, 
NO, SE (6)  

AT, BE, BG, 
HR, CY, CZ, 
DK, EE, FI, 
FR, DE, EL, 
HU, IE, IT, 
LV, LT, LU, 
MT, NO, PL, 
PT, RO, SK, 
SI, ES, SE, 
NL, UK (29) 
EEA, EU28 

CZ, FI, DE, 
IE, LT, NO, 
PL, SI, NL, 
UK (10)  
AT,  BE,  CZ, 
DK,  EE,  FI, 
DE, 
IE,  NO, 
PT, SK, NL, UK 
(13) 
EEA, 
EU28 

664 

 
 
 
  
  
  
  
  
  
  
 
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 143:  Above average vulnerability in countries due to market-related and experience drivers 

(knows contract, compares deals - finance) 

D  Indicator 

Knows bank contract conditions 

Compares deals from banks 

Not at all 

Not very 

Fair 
amount 

Compl
etely 

Time 
to 
time 

AT,  EE,  DE,  IS,  LT, 
LU,  NO,  RO,  ES,  NL 
(10)  

FI, IT, PL, SK, UK (5)  

IS 

HR,  FI, 
IS,  LU, 
NL (5)  

When 
need to 
renew 

HR, 
EE, 
FR,  DE, 
EL,  HU, 
IS, IE, IT, 
LU,  NO, 
PT,  RO, 
SI, 
ES, 
SE,  NL, 
UK 
(18) 
EEA, 
EU28 

Sporadi
cally 

Only the 
first time 

Never 

BG 

HR,  CY,  CZ,  EE,  FI, 
LT,  LU,  NO,  PT,  RO, 
SK, NL, UK (13)  

CZ,  EE,  FR,  NO,  SE, 
UK (6)  

AT,  DE, 
IS 

IS 

AT,  DK, 
DE,  MT 
(4)  

CZ 

NO, SK 

AT,  BE,  BG,  CY,  DK, 
EE,  FI,  EL,  HU,  IE, 
LT,  LU,  NO,  PL,  PT, 
SI,  ES,  SE,  NL,  UK 
(20) EEA, EU28 

AT,  CZ,  DK,  FI,  FR, 
DE,  IS,  IE,  LV,  NO, 
ES,  SE,  UK 
(13) 
EEA, EU28 

DE,  LU, 
MT 

IS,  LU, 
NL 

AT,  DE, 
LV, 
IS, 
LU, 
MT 
(6)  

NO 

DK 

BE,  BG,  EL,  IE,  LT,  NO, 
SK, SI, ES, UK (10) EEA, 
EU28 

1  2. Overpaid for 

services due to being 
unable to use certain 
payment methods 

2  4. Perception of own 

vulnerability due to 
personal 
characteristics 
In this section 
referred to as ‘Feels 
vulnerable to 'a great 
extent' due to 
employment situation’ 

2  5. Perception of own 

vulnerability due to 
personal 
characteristics 
In this section 
referred to as ‘Feels 
vulnerable to 'a great 
extent' due to 
financial 
circumstances’ 

665 

 
 
 
  
  
  
  
  
  
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 143:  Above average vulnerability in countries due to market-related and experience drivers 

(knows contract, compares deals - finance) 

D  Indicator 

Knows bank contract conditions 

Compares deals from banks 

Not at all 

Not very 

Fair 
amount 

Compl
etely 

Time 
to 
time 

When 
need to 
renew 

Sporadi
cally 

Only the 
first time 

Never 

2  10. Perception of own 
vulnerability due to 
personal 
characteristics 
In this section 
referred to as ‘Feels 
vulnerable to 'a great 
extent' due to any 
personal 
characteristic’ 
3  15. Has problems 

comparing deals due 
to information-related 
factors- energy 
sector* 

3  17. Has problems 

comparing deals due 
to information-related 
factors – financial 
sector* 

4  21. Does not compare 

product deals - 
energy sector 

4  22. Finds it very 

difficult to compare 
product deals - 
energy sector 

BG,  HR,  CY,  CZ,  DK, 
EE,  FI,  DE,  IS,  IE, 
LV,  LT,  LU,  NO,  PT, 
SK,  ES,  NL,  UK  (19) 
EEA, EU28 

CZ,  EE,  FR,  IS,  IE, 
LV, NO, SE, UK (9)  

LU,  MT, 
SI 

IS,  LU, 
NL 

AT,  DE, 
IS, 
LV, 
LU,  MT, 
UK 
NL, 
(8)  

NO 

DK 

BG, IE, LT, NO, SK, SI (6)  

AT,  BE,  CZ,  FI,  FR, 
DE,  IE,  RO,  SK,  UK 
(10) EEA, EU28 

BE,  CZ,  DK,  EE,  FI, 
DE,  IE,  LU,  NO,  PT, 
SK,  SE,  UK  (13) 
EEA, EU28 

AT,  BE,  CZ,  EE,  FI, 
FR,  DE,  IE,  LU,  NO, 
NL,  UK  (12)  EEA, 
EU28 
AT,  BE,  HR,  CZ,  DK, 
EE,  FI,  FR,  DE,  EL, 
HU,  IE,  IT,  LV,  LU, 
NO,  PL,  PT,  RO,  SK, 
SI,  ES,  SE,  NL,  UK 
(25) EEA, EU28 
AT,  BE,  BG,  HR,  CY, 
CZ,  DK,  EE,  FI,  FR, 
DE,  EL,  HU,  IS,  IE, 
IT,  LV,  LT,  LU,  MT, 
NO,  PL,  PT,  RO,  SI, 
ES,  SE,  NL,  UK  (29) 
EEA, EU28 

EE,  FI,  DE,  IE,  LT, 
LU,  NO,  SK  (8)  EEA, 
EU28 
AT,  BE,  CZ,  DK,  EE, 
FI,  DE,  EL,  HU,  IE, 
IT,  LV,  LT,  LU,  NO, 
PL,  PT,  SK,  SI,  ES, 
SE,  NL,  UK 
(23) 
EEA, EU28 

AT,  HR,  CZ,  EE,  FI, 
FR,  DE,  IT,  LU,  NO, 
PL,  SK,  SE,  NL  (14) 
EEA, EU28 

CZ 

EE, PT 

EE, 

DK, 
DE 

EE,  LT,  RO, 
SE,  NL,  UK 
(6)  

AT, FI, IE 

CZ, EE 

EE, LT 

IE 

AT,  CZ,  EE, 
FI,  DE,  IE, 
LV, MT, NO, 
PL,  SK,  SI, 
ES, SE (14)  

AT,  BE,  BG,  HR,  CZ,  DK, 
EE,  FI,  FR,  DE,  EL,  HU, 
IE, IT, LV, LT, LU, NO, PL, 
PT,  RO,  SK,  SI,  ES,  SE, 
NL, UK (27) EEA, EU28 

AT,  BE,  BG,  HR,  CZ,  DK, 
EE, FI, DE, EL, HU, IE, IT, 
LV,  LT,  LU,  NO,  PL,  PT, 
RO,  SK,  SI,  ES,  SE,  UK 
(25) EEA, EU28 

666 

 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 143:  Above average vulnerability in countries due to market-related and experience drivers 

(knows contract, compares deals - finance) 

D  Indicator 

Knows bank contract conditions 

Compares deals from banks 

Fair 
amount 

Compl
etely 

Time 
to 
time 

When 
need to 
renew 

Sporadi
cally 

Only the 
first time 

Never 

AT,  BE,  BG,  HR,  CZ,  DK, 
EE,  FI,  FR,  DE,  EL,  HU, 
IE,  IT,  LV,  LT,  LU,  MT, 
NO,  PL,  PT,  RO,  SK,  SI, 
ES,  SE,  UK  (27)  EEA, 
EU28 

DE, IE 

AT,  BE,  CZ,  FI,  DE,  IE, 
NO,  PT,  RO,  SK,  SE,  NL, 
UK (13) EEA, EU28 

AT,  FI,  EL, 
NO,  PL,  PT, 
SK,  SI,  ES 
(9)  

AT,  BE,  BG,  HR,  CY,  CZ, 
DK,  EE,  FI,  FR,  DE,  HU, 
IS, IE, IT, LV, LU, PL, PT, 
RO,  SK,  SI,  ES,  SE,  NL, 
UK (26) EEA, EU28 

LT 

HR, EL 

FR, IS 

LT 

LT, NO, SK 

DE, IE 

4  23. Does not compare 

product deals - 
finance sector 

4  24. Finds it very 

difficult to compare 
product deals - 
finance sector 

4  25. Has problems 

comparing deals due 
to personal factors – 
energy sector* 
4  26. Has problems 

comparing deals due 
to personal factors – 
on-line sector 

4  27. Has problems 

comparing deals due 
to personal factors – 
finance sector* 

4  28. Has problems 

comparing deals due 
to market-related 
factors – energy 
sector* 

Not at all 
AT,  BE,  BG,  HR,  CY, 
CZ,  DK,  EE,  FI,  FR, 
DE,  EL,  HU,  IS,  IE, 
IT,  LV,  LT,  LU,  MT, 
NO,  PL,  PT,  RO,  SK, 
SI,  ES,  SE,  NL,  UK 
(30) EEA, EU28 
AT,  BE,  BG,  HR,  CY, 
CZ,  DK,  EE,  FI,  FR, 
DE,  EL,  HU,  IS,  IE, 
IT,  LV,  LT,  LU,  MT, 
NO,  PL,  PT,  RO,  SK, 
SI,  ES,  SE,  NL,  UK 
(30) EEA, EU28 

AT,  BE,  CZ,  DK,  EE, 
FI,  FR,  DE,  IE,  LT, 
SK,  SE,  NL,  UK  (14) 
EEA, EU28 
AT,  BE,  BG,  HR,  CY, 
CZ,  DK,  EE,  FI,  FR, 
DE,  EL,  HU,  IS,  IE, 
IT,  LV,  MT,  PL,  RO, 
ES,  SE,  UK 
(23) 
EEA, EU28 

AT,  BE,  CZ,  DK,  EE, 
FI,  DE,  IS,  IE,  PT, 
SK,  SE,  NL,  UK  (14) 
EEA, EU28 

Not very 
AT,  BE,  BG,  HR,  CY, 
CZ,  DK,  EE,  FI,  FR, 
DE,  EL,  HU,  IS,  IE, 
IT,  LV,  LT,  LU,  MT, 
NO,  PL,  PT,  SK,  SI, 
ES,  SE,  NL,  UK  (29) 
EEA, EU28 

CZ,  DK,  EE,  FI,  FR, 
DE,  IE,  LU,  NO,  SK, 
SE, UK (12)  

AT,  BE,  CZ,  DK,  EE, 
FI,  DE,  IE,  NO,  SK, 
SE,  NL  (12)  EEA, 
EU28 
AT,  BE,  BG,  HR,  CY, 
CZ,  DK,  EE,  FI,  DE, 
HU,  IT,  LU,  NO,  PL, 
PT,  RO,  SK,  SI,  ES, 
SE,  NL,  UK 
(23) 
EEA, EU28 
AT,  BE,  CZ,  DK,  EE, 
FI,  FR,  DE,  IS,  IE, 
LT,  LU,  NO,  PT,  SK, 
SE,  NL,  UK 
(18) 
EEA, EU28 

AT,  CZ,  EE,  FI,  DE, 
IS, IE, RO (8)  

AT,  CZ,  EE,  FI,  DE, 
IE,  NO,  PT,  UK  (9) 
EEA, EU28 

667 

 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 143:  Above average vulnerability in countries due to market-related and experience drivers 

(knows contract, compares deals - finance) 

D  Indicator 

Knows bank contract conditions 

Compares deals from banks 

4  29. Has problems 

comparing deals due 
to market-related 
factors – on-line 
sector 

4  30. Has problems 

comparing deals due 
to market-related 
factors – finance 
sector* 

Not at all 

AT,  HR,  CZ,  EE,  FI, 
DE,  EL,  HU,  IE,  IT, 
LV,  PL,  PT,  RO,  SK, 
ES,  NL,  UK 
(18) 
EEA, EU28 

AT,  CZ,  EE,  FI,  DE, 
IE,  LU,  NO,  RO,  NL 
(10)  

Not very 
AT,  BE,  BG,  HR,  CZ, 
EE,  FI,  DE,  EL,  HU, 
IE,  IT,  LV,  LT,  NO, 
PL,  PT,  RO,  SK,  SI, 
ES,  SE,  UK 
(23) 
EEA, EU28 

AT,  BE,  CZ,  DK,  EE, 
FI,  FR,  DE,  IS,  IE, 
LT,  LU,  NO,  PT,  RO, 
SK,  SE,  NL,  UK  (19) 
EEA, EU28 

Note: * -= Following 11 countries not covered: BG, HR, CY, EL, HU, 
the indicator  
Source: London Economics 

Fair 
amount 

Compl
etely 

Time 
to 
time 

When 
need to 
renew 

Sporadi
cally 

Only the 
first time 

Never 

AT,  CY,  CZ, 
IE,  IT,  LT, 
NO, PL, RO, 
SK,  SI,  ES 
(12)  

HR,  CZ, 
DK, IE, LT 
(5)  

RO 

BG, DE, HU, PL, PT (5)  

IT, LV, MT, PL, SI, ES. Frequency to compare deals from online provider is used to construct 

Table 144:  Above average vulnerability in countries due to market-related and experience drivers (read 

last communication - finance) 

D  Indicator 

Read last communication from bank 

In 
detai
l 

Glanced 
or skim 
read 

Saw what 
it was 

Not at all 

Don’t know 

No response  
knowledge / 
read 

Reading last communication from bank 

Very 
easy 

Eas
y 

Difficult 

Very difficult  No response 

1  2. Overpaid for 
services due to 
being unable to 
use certain 
payment methods 

BG, DK, EE, 
DE, LU, NO, 
PL,  ES,  UK 
(9)  

AT, LU, NO 

CZ,  EE,  PT, 
ES (4)  

AT,  CZ,  DK, 
MT,  NO,  SK, 
UK (7)  

FI,  FR,  DE, 
EL,  LU,  SE, 
NL (7)  

MT 

AT,  BE,  HR,  CY, 
CZ,  DK,  FI,  DE, 
IE,  IT,  LV,  LT, 
LU,  PT,  RO,  SK, 
NL 
(17)  EEA, 
EU28 

AT,  DK,  LU, 
NO (4)  

668 

 
 
 
  
  
  
  
  
  
  
  
  
  
 
 
 
 
 
  
  
  
 
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 144:  Above average vulnerability in countries due to market-related and experience drivers (read 

last communication - finance) 

D  Indicator 

Read last communication from bank 

In 
detai
l 

Glanced 
or skim 
read 

Saw what 
it was 

Not at all 

Don’t know 

No response  
knowledge / 
read 

Reading last communication from bank 

Very 
easy 

Eas
y 

Difficult 

Very difficult  No response 

2  4. Perception of 

own vulnerability 
due to personal 
characteristics 
In this section 
referred to as 
‘Feels vulnerable 
to 'a great extent' 
due to 
employment 
situation’ 

2  5. Perception of 

own vulnerability 
due to personal 
characteristics 
In this section 
referred to as 
‘Feels vulnerable 
to 'a great extent' 
due to financial 
circumstances’ 
2  10. Perception of 
own vulnerability 
due to personal 
characteristics 
In this section 
referred to as 
‘Feels vulnerable 
to 'a great extent' 
due to any 
personal 
characteristic’ 

669 

IS 

AT,  DK,  LU, 
UK (4)  

IS, 
AT,  FI, 
LU,  NO,  RO, 
UK (7)  

CY,  LT,  MT, 
NO,  PT,  ES, 
NL (7)  

BG,  HR,  DE, 
HU, IE, IT, LU, 
MT,  PT,  SK, 
ES,  NL  (12) 
EEA, EU28 

BE, NO 

AT, HR, CZ, 
DE,  SK,  NL, 
UK (7)  

BE,  BG,  HR,  FR, 
DE,  HU,  IS,  LT, 
PT,  SI,  NL,  UK 
(12) EEA, EU28 

AT,  DK,  LU, 
UK (4)  

DE, IS 

LV 

AT, CZ, DK, 
SE,  NL,  UK 
(6)  

AT,  BE,  HR, 
CZ,  DK,  EE, 
FI,  FR, 
IE, 
LV,  LU,  NO, 
ES,  SE,  UK 
(15) 
EEA, 
EU28 

BE,  CY,  FI, 
HU,  LT,  LU, 
NO,  PT,  SK, 
ES (10)  

BE,  BG,  HR, 
CY,  CZ,  DE, 
HU, IT, LT, LU, 
MT,  PT,  RO, 
SK,  SI,  ES,  NL 
(17) 
EEA, 
EU28 

CZ,  EE,  FI, 
DE,  IS,  LV, 
LU,  MT,  PT, 
SK,  SE,  NL, 
UK (13)  

AT,  BE,  BG,  HR, 
DK,  EE,  FR,  DE, 
EL,  IS,  IE,  LT, 
PT,  ES,  NL,  UK 
(16) EEA, EU28 

AT,  CZ,  DK, 
LT,  LU,  NO, 
SK, NL, UK (9) 
EEA, EU28 

IS 

AT,  CY,  CZ, 
DK,  NL,  UK 
(6)  

AT,  BE,  HR, 
DK,  EE,  FI, 
IE,  LV,  NO, 
SE, UK (11)  

AT,  CY,  CZ, 
LT,  LU,  NO, 
PT,  ES,  UK 
(9)  

AT,  BG,  HR, 
CZ, DK, FI, FR, 
DE, HU, IS, IE, 
IT, LT, LU, MT, 
NO,  PL,  PT, 
RO,  SK,  SI, 
ES,  SE,  NL 
(24) 
EEA, 
EU28 

EE 

CZ,  EE,  FR, 
DE,  IS,  LV, 
LU,  MT,  PT, 
SK,  NL,  UK 
(12)  

AT,  BE,  BG,  HR, 
CZ,  DK,  EE,  FR, 
DE,  EL,  IS,  IE, 
LV,  LT,  PT,  ES, 
NL, UK (18) EEA, 
EU28 

AT,  CZ,  DK, 
IE,  LT,  NO, 
SK,  UK 
(8) 
EEA, EU28 

 
 
 
  
  
  
  
  
  
Table 144:  Above average vulnerability in countries due to market-related and experience drivers (read 

Annex 4│ Incidence rate of vulnerability by sub-group 

last communication - finance) 

D  Indicator 

Read last communication from bank 

In 
detai
l 

Glanced 
or skim 
read 

Saw what 
it was 

Not at all 

Don’t know 

No response  
knowledge / 
read 

Very 
easy 

Eas
y 

3  15. Has problems 

comparing deals 
due to 
information-
related factors- 
energy sector* 

3  17. Has problems 

comparing deals 
due to 
information-
related factors – 
financial sector* 

4  21. Does not 

compare product 
deals - energy 
sector 

4  22. Finds it very 
difficult to 
compare product 
deals - energy 
sector 

AT,  FI,  FR, 
DE,  LT,  LU, 
RO,  SK  (8) 
EEA, EU28 

AT,  BE,  EE, 
DE,  IE,  RO 
(6) 
EEA, 
EU28 

AT,  EE,  FI, 
FR,  IE,  NO, 
PT,  RO,  SK, 
SE,  UK  (11) 
EEA 

PT 

AT, IE, NO, SK 
(4)  

LT 

DK,  DE,  IS, 
LU,  SK,  SE 
(6)  

CZ, DK, EE, 
FI,  DE,  LV, 
PL,  PT,  SE 
(9)  

AT 

DE, LU, SE 

NO 
AT,  BE,  HR, 
CZ,  DK,  EE, 
FI,  FR,  DE, 
EL,  HU,  IE, 
IT,  LV,  LU, 
NO,  PL,  PT, 
SK,  SI,  ES, 
SE,  NL,  UK 
(24) 
EEA, 
EU28 
BE,  BG,  HR, 
CZ,  DK,  EE, 
FI,  FR,  DE, 
EL, 
IT, 
IE, 
LU,  MT,  PL, 
PT,  SK,  SI, 
ES,  NL,  UK 
(21) 
EEA, 
EU28 

CZ, UK 
AT,  BE,  HR, 
CZ,  DK,  EE, 
FI,  FR,  DE, 
EL,  HU,  IE, 
IT,  LV,  NO, 
PL,  PT,  RO, 
SK,  SI,  ES, 
SE,  NL,  UK 
(24) 
EEA, 
EU28 

AT,  BG,  CY, 
CZ,  EE,  FI, 
FR,  DE,  EL, 
HU,  LV,  LT, 
NO,  PT,  SK, 
SI,  ES,  SE, 
NL,  UK  (20) 
EEA, EU28 

IE,  NO, 

EE, 
SK, SE (5)  

AT,  BE,  HR, 
CZ, FI, FR, DE, 
EL,  IE,  IT,  LT, 
LU,  PL,  PT, 
RO,  SK,  SI, 
ES, SE, NL, UK 
(21) 
EEA, 
EU28 
BE,  BG,  HR, 
CZ, EE, FI, FR, 
DE,  EL,  HU, 
IE,  IT,  LV,  LT, 
NO,  PL,  PT, 
RO,  SK,  SI, 
SE,  NL,  UK 
(23) 
EEA, 
EU28 

ES 

Reading last communication from bank 

Difficult 
AT,  BE,  CZ, 
DK,  EE,  FI, 
DE,  IE,  LT, 
LU,  NO,  PT, 
RO, SK, SE, 
NL, UK (17) 
EEA, EU28 
AT,  BE,  CZ, 
DK,  EE,  FI, 
FR,  DE,  IS, 
LT,  LU,  NO, 
PT,  RO,  SK, 
SE,  NL,  UK 
(18)  EEA, 
EU28 

Very difficult  No response 

AT,  BE,  CZ,  DK, 
EE,  FR,  DE,  IS, 
IE,  PT,  RO,  UK 
(12) EEA, EU28 

AT, FI, DE, IE, 
SK (5)  

AT,  DK,  EE,  FI, 
FR,  DE,  IS,  IE, 
LT,  PT,  RO,  NL 
(12) EEA, EU28 

NO, UK 

AT,  BE,  HR, 
CZ,  DK,  EE, 
FI,  FR,  DE, 
HU, IE, IT, LV, 
NO,  PL,  PT, 
SK,  SI,  ES, 
SE,  NL,  UK 
(22) 
EEA, 
EU28 

AT,  BE,  BG,  DE, 
IE,  LT,  MT,  NO, 
PL,  RO,  SE,  NL 
(12)  

DE, NO 

AT, CZ, DK, 
EE,  FI,  FR, 
DE,  IE,  LU, 
NO, PT, SK, 
SI,  NL,  UK 
(15)  EEA, 
EU28 

AT,  BE,  BG,  HR, 
DK,  EE,  FI,  FR, 
DE,  EL,  HU,  IE, 
IT,  LV,  LT,  MT, 
NO,  PL,  PT,  RO, 
SK,  SI,  ES,  SE, 
NL, UK (26) EEA, 
EU28 

AT,  BE,  BG, 
HR,  CZ,  EE, 
FI, FR, DE, EL, 
IE,  IT,  LV,  LT, 
NO,  PL,  PT, 
SK,  SI,  SE, 
NL,  UK  (22) 
EEA, EU28 

670 

 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 144:  Above average vulnerability in countries due to market-related and experience drivers (read 

No response  
knowledge / 
read 

BE,  BG,  HR, 
CY,  CZ,  EE, 
FR, EL, HU, IS, 
IE,  IT,  LV,  LT, 
MT,  PL,  PT, 
RO,  SK,  SI, 
ES, SE, NL, UK 
EEA, 
(24) 
EU28 
AT,  BE,  BG, 
HR,  CY,  CZ, 
DK, EE, FI, FR, 
DE,  EL,  HU, 
IS,  IE,  IT,  LV, 
LT,  LU,  MT, 
NO,  PL,  PT, 
RO,  SK,  SI, 
ES, SE, NL, UK 
(30) 
EEA, 
EU28 

AT,  IS,  IE,  LT, 
RO,  UK 
(6) 
EEA, EU28 

last communication - finance) 

D  Indicator 

Read last communication from bank 

In 
detai
l 

Glanced 
or skim 
read 

Saw what 
it was 

AT, CZ, DK, 
EE,  FI,  FR, 
DE,  IS,  MT, 
NO, PT, SK, 
SE (13)  

AT,  EE,  DE, 
LU,  NO,  UK 
(6)  

CZ, DK, DE, 
NO,  SK,  UK 
(6)  

AT 

CZ,  DK,  EL, 
HU, LT, NO, 
PL,  PT,  SI, 
ES,  SE,  NL, 
UK (13)  

Not at all 
AT,  BE,  BG, 
HR,  CY,  CZ, 
DK,  EE,  FI, 
FR,  DE,  EL, 
HU,  IS,  IE, 
IT,  LV,  LT, 
LU,  MT,  NO, 
PL,  PT,  SK, 
SI,  ES,  SE, 
NL,  UK  (29) 
EEA, EU28 

Don’t know 
AT,  BE,  BG, 
HR,  CY,  CZ, 
DK,  EE,  FI, 
FR,  DE,  EL, 
HU,  IS,  IE, 
IT,  LV,  LT, 
LU,  MT,  NO, 
PL,  PT,  RO, 
SI,  ES,  SE, 
NL,  UK  (29) 
EEA, EU28 

AT,  BE,  HR, 
DK,  EE,  FI, 
FR,  DE,  IS, 
IE, 
IT,  LU, 
NO,  PL,  PT, 
SK,  ES,  SE, 
NL,  UK  (20) 
EEA, EU28 

AT,  BE,  EE, 
FI,  FR,  DE, 
IE,  NO,  SE, 
NL,  UK  (11) 
EEA, EU28 
BG,  HR,  CZ, 
FI,  DE,  EL, 
HU,  IE,  IT, 
LV,  LU,  PL, 
PT,  RO,  SK, 
ES,  SE,  NL, 
UK (19) EEA, 
EU28 

CY,  CZ,  FI, 
FR,  DE,  EL, 
HU,  IT,  LV, 
LU,  MT,  NO, 
ES,  SE,  NL, 
UK  (16)  EEA, 
EU28 
AT,  BE,  CZ, 
FI,  DE, 
IE, 
LU,  NO,  SK, 
SE,  NL,  UK 
(12) 
EEA, 
EU28 

AT,  BE,  BG, 
CY,  CZ,  DK, 
FI,  FR,  DE, 
IT,  LT,  NO, 
RO,  SK,  NL, 
UK  (16)  EEA, 
EU28 

4  23. Does not 

compare product 
deals - finance 
sector 

4  24. Finds it very 
difficult to 
compare product 
deals - finance 
sector 

4  25. Has problems 

comparing deals 
due to personal 
factors – energy 
sector* 

4  26. Has problems 

comparing deals 
due to personal 
factors – on-line 
sector 

671 

Reading last communication from bank 

Very 
easy 

Eas
y 

Difficult 

DK, NO 

Very difficult  No response 
AT,  BE,  BG, 
HR,  CY,  CZ, 
DK, EE, FI, FR, 
DE,  EL,  HU, 
IS,  IE,  IT,  LV, 
LT,  LU,  MT, 
NO,  PL,  PT, 
RO,  SK,  SI, 
ES, SE, NL, UK 
EEA, 
(30) 
EU28 

AT,  DE,  IE,  LT, 
NO,  PT,  RO,  SI, 
ES, SE, NL (11)  

AT,  BE,  BG,  HR, 
CZ,  DK,  EE,  FI, 
FR,  DE,  EL,  HU, 
IS, IE, IT, LV, LT, 
LU,  NO,  PL,  PT, 
RO,  SK,  SI,  ES, 
SE,  NL,  UK  (28) 
EEA, EU28 

AT,  CZ,  FI, 
FR,  DE,  IS, 
IE,  NO,  SK, 
SI,  SE,  NL, 
UK (13)  

AT, CZ, DK, 
DE, LU, NO, 
SE,  UK  (8) 
EEA, EU28 

AT,  BE,  CZ,  DE, 
PT,  SE,  NL,  UK 
(8) EEA, EU28 

AT,  BE,  BG, 
HR,  CZ,  DK, 
EE, FI, FR, DE, 
EL,  IE,  LV,  LT, 
LU,  MT,  NO, 
PL, PT, SK, SI, 
SE,  NL,  UK 
(24) 
EEA, 
EU28 

BE,  CZ,  FI, 
DE, 
IE,  NO, 
SK,  SE,  NL, 
UK  (10)  EEA, 
EU28 

BG,  HR,  CZ, 
DK,  FI,  DE, 
EL, HU, IE, LV, 
LT, PL, PT, RO, 
SK,  ES,  SE, 
NL,  UK  (19) 
EEA, EU28 

HR,  CY,  CZ, 
DK, EE, FR, IE, 
LT, LU, MT, PL, 
SK,  ES,  UK 
(14)  

LT 

BE, BG, HR, 
CZ, DE, HU, 
LU,  NO,  PL, 
PT,  SK,  SE 
(12)  

AT,  BE,  HR,  CY, 
DK,  DE,  IS,  IT, 
LT,  MT,  PT,  SI, 
SE 
(13)  EEA, 
EU28 

 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Table 144:  Above average vulnerability in countries due to market-related and experience drivers (read 

Annex 4│ Incidence rate of vulnerability by sub-group 

last communication - finance) 

D  Indicator 

Read last communication from bank 

No response  
knowledge / 
read 

Reading last communication from bank 

Very 
easy 

Eas
y 

Difficult 

Very difficult  No response 

Don’t know 

4  27. Has problems 

comparing deals 
due to personal 
factors – finance 
sector* 

4  28. Has problems 

comparing deals 
due to market-
related factors – 
energy sector* 

4  29. Has problems 

comparing deals 
due to market-
related factors – 
on-line sector 

4  30. Has problems 

comparing deals 
due to market-
related factors – 
finance sector* 

In 
detai
l 

Glanced 
or skim 
read 

Saw what 
it was 

AT, CZ, DK, 
EE,  FI,  DE, 
IS,  LU,  NO, 
PT,  SE,  UK 
(12)  EEA, 
EU28 

CZ,  FI,  DE, 
LT,  NO,  PT, 
RO,  SE,  UK 
(9) 
EEA, 
EU28 

BG, CY, CZ, 
EE,  FI,  EL, 
IT,  LT,  MT, 
PT,  RO,  SK, 
SI,  ES,  NL 
(15)  

AT,  CZ,  EE, 
DE,  IS,  IE, 
LT,  LU,  NO, 
PT,  SE,  UK 
(12)  EEA, 
EU28 

Not at all 
AT,  CZ,  DK, 
EE,  FI,  DE, 
IS, 
IE,  LT, 
LU,  NO,  PT, 
SK,  SE,  NL, 
UK (16) EEA, 
EU28 

AT,  CZ,  FI, 
DE,  IE,  LT, 
SE,  UK  (8) 
EEA, EU28 

CZ, EE, IE, LT, 
LU,  PT,  RO, 
SK,  UK 
(9) 
EEA, EU28 

DK,  EE,  DE, 
IE,  RO,  UK 
(6)  

IE 

EE,  IS,  IE,  LU, 
NO, SK (6)  

BG,  HR,  CZ, 
EE,  FI,  FR, 
DE,  EL,  IE, 
IT,  LV,  LU, 
PL,  RO,  SK, 
SE,  UK  (17) 
EEA, EU28 

AT,  BG,  CY, 
CZ,  FI,  HU, 
IE, IT, LT, LU, 
PT,  SK,  ES 
(13)  

BG,  CY,  LT, 
PT,  SI,  ES,  UK 
(7)  

NL 

EE,  FI, 
NO (4)  

IE, 

AT,  CZ,  IE, 
RO (4)  

Note: * -= Following 11 countries not covered: BG, HR, CY, EL, HU, IT, LV, MT, PL, SI, ES. 
Source: London Economics 

AT,  BE,  CZ, 
DK,  FI,  DE, 
IS,  LT,  NO, 
SK,  SE,  NL 
(12)  EEA, 
EU28 
AT,  BE,  CZ, 
EE,  FI,  DE, 
LT,  LU,  NO, 
PT,  RO,  SK, 
SE,  NL,  UK 
(15)  EEA, 
EU28 
AT,  BE,  BG, 
HR, CY, CZ, 
EE,  FI,  FR, 
DE,  HU,  IE, 
IT,  LT,  LU, 
MT, NO, PL, 
RO, SK, ES, 
SE,  NL,  UK 
(24)  EEA, 
EU28 
AT,  BE,  CZ, 
DK,  FI,  FR, 
DE,  IS,  IE, 
LT,  NO,  PT, 
RO, SK, SE, 
NL, UK (17) 
EEA, EU28 

AT,  BE,  CZ,  DK, 
FR,  DE,  IS,  LT, 
NO,  PT,  RO,  SE, 
NL 
(13)  EEA, 
EU28 

AT, CZ, EE, FI, 
DE, IE, LT, LU, 
NO,  PT,  RO, 
SK,  SE,  NL, 
UK  (15)  EEA, 
EU28 

AT,  BE,  CZ,  DK, 
FR,  DE,  IS,  IE, 
NO,  PT,  RO,  NL, 
UK 
(13)  EEA, 
EU28 

DE, IE 

BE,  BG,  HR,  DK, 
FR,  DE,  HU,  IS, 
IE,  IT,  LT,  MT, 
PT,  SI,  UK  (15) 
EEA, EU28 

BG,  CY,  CZ, 
FI,  EL,  LT,  PT, 
RO,  SK,  SI, 
ES, UK (12)  

AT,  BE,  CZ,  EE, 
FI,  FR,  DE,  IS, 
IE,  LT,  LU,  NO, 
PT,  RO,  NL,  UK 
(16) EEA, EU28 

CZ, EE, IE, NO 
(4)  

672 

 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
Annex 4│ Incidence rate of vulnerability by sub-group 

Access drivers 

Table 145:  Above average vulnerability in countries due to access drivers (type) 
D  Indicator 

Infrequent internet use (i.e. less than once a month) 

 Search 

 Compare prices 

 Banking 

 Purchases 

 Selling 

 Social 

 Email 

1  2. Overpaid for services due to 
being unable to use certain 
payment methods 
2  4. Perception of own 

vulnerability due to personal 
characteristics 
In this section referred to as 
‘Feels vulnerable to 'a great 
extent' due to employment 
situation’ 

2  5. Perception of own 

vulnerability due to personal 
characteristics 
In this section referred to as 
‘Feels vulnerable to 'a great 
extent' due to financial 
circumstances’ 

2  10. Perception of own 

vulnerability due to personal 
characteristics 
In this section referred to as 
‘Feels vulnerable to 'a great 
extent' due to any personal 
characteristic’ 

3  15. Has problems comparing 

deals due to information-related 
factors- energy sector* 
3  17. Has problems comparing 

deals due to information-related 
factors – financial sector* 
4  21. Does not compare product 

deals - energy sector 

673 

FI, DE, IE, NO, SE, NL, 
UK (7)  

DE, NO 

FI,  DE,  IS,  LU, 
NO, NL (6)  

DE 

NO 

DK,  FI,  DE,  NO, 
SE, NL, UK (7)  

HU, UK 

SK 

UK 

HU, UK 

HR, CY, EE, LT, MT, SI, 
SE (7)  

HR, EE, LT, SI (4)  

HR,  EE,  IS,  LT, 
MT, SI, SE (7)  

EE,  IS,  LT,  SI  (4) 
EEA, EU28 

EE 

HR, CY, EE, LT, MT, 
RO, SE (7)  

BE,  HR,  CY,  EE,  HU, 
LT,  MT,  NO,  RO,  SK, 
SI,  SE,  NL,  UK  (14) 
EEA, EU28 

HR, DK, LT, SK (4)  

HR,  IS,  LT,  MT, 
NO, SK,  SE,  NL 
(8) EEA, EU28 

LT, SK, UK 

HR, LT 

DE, SK 

SE 

FI,  DE,  NO,  NL 
(4)  

IE, NO 

CZ, UK 

BE, HR, EE, HU, LT, 
MT,  NO,  RO,  SK, 
SE,  NL,  UK  (12) 
EEA, EU28 

CZ,  FR, LU,  SK,  SE 
(5)  

DE, NO, SK 

AT,  BE,  HR,  CZ,  DK, 
EE,  FI,  FR,  DE,  EL,  IE, 
IT, LV, LT, NO, PL, PT, 
RO, SI, ES, SE, NL, UK 
(23) EEA, EU28 

AT,  BE,  HR,  CZ, 
DK, EE, FI, FR, DE, 
EL,  IE,  IT,  LV,  LT, 
NO,  PL,  PT,  RO, 
SK, SI, ES, SE, NL, 
EEA, 
UK 
EU28 

(24) 

NO 
AT, BE, HR, CZ, 
DK,  EE,  FI,  DE, 
EL,  IE,  IT,  LV, 
LT,  NO,  PL,  PT, 
SI,  ES,  SE,  NL, 
UK  (21)  EEA, 
EU28 

CZ, IS 

SK 

AT,  BE,  HR,  CZ, 
DK, EE, FI, DE, EL, 
IE,  IT,  PL,  PT,  SK, 
SI,  ES,  SE,  NL,  UK 
(19) EEA, EU28 

HR,  CZ, 
DE, 
IE, 
IT,  PT, 
SI,  UK 
(8)  

AT,  HR,  CZ,  EE, 
FI,  DE,  EL,  IE, 
IT,  LV,  LT,  PL, 
PT,  RO,  SI,  ES, 
(18) 
NL,  UK 
EEA, EU28 

AT,  BE,  HR,  CZ, 
DK, EE, FI, DE, EL, 
IE,  IT,  LV,  LT,  PL, 
PT, RO, SI, ES, SE, 
NL,  UK  (21)  EEA, 
EU28 

 
 
 
  
  
  
  
  
  
  
  
  
  
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 145:  Above average vulnerability in countries due to access drivers (type) 
D  Indicator 

Infrequent internet use (i.e. less than once a month) 

4  22. Finds it very difficult to 

compare product deals - energy 
sector 

4  23. Does not compare product 

deals - finance sector 

4  24. Finds it very difficult to 

compare product deals - finance 
sector 

4  25. Has problems comparing 

deals due to personal factors – 
energy sector* 

4  26. Has problems comparing 

deals due to personal factors – 
on-line sector 

4  27. Has problems comparing 

deals due to personal factors – 
finance sector* 

 Search 

 Compare prices 

AT,  BE,  HR,  CZ,  DK, 
EE, FI, FR, DE, EL, HU, 
IE, IT, LV, LT, LU, NO, 
PL, PT, RO, SK, SI, ES, 
(25)  EEA, 
NL,  UK 
EU28 

AT,  BE,  HR,  CZ, 
DK, EE, FI, FR, DE, 
EL,  HU,  IE,  IT,  LV, 
LT, NO, PL, PT, RO, 
SK, SI, ES, SE, NL, 
EEA, 
UK 
EU28 

(25) 

AT,  BE,  BG,  HR,  CY, 
CZ, DK, EE, FI, FR, DE, 
EL,  HU,  IS,  IE,  IT,  LV, 
LT, LU, MT, NO, PL, PT, 
RO,  SK,  SI,  ES,  SE, 
NL,  UK 
(30)  EEA, 
EU28 
AT,  BG,  HR,  CZ,  DK, 
EE, FI, DE, EL, HU, IE, 
IT, LV, LT, LU, MT, PL, 
RO,  SK,  SI,  ES,  SE, 
NL,  UK 
(24)  EEA, 
EU28 

FI,  DE,  LU,  NO,  PT, 
RO,  SK,  NL,  UK  (9) 
EEA, EU28 

HR, CY, DK, EE, FI, FR, 
DE, IS, IT, LV, LT, MT, 
PL, PT, SK, SI, ES, NL, 
UK (19) EEA, EU28 

BE, IS, LT, PT, RO, SK, 
SE, UK (8) EEA, EU28 

AT,  BE,  BG,  HR, 
CY, CZ, DK, EE, FI, 
FR, DE, EL, HU, IS, 
IE,  IT,  LV,  LT,  LU, 
MT,  NO,  PL,  PT, 
RO, SK, SI, ES, SE, 
NL,  UK  (30)  EEA, 
EU28 

AT,  BG,  HR,  CZ, 
EE, DE,  HU,  IE,  IT, 
LV, LT, LU, PL, RO, 
SK,  SI,  ES,  NL,  UK 
(19) EEA, EU28 
AT, BE, DK, FI, DE, 
IE, NO, PT, RO, SK, 
SE,  NL,  UK  (13) 
EEA, EU28 
AT,  HR,  CY,  DK, 
EE,  FI,  FR,  DE,  EL, 
HU,  IS,  IE,  IT,  LV, 
LT, LU, MT, PL, PT, 
RO, SK, SI, ES, SE, 
NL,  UK  (26)  EEA, 
EU28 
AT, CZ, FR, DE, IE, 
LT, PT, RO, SK, SE, 
NL,  UK  (12)  EEA, 
EU28 

 Banking 
AT,  BE,  HR,  CZ, 
DK,  EE,  FI,  FR, 
DE,  EL,  HU,  IE, 
IT,  LV,  LT,  LU, 
NO,  PL,  PT,  SK, 
SI,  ES,  SE,  NL, 
(25)  EEA, 
UK 
EU28 
AT,  BE,  BG, 
HR,  CY,  CZ, 
DK,  EE,  FI,  FR, 
DE,  EL,  HU,  IS, 
IE,  IT,  LV,  LT, 
LU, MT, NO, PL, 
PT, RO, SK,  SI, 
ES,  SE,  NL,  UK 
(30) 
EEA, 
EU28 
AT, BE, CZ, DK, 
EE,  FI,  DE,  IE, 
LV, LT, LU, NO, 
PL,  SK,  SI,  SE, 
NL,  UK 
(18) 
EEA, EU28 

BE,  FI,  IS,  IE, 
NO,  PT,  UK  (7) 
EEA, EU28 
AT, BE, HR, EE, 
FI,  DE,  EL,  IS, 
IT,  LT,  LU,  MT, 
NO, PL, PT, RO, 
SK,  ES,  UK 
(19) 
EEA, 
EU28 
AT,  FI,  IE,  LT, 
LU, NO, PT, SK, 
NL,  UK 
(10) 
EEA, EU28 

 Purchases 

 Selling 

 Social 

 Email 

AT,  HR,  CZ,  DK, 
EE,  FI,  DE,  EL, 
IE,  IT,  LV,  LT, 
NO,  PL,  PT,  SI, 
(17)  EEA, 
UK 
EU28 

AT,  HR,  CZ,  DK, 
EE,  FI,  DE,  EL, 
HU,  IE,  IT,  LV, 
LT,  LU,  NO,  PL, 
PT,  RO,  SK,  SI, 
ES,  NL,  UK  (23) 
EEA, EU28 

AT,  BE,  HR,  CZ, 
DK, EE, FI, FR, DE, 
EL,  HU,  IE,  IT,  LV, 
LT, LU, NO, PL, PT, 
RO, SK, SI, ES, SE, 
NL,  UK  (26)  EEA, 
EU28 

AT,  BE,  BG,  HR, 
CZ, DK, EE, FI, FR, 
DE,  EL,  HU,  IE,  IT, 
LV, LT, LU, NO, PL, 
PT, RO, SK, SI, ES, 
SE,  NL,  UK  (27) 
EEA, EU28 

BG,  CZ, 
DE, 
IT, 
PL,  RO, 
SI (7)  

AT, CZ, EE, DE, IE, 
LT, LU, UK (8)  

AT,  BG,  HR,  CZ, 
DK,  EE,  FI,  DE, 
EL,  HU,  IE,  IT, 
LV,  LT,  LU,  MT, 
PL,  PT,  RO,  SK, 
SI, ES (22) EEA, 
EU28 
AT,  BG,  HR,  CZ, 
EE,  HU,  IE,  IT, 
LV,  LT,  LU,  MT, 
PL,  RO,  SK,  SI, 
ES,  UK 
(18) 
EEA, EU28 

AT,  NO,  UK  (3) 
EEA, EU28 

PT, RO, UK 

AT,  HR,  EE, EL,  IE, 
IT, LT, MT, NO, PT, 
SE (11)  

EL,  HU,  IS,  IT, 
LV,  LU,  MT,  PT, 
UK (9)  

LT, UK (2) EU28 

LT, PT, RO 

AT,  BE,  BG,  HR, 
CY, CZ, DK, EE, FI, 
FR, DE, EL, HU, IS, 
IE,  IT,  LV,  LT,  LU, 
MT,  NO,  PL,  PT, 
RO, SK, SI, ES, SE, 
NL,  UK  (30)  EEA, 
EU28 
AT,  BG,  HR,  CZ, 
DK, EE, FI, DE, HU, 
IE,  IT,  LV,  LT,  LU, 
MT, PL, RO, SK, SI, 
ES,  SE,  NL,  UK 
(23) EEA, EU28 

AT, IE, PT, RO, SK, 
SE,  UK  (7)  EEA, 
EU28 

AT,  BE,  HR,  CZ, 
DK, FI, FR, DE, HU, 
IS,  IE,  IT,  LV,  MT, 
NO,  PL,  SK,  ES, 
SE,  UK  (20)  EEA, 
EU28 

DE, IE, LT, PT, RO, 
SK,  SE,  UK  (8) 
EEA, EU28 

674 

 
 
 
  
  
  
  
  
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 145:  Above average vulnerability in countries due to access drivers (type) 
D  Indicator 

Infrequent internet use (i.e. less than once a month) 

 Search 

 Compare prices 

 Banking 

 Purchases 

 Selling 

 Social 

 Email 

4  28. Has problems comparing 
deals due to market-related 
factors – energy sector* 
4  29. Has problems comparing 
deals due to market-related 
factors – on-line sector 

4  30. Has problems comparing 
deals due to market-related 
factors – finance sector* 

DE, IE 

EE, DE, LU, NO, 
UK (5)  

HR, CY, EE, FR, DE, IT, 
LV,  LU,  NO,  PT,  SK, 
ES,  UK 
(13)  EEA, 
EU28 

AT, HR, CY, EE, FI, 
DE,  EL,  HU,  IE,  IT, 
LV, LT, PT, SK, ES, 
SE 
EEA, 
EU28 

(16) 

EE, LT, LU, NO, 
SK, UK (6)  

AT,  BG,  CY,  CZ, 
EE, EL, IE, LT (8)  

AT, CZ 
AT,  BE,  HR,  CZ, 
DK,  EE,  FR,  EL, 
IT,  LV,  LT,  LU, 
NO,  PL,  RO,  ES, 
SE,  UK 
(18) 
EEA, EU28 

LU, SE 

HR,  CY,  CZ,  DK, 
FR, IT, SK (7) EEA, 
EU28 

EE 

IE 

AT 

IE 

NO 

AT, RO, SE 

Note: * -= Following 11 countries not covered: BG, HR, CY, EL, HU, IT, LV, MT, PL, SI, ES. 
Source: London Economics 

675 

 
 
 
  
  
  
  
  
 
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 146:  Above average vulnerability in countries due to access drivers (frequency) 
D  Indicator 

Uses internet at least once per month (out of 7 activities) 

1  2. Overpaid for services due 
to being unable to use 
certain payment methods 

2  4. Perception of own 

vulnerability due to personal 
characteristics 
In this section referred to as 
‘Feels vulnerable to 'a great 
extent' due to employment 
situation’ 

2  5. Perception of own 

vulnerability due to personal 
characteristics 
In this section referred to as 
‘Feels vulnerable to 'a great 
extent' due to financial 
circumstances’ 

2  10. Perception of own 

vulnerability due to personal 
characteristics 
In this section referred to as 
‘Feels vulnerable to 'a great 
extent' due to any personal 
characteristic’ 

3  15. Has problems comparing 
deals due to information-
related factors- energy 
sector* 

3  17. Has problems comparing 
deals due to information-
related factors – financial 
sector* 

0 

1 

DK,  FI,  DE,  IS,  IE, 
NO, SE, NL, UK (9)  

FI,  FR,  DE, 
LU,  MT,  RO, 
SK, SE (8)  

2 
AT,  CY,  FR,  DE, 
IT,  LT,  NO,  PL, 
PT,  RO,  SK,  UK 
(12)  

AT,  CZ,  EE, 
FR,  DE,  EL, 
MT,  ES,  UK 
EEA, 
(9) 
EU28 

BE,  HR,  CZ,  EE, 
IE,  LT,  PT,  RO, 
SK, SI (10)  

HU, UK 

3 

4 

5 

6 

HR, EE, UK 

EE, LU, MT 

7 
AT,  BE,  BG,  CY,  DK, 
EE,  FR,  EL,  IT,  MT, 
PL,  PT,  ES,  SE,  UK 
(15) EEA, EU28 

BE,  HR,  NL,  UK 
(4)  

DK,  DE,  IE,  LT,  SI, 
ES, SE, NL (8)  

EE, PL 

AT, DK,  DE,  LU,  NO, 
SE (6)  

HR,  CY,  EE,  LT,  MT, 
SE, UK (7)  

CZ,  DE,  EL, 
MT,  RO,  ES 
(6)  

BE,  HR,  CZ,  EE, 
FI,  FR,  DE,  EL, 
HU,  IE,  LT,  PL, 
PT,  SI,  ES  (15) 
EEA, EU28 

BE,  DK,  FI,  EL, 
HU,  IS,  IE,  IT, 
SI,  ES,  SE,  NL 
(12)  

BG,  DK,  FR,  DE,  IE, 
IT,  LV,  LT,  LU,  PL, 
SI,  ES  (12)  EEA, 
EU28 

EE,  FI,  IS, 
PL,  SK,  SE 
(6)  

LU  AT, CZ, SE, UK (4)  

HR,  CY,  EE,  HU,  LT, 
MT,  NO,  RO,  SK,  SE, 
NL,  UK  (12)  EEA, 
EU28 

AT,  BE,  EE, 
FR,  DE,  EL, 
MT,  PT,  RO, 
ES 
(10) 
EEA, EU28 

BE,  HR,  CZ,  DK, 
FR,  DE,  LT,  PL, 
PT,  SI,  ES  (11) 
EEA, EU28 

EL, IS, NL 

DK, IS, IE, IT, LT, ES 
(6)  

FI 

AT,  DK,  LV,  PL,  SE 
(5)  

FR, SK, UK 

CZ,  FI,  IS, 
NL (4)  

DK,  FI,  FR,  IE, 
RO,  SE  (6)  EEA, 
EU28 

EE,  DE,  IS,  IE, 
NO,  UK 
(6) 
EEA, EU28 

SK 

IE, NO 

FI, 
LT 

IE, LT 

BE, NO, SK 

BE, RO 

DE,  PT,  RO,  UK 
(4)  

IS, UK 

IE 

676 

 
 
 
 
  
  
  
  
  
  
  
Annex 4│ Incidence rate of vulnerability by sub-group 

Table 146:  Above average vulnerability in countries due to access drivers (frequency) 
D  Indicator 

Uses internet at least once per month (out of 7 activities) 

2 

3 

4 

5 

6 

7 

0 

AT,  BE,  HR,  CZ,  DK, 
EE, FI, DE, EL, IE, IT, 
LV,  LT,  PL,  PT,  RO, 
SI,  ES,  SE,  NL,  UK 
(21) EEA, EU28 
AT,  BE,  HR,  CZ,  DK, 
EE,  FI,  FR,  DE,  EL, 
HU,  IE,  IT,  LV,  LT, 
LU,  NO,  PL,  PT,  RO, 
SK,  SI,  ES,  NL,  UK 
(25) EEA, EU28 
AT,  BE,  BG,  HR,  CY, 
CZ,  DK,  EE,  FI,  FR, 
DE,  EL,  HU,  IE,  IT, 
LV,  LT,  LU,  MT,  NO, 
PL,  PT,  RO,  SK,  SI, 
ES, SE, UK (28) EEA, 
EU28 
AT,  BG,  HR,  CZ,  DK, 
EE,  FI,  DE,  HU,  IE, 
IT,  LV,  LT,  LU,  MT, 
NO,  PL,  RO,  SK,  SI, 
ES,  SE,  NL,  UK  (24) 
EEA, EU28 

AT,  BE,  DE,  IS,  PT, 
RO,  SK,  NL,  UK  (9) 
EEA, EU28 

1 
AT,  HR,  CZ, 
EE,  FI,  FR, 
DE,  EL,  HU, 
IE,  LV,  LT, 
LU,  RO,  SK, 
SI,  SE,  NL, 
(19) 
UK 
EEA, EU28 
AT,  BG,  HR, 
CY,  CZ,  FR, 
DE,  HU,  IS, 
IE,  LV,  LT, 
NL,  UK  (14) 
EEA, EU28 
BG,  CY,  CZ, 
DK,  EE,  FR, 
EL,  IS,  IE, 
LV,  LT,  LU, 
MT,  RO,  NL, 
UK 
(16) 
EEA, EU28 

BG,  CY,  DK, 
IS,  IE,  LV, 
LT,  LU,  RO, 
NL, UK (11)  
CZ,  FI,  FR, 
IS,  IE,  SK, 
NL,  UK  (8) 
EEA, EU28 

BE,  HR,  DK,  FI, 
DE,  HU,  IE,  LV, 
LT,  NO,  PL,  PT, 
RO,  SI,  ES,  SE, 
NL,  UK  (18)  EEA, 
EU28 

HR,  DK,  FI,  FR, 
DE,  EL,  HU,  IS, 
PT,  SK,  SI,  ES, 
SE,  NL,  UK  (15) 
EEA, EU28 
AT,  BE,  HR,  CZ, 
DK,  EE,  FI,  DE, 
EL, HU, IS, IE, IT, 
LV,  LT,  LU,  NO, 
PL,  RO,  SK,  SI, 
SE,  NL,  UK  (24) 
EEA, EU28 

FR,  DE,  EL,  LU, 
PT,  SK,  NL,  UK 
(8) EEA, EU28 
AT,  BE,  DK,  FI, 
DE,  IE,  LT,  LU, 
NL,  UK  (10)  EEA, 
EU28 

BE,  HR,  CY,  HU,  LV, 
MT,  NO,  SK,  ES,  NL 
(10) EEA, EU28 

AT,  CZ,  FI, 
FR,  IS,  IE, 
MT,  SK,  NL 
(9) 
EEA, 
EU28 

AT,  BE,  CZ,  DK, 
FR,  HU,  IS,  IE, 
IT,  LT,  LU,  MT, 
SI,  ES,  SE,  NL, 
UK 
(17)  EEA, 
EU28 

AT, BE,  CZ, DK, 
FI,  FR,  DE,  EL, 
HU,  IE,  IT,  LV, 
LT,  NO,  PL,  PT, 
RO,  SE,  NL,  UK 
EEA, 
(20) 
EU28 

BE,  CZ, DK,  EE, 
FI,  LV,  LU,  NO, 
SK, SE, NL (11)  
BE, CY, CZ, DK, 
FI,  FR,  DE,  EL, 
HU,  IS,  IE,  IT, 
LV,  LT,  LU,  PL, 
PT,  RO,  ES,  SE, 
NL,  UK 
(22) 
EEA, EU28 

AT,  BE,  CZ,  FI,  IE, 
IT,  LV,  NO,  PL,  SK, 
ES,  SE,  NL,  UK  (14) 
EEA, EU28 

AT,  DK,  FI,  DE,  LV, 
LU, SE (7)  

AT,  DK,  EE,  FI,  DE, 
HU,  IS,  IE,  IT,  LU, 
NO,  PT,  SK,  ES,  SE, 
NL (16) EEA, EU28 

AT,  BE,  CZ,  EE, 
FR,  IS,  LV,  PL, 
NL,  UK 
(10) 
EEA, EU28 

IE, LU 

AT,  BE,  DK,  EE, 
DE, IE, SE (7)  
AT, BE, BG, HR, 
CY,  DK,  EE,  FI, 
DE,  EL,  HU,  IS, 
IT,  LT,  LU,  NO, 
PL,  PT,  RO,  SK, 
SI,  ES,  SE,  UK 
(24) 
EEA, 
EU28 

AT,  BE,  FI,  IE,  NO, 
NL (6)  

AT,  DK,  EE,  FI,  FR, 
DE,  EL,  IE,  IT,  LV, 
LT,  LU,  PL,  PT,  SK, 
SE,  NL  (17)  EEA, 
EU28 

UK 

MT 

IS 

4  21. Does not compare 
product deals - energy 
sector 

4  22. Finds it very difficult to 
compare product deals - 
energy sector 

4  23. Does not compare 
product deals - finance 
sector 

4  24. Finds it very difficult to 
compare product deals - 
finance sector 

4  25. Has problems comparing 

deals due to personal 
factors – energy sector* 

4  26. Has problems comparing 

deals due to personal 
factors – on-line sector 

677 

 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Table 146:  Above average vulnerability in countries due to access drivers (frequency) 
D  Indicator 

Uses internet at least once per month (out of 7 activities) 

Annex 4│ Incidence rate of vulnerability by sub-group 

4  27. Has problems comparing 

deals due to personal 
factors – finance sector* 

4  28. Has problems comparing 
deals due to market-related 
factors – energy sector* 
4  29. Has problems comparing 
deals due to market-related 
factors – on-line sector 

0 

1 

LT,  PT,  RO,  SK,  NL, 
UK (6) EEA, EU28 

BE, SK, NL 

CZ,  IS,  IE, 
LU,  SK,  UK 
(6) 
EEA, 
EU28 

BE,  DK,  LU, 
UK (4)  

BE,  HR,  CY,  FR,  SK, 
(7)  EEA, 
ES,  NL 
EU28 

CZ, UK 

2 
AT,  CZ,  DK,  FI, 
FR,  DE,  IE,  LT, 
PT,  RO,  SE,  NL, 
UK 
(13)  EEA, 
EU28 

AT,  DK,  DE,  IE, 
PT,  RO,  SE,  UK 
(8) EEA, EU28 

BE,  HR,  DK,  FI, 
DE,  HU,  IE,  IT, 
LU,  SE,  UK  (11) 
EEA, EU28 

4  30. Has problems comparing 
deals due to market-related 
factors – finance sector* 

AT, BE, FI, FR (4)  
Note: * -= Following 11 countries not covered: BG, HR, CY, EL, HU, IT, LV, MT, PL, SI, ES. 
Source: London Economics 

DK, PT, SE 

FI, RO, UK 

3 

4 

5 

6 

7 

AT,  CZ,  FR,  DE, 
IS,  IE,  LT,  SK 
(8)  

EE,  DE,  IS,  IE, 
NO, UK (6)  
AT,  CY,  EE,  DE, 
EL,  IS,  IT,  LT, 
LU,  NO,  PT,  SI, 
(14) 
ES,  SE 
EEA, EU28 

DE, IE, SE, NL (4)  

CZ, DE, LT 

LT, 
PT 

LT, PT 

AT,  BG,  CZ,  DK,  EE, 
FI,  DE,  EL,  LV,  MT, 
NO, UK (12)  

EE,  IE,  LT, 
MT, PL (5)  

RO 

AT,  IS,  IE,  LT, 
SK (5)  

FI, NO 

PT 

678 

 
 
 
  
  
  
  
  
  
  
 
 
Annex 5│ Sector-level information on where consumers get information when comparing 
deals 

Annex 5  Sector-level  information  on  where  consumers  get 

information when comparing deals 

The  present  annex  discusses  the  `Getting  information  from  a  few  sources’  indicator, 
which  is  operationalised  in  the  survey  by  the  `Compares  product  deals  by  using 
information  from  advertisements  only’  indicator389  within  dimension  3.  As  survey 
respondents are asked about a variety of other information gathering channels, namely: 

  online search; 

 

comparison sites; 

  national authorities and/or consumer organisations; 

 

friends and/or family;  

  advertisements; and 

  other, 

A comparison between all channels is drawn in this section, although getting information 
from ‘advertisements only’ is the indicator of consumer vulnerability.  The prevalence  of 
the  aforementioned  product  information  gathering  channels  in  the  EU28  as  well  as  by 
Member State is discussed below, organised by sector. 

A5.1  Energy sector 

  Online  search  and  comparison  sites  in  particular  are  the  most  popular 
information channels when comparing energy deals, used on average390 by 82% 
and  51%  of  respondents  respectively.  The  country  with  smallest  share  of 
consumers  who  use  online  search  to  compare  energy  deals  is  Romania  with 
55%,  and  Luxembourg  with  21%  of  respondents  who  use  comparison  sites  in 
particular.  The  country  where  most  consumers  research  energy  deals  online  is 
Germany  -  89%  and  where  most  consumers  compare  energy  deals  on 
designated websites – the Czech Republic with 65%. 

  The  second  most  popular  channel  for  comparing  energy  deals  is  ‘friends  and 
family’,  averaging  26%  of  use  on  average  across  the  EU28.  Respondents  from 
Romania are the ones who use this channel the most of all countries - 50%. This 
method for product comparison is least popular in Finland with a 15% share. 
  On  average,  17%  of  respondents  use  national  authorities  and  consumer 
organisations.  This  channel  holds  its  lowest  share  in  Iceland  with  8%  and  its 
highest – in Austria with 31%. 

  `Advertisements  only’  constitutes  the  smallest  share  -  4%  on  average  –  of 
comparison channels. This method is comparatively more popular in Luxembourg 
and  Portugal  with  around  9%  of  respondents  reporting  to  use  it  exclusively  for 
comparing  energy  deals,  and  least  popular  –  in  the  Czech  Republic,  Germany, 
Sweden and the Netherlands with around 2%. 

389 This indicator is part of dimension 3. 
390  An  average  across  17  of  the  EU28  member  states,  excluding  Bulgaria  (BG),  Croatia  (HR), 
Cyprus  (CY),  Greece  (EL),  Hungary  (HU),  Italy  (IT),  Latvia  (LV),  Malta  (MT),  Poland  (PL), 
Slovenia  (SI)  and  Spain  (ES),  where  the  relevant  survey  question  for  the  energy  sector  was 
not asked. 

679 

 
 
 
                                                 
Online search 

All 

Compa
-rison 
-sites 
51% 
49% 
52% 

Annex 5│ Sector-level information on where consumers get information when comparing 
deals 

Table 147:  Indicators of ‘getting information from few sources’: energy 

sector 

Share of consumers who use the following sources of information : 

Friends 
and/or 
family 

National 
authorities 
and/or 
consumer 
organisati-
ons 

Other  Advertisem
-ents ONLY 

Observatio
-ns 

84% 

10% 

41% 

4% 
4% 
3% 

4% 
3% 
5% 

82% 
84% 
82% 

17% 
31% 
28% 

26% 
24% 
18% 

9,138 
595 
582 

2% 
5% 
4% 
4% 
5% 
3% 

2% 
4% 
4% 
6% 
6% 
2% 

562 
546 
291 
622 
359 
712 

88% 
84% 
84% 
85% 
73% 
89% 

14% 
13% 
21% 
13% 
23% 
13% 

41% 
18% 
31% 
15% 
28% 
24% 

65% 
51% 
58% 
52% 
27% 
59% 

EU28 
AT 
BE 
BG 
HR 
CY 
CZ 
DK 
EE 
FI 
FR 
DE 
EL 
HU 
IE 
IT 
LV 
LT 
LU 
MT 
PL 
PT 
RO 
SK 
SI 
ES 
SE 
NL 
UK 
IS 
NO 
Note: Results weighted using cross-country weights for the EU28 measure and country weights for the country 
measures. In contrast to Table 12, the base of the indicators here excludes those respondents who have never 
compared deals. Questions only asked in some countries/via some modes.  
Source: London Economics analysis  

48% 
57% 
58% 
34% 
40% 

21% 
16% 
21% 
29% 
22% 

12% 
25% 
14% 
8% 
24% 

84% 
87% 
83% 
83% 
83% 

7% 
6% 
4% 
13% 
6% 

629 
669 
746 
90 
604 

2% 
2% 
4% 
4% 
5% 

34% 
24% 
50% 

39% 
50% 
48% 

25% 
22% 
18% 

61% 
55% 
75% 

704 
421 
524 

8% 
7% 
3% 

9% 
4% 
4% 

35% 
21% 

34% 
47% 

21% 
21% 

79% 
68% 

270 
193 

3% 
6% 

4% 
9% 

32% 

713 

5% 

1% 

A5.2  Online sector 

  Online  search  is  used  on  average  by  84%  of  respondents  in  the  online  sector, 
which is higher than its share in the energy sector; yet, the share of respondents 

680 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 5│ Sector-level information on where consumers get information when comparing 
deals 

who  use  comparison  tools  to  search  online  is  only  38%,  compared  to  51%  for 
energy391.  

  The  share  of  on-line  search  is  largest  in  Croatia  with  95%,  and  that  of 

comparison sites – in Bulgaria and the UK with 51% .  

  The  shares  of  online  search  and  comparison  sites  are  lowest  Malta  with 

51% and 4%respectively.  

  Friends and family is the second-most used channel for comparing product deals 
in the on-line sector as well. The average proportion of respondents  to use this 
channel  is  29%;  with  the  highest  proportion  in  Slovakia  (48%)  and  the  lowest 
proportion – in Malta (17%). 

  National authorities and consumer organisations has a take-up of 9% on average 
in  the  on-line  sector,  lower  than  the  energy  sector.  This  sector  is  highest  in 
Portugal with 20%, and lowest in Hungary with 4%. 

  On  average  only  5%  of  respondents  who  compare  deals  use  advertisements 
only, but there is a large variation between countries (ranging from 2% to 24%). 

Table 148:  Indicators of ‘getting information from few sources’: On-line 

sector 

Share of consumers who use the following sources of information : 

Online search 

All 

84% 
83% 
79% 
86% 
95% 
52% 
81% 
86% 
82% 
86% 
81% 
85% 
87% 
81% 
82% 
87% 
74% 
88% 
60% 
51% 
79% 
84% 
74% 

Compa
-rison 
-sites 
38% 
35% 
35% 
51% 
39% 
20% 
42% 
44% 
39% 
38% 
23% 
49% 
41% 
22% 
33% 
40% 
20% 
27% 
13% 
4% 
22% 
40% 
23% 

National 
authorities 
and/or 
consumer 
organisatio-
ns 

Friends 
and/or 
family 

Other  Advertisem
-ents ONLY 
(indicator 
14) 

Observatio
-ns 

9% 
14% 
15% 
11% 
5% 
6% 
9% 
7% 
15% 
6% 
10% 
7% 
9% 
4% 
10% 
11% 
9% 
7% 
11% 
9% 
5% 
20% 
7% 

29% 
29% 
23% 
47% 
41% 
41% 
45% 
21% 
38% 
22% 
33% 
29% 
44% 
29% 
33% 
24% 
34% 
26% 
41% 
17% 
33% 
34% 
38% 

4% 
3% 
6% 
2% 
5% 
4% 
3% 
5% 
7% 
5% 
3% 
5% 
6% 
3% 
2% 
2% 
5% 
3% 
4% 
20% 
5% 
7% 
4% 

5% 
6% 
4% 
4% 
2% 
24% 
3% 
4% 
7% 
5% 
5% 
3% 
5% 
9% 
5% 
4% 
13% 
4% 
17% 
18% 
8% 
4% 
9% 

15,566 
695 
567 
475 
635 
231 
602 
732 
306 
680 
623 
737 
526 
601 
748 
572 
304 
565 
345 
266 
633 
589 
474 

EU28 
AT 
BE 
BG 
HR 
CY 
CZ 
DK 
EE 
FI 
FR 
DE 
EL 
HU 
IE 
IT 
LV 
LT 
LU 
MT 
PL 
PT 
RO 

391 This could be due to the sampling differences in the two sectors – the question was not asked 

for the energy sector in 11 of the countries. 

681 

 
 
 
 
                                                 
Annex 5│ Sector-level information on where consumers get information when comparing 
deals 

81% 
84% 
83% 
86% 
85% 
88% 
79% 
85% 

48% 
37% 
27% 
38% 
47% 
51% 
26% 
40% 

SK 
SI 
ES 
SE 
NL 
UK 
IS 
NO 
Note: Results weighted using cross-country weights for the EU28 measure and country weights for the country 
measures. In contrast to Table 12, the base of the indicators here excludes those respondents who have never 
compared  deals.  Questions  only  asked  in  some  countries/via  some  modes.  Missing  data  is  due  to  particular 
survey questions not being asked in all countries.  
Source: London Economics analysis of survey data 

48% 
40% 
30% 
23% 
21% 
19% 
32% 
22% 

8% 
9% 
7% 
7% 
16% 
9% 
6% 
16% 

3% 
1% 
3% 
7% 
5% 
2% 
11% 
5% 

644 
349 
610 
662 
710 
685 
352 
577 

3% 
7% 
6% 
2% 
2% 
3% 
5% 
5% 

A5.3  Finance sector 

  Online search makes up for the largest proportion of product comparison in the 
finance  sector  -  66%  on  average,  with  a  highest  take-up  in  the  Czech  Republic 
(80%)  and  the  lowest  in  Luxembourg  (44%).  The  average  use  of  online  search 
for  comparing  financial  products  is  much  lower  than  for  the  energy  and  online 
sectors. This is also reflected in the use of comparison sites, which is reported by 
29%  of  all  respondents,  the  largest  share  being  in  Denmark  (40%)  and  the 
lowest – once again in Luxembourg (6%).  

  Unlike the energy and on-line sectors, with an average of 40%, the second most 
popular channel for the comparison of financial product deals is through the bank 
or a similar institution. This proportion is highest in Estonia (40%), and lowest in 
the Netherlands (24%). 

  Friends  and  family  are  used  by  26%  of  respondents,  a  share  similar  to  that  in 

the energy and online sectors. 

  National authorities and consumer organisations are on average used only by an 

average of 10% of respondents. 

  Advertisements  only  are  used  by  3%  of  respondents  to  compare  financial 
products.  This  share  is  highest  in  Luxembourg  with  8%  and  lowest  in  Sweden 
with 1%. 

Table 149:  Indicators of ‘getting information from few sources’: financial 

sector 

Share of consumers who use the following sources of information : 

Online search 

All 

66% 
72% 
62% 

Compa
-rison 
-sites 
29% 
31% 
22% 

National 
authorities 
and/or 
consumer 
organisatio-
ns 

Friends 
and/or 
family 

Bank 
and/or 
Other 

Advertisem
-ents ONLY 
(indicator 
15) 

Observatio
-ns 

10% 
12% 
16% 

26% 
27% 
26% 

41% 
42% 
36% 

EU28 
AT 
BE 
BG 
HR 
CY 
CZ 
DK 

80% 
74% 

38% 
40% 

9% 
10% 

37% 
21% 

32% 
32% 

3% 
3% 
5% 

3% 
3% 

9,386 
638 
493 

626 
601 

682 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 5│ Sector-level information on where consumers get information when comparing 
deals 

5% 

624 

10% 

38% 

70% 

28% 

26% 

7% 
7% 

71% 
44% 

22% 
6% 

3% 
4% 
2% 
3% 

289 
584 
491 
660 

10% 
6% 
12% 
8% 

70% 
72% 
56% 
69% 

65% 
46% 
50% 
38% 

31% 
21% 
25% 
28% 

25% 
25% 
16% 
37% 

EE 
FI 
FR 
DE 
EL 
HU 
IE 
IT 
LV 
LT 
LU 
MT 
PL 
PT 
RO 
SK 
SI 
ES 
SE 
NL 
UK 
IS 
NO 
Note: Results weighted using cross-country weights for the EU28 measure and country weights for the country 
measures. In contrast to Table 12, the base of the indicators here excludes those respondents who have never 
compared deals. Questions only asked in some countries/via some modes.  
Source: London Economics analysis of survey data 

25% 
31% 
31% 
20% 
29% 

25% 
20% 
18% 
24% 
22% 

42% 
24% 
31% 
49% 
28% 

68% 
74% 
71% 
68% 
79% 

6% 
15% 
9% 
4% 
19% 

619 
432 
581 
263 
644 

1% 
2% 
4% 
3% 
4% 

19% 
17% 
31% 

33% 
32% 
38% 

60% 
61% 
46% 

48% 
51% 
69% 

14% 
7% 
4% 

633 
569 
642 

4% 
5% 
4% 

21% 
39% 

45% 
57% 

569 
335 

2% 
8% 

683 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 6│ Detailed analysis of vulnerability and non-vulnerability across a number of 
dimensions 

Annex 6  Detailed  analysis  of 

vulnerability  and  non-

vulnerability across a number of dimensions 

The  present  annex  examines,  as  an  alternative  approach  to  analysing  the  data,  the 
extent  to  which  individuals  identified  as  being  vulnerable/non-vulnerable  according  to 
one  vulnerability  dimension  are  also  vulnerable/non-vulnerable  according  to  another 
vulnerability dimension.  

For  the  purpose  of  this  analysis,  the  survey-  and  experiment-based  vulnerability 
indicators  used  to  assess  the  incidence  of  vulnerability  across  the  five  dimensions  have 
been further regrouped into: 

  Experience-based measures of vulnerability; 
  Perception-based measures of vulnerability; and, 
  Outcomes of decision making tests. 

Moreover,  each  of  these  measures  is  either  non-sector-specific  or  sector-specific 
measures of vulnerability. 

Thus,  as  a  first  step  in  this  analysis,  every  vulnerability  indicator  is  allocated  to  one  of 
the  three  groups  of  vulnerability  measures  and,  within  each  of  the  three  groups,  to 
either the group of non-sector-specific or sector-specific measures in Table 150. 

684 

 
 
Annex 6│ Detailed analysis of vulnerability and non-vulnerability across a number of 
dimensions 

Table 150:  Categories of variables to be used to operationalise consumer 

vulnerability 

Non-sector specific 

Sector specific 

Experience-
based 
(e.g. 
experience 
of problems) 

Perception-
based 
measures 
(e.g. feeling 
vulnerable) 

Decision 
making tests 

Did not take action when experienced 
a problem when buying or using goods 
or services in last 12 months (Q22) 
Paid more for services in last 12 
months due to being unable to use a 
certain payment method (Q21.1) 
Has not switched in last 12 months 
due to termination costs or bundling 
(Q21.3, Q21.6) 
Did not make a purchase online in last 
12 months due to difficulty of process 
or not having payment  card (Q21.4, 
Q21.5) 
Feels vulnerable because of health 
problems (Q16.1), financial 
circumstances (Q16.2), employment 
situation (Q16.3), age (Q16.5), 
belonging to a minority group 
(Q16.6), personal issues (Q16.7), 
personal issues (Q16.8) 
How informed feels about prices etc. 
when buying goods and services 
(Q17.1) 
Feels vulnerable because offers, terms 
or conditions are too complex (Q16.4)  
Experiment choices in the cross-
cutting experiment and aggregated 
across experiments  

Where gets information to compare 
deals  (A7, B6, C4)  
Whether compares deals (A6, B5, C3) 
Why finds it difficult to compare deals 
(A9a, B8a, C6a) 
Why never compares deals (A9b, B8b, 
C6b)  
Whether has switched in last 5 years 
(A21, B13, C11) 
Why has never switched (A23, B15, 
C13) 
Whether has tried but failed to obtain 
a loan in the last 5 years (Q13b)  
How difficult finds it to compare deals 
(A8, B7, C5) 

Experiment choices in the sector 
specific experiments  

Respondents may be considered as being vulnerable based on their answers to different 
categories of question from the table above, and some  consumers may be identified as 
vulnerable  based  on  questions  in  more  than  one  category.  For  example,  some 
consumers  may  have  experienced  problems  (e.g.  based  on  Q21  or  Q22)  and  also  feel 
vulnerable (based on Q16) (see figure below). 

A6.1  Economic-wide findings 

The key results of such a two-dimensional analysis when considering the subjective and 
objective measures of vulnerability across the EU28 are as follows: 

A6.1.1  Feeling  vulnerable  due  to  personal  characteristics  and  having 

experienced problems Figure 49) 

  45% of the EU28 survey respondents do not feel vulnerable because of personal 
characteristics and have not experienced any problem when purchasing or using 
any goods or service. 

  Another  30%  of  the  survey  respondents  experienced  at  least  one  problem  but 

nevertheless do not feel vulnerable. 

  At  the  other  end  of  the  spectrum,  15%  of  survey  respondents  feel  vulnerable 
and have experienced at least one problem and 10% feel vulnerable even though 
they did not experience any of the problems listed earlier on. 

685 

 
 
 
 
Annex 6│ Detailed analysis of vulnerability and non-vulnerability across a number of 
dimensions 

Figure 49:  Consumer grouping based on perception-based measure 

(“feeling vulnerable”) and experience-based measure (“experience of 
problems”) – EU28 

Experienced problems (Experience-based) 

No 

Yes 

)
d
e
s
a
b
-
n
o
i
t
p
e
c
r
e
P
(

No 

Don’t feel vulnerable AND 
haven’t experienced any 
problems 

Don’t feel vulnerable AND have 
experienced at least one 
problem 

(45% of EU28) 

(30% of EU28) 

Yes 

Feel vulnerable AND haven’t 
experienced any problems 

(10% of EU28) 

Feel vulnerable AND have 
experienced at least one 
problem 

(15% of EU28) 

l

e
b
a
r
e
n
u
v

l

l

e
e
F

Note: Results weighted using cross-country weights. EU28 N = 18,550. 
Source: London Economics analysis of survey data 

The  distribution  of  consumers  between  the  four  categories  varies  greatly  across  the 
EU28 Member States and Norway and Iceland (Figure 50). In particular: 

 

 

In  a  number  of  Northern  and  Western  European  countries  (the  Netherlands, 
Denmark,  the  United  Kingdom,  Sweden,  Iceland,  Denmark  and  Norway),  a 
majority  of  consumers  have  not  experienced  any  problems  and  do  not  feel 
vulnerable on the basis of their personal characteristics. 

In contrast, a relatively higher proportion of consumers in Central and Southern 
Europe  have  experienced  problems  and  feel  vulnerable  on  the  basis  of  their 
personal characteristics. 

686 

 
 
 
 
 
 
 
 
 
 
 
Annex 6│ Detailed analysis of vulnerability and non-vulnerability across a number of 
dimensions 

Figure 50:  Consumer grouping based on perception-based measure (“feeling 

vulnerable”) and experience-based measure (“experience of 
problems”) – EU28 + NO and IS 

Note: Results weighted using cross-country weights for the EU28 measure and country weights for the country 
measures. EU28 N = 18,550. 
Source: London Economics analysis of survey data 

A6.1.2  Feeling uninformed and having experienced problems (Figure 51)  

  48% do not feel uninformed and have not experienced any problem; 
  10% do feel uninformed and have not experienced any problems  
  Among the 45% who have experienced problems, 15% feel uninformed and 30% 

do not feel uninformed. 

687 

66%59%59%59%54%49%45%45%43%43%42%40%39%38%37%37%37%35%34%33%33%31%25%24%24%22%22%18%16%54%53%23%29%22%25%28%31%42%30%32%30%41%42%28%35%32%27%33%41%38%36%33%27%47%28%39%42%45%58%39%33%33%6%5%10%8%8%7%3%10%12%10%6%5%14%9%10%18%10%8%9%10%15%14%5%11%11%10%9%4%10%4%7%5%7%9%8%11%13%9%15%13%16%11%13%19%18%21%18%20%17%19%21%20%29%23%36%26%26%24%19%35%9%7%0%10%20%30%40%50%60%70%80%90%100%Don’t feel vulnerable AND haven’t experienced any problemsDon’t feel vulnerable AND have experienced at least one problemFeel vulnerable AND haven’t experienced any problemsFeel vulnerable AND have experienced at least one problem 
 
 
 
 
Annex 6│ Detailed analysis of vulnerability and non-vulnerability across a number of 
dimensions 

Figure 51:  Consumer grouping based on perception-based measure 

(“feeling uninformed”) and experience-based measure (“experience 
of problems”) – EU28 

Experienced problems (Experience-based) 

No 

Yes 

d
e
m
r
o
f
n
n
u

i

l

e
e
F

)
d
e
s
a
b
-
n
o
i
t
p
e
c
r
e
P
(

No 

Feel informed AND haven’t 
experienced any problems 

(48% of EU28) 

Yes 

Feel uninformed AND haven’t 
experienced any problems 

(7% of EU28) 

Feel informed AND have 
experienced at least one 
problem 

(37% of EU28) 

Feel uninformed AND have 
experienced at least one 
problem 

(8% of EU28) 

Note: Results weighted using cross-country weights. EU28 N = 18,550. 
Source: London Economics analysis of survey data 

  A relative majority of consumers in almost all countries feels informed and have 

experienced problems at the same time. 

 

In only 8 countries (Bulgaria, Cyprus, Finland, Hungary, Lithuania, Malta, Poland 
and  Slovakia)  do  the  proportion  of  vulnerable  consumers  due  to  lack  of 
information and having experienced problems exceed 10%. But, in these cases, 
the proportion never exceeds 20%. 

688 

 
 
 
 
 
 
 
 
 
 
 
Annex 6│ Detailed analysis of vulnerability and non-vulnerability across a number of 
dimensions 

Figure 52:  Consumer grouping based on perception-based measure (“feeling 
uninformed”) and experience-based measure (“experience of 
problems) – EU28 + NO and IS 

Note: Results weighted using cross-country weights for the EU28 measure and country weights for the country 
measures. EU28 N = 18,550. 
Source: London Economics analysis of survey data 

A6.2  Sectoral analysis  

The  sectoral  analysis  focuses  on  perception-based  indicators  (either  feeling  vulnerable 
due  to  personal  characteristics  or  feeling  uninformed)  and  the  experience-based 
indicator of comparing deals. 

A6.2.1  Sectoral analysis - feeling vulnerable due to personal characteristics 

  The  proportion  of  consumers  who  do  not  feel  vulnerable  and  do  not  compare 
deals  ranges  from  24%  for  the  on-line  sector  to  41%  for  the  financial  services 
sector. 

  The  proportion  of  consumers  who  do  not  feel  vulnerable  and  do  compare  deals 
ranges from 33% for the financial services sector to 52% for the on-line sector. 
In  contrast,  the  share  of  consumers  who  feel  vulnerable  and  do  not  compare 
deals  ranges  from  8%  for  the  on-line  sector  to  17%  for  the  financial  services 
sector; and, 

 

  The share of consumers who feel vulnerable and do compare deals ranges from 

10% for the financial services sector to 17% for the online sector. 

In  the  case  of  energy  and  financial  services,  a  somewhat  smaller  proportion  of  those 
feeling vulnerable due to personal characteristics than those who do not feel vulnerable 
compare  deals  -  41%  vs.  50%  in  the  case  of  energy  and  37%  vs.  45%  in  the  case  of 
financial services. In contrast, in the case of on-line, the share of those comparing deals 
is the same for those feeling vulnerable and not feeling vulnerable.  

689 

64%63%61%56%51%50%48%48%46%44%44%44%43%43%40%40%40%38%37%37%37%32%31%29%29%27%25%23%22%55%48%26%26%30%30%30%43%38%37%40%36%49%44%47%30%44%46%41%44%36%46%45%57%40%45%61%63%67%49%71%35%33%8%6%6%8%11%3%8%7%7%11%3%5%4%12%7%5%7%6%10%6%6%3%12%7%2%3%1%9%1%6%10%2%5%3%6%9%4%5%8%7%9%4%8%6%15%8%9%12%12%17%11%13%8%17%19%8%7%7%19%6%4%9%0%10%20%30%40%50%60%70%80%90%100%Feel informed AND haven’t experienced any problemsFeel informed AND have experienced at least one problemFeel uninformed AND haven’t experienced any problemsFeel uninformed AND have experienced at least one problem 
 
 
Annex 6│ Detailed analysis of vulnerability and non-vulnerability across a number of 
dimensions 

Figure 53: 

 Energy sector: consumer grouping based on perception-based 

measure (“feeling vulnerable”) and experience-based measure 
(“comparing deals”) 

Compare market deals (Experience-based)392 

Yes 

No 

No 

Yes 

Don’t feel vulnerable AND 
compare deals 
(37% of EU28) 
Feel vulnerable AND compare 
deals 
(11% of EU28) 

Don’t feel vulnerable AND don’t 
compare deals 
(37% of EU28) 
Feel vulnerable AND don’t 
compare deals 
(16% of EU28) 

-
n
o
i
t
p
e
c
r
e
P
(

)
d
e
s
a
b

l

e
b
a
r
e
n
u
v

l

l

e
e
F

Note: Results weighted using cross-country weights. EU28 N = 25,151. 
Source: London Economics analysis  

Figure 54: 

 On-line sector: consumer grouping based on perception-based 

measure (“feeling vulnerable”) and experience-based measure 
(“comparing deals”) 

-
n
o
i
t
p
e
c
r
e
P
(

)
d
e
s
a
b

Compare market deals (Experience-based) 

Yes 

No 

No 

Yes 

Don’t feel vulnerable AND 
compare deals 
(52% of EU28) 
Feel vulnerable AND compare 
deals 
(17% of EU28) 

Don’t feel vulnerable AND don’t 
compare deals 
(24% of EU28) 
Feel vulnerable AND don’t 
compare deals 
(8% of EU28) 

l

e
b
a
r
e
n
u
v

l

l

e
e
F

Note: Results weighted using cross-country weights. EU28 N = 18,506. 
Source: London Economics analysis 

Figure 55: 

 Financial sector: consumer grouping based on perception-based 

measure (“feeling vulnerable”) and experience-based measure 
(“comparing deals”) 

l

e
e
F

r
e
n
u
v

l

l

e
b
a

e
c
r
e
P
(

-
n
o
i
t
p

)
d
e
s
a
b

Compare market deals (Experience-based) 

Yes 

No 

No 

Don’t feel vulnerable AND 
compare deals 

Don’t feel vulnerable  AND 
don’t compare deals 

392  The  experience-based  indicator  of  vulnerability  is  ‘Does  not  compare  market  deals’,  however 
the  opposite  statement  has  been  used  and  instead  the  ‘Yes’  and  ‘No’  columns  have  switched 
places compared to previous tables, in order to avoid double negation whilst at the same time 
keeping the meaning of the four quadrants of the table. 

690 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
                                                 
Annex 6│ Detailed analysis of vulnerability and non-vulnerability across a number of 
dimensions 

Yes 

(33% of EU28) 
Feel vulnerable AND compare 
deals 
(10% of EU28) 

(41% of EU28) 
Feel vulnerable AND don’t 
compare deals 
(17% of EU28) 

Note: Results weighted using cross-country weights. EU28 N = 25,151. 
Source: London Economics analysis 

As in the previous two cases, the distribution of consumers between the four categories 
varies greatly across the EU28 + Norway and Iceland (see figure below). However, in the 
present  cases,  typically  a  relative  majority  does  not  compare  deals  and  does  not  feel 
vulnerable. 

691 

 
 
  
Annex 6│ Detailed analysis of vulnerability and non-vulnerability across a number of 
dimensions 

Figure 56: 

 Energy sector: consumer grouping based on perception-based 

measure (“feeling vulnerable”) and experience-based meaure 
(“comparing deals”) – EU28 + NO and IS 

Note: Results weighted using cross-country weights for the EU28 measure and country weights for the country 
measures. EU28 N = 25,151. 
Source: London Economics analysis of survey data 

692 

56%51%50%49%48%47%46%42%42%41%38%36%36%36%35%34%32%31%29%24%18%17%17%14%12%11%11%9%8%40%8%32%37%28%22%35%42%24%36%33%42%43%37%31%30%38%28%34%32%44%38%32%47%50%68%47%64%51%54%40%46%79%8%6%12%18%9%8%19%10%13%10%8%11%17%17%10%20%16%16%10%17%14%11%7%4%9%4%6%4%7%6%1%4%6%9%11%8%4%11%11%12%8%10%16%16%18%17%18%18%21%18%20%35%25%26%14%32%21%32%33%45%9%12%0%10%20%30%40%50%60%70%80%90%100%Don’t feel vulnerable AND compare dealsDon’t feel vulnerable AND don’t compare dealsFeel vulnerable AND compare dealsFeel vulnerable AND don’t compare deals 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 6│ Detailed analysis of vulnerability and non-vulnerability across a number of 
dimensions 

Figure 57: 

 On-line sector: consumer grouping based on perception-based 

measure (“feeling vulnerable”) and experience-based measure 
(“comparing deals”) – EU28 + NO and IS 

Note: Results weighted using cross-country weights for the EU28 measure and country weights for the country 
measures. EU28 N = 18,506. 
Source: London Economics analysis of survey data 

693 

66%62%61%60%58%57%55%54%54%53%53%53%52%52%51%50%50%50%47%46%46%44%42%41%40%40%39%37%35%47%36%11%26%28%12%14%15%20%26%16%10%11%29%23%13%32%14%5%19%42%21%27%25%38%17%27%42%45%39%19%40%50%19%9%9%22%22%23%15%13%22%34%29%13%17%26%11%27%38%24%7%23%18%19%9%27%17%10%9%12%29%6%6%4%3%3%6%6%4%10%7%8%3%6%6%8%9%6%9%7%7%5%10%9%12%11%16%16%8%8%13%17%7%8%0%10%20%30%40%50%60%70%80%90%100%Don’t feel very vulnerable AND compare dealsDon’t feel very vulnerable  AND don’t compare dealsFeel very vulnerable AND compare dealsFeel very vulnerable AND don’t compare deals 
 
 
Annex 6│ Detailed analysis of vulnerability and non-vulnerability across a number of 
dimensions 

Figure 58: 

 Financial sector: consumer grouping based on perception-based 

measure (“feeling vulnerable”) and experience-based measure 
(“comparing deals”) – EU28 + NO and IS 

Note: Results weighted using cross-country weights for the EU28 measure and country weights for the country 
measures. EU28 N = 25,151. 
Source: London Economics analysis of survey data 

A6.2.2  Sectoral analysis - vulnerability due to lack of information 

Overall, 

 

In both the energy and the financial sectors, 81% of consumers feel informed; in 
the on-line sector the proportion of consumers feeling informed is even higher  - 
86%.  40%  and  45%  of  consumers  respectively  feel  informed  and  do  not 
compare deals in the energy and in the financial sector, while the corresponding 
share in the on-line sector is only 26%. 

  Among  the  consumers  who  feel  uninformed  (the  shares  of  which  range  from 
15%  in  the  on-line  sector  to  20%  in  the  financial  sector),  a  greater  share  does 
not  compare  deals  in  the  energy  sector  and  the  financial  sector  while  the 
opposite is true in the case of the on-line sector. 

694 

49%47%43%43%41%38%37%36%34%34%34%33%32%31%31%30%30%30%28%27%27%25%25%24%24%23%23%22%18%42%24%34%40%45%30%29%44%36%47%37%42%45%41%43%34%32%49%37%53%38%36%40%37%34%38%43%65%27%40%30%43%63%9%6%7%12%12%7%14%7%12%12%7%10%11%16%15%7%14%8%12%12%11%9%9%12%9%4%16%15%17%6%4%8%6%5%15%18%11%13%11%17%13%15%17%14%20%22%14%19%9%23%25%22%29%32%26%24%8%33%23%35%9%8%0%10%20%30%40%50%60%70%80%90%100%Don’t feel very vulnerable AND compare dealsDon’t feel very vulnerable  AND don’t compare dealsFeel very vulnerable AND compare dealsFeel very vulnerable AND don’t compare deals 
 
 
 
 
 
Annex 6│ Detailed analysis of vulnerability and non-vulnerability across a number of 
dimensions 

Figure 59:  Energy sector: consumer grouping based on perception-based 

measure (“feeling uninformed”) and experience-based measure 
(“comparing deals”) – EU28  

d
e
m
r
o
f
n
n
u

i

l

e
e
F

-
n
o
i
t
p
e
c
r
e
P
(

)
d
e
s
a
b

Compare market deals (Experience-based)393 

Yes 

No 

No 

Yes 

Feel informed AND compare 
deals 
(41% of EU28) 
Feel uninformed AND 
compare deals 
(7% of EU28) 

Feel informed AND don’t 
compare deals 
(40% of EU28) 
Feel uninformed AND don’t 
compare deals 
(12% of EU28) 

Note: Results weighted using cross-country weights. EU28 N = 25,151. 
Source: London Economics analysis of survey data 

Figure 60: 

 On-line sector: consumer grouping based on perception-based 

measure (“feeling uninformed”) and experience-based measure 
(“comparing deals”) – EU28  

d
e
m
r
o
f
n
n
u

i

l

e
e
F

-
n
o
i
t
p
e
c
r
e
P
(

)
d
e
s
a
b

Compare market deals (Experience-based) 

Yes 

No 

No 

Yes 

Feel informed AND compare 
deals 
(60% of EU28) 
Feel uninformed AND 
compare deals 
(9% of EU28) 

Feel informed AND don’t 
compare deals 
(26% of EU28) 
Feel uninformed AND don’t 
compare deals 
(6% of EU28) 

Note: Results weighted using cross-country weights. EU28 N = 18,506. 
Source: London Economics analysis of survey data 

393  The  experience-based  indicator  of  vulnerability  is  ‘Does  not  compare  market  deals’,  however 
the  opposite  statement  has  been  used  and  instead  the  ‘Yes’  and  ‘No’  columns  have  switched 
places compared to previous tables, in order to avoid double negation whilst at the same time 
keeping the meaning of the four quadrants of the table. 

695 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                                 
Annex 6│ Detailed analysis of vulnerability and non-vulnerability across a number of 
dimensions 

Figure 61:  Financial sector consumer grouping based on perception-based 

measure (“feeling uninformed”) and experience-based measure 
(“comparing deals”) – EU28  

d
e
m
r
o
f
n
n
u

i

l

e
e
F

-
n
o
i
t
p
e
c
r
e
P
(

)
d
e
s
a
b

Compare market deals (Experience-based) 

Yes 

No 

No 

Yes 

Feel informed AND compare 
deals 
(36% of EU28) 
Feel uninformed AND 
compare deals 
(7% of EU28) 

Feel informed AND don’t 
compare deals 
(45% of EU28) 
Feel uninformed AND don’t 
compare deals 
(13% of EU28) 

Note: Results weighted using cross-country weights. EU28 N = 25,151. 
Source: London Economics analysis of survey data 

The  distribution  of  consumers  between  the  four  categories  varies  greatly  across  EU28 
Member States (see Figure 62,Figure 63, Figure 64 and Figure 65). 

Figure 62:  Energy sector: consumer grouping based on perception-based 

measure (“feeling uninformed”) and experience-based measure 
(“comparing deals”) – EU28 + NO and IS  

Note: Results weighted using cross-country weights for the EU28 measure and country weights for the country 
measures. EU28 N = 25,151. 
Source: London Economics analysis of survey data 

696 

62%58%56%55%52%50%49%47%47%46%46%46%42%42%41%40%38%37%33%30%27%21%21%19%15%13%11%11%10%41%7%26%25%30%28%40%40%36%43%33%34%37%39%32%37%40%41%41%35%41%55%37%55%62%61%71%53%60%63%51%47%73%5%7%6%9%4%5%8%3%10%8%6%7%11%6%7%7%8%9%8%3%12%7%3%2%3%4%4%4%3%4%2%7%10%7%8%5%6%6%7%10%12%11%9%15%15%12%13%14%18%17%12%24%17%14%18%11%31%25%22%36%8%18%0%10%20%30%40%50%60%70%80%90%100%Feel informed AND compare dealsFeel informed AND don’t compare dealsFeel uninformed AND compare dealsFeel uninformed AND don’t compare deals 
 
 
 
 
 
 
 
 
 
Annex 6│ Detailed analysis of vulnerability and non-vulnerability across a number of 
dimensions 

Figure 63:  On-line sector: consumer grouping based on perception-based 

measure (“feeling uninformed”) and experience-based measure 
(“comparing deals”) – EU28 + NO and IS 

Note: Results weighted using cross-country weights for the EU28 measure and country weights for the country 
measures. EU28 N = 18,506. 
Source: London Economics analysis of survey data 

697 

82%80%77%75%75%71%64%63%63%62%62%61%60%60%60%58%58%57%57%55%52%52%50%49%49%45%44%44%37%42%39%10%13%12%15%18%19%16%24%9%27%21%25%32%28%26%24%16%25%14%26%28%32%36%25%41%42%46%41%36%38%51%7%5%10%6%5%6%14%8%20%8%12%9%3%7%9%12%19%10%23%10%10%10%7%15%5%7%3%5%12%10%3%2%2%1%4%2%3%7%5%8%3%5%6%4%5%6%6%8%8%6%9%9%6%7%11%5%7%7%9%16%10%7%0%10%20%30%40%50%60%70%80%90%100%Feel informed AND compare dealsFeel informed AND don’t compare dealsFeel uninformed AND compare dealsFeel uninformed AND don’t compare deals 
 
 
Annex 6│ Detailed analysis of vulnerability and non-vulnerability across a number of 
dimensions 

Figure 64:  Financial sector: consumer grouping based on perception-based 

measure (“feeling uninformed”) and experience-based measure 
(“comparing deals”) – EU28 + NO and IS 

Note: Results weighted using cross-country weights for the EU28 measure and country weights for the country 
measures. EU28 N = 25,151. 
Source: London Economics analysis of survey data 

A6.2.3  Feeling vulnerable and results of experiments  

The  figures  below  present  the  results  for  each  of  the  four  experiments  (energy,  online 
and  financial  sectors  as  well  as  cross-cutting),  for  each  of  the  two  treatments  –  the 
marketing  practice  and  the  remedy  treatment.  In  the  case  of  the  three  sectoral 
experiments  conducted  in  the  five  experiment  countries394,  out  of  those  respondents 
feeling vulnerable because of personal characteristics:  

 

In  the  case  of  energy  and  online,  among  those  consumers  feeling  vulnerable,  a 
majority chose  optimally (in the two rounds) under both the marketing practice 
treatment  and  the  remedy  treatment.  This  proportion  is  very  similar  for  both 
treatments  

  But only 50% chose the optimal solution in the case of the financial sector 
 

In the case of the cross-cutting experiment, a large majority – around 70% - of 
those  feeling  vulnerable  chose  the  optimal  solution.  This  proportion  is  slightly 
higher under the remedy treatment (70.8%) compared to the marketing practice 
(70.4%) treatment. 

394  The  countries  in  which  the  experiment  was  conducted  are  Denmark  (DK),  Lithuania  (LT), 

Portugal (PT), Romania (RO) and the United Kingdom (UK). 

698 

49%47%46%46%43%42%42%39%38%37%36%36%36%36%36%35%35%34%32%32%32%31%30%30%29%28%26%26%25%44%24%31%35%40%32%41%49%46%51%38%50%44%45%48%29%39%37%50%45%51%54%49%40%50%45%54%33%47%64%40%45%56%8%6%8%9%7%3%4%3%9%4%9%7%4%16%8%10%4%4%5%6%5%12%4%7%4%11%8%2%9%4%5%12%12%7%13%9%6%8%7%15%10%11%13%12%20%18%17%11%17%12%9%14%17%16%18%12%28%18%9%26%7%15%0%10%20%30%40%50%60%70%80%90%100%Feel informed AND compare dealsFeel informed AND don’t compare dealsFeel uninformed AND compare dealsFeel uninformed AND don’t compare deals 
 
 
                                                 
Annex 6│ Detailed analysis of vulnerability and non-vulnerability across a number of 
dimensions 

Figure 65: 

 Energy sector: consumer grouping based on perception-based 

measure (“feeling vulnerable”)  and experience-based measure (“not 
choosing optimally in experiment) – Experiment countries 

No 

Yes 

)
d
e
s
a
b

Poor experiment performance (Experience-based) 

No 

Yes 

Don’t feel vulnerable AND 
chose optimally in both 
rounds 
(Marketing practice: 12% 
across experiment countries) 
(Remedy: 26% across 
experiment countries) 
Feel vulnerable AND chose 
optimally in both rounds  
(Marketing practice: 60% 
across experiment countries) 
(Remedy: 46% across 
experiment countries) 

Don’t feel very vulnerable AND 
did not choose optimally in both 
rounds 
(Marketing practice: 3% across 
experiment countries) 
(Remedy: 8% across 
experiment countries) 
Feel vulnerable AND did not 
choose optimally in both rounds 
(Marketing practice: 25% 
across experiment countries) 
(Remedy: 21% across 
experiment countries) 

-
n
o
i
t
p
e
c
r
e
P
(

l

e
b
a
r
e
n
u
v

l

l

e
e
F

Note: Results weighted using cross-country weights. N = 2,522. 
Source: London Economics analysis  

Figure 66: 

 On-line sector: consumer grouping based on perception-based 

measure (“feeling vulnerable”)  and experience-based measure (“not 
choosing optimally in experiment) – Experiment countries 

No 

Yes 

)
d
e
s
a
b

Poor experiment performance (Experience-based) 

No 

Yes 

Don’t feel vulnerable AND 
chose optimally in both 
rounds  
(Marketing practice: 35% 
across experiment countries) 
(Remedy: 48% across 
experiment countries) 
Feel vulnerable AND chose 
optimally in both rounds  
(Marketing practice: 37% 
across experiment countries) 
(Remedy: 27% across 
experiment countries) 

Don’t feel vulnerable AND did 
not choose optimally in both 
rounds  
(Marketing practice: 12% 
across experiment countries) 
(Remedy: 11% across 
experiment countries) 
Feel vulnerable AND did not 
choose optimally  
(Marketing practice: 16% 
across experiment countries) 
(Remedy: 14% across 
experiment countries) 

-
n
o
i
t
p
e
c
r
e
P
(

l

e
b
a
r
e
n
u
v

l

l

e
e
F

Note: Results weighted using cross-country weights. N = 2,510. 
Source: London Economics analysis  

699 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 6│ Detailed analysis of vulnerability and non-vulnerability across a number of 
dimensions 

Figure 67: 

 Financial sector: consumer grouping based on perception-based 
measure (“feeling vulnerable”)  and experience-based measure (“not 
choosing optimally in experiment) – Experiment countries 

No 

Yes 

)
d
e
s
a
b

Poor experiment performance (Experience-based) 

No 

Yes 

Don’t feel vulnerable AND 
chose optimally in both 
rounds  
(Marketing practice: 57% 
across experiment countries) 
(Remedy: 64% across 
experiment countries) 
Feel vulnerable AND chose 
optimally in both rounds  
(Marketing practice: 16% 
across experiment countries) 
(Remedy: 9% across 
experiment countries) 

Don’t feel vulnerable AND did 
not choose optimally in both 
rounds  
(Marketing practice: 16% 
across experiment countries) 
(Remedy: 22% across 
experiment countries) 
Feel vulnerable AND did not 
choose optimally in both rounds  
(Marketing practice: 11% 
across experiment countries) 
(Remedy: 6% across 
experiment countries) 

-
n
o
i
t
p
e
c
r
e
P
(

l

e
b
a
r
e
n
u
v

l

l

e
e
F

Note: Results weighted using cross-country weights. N = 2,505. 
Source: London Economics analysis  

Figure 68: 

 Cross-cutting sector : consumer grouping based on perception-

based measure (“feeling vulnerable”)  and experience-based measure 
(“not choosing optimally in experiment) – Experiment countries 

No 

Yes 

)
d
e
s
a
b

Poor experiment performance (Experience-based) 

No 

Yes 

Don’t feel vulnerable AND 
chose optimally in both 
rounds  
(Marketing practice: 26% 
across experiment countries) 
(Remedy: 28% across 
experiment countries) 
Feel vulnerable AND chose 
optimally in both rounds  
(Marketing practice: 47% 
across experiment countries) 
(Remedy: 44% across 
experiment countries) 

Don’t feel vulnerable AND did 
not choose optimally in both 
rounds 
(Marketing practice: 8% across 
experiment countries) 
(Remedy: 10% across 
experiment countries) 
Feel vulnerable AND did not 
choose optimally in both rounds  
(Marketing practice: 20% 
across experiment countries) 
(Remedy: 18% across 
experiment countries) 

-
n
o
i
t
p
e
c
r
e
P
(

l

e
b
a
r
e
n
u
v

l

l

e
e
F

Note: Results weighted using cross-country weights. N = 2,565. 
Source: London Economics analysis  

The distribution of consumers between the four categories varies greatly across the five 
experiment countries (see Figure 69): 

 

In  the  case  of  energy  and  online,  among  those  consumers  feeling  vulnerable,  a 
majority  chose  optimally  in  both  the  marketing  practice  and  the  remedy 
treatment. 

700 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 6│ Detailed analysis of vulnerability and non-vulnerability across a number of 
dimensions 

  However, in the case of the financial sector, the results are not as clear-cut with 
occasionally, a larger proportion of respondents who feel vulnerable not choosing 
the  optimal  solution  (Romania  for  the  marketing  practice  treatment  and 
Lithuania for the remedy treatment). 

  Finally,  in  the  case  of  the  cross-cutting  experiment,  a  clear  majority  of 

vulnerable people chose the optimal solution. 

Figure 69:  Energy sector: consumer grouping based on perception-based 

measure (“feeling vulnerable”)  and experience-based measure (“not 
choosing optimally in experiment) – Experiment countries 

Marketing practice treatment: 

Remedy treatment: 

Note: Results weighted using country weights. N = 2,522. 
Source: London Economics analysis of survey data 

701 

12%6%6%8%15%2%4%3%2%3%63%54%57%51%63%22%36%34%39%19%0%10%20%30%40%50%60%70%80%90%100%DKLTPTROUKDon’t feel vulnerable AND chose optimally in both roundsDon’t feel very vulnerable AND did not choose optimally in both roundsFeel vulnerable AND chose optimally in both roundsFeel vulnerable AND did not choose optimally in both rounds28%20%21%12%31%5%6%11%8%7%53%47%38%52%45%15%27%30%29%17%0%10%20%30%40%50%60%70%80%90%100%DKLTPTROUKDon’t feel vulnerable AND chose optimally in both roundsDon’t feel very vulnerable AND did not choose optimally in both roundsFeel vulnerable AND chose optimally in both roundsFeel vulnerable AND did not choose optimally in both rounds 
 
 
 
 
 
Annex 6│ Detailed analysis of vulnerability and non-vulnerability across a number of 
dimensions 

Figure 70:  On-line sector: consumer grouping based on perception-based 

measure (“feeling vulnerable”)  and experience-based measure (“not 
choosing optimally in experiment) – Experiment countries 

Marketing practice treatment: 

Remedy treatment: 

Note: Results weighted using country weights. N = 2,510. 
Source: London Economics analysis of survey data 

702 

45%34%21%21%41%7%8%21%14%11%41%32%38%41%36%8%25%20%24%13%0%10%20%30%40%50%60%70%80%90%100%DKLTPTROUKDon’t feel vulnerable AND chose optimally in both roundsDon’t feel very vulnerable AND did not choose optimally in both roundsFeel vulnerable AND chose optimally in both roundsFeel vulnerable AND did not choose optimally in both rounds55%26%36%23%58%12%15%17%20%8%28%35%31%36%23%5%25%16%21%11%0%10%20%30%40%50%60%70%80%90%100%DKLTPTROUKDon’t feel vulnerable AND chose optimally in both roundsDon’t feel very vulnerable AND did not choose optimally in both roundsFeel vulnerable AND chose optimally in both roundsFeel vulnerable AND did not choose optimally in both rounds 
 
 
 
 
 
Annex 6│ Detailed analysis of vulnerability and non-vulnerability across a number of 
dimensions 

Figure 71:  Financial sector: consumer grouping based on perception-based 
measure (“feeling vulnerable”)  and experience-based measure (“not 
choosing optimally in experiment) – Experiment countries 

Marketing practice treatment: 

Remedy treatment: 

Note: Results weighted using country weights. N = 2,505. 
Source: London Economics analysis of survey data 

703 

68%46%43%47%63%12%33%22%26%11%16%10%20%13%17%4%10%15%14%9%0%10%20%30%40%50%60%70%80%90%100%DKLTPTROUKDon’t feel vulnerable AND chose optimally in both roundsDon’t feel very vulnerable AND did not choose optimally in both roundsFeel vulnerable AND chose optimally in both roundsFeel vulnerable AND did not choose optimally in both rounds70%58%49%48%71%17%28%33%33%16%9%4%10%10%9%5%10%9%9%4%0%10%20%30%40%50%60%70%80%90%100%DKLTPTROUKDon’t feel vulnerable AND chose optimally in both roundsDon’t feel very vulnerable AND did not choose optimally in both roundsFeel vulnerable AND chose optimally in both roundsFeel vulnerable AND did not choose optimally in both rounds 
 
 
 
 
Annex 6│ Detailed analysis of vulnerability and non-vulnerability across a number of 
dimensions 

Figure 72:  Cross-cutting sector : consumer grouping based on perception-

based measure (“feeling vulnerable”)  and experience-based measure 
(“not choosing optimally in experiment) – Experiment countries 
Marketing practice treatment: 

Remedy treatment: 

Note: Results weighted using country weights. N = 2,565. 
Source: London Economics analysis of survey data 

704 

29%22%15%12%33%7%5%11%10%6%51%45%48%43%47%13%27%26%34%14%0%10%20%30%40%50%60%70%80%90%100%DKLTPTROUKDon’t feel vulnerable AND chose optimally in both roundsDon’t feel very vulnerable AND did not choose optimally in both roundsFeel vulnerable AND chose optimally in both roundsFeel vulnerable AND did not choose optimally in both rounds36%16%19%17%33%5%8%12%8%10%48%44%43%39%46%11%32%26%36%11%0%10%20%30%40%50%60%70%80%90%100%DKLTPTROUKDon’t feel vulnerable AND chose optimally in both roundsDon’t feel very vulnerable AND did not choose optimally in both roundsFeel vulnerable AND chose optimally in both roundsFeel vulnerable AND did not choose optimally in both rounds 
 
 
 
 
 
 
 
 
 
Annex 7│ Cluster analysis of indicators of vulnerability 

Annex 7  Cluster analysis of indicators of vulnerability 

This  annex  provides  a  technical  justification  for  the  grouping  of  countries  into  separate 
groups. The grouping of countries is based on the indicators from the survey (discussed 
in Chapter 3 Table 5) and conducted through a cluster analysis of these indicators. The 
country  groups  are  used  in  the  country  level  analysis  in  Chapter  6.7  of  the  report  to 
determine if the drivers of and factors linked to vulnerability in the full sample hold on a 
country group level. 

A7.1  Method 

The  technique  used  to  assign  countries  to  groups  is  cluster  analysis.  This  method 
calculates  the  dissimilarity  between  countries  based  on  the  differences  between 
countries  in  terms  of  a  number  of  vulnerability  indicators395.  In  particular,  the  average 
incidence rates of the 30 countries on fifteen indicators are used in the cluster analysis, 
namely: 

  Unassertive (took no action) when experienced a problem (1) 

  Overpaid for services due to being unable to use certain payment methods (2)  
  Perception of own vulnerability due to personal characteristics (3)396 
  Does not feel informed about prices etc. (4)397 
  Does not compare product deals - energy sector (8_1_ene) 

  Finds it very difficult to compare product deals - energy sector (8_2_ene) 

  Does not compare product deals - finance sector (8_1_fin) 

  Finds it very difficult to compare product deals - finance sector (8_2_fin) 

  Does not compare product deals - online sector (8_1_onl) 

  Finds it very difficult to compare product deals - online sector (8_2_onl) 

  Has not switched due to bundling (9e_bun) 

  Has not switched due to termination costs (9d_ter) 

  Excluded from e-commerce due to not having a payment card (10b) 

  Excluded from e-commerce due to difficulty of the process (10a) 
  Perception of own vulnerability due to complexity of offers (12_off)398 

These fifteen indicators were selected for use in the cluster analysis since they provide a 
balance  across  the  three  key  sectors  of  the  study  (energy,  online  and  finance)  and  are 
available  for  all  30  countries.  In  the  cluster  analysis,  these  indicators  were  weighted 

395 The dissimilarity measure is the Eucledian distance between the values of the indicators for two 
countries (i.e. (Σ15
a=1(xia-xja)2)1/2, where a is the indicator (of which there are 15 in total), and 
i and j are the countries; i.e. xia is the value of indicator xa for country i, and xja is the value of 
indicator xa for country j). In some cases the indicators were first rescaled; see the following 
footnote.  

396 This indicator was originally a scale from 1 to 4. So that all indicators are on the same scale (so 
that no indicators disproportionally affect the results of the cluster analysis), for the purposes 
of  the  cluster  analysis  this  indicator  has  been  rescaled  to  a  0  to  1  (by  subtracting  1  and 
dividing by three). 
397 See the footnote above. 
398 See the footnote above. 

705 

 
 
                                                 
Annex 7│ Cluster analysis of indicators of vulnerability 

based on the number of indicators in each dimension, so that each dimension affects the 
results of the analysis to an equal extent399.  

The  specific  type  of  cluster  analysis  method  used  is  complete  linkage  cluster  analysis 
(also  known  as  the  ‘furthest  neighbour’  method),  with  a  simple  cut-off  based  on  a 
maximum  allowed  level  of  dissimilarity  between  members  of  a  group.  This  method 
divides  objects  (in  our  case  countries)  into  groups  based  on  the  distances  between  the 
furthest (most dissimilar) members of the groups. The advantage of this method is that 
it  provides  well-defined  groups  compared  to  other  cluster  analysis  methods  (e.g.  the 
single linkage or ‘nearest neighbour’ method). 

A7.2  Results 

Based  on  the  complete  linkage  cluster  analysis,  the  countries  may  be  separated  into 
either three  or five obvious groups, as shown in Figure 1. For example, Figure 1 shows 
that  the  maximum  dissimilarity  (on  the  dissimilarity  measure)  between  any  two 
countries in group 1 is 0.16, which is less than a cut-off point of 0.2. 

Five groups of countries are identified if the dissimilarity measure cut-off point is set at 
0.2, as shown in Figure 1.400 These groups are: 

  Group  1:  Germany,  Denmark,  Sweden,  Norway,  Luxembourg,  the  Netherlands, 

Austria, and Iceland. 

  Group 2: Czech Republic, United Kingdom, Finland, Belgium, Malta and Slovakia. 

  Group 3: Hungary, Estonia, Lithuania, Latvia, Bulgaria, Poland and Spain. 

  Group 4: Greece, Cyprus and Romania.  

  Group 5: Italy, Slovenia, France, Portugal, Ireland and Croatia 

As can also be seen from Figure 1, if the dissimilarity measure cut-off point is raised, the 
five groups set out above would be combined as follows (in no particular order): 

A.  Group 1 and group 2. 

B.  Group 3 and group 4. 

C.  Group 3, group 4 and group 5. 

399 Each indicator has been divided by 1/n, where n is the number of indicators in the dimension. 
400 The dissimilarity measure between two countries/groups is the Eucledean distance between them. 

706 

 
 
                                                 
Annex 7│ Cluster analysis of indicators of vulnerability 

Figure 73:  Cluster dendrogram and map 

Dissimilarity measure 
cut-off point yielding 
five groups 

        A.  

                 C.   

   B. 

   .16 
         Group 1 
           cut-off 

                                Group 1                         Group 2                        Group 3               Group 4             
Group 5  

Group 5 
Group 4 
Group 3 

Group 2 
Group 1 

C. 
B. 

A. 

707 

Cluster 4Cluster 3Cluster 5Cluster 2Cluster 10.1.2.3.4.5Dissimilarity measureATNLDKDELUNOSEISBEFICZUKMTSKBGLVEEHULTPLESCYELROHRIEFRITSIPT 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 7│ Cluster analysis of indicators of vulnerability 

Note: Based on indicators 1, 2, 3, 4, 8_1_ene, 8_2_ene, 8_1_fin, 8_2_fin, 8_1_onl, 8_2_onl, 9e_bun, 9d_ter, 
10b,  10a,  and  12_off,  weighted  1/n  where  n  is  the  number  of  indicators  in  the  dimension.  The  dissimilarity 
measure is the Eucledian distance (i.e. (Σ15
a=1(xia-xja)2)1/2, where a is the variable, and i and j are the country 
averages of the (rescaled) indicator of two different countries). A complete linkage is used for the dendrogram 
(i.e. each branch represents the ‘furthest neighbour’ distance).  The horizontal line in the top panel denotes the 
dissimilarity cut-off point for the clusters in the dendrogram. A, B, and C denote higher level cluster groupings 
in the analysis.  
Source: London Economics analysis of the survey data 

A7.2.1  Characteristics of the country groups identified via cluster analysis 

Five  country  groups  are  established  based  on  the  survey  data  using  cluster  analysis  in 
the  section  A7.1.  This  approach  assigns  countries  to  groups  based  on  the  incidence  of 
vulnerability on fifteen indicators.401 The five country groups are:402  

  Group  1.  A  group  consisting  mainly  of  Germanic  and  Scandinavian  countries. 
These  countries  exhibit  among  the  lowest  rates  of  vulnerability.  Countries 
included  in  this  group  are  Germany,  Denmark,  Sweden,  Norway,  Luxembourg, 
the Netherlands, Austria, and Iceland. 

  Group 2. A group consisting mostly of countries that are neighbours of group 1. 
These  countries  have  below  average  rates  of  vulnerability.  The  countries  in  this 
group  are  the  Czech  Republic,  United  Kingdom,  Finland,  Belgium,  Malta  and 
Slovakia. 

  Group 3. A group of countries many of which are Eastern European. These have 
above  average  rates  of  vulnerability.  Countries  included  in  this  group  are 
Hungary, Estonia, Lithuania, Latvia, Bulgaria, Poland and Spain. 

  Group 4. A group consisting of countries in South East Europe. These countries 
have  among  the  highest  rates  of  vulnerability,  with  especially  high  incidence 
rates  on several indicators.  Countries included in this group are  Greece,  Cyprus 
and Romania.  

  Group  5.  A  group  of  countries  located  either  on  the  Adriatic  Sea  or  Atlantic 
Ocean.  These  countries  exhibit  average  or  high  rates  of  vulnerability  on  some 
indicators, but low incidence of vulnerability on other indicators. The countries in 
this group are Italy, Slovenia, France, Portugal, Ireland and Croatia.  

Below we discuss the characteristics of the groups that have been established as a result 
of the cluster analysis. 

Overall vulnerability levels 
In  broad  terms,  the  country  groups  differ  in  terms  of  their  average  incidence  of 
vulnerability.  Looking  at  the  average  level  of  vulnerability  across  the  dimensions403, 
group  1  has  the  lowest  average  vulnerability  (17.7%);  followed  by  group  2  (20.3%), 
group  5  (22.2%),  group  3  (25.5%),  and  group  4  has  the  highest  average  incidence  of 
vulnerability  (27.3%),  as  shown  in  Figure  2.  All  countries  in  groups  1,  2,  and  5  have  a 
lower average incidence of vulnerability across the dimensions than countries in groups 3 
and 4. Compared to the outlier analysis, reported in Chapter 4, countries in group 1 are 

401  Specifically,  these  indicators  are  (see  Table  5  for  definitions):  1,  2,  3,  4,  8_1_ene,  8_2_ene,  8_1_fin, 

8_2_fin, 8_1_onl, 8_2_onl, 9e_bun, 9d_ter, 10b, 10a, and 12_off. 

402 The rate of vulnerability referred to in these bullet points is the average rate of vulnerability based on the 

indicators included in the cluster analysis. 

403  That  is,  the  average  of  the  indicators  for  each  dimension  is  calculated  first;  then  the  average  of  the 
dimension averages is calculated. This is based on the fifteen indicators included in the cluster analysis. 

708 

 
 
 
                                                 
Annex 7│ Cluster analysis of indicators of vulnerability 

mostly  low  outliers  on  both  dimension  2  and  5404,  while  countries  in  group  4  are  high 
outliers across all the dimensions. 

However,  the  countries  are  not  solely  grouped  based  on  the  average  incidence  of 
vulnerability  across  the  dimensions  (e.g.  some  countries  in  group  2  have  lower 
vulnerability than in group 1).  

Figure 74:  Group vulnerability level 

A  possible  alternative  approach 
to 
grouping  the  countries  would  be  to  rank 
the  countries  from  low-to-high  based  on 
the average vulnerability levels shown in 
Figure  74,  and  then  to  divide  the 
countries  in  five  roughly  equal  sized 
groups based on this ranking. While this 
approach  would  be  equivalent  to  the 
cluster  analysis  if  countries  with  higher 
vulnerability  on  some  indicators  had 
higher  vulnerability  across  all 
the 
indicators, 
‘rank-and-divide’ 
this 
approach  ignores  the  particular  type  of 
consumer  vulnerability  present  in  each 
country. 

The  cluster  analysis,  in  contrast,  groups 
countries according to the particular type 
of  consumer  vulnerability  present  in 
these  countries.  This  is  important  since 
two countries with similar average levels 
of  consumer  vulnerability  (e.g.  Iceland 
(19.4%)  and  the  UK (18.9%),  as  shown 
in  Figure  74)  may  be  quite  different  in 
terms of the type of vulnerability present 
in  those  countries  (e.g.,  as  reported  in 
section 4.1, Iceland mainly has issues relating consumer choice and switching, while the 
UK  has  a  higher  incidence  of  perceived  vulnerability  due  to  various  personal 
characteristics). 

That  said,  as  Figure  75  illustrates,  this  corresponds  fairly  well  with  the  level  of 
vulnerability – however countries cannot be solely characterised in terms of the level of 
vulnerability.  

To  explore  this  further,  additional  analysis  is  needed  on  the  specific  dimensions  of 
vulnerability  on  which  countries  can  be  grouped.  This  is  done  in  the  following  sub-
section. 

A7.2.2  Group specific vulnerability 

Building  upon  the  overall  vulnerability  level  of  the  country  groups  in  the  previous 
section, we explore in this sub-section the characteristics of the country groups in more 
detail. 

404 Sweden and Denmark are not identified as low outliers on either dimension 2 or 5, Luxembourg is only a 

low outlier on dimension 2. 

709 

15.0%17.5%20.0%22.5%25.0%27.5%30.0%PTHRSIITIEFRCYROELBGLVLTEEPLHUESMTSKFIBECZUKISLUDKSEDENONLATAverage of vulnerability dimension averagesGroup 5 Group 4           Group 3Group 2Group 1 
 
                                                 
 
Annex 7│ Cluster analysis of indicators of vulnerability 

To visualise the clusters of countries, we proceed with two steps: 

1)  A  factor  analysis  is  used  to  explore  how  the  vulnerability  dimensions405  can  be 

reduced; and 

2)  The  countries  are  plotted  on  the  reduced  dimensions  to  visualise  the  position  of 

the countries vis-à-vis one another. 

As  such,  the  “clusters”  of  countries  are  artificially  recreated  and  visualised  on  a  two 
dimensional plot through a combination of the dimensions.  

Factor analysis 
A  factor  analysis406  is  commonly  used  to  reduce  the  dimensionality  of  the  problem.  As 
shown  in  Table  1,  each  dimension  has  three  numbers:  a  factor  loading  on  each  of  the 
two factors (i.e. the latent root, conceptually the amount of variance accounted for by a 
factor), and a uniqueness score (i.e. the proportion not accounted for by either factor). 
The  higher  the  factor  loading,  the  better  this  factor  is  related  to  a  factor.  In  the  factor 
analysis, there are four factor loadings above 0.5. These have been highlighted in Table 
1. 

As  it  is  impractical  to  visualise  each  country  on  15  indicators,  from  the  factor  analysis, 
we  find  that  we  should  focus  on  dimension  2  +  dimension  5  and  dimension  1  + 
dimension 3 to provide  an informative picture of the locations of the countries in terms 
of vulnerability. In contrast, dimension 4 is not strongly associated with either factor and 
would obfuscate the picture (with two factors). 

Table  151:   Factor analysis of dimensions 

Dimension 

Factor 1 

Factor 2 

Uniqueness 

Dimension 1. Heightened risk of negative 
outcomes or impacts on well-being 

Dimension 2. Having characteristics that limit 
ability to maximise well-being 

Dimension 3. Having difficulty in obtaining or 
assimilating information 

Dimension 4. Inability or failure to buy, choose 
or access suitable products 

Dimension 5. Higher susceptibility to marketing 
practices 

0.1215 

0.7576 

0.3035 

0.8659 

0.1271 

0.1053 

-0.0986 

0.5397 

0.7613 

0.1949 

0.3763 

0.7345 

0.9267 

-0.0341 

0.1771 

Note: The factor analysis technique used is a principle factor (PF) with promax rotation to maximise the factor 
loadings of each dimension to the factors. 

405 The five dimensions with the fifteen indicators are used. The cluster analysis is conducted on the indicator 

level, however the dimension level facilitates the reduction of the factors.  

406 The factors have been rotated to  have some dimensions strongly related to the factors, and other factors 

not very much. The promax rotation variant has been used to keep the factors uncorrelated. 

710 

 
 
  
                                                 
Annex 7│ Cluster analysis of indicators of vulnerability 

Positions countries vis-à-vis  

Following  the  factor  analysis,  we  take  the  average  of  the  vulnerability  incidence  on 
dimension 2 + dimension 5 and dimension 1 + dimension 3 and plot these respectively 
on the x-axis and y-axis407. 

A  caveat  should  be  added  in  terms  of  the  visualisation  of  the  position  of  the  countries 
vis-à-vis  one  another  in  Figure  3,  namely  dimension  4,  which  is  not  incorporated,  and 
will for some country pairs decrease the distance between one another (e.g. Poland and 
Spain) and for other country pairs increase the distance (e.g. Romania and Cyprus). 

From  the  visualisation  of  the  positions  of  the  countries  vis-à-vis  one  another,  the  five 
country  groups  can  be  re-identified  as  neighbours  or  “clusters”  –  these  have  been 
marked  by  circles  in  Figure  3.  Closer  to  the  intercept  (bottom-left,  group  1)  the  low 
vulnerability  countries  are  located,  while  further  away  from  the  intercept  (top-right, 
group 5) the higher vulnerability countries are located.  

Figure 75:  Vulnerability characteristics clusters 

X1 

Note:  Based  on  indicators  1,  2,  3_max,  4,  8_1_ene,  8_2_ene,  8_1_onl,  8_2_onl,  8_1_fin,  8_2_fin,  9_bun, 
9_ter, 10_card, 10_diff, 12_off weighted by dimension. The factor analysis (ipf) shows that if countries would 
need to be plotted in a two dimensional space, the best  use of the axis would be Dimension 1 separately on 
one axis and Dimensions 2 and 5 combined on the other axis. 
Source: London Economics analysis of the survey data 

In  addition,  the  plot  in  Figure  75  shows  why  certain  countries  with  similar  average 
vulnerability  levels  are  not  grouped  together,  for  instance,  Iceland  and  the  UK.  It  also 

407 Dimension 1 is made up of two indicators, namely Indicator 1 ‘took no action when experienced a problem’ 

and Indicator 2 ‘Overpaid due to payment methods’. 

711 

 
 
 
 
                                                 
 
 
Annex 7│ Cluster analysis of indicators of vulnerability 

reveals  why  certain  countries  with  different  average  vulnerability  levels  are  grouped 
together, e.g. Greece and Cyprus, as these both score the highest on ‘perception of own 
vulnerability’ (dimensions 2 and 5).  

In  terms  of  characteristics,  a  rule  based  distinction  can  be  made  within  the  ‘low 
vulnerability countries’ (groups 1, 2, and 5) and the ‘high vulnerability countries’ (groups 
3, and 4) to further split the individual groups.  

Groups  1,  2  and  5  can  be  differentiated  from  each  other  by  examining  the  extent  that 
consumers in these country groups feel vulnerable for the  various reasons mentioned in 
the  survey  (age,  health,  employment,  complexity  of  offers,  etc.).  As  can  be  seen  from 
the top-right panel in Figure 3, countries in group 1 have the lowest ‘perception of own 
vulnerability’ (dimensions 2 and 5) among consumers, followed by countries in group 2, 
then countries in group 5 with the highest vulnerability on the x-axis of the graph.  

Similarly,  groups  3  and  4  can also be distinguished  from  each other by considering the 
average feeling of vulnerability among consumers in these country groups408. Consumers 
in group 3 countries on average feel less vulnerable due to the factors mentioned in the 
survey than consumers in group 4 countries. 

Thus  within  each  of  the  low  and  high  vulnerability  groups,  the  highest  country  has  a 
lower  vulnerability  than  the  lowest  of  the  next  group  on  the  ‘perception  of  own 
vulnerability’ (dimensions 2 and 5), i.e. Slovakia has a lower average ‘perception of own 
vulnerability’ than France, and puts Slovakia in group 2 and France in group 5. 

Variation  in  perceived  vulnerability  is  thus  the  method  to  distinguish  and  describe 
countries of the high and low vulnerability clusters into their own sub-clusters. 

Last, these dimensions (‘perception of own vulnerability’, dimensions 2 and 5) are once 
more  the  divisive  measure  to  cluster  country  groups  at  a  more  disaggregate  level 409. 
This can be done by grouping groups 1 and 2 together (Clusters A.) and groups 3, 4 and 
5 (Cluster C.)410 – the line X1 in Figure 3 marks the separation between the two clusters. 
Thus similar to the distinction of the low ‘vulnerability’ and ‘high vulnerability’ groups in 
their consistent groups, the more disaggregate level groups can be distinguished on the 
same dimensions (‘perception of own vulnerability’). 

408 Dimensions 2 and 5 
409  For  the  country  group  analysis  in  the  report  in  section  6.7,  country  groups  are  at  times  combined  to 

increase the sample size. 

410  Likewise  groups 3  and 4  can  be  combined  into  one  larger  group  (Cluster B.).  Cluster  B.  reflects  the close 

affinity of the two groups on all dimensions and intermediate grouping of Cluster C. 

712 

 
 
 
                                                 
Annex 7│ Cluster analysis of indicators of vulnerability 

A7.2.3  Sample sizes for each consumer group for each experiment 

Table 152:  Sample sizes for each consumer group for each experiment 

 Energy 

Online 

 Finance 

 Generic 

Personal & Demographic 
Age 35-44 (base) 
Age 16-24 
Age 25-34 
Age 45-54 
Age 55-64 
Age 65-74 
Age 75+ 
Female (base) 
Male 
High density region (base) 
Medium density region 
Low density region 
Native mother tongue (base) 
Foreign mother tongue 
High/Medium Education (base) 
Low Education 
Not long-term sick or disabled 
(base) 
Long-term sick or disabled 
Behavioural 
Risk taking: Not at all (base) 
Risk taking: Not very 
Risk taking: Fairly 
Risk taking: Very 
Trust 2: Very low (base) 
Trust 2: Low 
Trust 2: High 
Trust 2: Very high 
Impulsiveness: Very low (base) 
Impulsiveness: Low 
Impulsiveness: High 
Impulsiveness: Very high 
Credulity (base) 
No credulity 
Market-related and experience 
Know contract conditions: 
Completely (base) 
Know contract conditions: Fair 
amount 
Know contract conditions: Not 
very 
Know contract conditions: Not at 
all 
Compare deals: From time to time 
(base) 
Compare deals: When 
notified/need to renew 
Compare deals: Sporadically 
Compare deals: Only the first time 
Compare deals: Never 
Read communication: In detail 
(base) 

713 

484 
336 
496 
465 
379 
261 
101 
1316 
1206 
1197 
640 
685 
2319 
203 
1863 
659 

2455 

67 

464 
1134 
788 
98 
374 
946 
1001 
201 
638 
1049 
670 
165 
703 
1501 

261 

903 

894 

387 

460 

292 

276 
340 
1154 

581 

493 
359 
496 
429 
387 
258 
88 
1338 
1172 
1205 
633 
672 
2306 
204 
1876 
634 

2454 

56 

463 
1143 
759 
102 
355 
947 
994 
214 
585 
1078 
681 
166 
698 
1500 

444 

846 

345 

98 

678 

376 

243 
240 
229 

448 

506 
343 
446 
465 
397 
264 
84 
1363 
1142 
1183 
628 
694 
2317 
188 
1875 
630 

2422 

83 

476 
1174 
708 
109 
375 
915 
1024 
191 
639 
1060 
648 
158 
685 
1484 

480 

1067 

509 

105 

473 

213 

324 
494 
1001 

709 

439 
346 
480 
477 
443 
283 
97 
1337 
1228 
1205 
643 
717 
2340 
225 
1852 
713 

2489 

76 

495 
1199 
719 
111 
376 
926 
1043 
220 
608 
1093 
683 
181 
712 
1523 

350 

975 

607 

208 

510 

324 

293 
314 
858 

605 

 
 
 
Annex 7│ Cluster analysis of indicators of vulnerability 

Table 152:  Sample sizes for each consumer group for each experiment 
 Energy 

 Finance 

Online 

 Generic 

Read communication: Glanced or 
skim read 
Read communication: Saw total 
price/what it was 
Read communication: Not read it 
at all 
Read communication: Don't know 
Read bill: Very easy (base) 
Read bill: Easy 
Read bill: Difficult 
Read bill: Very difficult 
Source: London Economics 

726 

541 

417 

180 
268 
993 
493 
94 

700 

198 

244 

143 
392 
663 
84 
9 

779 

231 

211 

231 
457 
836 
172 
23 

729 

354 

279 

173 
374 
849 
261 
41 

714 

 
 
 
Annex 8│ Data analysis undertaken to examine the drivers of and factors linked to consumer 
vulnerability 

Annex 8  Data  analysis  undertaken  to  examine  the  drivers  of 
and factors linked to consumer vulnerability 

This annex describes the methodology and variables used in the analysis of Chapter 6 of 
the report. The analysis comprises of two main parts: 

1) 

2) 

An  analysis  of  factors  explaining  the  survey-based  indicators  of  vulnerability, 
and; 

Analysis of factors explaining the performance of participants in the experiments, 
and factors explaining the treatment effect in the experiments 

The analysis uses four main statistical tools: 

  Correlation analysis; 

  Multivariate  variable  selection  analysis  through  General-to-Specific  selection 

models. The models involved are the following: 

  Multivariate binary choice analysis through logistic regression models; 

  Multivariate  ordered    choice  analysis  through  ordered  logistic  regression 

models;  

Two  additional  univariate  statistical  tools  are  used  in  the  experiment  analysis  only  and 
are described in section A8.3:  

  Difference-in-proportion analysis; 

  Difference-in-differences analysis. 

The section below provides a brief description of the various models and techniques that 
are used in both main parts of the analysis. 

A8.1  Analytical tools common to both main parts of the analysis 

A8.1.1  Statistical methods 

Correlation analysis 

The correlation analysis focuses on the potential link between a dependent variable and 
an explanatory variable. In the correlation analysis, a respondent is included in the data 
sample  used  in  the  correlation  analysis  as  long  as  she/he  has  given  an  answer  to  the 
questions  regarding  the  dependent  and  explanatory  variables.  Thus  the  size  of  the 
sample  over  which  the correlation is  computed  will  vary.  This  approach  was  adopted  in 
order to maximise sample size while non-responses are excluded from the analysis and 
only reduce the number of respondents in each particular correlation computation.   

The  analysis  is  conducted  at  the  highest  level  of  detail  possible  of  the  explanatory 
variable  and  a  correlation  coefficient  is  computed  for  each  possible  answer  to  each 
question related to the set of potential vulnerability drivers411.  

General-to-specific selection models 

411  See  the  variable  list  in  A8.1.2.  Age  was  re-coded  into  age  bands.  Household  size  is  an 

exception. 

715 

 
 
                                                 
Annex 8│ Data analysis undertaken to examine the drivers of and factors linked to consumer 
vulnerability 

The econometric analysis is performed via General-to-Specific (G-S) modelling. The G-S 
approach  is  suitable  for  cases  where  there  are  a  large  number  of  potential  explanatory 
variables and it is not clear a priori which explanatory variables should be included in the 
regression model.412 

In  order  to  select  a  parsimonious  model  from  a  large  set  of  potential  explanatory 
variables, each dependent variable is first modelled using a general model that includes 
all potential explanatory variables as regressors. Then, the general model is reduced in 
complexity  by  eliminating  from  the  model,  one  at  a  time,  statistically  insignificant 
variables while checking the validity of the model reduction at every stage. 

The  general-to-specific  approach  used  here  is  ‘GETS’  in  STATA413.  This  approach 
removes  the  least  statistically  significant  variable  at  each  stage  in  an  iterative  process. 
To ensure that the validity of the model is maintained five statistical tests are conducted 
at each  stage  of the process.414 If validity of the model is maintained (according to the 
results  of  the  tests),  the  least  statistically  significant  variable  is  removed  at  each 
iteration and this process is repeated until either all variables remaining in the model are 
statistically  significant  or  no  variable  can  be  removed  without  failing  the  statistical 
tests415. 

Since  the  order  in  which  variables  are  removed  during  the  iterative  process  may  affect 
which  variables  are  included  in  the  final  regression  model,  five  different  ‘search  paths’ 
are  used  in  GETS.  The  first  path  starts  (at  the  first  iteration)  with  the  removal  of  the 
least  statistically  significant  variable,  the  second  path  starts  with  the  removal  of  the 
second  least  statistically  significant  variable,  etc.  Following  multiple  selection  paths  in 
this  way  reduces  the  likely  of  excluding  important  variables.  The  final  G-S  model  is 
selected  from  the  five  path  endpoints  based  on  the  lowest  Baysian  Information 
Criterion.416 

412  Promoted  by  the  London  School  of  Economics  and  Professor  Hendry.  Hendry,  D.  F.  (1986). 
Econometric modelling with cointegrated variables: an overview. Oxford Bulletin of Economics 
and Statistics, 48(3), 201-212 

413  Clarke,  D.  (2014).  General-to-specific  modeling  in  Stata.  Stata  Journal,  14(4),  895-908.  The 

module is also known as ‘GENSPEC’. 

414  These 

tests  are 

the  Doornik-Hansen 

for 
test 
heteroskedasticity;  Ramsey  RESET  test  for  linearity;  and,  in-  and,  out-of-sample  Chow  tests 
for structural breaks in the data. Tests that the model does not pass before the first iteration 
of  the  model  are  not  used  again  (in  practice  the  normality  and  heteroskedasticity  tests, 
because the dependent variable is frequently binary) 

for  normality;  Breusch-Pagan 

test 

415  It  was  not  practical  to  implement  the  general-to-specific  statistical  tests:  the  in-and,  out-of-
sample  Chow  tests  randomly  selects  50%  and  10%  of  the  sample  respectively  to  test  for 
structural breaks and external validity of the model. The random selection means that the test 
might  be  passed  or  failed  by  chance,  leading  to  non-reproducible  results  of  the  G-S  model. 
Statistical 
disabled. 
Robust  standard  errors  were  specified,  in  response  to  frequent  failures  in  particular  of  the 
Breusch-Pagan test for heteroskedasticity. 

were 

tests 

416 Schwarz, G. (1978), “Estimating the dimension of a model”, Annals of statistics, 6(2), 461-464. 
The Baysian Information Criterion trades-off the goodness-of-fit of the model with the number 
of  variables.  The  criterion’s  general  form  is  as  follows:  BIC  =  ln(n)k  –  2lnL,  where  n  is  the 
number  of  observations.  This  leads  in  general  to  a  more  parsimonious  model  selection  than 
other criteria (e.g. Akaike Information Criterion). The first part of the expression is a function 
of the number of free parameters to estimate, and thus represents the complexity introduced 
by  trying  to  fit  the  model  with  more  regressors.  The  second  part  includes  a  penalty  as  an 

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Annex 8│ Data analysis undertaken to examine the drivers of and factors linked to consumer 
vulnerability 

In  the  final  step,  in  the  case  of  variables  with  a  number  of  dummies  or  sub-variables 
(such  as  for  example  an  age  variable  included  in  the  analysis  as  a  series  of  age  range 
dummies – for example 0-19, 20-49, 50-69, 70+) the set of explanatory variables of the 
final  G-S  model  is  expanded  by  adding  back  into  a  model  all  the  dummies  or  sub-
variables  variables  belonging  to  the  same  group  of  a  particular  variable  even  if  these 
dummies or sub-variables had been eliminated during to the G-S process. This approach 
ensures  that  the  base  (i.e.,  the  particular  dummy  or  sub-variable  category  excluded 
from the model to avoid perfect multi-collinearity) is the same across all models. 

In  this  manner,  the  effect  of  variable  represented  by  a  series  of  dummies  is  in  effect 
estimated relative to a base group of respondents used for comparison that differ in the 
characteristic  of  the  same  dimension  (see  section  below  for  base  categories).  For 
example,  if  in  model  using  the  age  groupings  described  above,  the  age  group  0-19  is 
excluded,  the  econometrically  estimated  effect  of  belonging  to  an  older  age  group  will 
always be relative to effect the 0-19 age group.  

If only one dummy or sub-variable is excluded from the analysis, its effect is co-mingled 
with that of the true” constant term of the model. When several dummies are excluded, 
the  estimated  constant  of  the  model  reflects  the  combined  effect  of  all  the  excluded 
variables  and  the  “true”  constant,  and  it  is  not  possible  to  split  the  estimated  constant 
into its constituent components. 

The  relationship  between  the  dependent  variable  and  its  potential  drivers  is  then 
estimated with the complete set of explanatory variables using logistic or ordered logistic 
regression  models.  Marginal  effects  (and  odds-ratios)  are  reported  for  the  final 
regressions. 

A8.1.2  List of explanatory variables 

The set of potential vulnerability drivers has been selected based on literature research, 
expert  opinion  and  communication  between  LE,  VVA,  Ipsos  and  the  European 
Commission.  The  vulnerability  drivers 
to  personal  and  demographic 
characteristics,  behavioural  characteristics,  market-related  and  experience,  access 
characteristics and situational characteristics. In total, 189 drivers were derived from 49 
survey questions – this group of drivers also includes three sets of composite indicators 
related  to  the  (employment  status  and  time  in  the  current  status,  the  civil  status  of 
being single and a parent, and the number of purposes for which the internet is used on 
a monthly basis). 

relate 

All respondent specific drivers except household size, are coded as dummy variables, i.e. 
the  characteristic  can  be  only  true  or  false.  In  addition,  characteristics  related  to  a 
particular  survey  are  mutually  exclusive  –  these  groups  are  indicated  by  intermittent 
shades  in  Table  153.  For  example,  the  gender  of  respondents  is  coded  as  two  dummy 
variables  (female  and  male)  and  the  age  of  respondents  is  coded  as  six  age  group 
dummies (16-24, 25-34, 45-54, 55-64, 65-74 and 75+) 

As  already  noted  above,  the  coding  of  the  various  characteristics  of  respondents  as 
dummy variables implies that in the regression analysis one characteristic of a particular 
variable is  always  excluded  from  the  set  of  explanatory  variables.  Its  effect  is  captured 

increasing  function  of  the  number  of  estimated  parameters.  This  penalty  discourages  over-
fitting. Hence, the “best” model is the one with the lowest BIC value. 

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Annex 8│ Data analysis undertaken to examine the drivers of and factors linked to consumer 
vulnerability 

by the constant of the estimated model. In the discussion below, the excluded dummy is 
called the base category.  

From  a  statistical  point  of  view,  the  base  category  provides  the  null  hypothesis  that  a 
regression  analysis  sets  out  to  reject,  namely  that  a  particular  characteristic  has  the 
same  effect  on  the  dependent  variable  as  the  characteristic  reflected  in  the  base 
category. 

As  the  dependent  variables  are  indicators  of  vulnerability  and  measures  of  experiment 
performance, the categories we are most interested in are the more vulnerable groups a 
priori.  

The  key  purpose  of  the  selection  of  a  particular  base  category  is  to  facilitate  the 
comparison  of  the  drivers  of  vulnerability.  Therefore,  the  specific  selection  of  the 
characteristics  of  a  particular  variable  to  define  as  base  categories  was  based  on  the 
following considerations: 

  Largest group of respondents in the case of a number of objective features of 
respondents (e.g. married, female, full-time employed, no dependent children, 
native speaker, etc.); 
In the case of ordinal responses, the end-of-spectrum conservative response 
(e.g. very low impulsiveness, very risk averse, very low trust,  meeting ends is 
easy, complete knowledge of contract conditions, read last communication in 
detail, etc.); 

 

  Indicators least associated with vulnerability in the experiment (e.g. age 

groups 35 to 44, high education, answered the credulity question correctly, does 
not have difficulty with small print, monthly internet use, etc.); 

  Negative responses to a positive statement (e.g. incorrect answer to the 

computerised test 1&2, does not have friends that buy goods or services online, 
does not know the meaning of kWh/Mbps/interest rate, does not have monthly 
internet activities, etc.). 

In  addition,  there  are  three  contract-related  dummies  that  belong  to  three  sets  each. 
Namely, the  response  “Does not  have  a(n) … contract”417 did not get the follow-up 
questions on 1) the knowledge of the contract, 2) the level of detail the respondent read 
the contract and 3) the difficulty the respondent had to read the contract in that sector. 

In  the  correlation  analysis  respondents  without  a  contract  were  simply  left  out  as  non-
respondents and this did not pose a particular issue.  

In  the  regression  analysis  however,  a  large  and  important  group  of  respondents  would 
not  have  been  included  in  the  analysis  as  they  did  not  have  a  response  variable. 
Therefore, a dummy variable “Does not have a(n) … contract” was created to be used as 
the answer of respondents who did not answer this question. 

417  The  three  variables  are:  “Does  not  have  an  energy  contract”,  “Does  not  have  an  internet 

contract” or “Does not have an bank contract” 

718 

 
 
                                                 
Annex 8│ Data analysis undertaken to examine the drivers of and factors linked to consumer vulnerability 

Table 153:  Explanatory variables included in regressions 

Name/label 

Description 

Personal and demographic characteristics 

Age 16-24 

Age 25-34 

Age 45-54 

Age 55-64 

Age 65-74 

Age 75+ 

Male 

Low density region 

Medium density region 

Household size 

Low education 

Medium education 

Age group from 16 to 24 

Age group from 25 to 34 

Age group from 45 to 54 

Age group from 55 to 64 

Age group from 65 to 74 

Age group above 75 

Male respondent 

Respondent lives in low density region 

Respondent lives in medium density region 

Number of individuals in the household 

Low education level  

Medium education level 

Base category 

Age group 35 to 44 

Female 

High density region 

N/A 

High education level 

Non-native tongue, no diff’ty 

Mother tongue different from the official language, but this does not cause difficulty 

Native speaker 

Non-native tongue, difficulty 

Mother tongue different from the official language, and this causes difficulty 

Behavioural characteristics 

Trust 1: Low 

Trust 1: High 

Trust 1: Very high 

Trust 2: Low 

Trust 2: High 

Trust 2:Very high 

Low trust in people; Tends to agree with the statement "you need to be very careful in dealing 
with people" 

Very low trust 

High trust in people; Tends to disagree with the statement "you need to be very careful in 
dealing with people" 

Very high trust in people; Strongly disagrees with the statement "you need to be very careful 
in dealing with people" 

Low trust in people; Tends to disagree with the statement "I believe most people can be 
trusted" 

Very low trust 

High trust in people, Tends to agree with the statement "I believe most people can be trusted" 

Very high trust in people; Strongly agrees with the statement "I believe most people can be 
trusted" 

Credulity test: Incorrect 

Answered credulity test incorrectly  

Credulity test: Not taken 

Did not take credulity test 

Answered credulity test 
correctly  

Credulity: Low  

Low credulity; Tends to disagree  with the statement "Most advertisements report objective 
fact, I trust most of the information provided in advertisements" 

Very low credulity 

719 

 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
Annex 8│ Data analysis undertaken to examine the drivers of and factors linked to consumer vulnerability 

Table 153:  Explanatory variables included in regressions 

Name/label 

Credulity: High 

Credulity: Very high 

Impulsiveness 1: Low  

Impulsiveness 1: High 

Impulsiveness 1: Very high 

Impulsiveness 2: Low  

Impulsiveness 2: High 

Impulsiveness 2: Very high 

Impulsiveness 3: Low  

Impulsiveness 3: High 

Impulsiveness 3: Very high 

Impulsiveness 4: Low  

Impulsiveness 4: High 

Impulsiveness 4: Very high 

Risk taking: Not very  

Risk taking: Fairly 

Risk taking: Very 

Risk taking: No response  

Computation test 1 correct  

Computation test 2 correct  

Knows meaning of kWh 

Knows meaning of Mbps 

Description 

Base category 

High credulity; Tends to agree with the statement "Most advertisements report objective fact, I 
trust most of the information provided in advertisements" 

Very high credulity; Strongly agrees with the statement "Most advertisements report objective 
fact, I trust most of the information provided in advertisements" 

Low impulsiveness; Tends to disagree with the statement "I have a hard time breaking bad 
habits" 

Very low impulsiveness 

High impulsiveness; Tends to agree with the statement "I have a hard time breaking bad 
habits" 

Very high impulsiveness; Strongly agrees with the statement "I have a hard time breaking bad 
habits" 

Low impulsiveness; Tends to agree  with the statement “I’m good at resisting temptation” 

Very low impulsiveness 

High impulsiveness; Tends to disagree with the statement “I’m good at resisting temptation” 

Very high impulsiveness; Strongly disagrees with the statement “I’m good at resisting 
temptation” 

Low impulsiveness; Tends to agree  with the statement “People would say that I have very 
strong self-discipline” 

Very low impulsiveness 

High impulsiveness; Tends to disagree with the statement  “People would say that I have very 
strong self-discipline” 

Very high impulsiveness; Strongly disagrees with the statement  “People would say that I have 
very strong self-discipline” 

Low impulsiveness; Tends to disagree  with the statement “I’m impulsive in the purchase 
decisions I take” 

Very low impulsiveness 

High impulsiveness; Tends to agree with the statement “I’m impulsive in the purchase 
decisions I take” 

Very high impulsiveness; Strongly agrees with the statement “I’m impulsive in the purchase 
decisions I take” 

Not very willing to take risks 

Fairly willing to take risks 

Very willing to take risks 

Did not answer the question on willingness to take risks 

Answered first computation test correctly 

Answered second computation test correctly 

Knows the meaning of the term kWh 

Knows the meaning of the term Mbps 

Not at all willing to take risks 

Answered incorrectly 

Answered incorrectly 

Answered incorrectly 

Answered incorrectly 

720 

 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
Annex 8│ Data analysis undertaken to examine the drivers of and factors linked to consumer vulnerability 

Table 153:  Explanatory variables included in regressions 

Name/label 

Description 

Identified best interest rate 

Identified best interest rate for a savings account 

Market-related drivers and experience in markets 

Base category 

Answered incorrectly 

Unable to read Ts&Cs due to small print 

Unable to read energy, telecommunication or banking contract terms and conditions due to 
overly small print 

Able to read contract Ts&Cs 

Compare energy deals: When need to 
renew 

Compares energy deals from internet providers "but only when I need to renew my contract" 

Compares deals "from time 
to time"  

Compare energy deals: Sporadically 

Compares energy deals "but only sporadically" 

Compare energy deals: Only the first time  Compares energy deals from internet providers only "the first time I needed to choose a 

Compare energy deals: Never 

Never compared energy deals 

provider" 

Compare internet deals: When need to 
renew 

Compares deals from internet providers "but only when I need to renew my contract" 

Compare internet deals: Sporadically 

Compares deals from internet providers "but only sporadically" 

Compare internet deals: Only the first 
time 

Compares deals from internet providers only "the first time I needed to choose a provider" 

Compare internet deals: Never 

Never compared deals from internet providers 

Compare internet deals: No response 

Did not answer the question on frequency of comparing internet deals 

Compare deals from banks: When notified  Compares deals from banks "but only when I am notified that the conditions of my contract will 

be changed" 

Compare deals from banks: Sporadically 

Compares deals from banks "but only sporadically" 

Compare deals from banks: Only the first 
time 

Compares deals from banks only "the first time I needed to choose a bank" 

Compare deals from banks: Never 

Never compared deals from banks 

Know energy contract: Not at all 

Does not know energy contract conditions "at all" 

Know energy contract: Not very 

Does not know energy contract conditions "very much" 

Know energy contract: Fair amount 

Knows energy contract conditions a "fair amount" 

Know energy contract/Read bill: No 
response 

Does not have a gas or electricity contract (so no response to question on knowledge of 
contract/read last bill) 

Know internet contract conditions: Not at 
all 

Know internet contract conditions: Not 
very 

Does not know internet contract conditions "at all" 

Does not know internet contract conditions "very much" 

Compares deals "from time 
to time" 

Compares deals "from time 
to time" 

Know contract conditions 
completely 

Know contract conditions 
completely 

721 

 
 
  
  
  
  
  
  
  
  
  
  
 
  
Annex 8│ Data analysis undertaken to examine the drivers of and factors linked to consumer vulnerability 

Table 153:  Explanatory variables included in regressions 

Name/label 

Description 

Base category 

Know internet contract conditions: Fair 
amount 

Knows internet contract conditions a "fair amount" 

Know internet contract/read comm'n: No 
response 

Does not have internet contract (so no response to question on knowledge of contract/read 
communication) 

Know bank contract conditions: Not at all  Does not know bank contract conditions "at all" 

Know bank contract conditions: Not very 

Does not know bank contract conditions "very much" 

Know bank contract conditions: Fair 
amount 

Knows bank contract conditions a "fair amount" 

Know contract conditions 
completely 

Know bank contract/read comm'n: No 
response 

Does not have bank contract (so no response to question on knowledge of contract/read 
communication) 

Read energy bill: Glanced or skim read 

Read last bill from energy supplier, but "glanced over it or skim read it" 

Read last bill in detail 

Read energy bill: Looked at total price 

Read last bill from energy supplier, but "only looked at total price" 

Read energy bill: Not at all 

Did not read last bill from energy supplier 

Read energy bill: Don’t know 

Does not know or remember if read last bill from energy supplier 

Read energy bill: No response 

Does not have a gas or electricity contract  

Read internet communication: Glanced or 
skim read 

Read last internet provider communication, but "glanced over it or skim read it" 

Read communication in detail 

Read internet communication: Saw what 
it was 

Read last internet provider communication, but "only saw what it was (i.e. looked at the 
numbers)" 

Read internet communication: Not read it 
at all 

Did not read last internet provider communication "at all" 

Read internet communication: Don’t know  Does not know or remember if read last internet provider communication 

Read internet communication: No 
response 

Read bank communication: Glanced or 
skim read 

Read bank communication: Saw what it 
was 

Read bank communication: Not read it at 
all 

Does not have internet contract  

Read last bank communication, but "glanced over it or skim read it" 

Read communication in detail 

Read last bank communication and "only saw what it was (i.e. looked at the numbers)" 

Did not read last bank communication "at all" 

Read bank communication: Don't know 

Does not know or remember if read last bank communication 

Read bank communication: No response 

Does not have bank contract 

Read energy bill: Easy 

Read energy bill: Difficult 

Reading last bill from energy supplier was easy 

Reading last bill from energy supplier was difficult 

Very easy 

722 

 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Annex 8│ Data analysis undertaken to examine the drivers of and factors linked to consumer vulnerability 

Table 153:  Explanatory variables included in regressions 

Name/label 

Description 

Base category 

Read energy bill: Very difficult 

Reading last bill from energy supplier was very difficult 

Did not read energy bill 

Did not read the last bill from the energy supplier in detail, glanced over or skim read it (or has 
no energy supplier) 

Read internet communication: Easy 

Reading last internet provider communication was easy 

Very easy 

Read internet communication: Difficult 

Reading last internet provider communication was difficult 

Read internet communication: Very 
difficult 

Did not read internet communication 

Reading last internet provider communication was very difficult 

Did not read the internet provider communication in detail, glanced over or skim read it (or has 
no internet provider) 

Read bank communication: Easy 

Reading last bank communication was easy 

Read bank communication: Difficult 

Reading last bank communication was difficult 

Read bank communication: Very difficult 

Reading last bank communication was very difficult 

Very easy 

Did not read bank communication 

Did not read the bank communication in detail, glanced over or skim read it (or has no bank 
account) 

Access-related drivers 

Monthly internet activities: 7 

Monthly internet activities: 6 

Monthly internet activities: 5 

Monthly internet activities: 4 

Monthly internet activities: 3 

Monthly internet activities: 2 

Monthly internet activities: 1 

Uses internet at least once per month for 7 out of 7 activities (Search, Price comparison, Online 
banking , Online purchases, Online sales, Social network,  Email) 

Zero monthly internet 
activities 

Uses internet at least once per month for 6 out of 7 activities (Search, Price comparison, Online 
banking , Online purchases, Online sales, Social network,  Email) 

Uses internet at least once per month for 5 out of 7 activities (Search, Price comparison, Online 
banking , Online purchases, Online sales, Social network,  Email) 

Uses internet at least once per month for 4 out of 7 activities (Search, Price comparison, Online 
banking , Online purchases, Online sales, Social network,  Email) 

Uses internet at least once per month for 3 out of 7 activities (Search, Price comparison, Online 
banking , Online purchases, Online sales, Social network,  Email) 

Uses internet at least once per month for 2 out of 7 activities (Search, Price comparison, Online 
banking , Online purchases, Online sales, Social network,  Email) 

Uses internet at least once per month for 1 out of 7 activities (Search, Price comparison, Online 
banking , Online purchases, Online sales, Social network,  Email) 

Frequent internet use: Search 

Uses internet at least once a month for: Online search 

Frequent internet use: Compare prices 

Uses internet at least once a month for: Price comparison 

Frequent internet use: Banking 

Uses internet at least once a month for: Online banking 

Frequent internet use: Purchases 

Uses internet at least once a month for: Online purchases 

Frequent internet use: Selling 

Uses internet at least once a month for: Online sales 

Does not... 

Does not... 

Does not... 

Does not... 

Does not... 

723 

 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Annex 8│ Data analysis undertaken to examine the drivers of and factors linked to consumer vulnerability 

Table 153:  Explanatory variables included in regressions 

Name/label 

Description 

Frequent internet use: Social 

Uses internet at least once a month for: Social network 

Frequent internet use: Email 

Uses internet at least once a month for: Email 

Situational drivers 

Employed part-time 

Self-employed full-time 

Self-employed part-time 

Unemployed & looking 

Unemployed & not looking 

Long-term sick or disabled 

Housewife/Homemaker 

Retired 

Employed part-time 

Self-employed full-time 

Self-employed part-time 

Unemployed & looking 

Unemployed & not looking 

Long-term sick or disabled 

Housewife/Homemaker 

Retired 

In full time education 

In full time education 

Studying with part-time job 

Studying with part-time job 

Unemployed & looking 5+ years 

Has been unemployed & looking for work for at least 5 years 

Unemployed & not looking 5+ years 

Has been unemployed & not looking for at least 5 years 

Long-term sick or disabled 5+ years 

Has been long-term sick or disabled for at least 5 years 

Housewife/Homemaker 5+ years 

Has been a housewife or homemaker for at least 5 years 

Retired 5+ years 

Remarried 

Living with a partner 

Single 

Has been retired for at least 5 years 

Marital status: remarried 

Marital status: not married living with a partner 

Marital status: single 

Divorced or separated 

Marital status: divorced or separated 

Widowed 

Other 

Single parent 

1 dependent child 

2 dependent children 

3+ dependent children 

Single person household 

Friends buy online 

Friends buy on credit 

Marital status: widowed 

Marital status: other 

Is a single parent 

One child in the household  

Two children in the household 

Three or more children in the household 

Only member of the household 

Base category 

Does not... 

Does not... 

Employed full-time 

Not... 

Not... 

Not... 

Not... 

Not... 

Married 

Not... 

No dependent children 

Agree or strongly agree that "Most of my friends and relatives buy goods and services online" 

Does not... 

Agree or strongly agree that "Most of my friends and relatives buy goods and services on 
credit" 

Does not... 

724 

 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Annex 8│ Data analysis undertaken to examine the drivers of and factors linked to consumer vulnerability 

Table 153:  Explanatory variables included in regressions 

Name/label 

Description 

Friends can’t make ends meet 

Agree or strongly agree that "Most of my friends and relatives find it difficult to make ends 
meet every month" 

Making ends meet: Fairly easy 

Making ends meet is fairly easy 

Making ends meet: Fairly difficult 

Making ends meet is fairly difficult 

Making ends meet: Very difficult 

Making ends meet is very difficult 

Making ends meet: Prefer not to say 

Prefers not to answer the statement 

Base category 

Does not... 

Making ends meet is easy 

725 

 
 
  
 
  
 
Annex 8│ Data analysis undertaken to examine the drivers of and factors linked to 
consumer vulnerability 

A8.2  Econometric  analysis  of  the  survey  data  –  drivers  of 

consumer vulnerability 

The econometric analysis of the survey results aims to identify the drivers of consumer 
vulnerability. 

Consumer  vulnerability  was  described  and  operationalised  using  a  number  of 
quantifiable indicators as described in section 3.6. 

The analysis consists of the following steps: 

1.  Selection of a set of representative vulnerability indicators  
2.  Selection of a set of vulnerability drivers 
3.  Preliminary  correlation  analysis  (both  between  indicators  and  explanatory 

variables, and among explanatory variables) 

4.  General-to-specific  modelling  on  whole  survey  sample  (i.e.  combining  all 

countries) 

The selection of vulnerability drivers is the full set of variables described in the section 
A8.1.2. 

From  the  full  list  of  over  100  quantitative  indicators,  a  total  of  37  indicators  of 
vulnerability indicators, or dependent variables, were selected. 

A8.2.1  Dependent variables 

The G-S analysis examines the relationships between the explanatory variables set out 
in section A8.1.2 and the following survey-based indicators of vulnerability, which are 
the dependent variables in the regressions (these indicators are defined in section 3.6; 
the indicator codes in parentheses below match those in Table 5 in section 3.6). 

 

 

 

 

 

 

 

 

 

Indicator 1. Unassertive (took no action) when experienced a problem (1). 

Indicator 2. Overpaid for services due to being unable to use certain payment 
methods (2). 

Indicator 3. Perception of own vulnerability due to personal characteristics (3). 

Indicator 4. Does not feel informed about prices etc. (4). 

Indicator  5.  Gets  information  from  advertisements  only;  separately  for  the 
energy (5_adv_ene), online (5_adv_onl) and finance (5_adv_fin) sectors. 

Indicator 6. Has problems comparing deals due to information-related factors; 
separately for the energy (6_ene), online (6_onl) and finance (6_fin) sectors. 

Indicator 7. Has not switched due to information-related factors; separately for 
the energy (7_ene), online (7_onl) and finance (7_fin) sectors. 

Indicator 8a. Has problems comparing deals due to personal factors; separately 
for the energy (8a_ene), online (8a_onl) and finance (8a_fin) sectors. 

Indicator  8b.  Has  problems  comparing  deals  due  to  market-related  factors; 
separately  for  the  energy  (8b_ene),  online  (8b_onl)  and  finance  (8b_fin) 
sectors. 

726 

 
 
Annex 8│ Data analysis undertaken to examine the drivers of and factors linked to 
consumer vulnerability 

 

 

 

 

 

 

 

 

 

 

Indicator  8c.  Has  problems  comparing  deals  due  to  access-related  factors; 
separately  for  the  energy  (8c_ene),  online  (8c_onl)  and  finance  (8c_fin) 
sectors. 

Indicator  9a.  Has  not  switched  due  to  personal  factors;  separately  for  the 
energy (9a_ene), online (9a_onl) and finance (9a_fin) sectors. 

Indicator  9b.  Has  not  switched  due  to  market-related  factors;  separately  for 
the energy (9b_ene), online (9b_onl) and finance (9b_fin) sectors. 

Indicator 9c. Has not switched due to access-related factors; separately for the 
energy (9c_ene), online (9c_onl) and finance (9c_fin) sectors. 

Indicator 9d. Has not switched due to termination costs. 

Indicator 9e. Has not switched due to bundling. 

Indicator 10a. Excluded from e-commerce due to difficulty of the process. 

Indicator 10b. Excluded from e-commerce due to not having a payment card. 

Indicator 11. Declined for a loan. 

Indicator 12. Perception of own vulnerability due to complexity of offers. 

A8.2.2  Statistical models and tests 

The general-to-specific selection model was applied as described in A8.1.1.  

The ordered logistic regression was used after the G-S selection model for Indicator 3 
and Indicator 4.  

An ordered logistic regression was used for Indicator 12, as well as a logistic regression 
on  a  dummy  of  Indicator  12  for  respondents  who  indicated  to  feeling  vulnerable 
'because offers, terms or conditions are too complex '“to a great extent”.  

A standard logistic regression has been used for all other indicators. 

A8.2.3  Results available in Excel format 

For  the  empirical  analysis  of  the  drivers  of  vulnerability  using  the  survey-based 
indicators  of  vulnerability,  the  estimation  results  are  available  in  Excel  format  in  the 
spreadsheet: 

  Survey_G-S.xlsx 

The reported values are the marginal effects  and p-values for the logistic regressions, 
and odds-ratios and p-values for ordered logistic regressions. 

The analysis was undertaken on the entire sample and for individual countries  

A8.3  Analysis  of  experiments  –  relating  performance  to  drivers 

of vulnerability, marketing practice and remedy treatment 

The  aim  of  the  analysis  of  the  experiment  results  is  to  relate  performance  in  the 
experiments  to  the  drivers  of  vulnerability,  and  the  market  conditions/remedy 
treatment the respondent observed. 

The analysis of the factors linked to experiment performance consists of the following 
steps: 

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consumer vulnerability 

1.  Selection of a set of indicators for experiment performance; 
2.  Selection of a set of vulnerability drivers; 
3.  Difference-in-proportion tests of the marketing practice and remedy treatment; 
4.  Correlation  analysis  between  experiment  performance  and  explanatory 

variables; 

5.  Regression  analysis  for  each  group  of  explanatory  variables  (personal  and 
demographic,  behavioural,  market-related  and  experience,  access,  and 
situational); 

6.  General-to-specific modelling on whole survey sample (i.e. combining Denmark, 

Lithuania, Portugal, Romania, United Kingdom); and, 

7.  General-to-specific modelling on separate countries 

The  next  aim  of  the  analysis  is  to  disentangle  the  different  treatment  effects  on 
performance of various subgroups. More specifically, the aim is to identify the groups 
which  are  more  susceptible  to  the  marketing  practice  treatment,  or  in  other  words, 
benefit more from the remedy treatment. 

The analysis of the factors linked to the treatment effect in the experiments consists of 
the steps: 

8.   Difference-in-differences  estimations  of  the  marketing  practice  and  remedy 

treatment; 

9.  General-to-specific  modelling  on  the  whole  survey  sample  (i.e.  combining 
Denmark,  Lithuania,  Portugal,  Romania,  United  Kingdom)  and  on  separate 
countries; 

a.  Remedy treatment with full set of interaction terms; 
b.  Difference in treatment performance as dependent variable. 

A8.3.1  Dependent variables 

Performance 

The experiment performance variable comprises nine different measures, namely: 

  2 correct or incorrect responses in the energy sector (round 1 and round 2); 

  2 correct or incorrect responses in the online sector (round 1 and round 2); 

  2 correct or incorrect responses in the finance sector (round 1 and round 2); 

  2  correct  or  incorrect  responses  in  the  energy  sector  (round  1  and  round  2), 

and; 

  1  overall  score  for  the  total  number  of  correct  responses  from  the  four 

responses. 

To recap, each experiment choice had an optimal offer. In the first round, one deal was 
at least as good as–or better than– the other deal on all aspects (price, quality (if any), 
etc.). In the second round, one deal was at least as good as–or better than– two other 
deals  on  all  aspects.  The  result  of  the  different  choices  provides  a  single  binary 
measure, correct or incorrect, for both rounds in the four sectors. 

The variables used for experiment performance in the empirical analysis are the eight 
choices of deals, with two choices in each sector. For each choice decision, an optimal 
deal  could  be  selected.  The  performance  variable  is  thus  a  binary  variable:  did  the 
respondent select the optimal deal presented to him/her. 

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Annex 8│ Data analysis undertaken to examine the drivers of and factors linked to 
consumer vulnerability 

Each respondent went through the first and second round choice selection n two of the 
four  sectors.  The  number  of  correct  answers  is  a  simple  sum  of  all  correct  choices  in 
order  to  provide  an  overall  score  on  a  discrete  scale  from  0,  in  case  of  no  correct 
answers, to 4, if all were choices were answered correctly. 

Variation in treatment effects 

The  eight  performance  measures  that  are  specific  to  the  sectors  and  choice  selection 
rounds are used as dependent variable in the regression. 

For  the  assessment  of  overall  experiment  performance,  a  tenth  and  last  measure  is 
used, namely:  

  The difference between marketing practice and treatment performance 

The difference in treatment performance is for each respondent the number of correct 
deals chosen in the remedy treatment minus the number of correct deals chosen in the 
marketing practice.  

On a five point scale, the variable is a score ranging from a maximum of 2 if the best 
deals chosen were only the remedy treatment to -2 if only the best deals chosen were 
in the marketing practice.  

Each  respondent  was  allocated  randomly  to  the  treatment  and  sector,  and  made  two 
choices  in  the  remedy  treatment  part  of  the  experiment  and  two  choices  in  the 
marketing practice part of the experiment. 

The  random  allocation  allows  the  relationship  of  the  dependent  variable  with  the 
explanatory  variables  to  be  interpreted  as  characteristics  related  to  greater  or  lesser 
susceptibility to the marketing practice, or equivalently a greater or lesser benefit from 
the remedy. 

A8.3.2  Statistical models and tests 

Difference-in-proportions 

Difference-in-proportions  tests  were  used  to  assess  the  experiment  performance  with 
the marketing practice and remedy treatment.  

This  test  compares  the  proportions  of  respondents  that  chose  the  correct  deal  in  the 
marketing  practice  with  the  proportions  of  respondents  that  chose  the  correct  deal  in 
the remedy treatment (between-subject comparison). 

No  correlation  between  the  treatment  conditions  and  any  other  characteristics  is 
expected, due to the random allocation of respondents between marketing practice and 
remedy treatment in the experiment design. 

Difference-in-differences 

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consumer vulnerability 

The  difference-in-differences  of  the  remedy  treatment  of  various  groups  were 
calculated  after  the  difference-in-proportions  test.  The  significance  test  used  was  a 
standard t-test418.  

Logistic regression models 

A  logistic  regression  model  is  used  for  each  of  the  eight  binary  dependent  variables, 
i.e. one for each round of each experiment. As in the case of the G-S models discussed 
earlier,  robust  standard  errors  were  calculated.  Subsequently,  marginal  effects  and 
standard errors were calculated at the means using the mfx command in Stata. 

Logistic  regressions  were  performed  for  each  of  the  eight  models  for  each  group  of 
explanatory variables as well (personal and demographic, behavioural, market-related 
and  experience,  access,  and  situational)  and  the  estimation  results  are  available  as 
Excel files. 

Ordered logistic regression models 

An  ordered  logistic  regression  model  is  used  for  the  overall  score.  Coefficients  were 
transformed to odds ratios. 

Ordered  logistic  regressions  were  estimated  for  each  of  the  eight  models  for  each 
group  of  explanatory  variables  as  well  (personal  and  demographic,  behavioural, 
market-related and experience, access, and situational) and the regression results are 
available as Excel file. 

General-to-specific selection models 

The general-to-specific selection model was applied as described earlier.  

The final G-S model was selected using 107 potential explanatory variables in total, 90 
variables  from  the  demographic  and  personal,  behavioural,  access,  and  situational 
characteristics  and  the  17  market-related  and  experience  characteristics  as  described 
in the section below. 

The  designated  base  category  from  the  variable  list  was  not  included  in  the  potential 
set of explanatory variables in the general model. 

The  analysis  of  the  online  sector  experiment  started  with108  potential  explanatory 
variables in total and the analysis of the overall score began 106 potential explanatory 
variables. 

The  general-to-specific  selection  model  was  applied  for  each  of  the  nine  dependent 
variables  for  Denmark,  Lithuania,  Portugal,    Romania,  and  the  United  Kingdom 
separately and combined to produce 54 regression selection models.  

418 The diff Stata package was used in this calculation. The violation of normality of the standard 
errors due to the comparison of binary outcome variables means some caution needs to be 
taken, in particular for the significance in the rounds of the finance experiment. 

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Annex 8│ Data analysis undertaken to examine the drivers of and factors linked to 
consumer vulnerability 

A8.3.3  List of explanatory variables 

The explanatory variables consist of those used in the section above and described in 
section  A8.1.2.  However,  there  are  two  exceptions  which  are  described  in  further 
details below:  

  The market-related and experience variables are the only group of variables 

that a more parsimonious sector specific group is used instead.  

  The treatment and remedy condition in the experiment provide an additional set 

of sector specific variables. 

The  relationship  between  experiment  performance  and  the  market-related  and 
experience drivers of vulnerability are tested by using a tailored set of variables. 

Just  one  variable  is  used  for  the  contract  knowledge,  the  frequency  deals  are 
compared, the detail bill or communication are read, and the difficulty to read the last 
bill  or  communication,  namely  the  variable  which  relates  to  the  experiment  (e.g., 
knowledge  of  energy  contract  for  the  rounds  of  the  energy  sector  experiment).  As 
such,  the  performance  in  the  experiment  is  related  to  the  experience  in  the  same 
sector419. 

Table 154:  Market-related and experience variables 

Description 

Energy 
sector 

Online 
sector 

Finance 
sector 

Cross-
cutting 

Overall 

know_contra
ct_ene_* 
compare_de
als_ene_** 
read_comm_
ene_* 

know_contra
ct_onl_* 
compare_de
als_onl_* 
read_comm_
onl_* 

know_contra
ct_fin_* 
compare_de
als_fin_** 
read_comm_
fin_* 

Knows the contract 
conditions of the sector 
Frequency of comparing 
deals of the sector 
Depth in reading 
communications of the 
sector 
Difficulty to read 
communications of the 
sector  
Marketing condition (=0) 
or treatment (=1) 
Note: Shade indicates that the variable is a composite variable from the other variables to the left of it. 
*coded as five or six separate dummies for each possible response and no response 
**no response is not an option 

know_contract
_gen_* 
compare_deal
s_gen_** 
read_comm_g
en_* 

read_comm_di
ff_gen_* 

read_comm_
diff_onl_* 

read_comm_
diff_fin_* 

read_comm_
diff_ene_* 

know_contract_
exp_* 
compare_deals_
exp_** 
read_comm_exp
_* 

read_comm_diff
_exp_* 

g_treatment 

o_treatment 

e_treatment 

f_treatment 

N/A 

Similarly,  the  cross-cutting  experiment  has  three  frames:  energy,  online  and  finance. 
The market experience used in the analysis depends on the frame that the respondent 
faced  (e.g.  knowledge  of  energy  contract  for  the  rounds  in  the  cross-cutting 
experiment for a respondent in the energy frame).  

In  contrast,  the  overall  experiment  performance  consists  of  the  performance  in  two 
different frames. The overall experiment performance can thus not be assessed against 
a  single  sector  experience  measure.  Instead,  the  performance  in  the  overall 

419 Noteworthy, the performance in the online sector of the experiment has one more potential 
respondents 

explanatory variable as a result: “Compare internet deals: No response” 
that did not answer the question on frequency of comparing internet deals. 

 for 

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Annex 8│ Data analysis undertaken to examine the drivers of and factors linked to 
consumer vulnerability 

experiment  is  evaluated  against  the  highest  level  of  experience  of  the  two  markets 
frames the respondent  observed. For  example, if the respondent was  allocated to the 
energy  and  online  sector  experiments  and  indicated  to  know  his/her  energy  contract 
“not very much” and internet conditions “a fair amount”, the respondent was scored to 
have “a fair amount” of knowledge of contract conditions. This reveals the experience 
of  the  respondent  in  a  broader  sense,  yet  still  adapts  the  variable  to  the  frame 
observed in the experiment. 

No  treatment  effect  can  be  determined  for  the  overall  experiment  performance. 
Respondents  observed  the  marketing  practice  for  one  sector  and  the  treatment 
condition for the other sector. As the overall experiment performance is the number of 
best deals chosen among the four choices from the two rounds in the two sectors, each 
respondent was both in the marketing practice and remedy condition. 

The  variable  reflecting  the  respondents  who  were  unable  to  read  energy, 
telecommunication or banking contract terms and conditions due to overly small print, 
“Unable to read Ts&Cs due to small print”, was the only variable in the market-related 
and experience group identical for all performance indicators. 

A8.4  Results available in Excel format 

For  the  experiment  results,  the  following  empirical  results  are  available  in  Excel 
format: 

  Experiment_Correlations.xlsx 

o  Pairwise correlations for each of the nine performance indicators 

  Experiment_Regressions.xlsx  

o  Six separate regressions for each performance indicator/country 

  demographic and personal characteristics 
  behavioural characteristics 
  market-related and experience characteristics 
  access characteristics I: frequency of access 
  access characteristics II: type of access 
 

situational characteristics 

o  Nine performance indicators for the total sample and five countries. 

  Experiment_G-S.xlsx 

o  General-to-Specific model  selected sets of explanatory variables; 
o  Regressions for nine performance indicators for the total sample and five 

countries 

  Experiment_marketing susceptibility.xlsx 

o  General-to-Specific model  selected explanatory variables and interaction 

terms; 

o  General-to-Specific model for the Remedy Treatment-Marketing Practice 

difference variable 

The regressions are all logit regressions for binary dependent variables (i.e. the rounds 
of  the  experiments  by  sector)  and  ordered  logit  regressions  for  the  multi-outcome 
variables (i.e. scores). 

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Annex 8│ Data analysis undertaken to examine the drivers of and factors linked to 
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The values reported are the logit marginal effects and ordered logit odds-ratios with p-
values.  

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Annex 9│ Factor analysis undertaken in the validation of the operationalisation of 
vulnerability 

Annex 9  Factor  analysis  undertaken  in  the  validation  of  the 

operationalisation of vulnerability 

The dimensions of vulnerability set out in Chapter 3 are operationalised with indicators 
based on the literature. However, factor analysis can provide further insights regarding 
the construct validity of the dimensions vis-à-vis the indicators. 

Construct  validity  is  the  extent  to  which  a  survey  question  or  test  measures  what  is 
claimed,  or  purported,  it  measures  (Cronbach  &  Meehl,  1955).  We  use  construct 
validity  as  a  methodology  to  assess  the  soundness  of  the  grouping  of  indicators  into 
dimensions. 

The  factor  analysis  is  the  tool  we  use  to  assess  the  construct  validity.  The  factor 
analysis is one of the  most widely used statistical techniques in social sciences and is 
commonly  used  to  reduce  the  dimensionality  of  a  problem  (Kim  and  Mueller,  1978). 
Essentially,  factor  analysis is  a  method  for  expressing  a  number  of  variables  in  terms 
of  a  smaller  number  of  ‘factors’.  Factors  are  unobserved  latent  variables  created  to 
capture the underlying shared component (i.e. the shared essence and ultimate cause) 
of the observed variables. 

We  use  factor  analysis  to  establish  how  the  data  from  the  consumer  survey  and 
behavioural experiment suggests the indicators should be grouped, and compare these 
results  with  our  operationalisation  based  on  the  literature.  The  factor  analysis  is  thus 
used to assess the validity of the vulnerability dimensions, rather than to redefine and 
operationalise the dimensions.  

The specific factor analysis method we have used is a principle factor analysis without 
rotation420.  

Applied  to  our  dataset,  there  is  a  trade-off  between  the  sample  size  and  the  set  of 
indicators  that  can  be  included  in  the  factor  analysis,  since  not  all  indicators  are 

420 In a principal factor analysis, the first factor is a linear combination of the correlation matrix 
(with  communalities  as  the  diagonal)  of  indicators  that  captures  the  maximum  variance  of 
the matrix; the second factor is a linear combination that captures the maximum variance of 
the  matrix  and  is  uncorrelated  to  the  first  factor,  etc.  A  principal  factor  analysis  is  for  the 
preferred  method  in  exploratory  factor  analysis,  since  it  makes  no  assumptions  about  the 
number of patterns among the indicators or the underlying factors of the indicators (Fabrigar 
and  Wegener  2011).  While  principal  factor  analysis  does  not  allow  the  statistical  tests  of 
other  approaches,  important  advantages  of  the  approach  are  that  it  finds  a  unique  factor 
solution,  has  a  simple  structure,  and  provides  comparable  results  to  other  factor  analysis 
techniques.  For  these  reasons,  the  principal  factor  method  is  commonly  used  in  the 
exploratory  part  of  factor  analysis.  Rotation  (i.e.  redefining  factors  such  that  loadings  on 
various  factors  tend  to  be  very  high  or  very  low)  is  not  deemed  necessary  in  our  analysis 
since  the  purpose,  in  this  case,  is  not  to  link  individual  groups  of  variables  in  order  to 
construct  factors  from  these  variables,  but  to  identify  the  shared  underlying  shared 
components  of  the  variables.  The  principal  factor  analysis  we  have  undertaken  provides  a 
better  understanding  of  the  underlying  shared  components  of  the  variables,  and  does  not 
force variables into distinct groups, making it the preferred method of analysis  

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vulnerability 

available  for  every  respondent421.  Therefore,  the  factor  analysis  is  run  with  four 
different set of indicators:422  

  52 indicators (5 countries, 642 respondents); 

  51 indicators (5 countries, 1,552 respondents); 

  37 indicators (19 countries, 2,560 respondents); and 

  22 indicators (30 countries, 6,678 respondents). 

When a FA is carried out, the analyst has a choice to make regarding how many factors 
to  analyse,  given  that  each  additional  factor  adds  less  value  (incrementally)  to  the 
analysis.  In  our  case,  only  the  first  three  factors  are  analysed  (the  reason  for  this  is 
explained in the box below). 

421  This  is  due  to  the  fact  that  not  all  survey  questions  were  asked  to  all  respondents  in  every 

country. 

422  The  sample  sizes  here  are  the  numbers  of  respondents  for  whom  data  are  available  for  all 

indicators.  

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Annex 9│ Factor analysis undertaken in the validation of the operationalisation of 
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Box 9:  Factor analysis – number of factors: screeplots 

The number of factors  examined is either determined based on a certain rule (most 
commonly,  an  eigenvalue  of  1  is  used  as  a  cut-off,  which  implies  that  additional 
factors  explain  less  of  the  underlying  shared  components  than  is  used  to  create 
another distinct variable) or visually  by observing from a screen-plot of  eigenvalues 
(see  below)  the  point  at  which  an  additional  factor  captures  considerably  less 
communality than the previous factor. Based on either approach, in our analysis the 
result is that no more than three factors should be analysed. 

      52 indictors (thus up to 52 factors)        51 indictors (thus up to 51 factors) 

      37 indictors (thus up to 37 factors)        22 indictors (thus up to 22 factors) 

A9.1  Results of the factor analysis 

Factor analysis produces two types of result:  

  Factor loadings, which measure how much a factor explains an indicator, show 
the association of each indicator with each factor. Low factor loadings note an 
indicator  is  not  closely  associated  with  a  factor.  Conversely,  high  factor 
loadings mean an indicator is associated with a factor. A number of high factor 
loadings  from  the  same  factor  group  indicators  that  define  and  express  the 
same factor. 

  Uniqueness  scores,  which  measure  how  much  of  the  indicator  is  explained  by 
none  of  the  factors,  show  indicators  that  are  not  associated  strongly  by  any 
factor. These indicators cannot be grouped with other indicators. 

736 

01234Eigenvalues01020304050NumberScree plot of eigenvalues after factor01234Eigenvalues01020304050NumberScree plot of eigenvalues after factor01234Eigenvalues010203040NumberScree plot of eigenvalues after factor0123Eigenvalues05101520NumberScree plot of eigenvalues after factor 
 
 
 
 
 
  Annex 9│ Factor analysis undertaken in the validation of the operationalisation of 
vulnerability 

It is common practice to look at factor loadings of above 0.3 or below -0.3 in order to 
determine  which  variables  (i.e.  our  indicators)  are  linked  to  the  same  factor.423  
Conversely, variables with a uniqueness score above 0.9 will not have any factor that 
explains 10%  or more  of the variable variance and will not be  explained substantially 
by any shared factor.  

Factor loadings above 0.3 are highlighted in Table 155 in green, yellow and red for the 
first,  second  and  third  factors,  for  each  of  the  four  factor  analyses  undertaken. 
Uniqueness  scores  above  0.9  are  highlighted  in  blue  in  the  same  figure424.  The 
indicators upon which each factor analysis is based are listed on the right hand side of 
Table 155.  

Hence,  the  colour  coding  of  the  factor  loadings  in  Table  155  provides  an  overview  of 
the set of indicators associated with each factor. It should be noted that the factors in 
the  four  factor  analyses  do  not  have  the  same  meaning,  although  the  fact  that  the 
colour  coding  of  each  indicator  is  often  the  same  across  all  four  analyses  illustrates 
that the meanings of the factors are similar across the analyses.  

Comparison  of  the  colour  coding  within  a  factor  analysis,  and  particularly  within  a 
dimension  within  a  factor  analysis,  provides  an  indication  of  the  construct  validity  of 
the dimensions. In particular, if the factor loadings in a dimension are coloured mainly 
with a single colour (i.e. green, yellow or red) this indicates that the dimension has a 
high  level  of  construct  validity,  especially  if  that  colour  is  rarely  present  in  other 
dimensions. Conversely, if the factor loadings in a dimension are coloured with a mix of 
different colours, or the uniqueness scores in a dimension are high, this implies lower 
construct validity. 

423  Factor  loadings  of  above  0.3  or  below  -0.3  imply  that  10%  or  more  of  the  variation  in  a 

variable is explained by the factor. 

424  This  should  be  taken  as  a  fairly  high  cut-off  point.  For  example,  Fabrigar,  Wegener, 
MacCallum,  &  Strahan  (1999)  provide  an  indicative  level  of  0.7  above  which  indicators  do 
not represent any factor. 

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Annex 9│ Factor analysis undertaken in the validation of the operationalisation of vulnerability 

Table 155:  Results of factor analysis on the survey indicators of vulnerability 

738 

Factor 1Factor 2Factor 3Factor 1Factor 2Factor 3Factor 1Factor 2Factor 3Factor 1Factor 2Factor 3DescriptionD110.1640.1690.3470.8240.1170.0950.1540.9540.0940.0420.2210.9411. Unassertive (took no action) when experienced a problem20.287-0.0430.3580.7880.295-0.0840.3680.7710.299-0.1120.4260.7170.293-0.2890.2900.7462. Overpaid for services due to being unable to use certain payment methodsD23_b2_age0.490-0.277-0.1060.6720.395-0.245-0.0730.7780.457-0.178-0.0430.7580.433-0.027-0.0650.8084. Perception of own vulnerability due to age3_b2_emp0.480-0.085-0.1880.7270.515-0.183-0.1620.6760.523-0.115-0.1690.6840.5680.086-0.1180.6565. Perception of own vulnerability due to employment situation3_b2_fin0.597-0.118-0.1370.6110.582-0.223-0.2000.5710.629-0.074-0.2050.5570.6560.146-0.1510.5256. Perception of own vulnerability due to financial circumstances3_b2_hea0.558-0.249-0.1750.5960.446-0.231-0.0990.7380.541-0.166-0.1020.6700.474-0.008-0.1060.7647. Perception of own vulnerability due to health problems3_b2_min0.474-0.291-0.0470.6890.410-0.272-0.0300.7570.487-0.200-0.0520.7200.444-0.056-0.0830.7938. Perception of own vulnerability due to belonging to a minority group3_b2_per0.549-0.215-0.0440.6500.516-0.259-0.0360.6650.541-0.174-0.0970.6680.5590.017-0.0740.6829. Perception of own vulnerability due to personal issues3_b2_oth0.552-0.223-0.0030.6460.500-0.2500.0150.6880.479-0.169-0.0890.7340.5000.011-0.0630.74610. Perception of own vulnerability due to other reasons3_b2_any0.762-0.221-0.1660.3440.748-0.312-0.2290.2910.797-0.184-0.2130.2860.8280.102-0.1790.27211. Perception of own vulnerability due to any of the aboveD34_b-0.0180.023-0.0210.999-0.0090.0220.0210.9990.0290.0030.0720.9940.0440.0480.1230.98113. Feels 'not at all' informed about product price, quality, conditions, etc 5_adv_ene0.1980.0590.0410.9560.1320.0250.1080.9700.113-0.0130.1750.95614. Gets information from/compares product deals using adverts only – energy sector5_adv_onl0.1180.0910.1850.9440.0820.0800.2130.9420.0360.0240.1580.97315. Gets information from/compares product deals using adverts only – online sector5_adv_fin0.0690.0210.0770.9890.0330.0270.1160.9850.047-0.0390.0880.98916. Gets information from/compares product deals using adverts only – finance sector6_ene0.2240.559-0.0030.6370.2610.4770.0020.7040.1830.5290.0580.68417. Has problems comparing deals due to information factors – energy sector6_onl0.1500.515-0.1710.6830.1810.418-0.1340.7750.1610.4870.1250.72118. Has problems comparing deals due to information factors – online sector6_fin0.1340.427-0.1980.7610.1800.377-0.1460.8050.1180.4050.0240.82119. Has problems comparing deals due to information factors – finance sector7_ene0.106-0.0070.1030.9780.1190.0750.0520.97820. Has not switched due to information-related factors – energy sector7_onl0.0500.2020.0390.9550.0550.1520.0720.96921. Has not switched due to information-related factors – on-line sector7_fin-0.009-0.0140.0930.9910.0220.0910.0920.98322. Has not switched due to information-related factors – financial sectorD48_1_ene0.0360.2740.4000.7630.1190.2560.2890.8370.0060.264-0.1100.9180.0190.4350.2340.75523. Does not compare product deals – energy sector8_2_ene0.1570.369-0.0280.8390.1760.317-0.0840.8610.1380.3360.0440.8660.1060.3240.3030.79224. Finds it very difficult to compare product deals – energy sector8_1_onl0.0380.2550.1660.9060.1090.2660.0970.9080.0430.266-0.1800.895-0.0100.4000.1740.81025. Does not compare product deals – online sector8_2_onl0.1490.383-0.0580.8280.1250.261-0.0660.9120.1070.2890.1170.8910.1080.2570.2620.85426. Finds it very difficult to compare product deals – online sector8_1_fin0.0710.194-0.0670.9530.1060.186-0.0470.9520.0150.275-0.2440.8650.0120.4250.1400.80027. Does not compare product deals – finance sector8_2_fin0.1470.322-0.2530.8110.1770.278-0.1770.8600.1610.2860.0090.8920.1470.2480.2520.85428. Finds it very difficult to compare product deals – finance sector8a_ene0.1760.4650.1430.7330.2140.4150.0880.7750.1360.446-0.0490.78029. Has problems comparing deals due to personal factors – energy sector8a_onl0.1350.4680.0280.7620.1770.3850.0060.8210.1540.3740.0070.83630. Has problems comparing deals due to personal factors – on-line sector8a_fin0.1160.340-0.0860.8640.2030.303-0.0810.8600.1080.359-0.1410.84031. Has problems comparing deals due to personal factors – finance sector8b_ene0.1590.549-0.0600.6690.2370.523-0.0560.6670.1420.597-0.0110.62432. Has problems comparing deals due to market-related factors – energy sector8b_onl0.1350.535-0.1350.6780.1860.454-0.1550.7350.1500.5680.0540.65133. Has problems comparing deals due to market-related factors – on-line sector8b_fin0.0890.409-0.2390.7680.1990.377-0.1680.7900.1090.500-0.0730.73334. Has problems comparing deals due to market-related factors – finance sector8c_ene0.0050.1680.4290.7880.1180.1840.3350.8400.0670.1600.0590.96735. Has problems comparing deals due to access-related factors – energy sector8c_onl-0.0240.0620.0020.9960.0280.0870.0630.9880.0320.142-0.0340.97836. Has problems comparing deals due to access-related factors – on-line sector8c_fin0.0400.0220.0470.9960.0300.0290.0620.9940.1130.035-0.0040.98637. Has problems comparing deals due to access-related factors – financial sector9a_ene0.0430.1160.1490.9630.0770.0700.0890.98138. Has not switched due to personal factors- energy sector9b_ene0.0560.1280.0020.9800.0720.1580.0150.97039. Has not switched due to market-related factors – energy sector9c_ene0.0420.0160.3620.8670.0450.0180.2610.93040. Has not switched due to access-related factors – energy sector9a_onl0.0380.1750.0540.9650.0390.0780.0290.99241. Has not switched due to personal factors – on –line sector9b_onl0.0390.1560.0250.9740.0440.137-0.0130.97942. Has not switched due to market-related factors – on-line sector9c_onl-0.0170.0090.0770.9940.0310.0130.0790.99343. Has not switched due to access-related factors – on-line sector9a_fin0.0350.0150.1250.9830.0360.0070.0830.99244.Has not switched due to personal factors – financial sector9b_fin0.0260.136-0.0900.9730.0740.164-0.0870.96045. Has not switched due to market-related factors – financial sector9c_fin0.1990.168-0.1690.9040.1410.081-0.0430.97246. Has not switched due to access-related factors – financial sector9d_ter0.3190.0610.2890.8110.2990.0090.3120.8130.264-0.0160.4200.7530.174-0.2880.2970.79947. Has not switched due to termination costs9e_bun0.2960.0630.1800.8760.2610.0190.2390.8740.2430.0280.3410.8240.235-0.3280.3270.73048. Has not switched due to bundling10a0.3280.0710.2280.8350.2850.0500.2590.8490.2540.0390.3490.8120.258-0.2780.2880.77349. Excluded from e-commerce due to difficulty of the process10b0.353-0.1190.3770.7190.311-0.0940.3840.7470.270-0.1410.3820.7620.207-0.2330.3200.80050. Excluded from e-commerce due to not having a payment card 11_loan0.1720.0150.0870.9630.144-0.0300.0530.97651. Declined for a loanD512_off_b20.347-0.075-0.0420.8720.352-0.073-0.0310.8700.359-0.049-0.0060.8690.3500.0420.0580.87254. Perception of own vulnerability due to complexity of offers 'to great extend' (dummy)expt_mp-0.2090.042-0.2530.891-0.1240.019-0.3800.84055-7. Made the correct choice of offer under the marketing practice treatment treatment0.0400.0090.0600.995-0.0060.0020.2440.941- -. Treatment effects (i.e. correct choices remedy minus marketing practice treatment)IndicatorsDimensionFactor analysis 4 (22 indicators)Factor analysis 1 (52 indicators)Factor analysis 2 (51 indicators)Factor analysis 3 (37 indicators)Factor loadingsFactor loadingsFactor loadingsFactor loadingsUniquenessUniquenessUniquenessUniqueness 
 
 
  Annex 9│ Factor analysis undertaken in the validation of the operationalisation of 
vulnerability 

In  summary,  as  can  be  seen  from  the  colour  coding  in  Table  5,  overall  the  factor 
analysis supports the allocation of indicators to dimensions that has been made based 
on  the  literature,  since  most  indicators  of  the  same  colour  are  within  the  same 
dimension. The factor analysis supports the allocation of indicators to dimensions 1 and 
2 in particular and also suggests that many indicators allocated to dimensions 3 and 4 
based on the literature do indeed relate to a common factor.  

However,  all  dimensions  include  highly  unique  indicators  (coloured  blue)  and/or 
indicators  that  share  a  common  factor  with  indicators  in  other  dimensions  (coloured 
green,  yellow  or  red).  Thus,  the  allocation  of  indicators  to  dimensions  based  on  the 
data  would  be  somewhat  different  to  that  which  is  based  on  the  literature  (in  fact, 
indicators that are found to be highly unique based on the factor analysis could not be 
allocated to any common dimension based only on the data). 

Given the broad nature of the dimensions, one would not expect all indicators that can 
be  allocated  to  a  certain  dimension  (based  on  theory)  to  measure  the  same 
phenomenon.  For  example,  in  dimension  4,  failure  to  make  a  correct  product  choice 
may be driven by a range of different factors (personal, market-related, etc.) and one 
would  not  necessarily  expect  these  factors  to  be  associated.  Therefore,  the  fact  that 
the  results  of  the  factor  analysis  to  some  extent  contradict  the  literature-based 
allocation  of  indicators  is  not  unexpected  and  does  not  invalidate  the  allocation  of 
indicators to dimensions in the study. 

The dimensions themselves are linked to the factors as follows: 

 

 

 

 

 

In  Dimension  1  ‘Heightened  risk  of  negative  outcomes  or  impacts  on  well-
being’ indicators are related in general to Factor 3, however not very well. The 
two  indicators  in  this  dimension  share  this  factor  with  several  indicators  in 
Dimension 4 and Dimension 5. In addition, one of the two indicators (Indicator 
1) is fairly unique. 

In Dimension 2 ‘Having characteristics that limit ability to maximise well-being’ 
indicators  are  strongly  associated  with  Factor  1.  Indicators  in  Dimension  2 
share  a  common  factor  with  several  indicators  in  Dimension  4  and  one 
indicator  in  Dimension  5.  Nevertheless,  the  indicators  in  Dimension  2  have 
higher  factor  loadings  in  Factor  1  than  any  other  indicator  (this  holds  across 
the four analyses). In addition, no indicators in Dimension 2 are highly unique. 
Overall, this reflects the soundness of the grouping of these indicators into the 
dimensions. 

In  Dimension  3  ‘Having  difficulty  in  obtaining  or  assimilating  information’ 
indicators  are  generally  associated  to  Factor  2.  Several  indicators  in  this 
dimension share the factor with indicators in Dimension 4. However, more than 
half of the indicators in Dimension 3 in all four analyses are highly unique. 

In Dimension 4 ‘Inability or failure to buy, choose or access suitable products’ 
indicators  are  generally  related  to  Factor  2.  However,  there  are  several 
indicators broadly related to Factor 2, others to Factor 3, and a  couple (some 
just  below  the  0.3  highlight  threshold)  to  Factor  1.  Indicators  in  Dimension  4 
thus  share  a  factor  with  indicators  from  each  of  the  before  mentioned 
dimensions.  In  addition,  dependent  on  the  analysis,  Dimension  4  has  a 
significant number of its indicators high in uniqueness. 

In Dimension 5 ‘Higher susceptibility to marketing practices’ one indicators are 
broadly  speaking  associated  with  Factor  1  and  Factor  3.  Indicators  in  this 
dimension  share  the  factors  with  indicators  in  Dimension  1,  Dimension  2  and 

739 

 
 
Annex 9│ Factor analysis undertaken in the validation of the operationalisation of 
vulnerability 

Dimension  4.  In  addition,  dependent  on  the  analysis,  Dimension  5  has  an 
indicator high in uniqueness. 

As concluding comment, across the dimensions a significant number of indicators have 
generally  high  uniqueness  scores  in  our  factor  analysis  which  relates  to  the  high 
dimensionality  of  vulnerability.  One  interpretation is  that  our  indicators  measure  a  lot 
of  different  aspects  in  consumer  vulnerability.  Consumer  vulnerability  is  widely 
acknowledged  to  be  an  extremely  multi-faceted  concept  and  this  is  to  some  extend 
reflected  in  the  indicators.  Another  reading  is  that  our  indicators  capture  a  lot  of 
different  features  of  vulnerability  itself  and  certain  aspects  of  our  dimensions  are 
consequently  still  too  complex.  The  indicators  with  high  uniqueness  scores  are  in  this 
reading insufficiently understood through an analysis with a high level of generality and 
several  indicators425  require  additional  exploration  and  further  research  from  a 
conceptual perspective of consumer vulnerability. 

425  Broadly  the  indicators  that  relate  to:  how  consumers  get  information  (13-16) ;  reasons  for 
consumers not to switch in specific markets (20-22 and 38-46) ; problems to compare deals 
in broadband and banking due to access reasons (36-37) ; whether consumers are declined 
for  a  loan  (51) ;  and  corrections  to  marketing  practices  (--).  (N.B.  The  numbers  between 
brackets refer to the specific indicators described in Table 5). 

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Annex 10│ Findings from the in-depth consumer interviews 

Annex 10 Findings from the in-depth consumer interviews 

This  annex  reports  the  findings  from  the  in-depth  consumer  interviews.  These 
interviews  were  conducted  in  five  Member  States:  Denmark,  Lithuania,  Portugal, 
Romania and the UK.  

A  total  of  nine  participants  were  interviewed  for  each  country.  In  order  to  obtain 
detailed  information  about  the  research  topic,  each  interview  was  scheduled  to  last 
between one hour and one hour and a half. 

All respondents had previously participated in the quantitative survey. Recruitment was 
done based on their answers to the survey. The aim was to include people belonging to 
the following pre-defined typologies: 

  Typology I: feels vulnerable and has experienced a problem;  

  Typology II: feels vulnerable and has not experienced problem;  

  Typology III: does not feel vulnerable and has experienced problem.  

The  three  typologies  are  defined  below,  based  on  questions  Q16  and  Q21  from  the 
questionnaire which was used for the quantitative survey: 

  Typology I: Feels vulnerable & has experienced problem; Selected “To a great 
extent” or “To some extent” for at least one reason in Q16 AND selected "Yes" 
for at least one situation in Q21 

  Typology  II:  Feels  vulnerable  &  has  not  experienced  problem;  Selected  “To  a 
great extent” or “To some extent” for at least one reason in Q16 AND selected 
"No" for all situations in q21 

  Typology  III:  Does  not  feel  vulnerable  &  has  experienced  problem;  Selected 
“Hardly  at  all”  or  “Not  at  all”  for  all  reasons  in  Q16  AND  selected  "No"  for  all 
situations in q21 

The following sections report the findings of the interviews. 

A10.1   Drivers of and factors linked to consumer vulnerability 

The  qualitative  exercise  shows  that  consumer  attitudes  which  help  avoid  vulnerability 
vary by sector, as well as by country. 

A10.1.1  Energy sector 

Differs across the five countries. In the UK, many consumers sense an opening of the 
market  in  terms  of  competition,  while  in  countries  like  Romania  and  Lithuania,  the 
general belief is that there is only one energy supplier.  

In Portugal, due to the transition to an open market, consumers gained access to more 
energy  suppliers.  However,  respondents  in  this  country  agree  that  the  market  offers 
the same (or very similar) prices and service quality. 

In Denmark and in the UK, supplier selection is done mainly based on price. 

Across  the  five  countries,  interest  and  understanding  of  energy  bills  are  low,  and 
reading energy bills is perceived as a great effort. This can become a disadvantage, as 
for  some  of  the  respondents,  the  main  difficulties  encountered  were  related  to  price 
(e.g. bill increasing unexpectedly, for reasons which respondents do not understand).  

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Annex 10│ Findings from the in-depth consumer interviews 

In countries such as Romania and Lithuania, consumers’ involvement is rather low, as 
the  choice  of  suppliers  is  perceived  as  unavailable.  In  these  countries,  the  main 
attitudes  aimed  to  avoid  vulnerability  involve  investing  an  effort  in  understanding  the 
bill (for  example,  by  looking  up  the  meaning of  each  of  the  different  terms  online,  or 
asking  the  supplier  to  provide  details  about  their  consumption).  Other  strategies 
include  closely  monitoring  the  consumption,  by  regularly  checking  the  price,  or  by 
writing down the consumption and the amount of money spent each time. 

In Portugal, where offers are perceived to be very similar, consumer attitudes aimed to 
prevent vulnerability situations are somewhat in-line with those described in Romania 
and in Lithuania: 

- 
Increased attention to price 
-  Close monitoring of consumption 
- 
-  Keeping informed through all available channels (telephone, internet, face-to-

Inquiries about terms of payment, contracts, returns, warranties etc. 

- 

face contact) 
Filing complaints in the official complaints book or with the regulatory or 
supervisory authorities. 

These strategies reveal the growing empowerment of Portuguese consumers. 

In  the  UK,  where  the  market  is  perceived  as  being  very  competitive,  switching 
providers  on  a  yearly  basis  is  becoming  more  and  more  common.  Switching is  driven 
mainly  by  price,  or  by  other  incentives  (loyalty  points,  vouchers  etc.),  rather  than  by 
service quality or satisfaction with the supplier. 

involve  close  monitoring  (keeping  detailed  records  of  past 
Other  strategies 
consumption data and interactions with the supplier), or carefully selecting the means 
of payment (one of the respondents preferred using prepaid meters, as this offered her 
more control over her weekly budget and the ability to alter consumption accordingly). 

A10.1.2  Financial sector 

When it comes to the banking sector, across the five countries, most respondents are 
generally  loyal  to  one  bank.  Switching  is  not  a  very  common  behaviour  (although  in 
some  interviewees  may  have  two  bank  accounts,  either  with  the  same  bank,  or  with 
two different banks).  

However, consumer attitudes with regards to the financial sector vary by country. 

In  Denmark  and  in  the  UK,  people’s  attitudes  towards  their  bank  are  mainly  positive. 
Respondents  in  these  two  countries  are  savvier  than  those  in  than  those  in  other 
countries.  These  respondents  tend  to  read  and  understand  communication  from  their 
banks. Those who have not experienced any difficulties are generally satisfied with the 
service.  

All  respondents  in  these  two  countries  (apart  from  two  of  the  ‘offline’  respondents  in 
Denmark)  use  online  banking  -  whether  it  is  for  receiving  communication,  checking 
their  balance,  or  making  money  transfers  and  payments  –  and  find  it convenient  and 
user-friendly.  

Consumer  strategies  to  avoid  vulnerability  in  these  two  countries  can  be  summarised 
as following: 

742 

 
 
Annex 10│ Findings from the in-depth consumer interviews 

-  Reading communication received from the bank is seen as very important. 

Consumers do this in order to make sure that they don’t miss out on any 
relevant information. 

-  Although switching banks is not very common, respondents (particularly those 
in the UK) keep informed about the latest options on the market and how they 
could benefit from them. British respondents frequently check comparison 
websites and newsletters, which they consider very easy to understand as they 
provide rankings which take into account all the elements of the offers. 

-  Switching is seen as a relatively easy process, particularly in the UK. 

- 

- 

Respondents are relatively informed about the procedure, and some cite the 7 
day switch guarantee imposed by the government. 
In Denmark, although respondents are satisfied with the service they have, 
some envisage the possibility of switching banks in case of a change in their 
status (e.g. when buying a house, or finishing their studies). 
In both countries, most respondents use online banking (whether from a 
computer of via mobile apps), and find the process easy and convenient. When 
it comes to security concerns, one of the respondents in the UK mentioned that 
he preferred accessing his account from his office, as he felt that his home 
internet connection was not secure enough. Others experienced cases of fraud, 
which however were resolved in an efficient and timely manner by their banks. 

-  Respondents are generally used to handling a large number of financial 

instruments (current accounts, credit cards, personal loans, savings account, 
investment/retirement vehicles etc.). Savvier ones developed different 
strategies in order to maximise their benefits (e.g. receiving their salary on one 
account, transferring part of it towards another, with a better interest, and part 
on the credit balance etc.). 

In  Portugal,  where  the  reputation  of  the  banking  sector  seems  to  be  deteriorating 
(under  the  impact  of  the  crisis,  as  well  as  of  certain  financial  scandals),  people’s 
relationships with banks are more complex. Consumers are persuaded that they do not 
benefit from the best market conditions, and understanding of products and services is 
lower  in  this  country  (respondents  admit  that  they  do  not  read  or  do  not  fully 
understand communication with their banks, and are therefore often surprised by fees 
or terms that they were unaware of). 

Most respondents use online banking, and are satisfied with the convenience and user-
friendliness  of  the  service.  Those  who  don’t  use  it  don’t  perceive  it  as  an 
inconvenience,  as  they  can  access  most  services  (bank  transfers,  checking  balance, 
etc.) at any ATM machine. 

In  terms  of  strategies  to  avoid  consumer  vulnerability,  communication  with  account 
managers  comes  across  as  a  key  element.  This  aspect  is  usually  satisfactory,  and  is 
achieved through multiple channels (face-to-face, telephone, email). Other behaviours 
involve  carefully  examining  the  messages  from  promotional  communication,  and 
asking  further  explanations  from  bank  employees,  as  well  as  keeping  informed  about 
how  to  file  complaints,  both  through  regulatory  authorities  and  consumer  authorities 
(DECO).  Being  a  loyal  client  (using  the  services  of  the  same  bank  for  a  long  time)  is 
also  perceived  as  a  potential  strategy,  as  respondents  believe  that  this  will  increase 
their  chances  of  obtaining  a  loan  (unlike  in  Denmark,  where  some  would  consider 
switching if they were to search for a loan for purchasing a house). Overall, however, 

743 

 
 
 
Annex 10│ Findings from the in-depth consumer interviews 

consumers  feel  unprotected  in  their  relationship  with  banks,  and  lack  trust  in  these 
institutions. 

Similarly to Portugal, scepticism towards financial institutions is high also in Lithuania 
and  Romania.  Banks  are  selected  according  to  geographical  criteria  (proximity  to 
home), or recommendations from employers. Only more educated consumers make a 
thorough research before selecting a bank. 

Respondents in these countries feel exposed to risk when dealing with banks, and the 
general  belief  is  that  banks  are  oriented  towards  their  own  gain,  without  focusing  on 
consumer benefits. 

Unlike  in  Portugal,  where  communication  with  account  managers  is  seen  as  very 
important, consumers in Lithuania and Romania have negative views regarding contact 
with  bank  staff.  Bank  employees’  attitude  is  perceived  as  disregardful,  often  making 
consumers  feel  that  they  are  in  a  disadvantaged  position  (by  making  clients  wait  too 
long,  showing  an  unpleasant/superior  attitude,  using 
financial  terms  without 
thoroughly explaining them, showing no availability etc.). 

In  these  two  countries,  consumer  strategies  to  avoid  vulnerability  when  dealing  with 
this sector are limited: 

-  Avoidance is one of the options, as some respondents do everything they can to 

have as little as possible to do with banks/bank employees (for example, doing 
everything through ATMs). 

-  Keeping one’s savings at home, rather than in a savings account (after a 

negative experience with the bank). 

-  Using mainly cash, in order to have a better control over one’s spendings, and 

to avoid unforeseen charges. 

-  Opening accounts with several banks, and transferring the income to each of 

these. 

-  Switching banks. 
-  Monitoring bank charges closely, by adding up all expenses, and comparing 

them to the balance of the account. 

-  More educated consumers prefer keeping close contact with the bank (receiving 
notifications by SMS, contact with the staff when needing more information, 
using online banking).  

A10.1.3  Online sector 

Respondents are more optimistic with regards to the internet sector (particularly in the 
UK,  Denmark  and  Romania).  This  is  generally  due  to  the  quality  of  the  services,  the 
affordability,  but  also  to  the  number  of  options  available  on  the  market,  and  to  the 
easiness of switching providers. 

The main criteria when selecting an ISP is price and speed. In some cases, in the UK, 
the offer is restrained by the geographical coverage of some of the different providers. 
In Denmark, some of the respondents were not able to select the ISP themselves, due 
to the fact that they were renters. 

Many  consumers  have  bundled  contracts  (e.g.  the  internet  provider  is  the  same  with 
the TV cable and fixed telephone provider). 

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Strategies aimed at avoiding vulnerability in this sector are: 

-  Comparing offers and keeping informed about the possibilities of switching  
-  Having mobile internet as an alternative  
-  Taking into account possible security issues, and installing anti-virus software 
In the UK, some respondents have ad blockers installed on their browsers, in 
- 
order to avoid targeted advertising (in some of the other countries however, 
many respondents do not understand how targeted advertising works). 

In  Portugal,  respondents  are  somewhat  more  critical  regarding  companies  which 
provide  these  services.  One  of  the  reasons  for  this  is  the  retention  period  (24-month 
contracts), but also due to issues such as non-compliance with promotional campaigns, 
inaccuracies with regards to billing, difficulties to terminate a contract, and aggressive 
commercial practices. Switching is rather common, but ‘threatening’ to switch is even 
more frequent (just by manifesting the intention to switch, consumers often succeed in 
obtaining  better  offers).  The  main  barrier  to  switching  is  the  imposed  24-month 
retention period. 

A10.2   Role of marketing practices 

Difficulties  encountered  by  consumers  when  dealing  with  retailers  come  from  various 
sources.  In  many  cases,  feelings  of  vulnerability  are  a  result  of  a  combination  of 
factors, rather than an outcome of specific isolated elements. 

Factors  related  to  low  income  and  low  socio-economic  status  are  mainly  cited  in 
Portugal,  Romania  and  Lithuania.  Not  only  these  factors  limit  consumers’  access  to 
goods and services, but they also have a discouraging effect, as respondents who are 
in  this  situation  do  not  feel  empowered  when  dealing  with  retailers.  In  Portugal,  low 
income,  along  with  unemployment  (situations  where  the  respondents  or  members  of 
their household are unemployed) and work market instabilities have a negative impact 
on  people’s  self-confidence,  and  make  them  feel  less  secure  when  dealing  with 
companies. 

Similarly, in Romania and in Lithuania, low income (sometimes combined with lack of 
education/information,  or  old  age)  is  one  of  the  factors  which  influence  consumer 
vulnerability. 

Factors  related  to  perceived  problematic  marketing  practices  are  mainly  cited  in 
Romania and in Lithuania, in the context of the financial sector. 

In  Romania,  this  is  very  much  related  to  people’s  negative  attitudes  and  experiences 
with  the  staff.  Regardless  of  the  strategy  they  adopt,  respondents’  behaviour  when 
dealing  with  this  sector  is  mainly  oriented  towards  avoiding  the  risk  of  being 
‘scammed’.    Staff  is  often  seen  as  intimidating  and  unhelpful  when  it  comes  to 
negotiating, explaining terms and conditions or financial vocabulary. Some consumers 
therefore feel the need to make an effort themselves towards keeping informed about 
products,  terms  and  conditions,  searching  for  ‘hidden  costs’,  in  order  to  be  prepared 
for  any  risk.  Others  simply  avoid  taking  an  interest  in  financial  products,  or  prefer 
asking for advice among friends and family rather than from their financial advisor. 

In Lithuania, despite the fact that in most cases, communication with staff is perceived 
as  pleasant  and  polite,  part  of  the  respondents  believe  that  banks  do  not  provide 
sufficient  information  about  the  pricing  of  services  and  other  terms  and  conditions. 

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Consumers in this country (particularly the elderly) tend to rely mainly on information 
received  from  bank  employees,  rather  than  read  through  the  terms  and  conditions. 
Some  therefore  believe  that  the  staff  is  deliberately  not  providing  them  with  all  the 
information. 

One  respondent  noticed  that  money  was  missing  from  her  account,  and 
approached  the  customer  service  department  in  order  to  clarify  the  situations. 
It  was  only  after  visiting  three  different  departments  that  it  was  explained  to 
her that this was due to fees for the bank services. The respondent felt that the 
bank  was  reluctant  to  disclose  information:  “I  do  not  know  for  sure  why  they 
did not explain it immediately. I think that the bank is hiding all of its fees from 
their customers. To me it seems that it is so.” (female, 65, Lithuania, typology 
2). 

Some  of  the  difficulties  encountered  by  Lithuanian  consumers  are  related  to  fees 
which, according to the respondents, were not sufficiently explained.  

 “I did not read the contract. I trusted the bank employee. But she certainly did 
not tell me that I will have to pay for the account annually. If I only knew about 
the  fee  I  would  have  never  opened  an  account”.  (female,  73,  Lithuania, 
typology 3) 

This type of attitude from retailers is often perceived as being a consequence  of poor 
socio-economic status. Some respondents consider that the bank’s negligence or poor 
customer service is related to their income  - in the idea that clients with low incomes 
are “of no interest” to the bank (female, 65, Lithuania, typology 2). This is due to the 
fact that respondents who are in this situation earn too little to consider either saving 
or  investing  (“it  is  not  interesting  to  me,  because  I  have  no  money,  I  don’t  have 
anything to save” – male, 66, Lithuania, typology 2) 

Difficulties when dealing with the financial sector are therefore based, on the one hand, 
on  respondents’  poor  financial  situation,  correlated  with  fear  of  losing  money  or 
property. On the other hand, belief that banks put their interest first (rather than that 
of  the  consumer)  is  strong  and  plays  an  important  role  in  strengthening  people’s 
scepticism towards the sector. 

The  two  elements  (socio-economic  status  and  problematic  marketing  practices)  are 
sometimes  perceived  as  interrelated:  some  feel  that  the  bank  staff  is  not  providing 

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them with enough information and support because they don’t qualify as “interesting” 
costumers, due to their low income. 

A10.3   Complexities  of  consumer  vulnerability  across  different 

sectors 

The nature of the difficulties encountered by consumers when dealing with companies 
varies by country, as well as by sector. 

Isolated incidents 
Among  the  five  countries,  the  British  market  stands  out  as  a  mature  and  developed 
one  when  it  comes  to  customer  protection,  and  there  are  a  number  of  government 
bodies  in  place  designed  to  regulate  the  services  provided  to  consumers.  As  a  result, 
consumers  are  empowered  through  a  series  of  mechanisms  which  they  use  when  it 
comes to dealing with different types of incidents. 

Difficulties  reported  by  consumers  in  this  country  are  generally  presented  as  isolated 
incidents, rather than vulnerability patterns (e.g. problems with energy bills, technical 
problems  with  bank  cards  or  with  submitting  online  applications,  misunderstandings 
with  the  insurance  company  etc.).  In  most  cases,  consumers  take  the  necessary 
actions and get to a satisfactory result. 

The  main  disadvantage  identified  relies  in  the  way  in  which  most  of  these  cases  are 
treated. The onus is always on the consumer, who needs to systematically verify that 
they  have  the  most  appropriate  deal,  that  the  company  is  fulfilling  their  part  of  the 
agreement,  to  gather  and  provide  proof  in  order  to  defend  their  interest  and  to  take 
charge  of  escalating  matters  when  necessary.  Given  the  time  and  energy  required, 
consumers are in a position where they need to evaluate whether pursuing the matter 
will actually benefit them or not, and may decide not to go further if the process seems 
too complicated, or if they don’t see clear chances of immediate success. 

Across  the  other  four  countries,  consumer  vulnerability  is  related  to  various  types  of 
factors, which (independently or combined) can make respondents feel disadvantaged 
when dealing with retailers: 

A10.4   Socio-demographic factors 

The  main  socio-demographic  factor  associated  with  consumer  vulnerability  is  low 
income. This aspect is mainly raised in Portugal, Romania and Lithuania. Respondents 
who have low incomes do not feel empowered when dealing with retailers, and tend to 
feel disregarded and intimidated when interacting with staff. 

In Lithuania, old age is also perceived as a disadvantage (particularly when combined 
with low incomes from small retirement pensions).  

“You  know,  it  seems  that  they  look  at  me  and  think  that,  oh,  an  old person,  I 
can sell her anything. It seems that they are dissatisfied with the fact that I am 
interested,  that  I  want  to  know  something  more”.  (female,  65,  Lithuania, 
typology 2) 

Older  consumers  are  less  savvy  when  it  comes  to  market  practices  (terms  and 
conditions,  tariffs,  guarantees,  consumer  rights  etc.),  and  less  able  to  search  for 
information  themselves.  Therefore,  they  mainly  rely  on  advice  and  information 
received  from  staff,  which  they  often  find  insufficient  (particularly  when  dealing  with 

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the  financial  sector).  Furthermore,  these  consumers  are  often  unable  to  use  the 
internet, which limits even more their access to information. 

Health  problems  constitute  another  vulnerability  factor,  as  some  of  the  elder 
consumers interviewed in Lithuania have serious health problems, finding it difficult to 
move.  Shopping  or  visiting  retailers  at  a  branch  can  become  a  challenge  for  these 
people. 

Old age is therefore associated with other biographical factors: 

Income decrease due to retirement 
Lack of involvement in the active life 

- 
- 
-  Health problems 
- 
- 

Lack of certain technical skills (e.g. in some cases, computer skills) 
Fewer abilities to absorb new information. 

The language barrier is another possible vulnerability factor. Part of the respondents 
in  Lithuania  speak  Russian.  Some  can  understand  Lithuanian  but  don’t  speak  it  very 
well. Problems may occur when faced with the official Lithuanian language (contracts, 
information letter etc.). 

In Portugal, low income – sometimes associated with factors such as unemployment 
(situations  where  either  the  respondents  or  other  members  of  their  household  are 
unemployed) or precarious employment not only limit people’s access to goods and 
services, but also make them feel less empowered when dealing with retailers.  

In  Denmark,  respondents  generally  feel  confident  when  dealing  with  companies,  and 
believe that the Danish system is able to protect them in case something went wrong. 
Some however mention feeling disadvantaged due to certain personal factors, such as 
being a widow and living alone, and not having anyone to discuss with before making 
an important decision (woman, 77, Denmark, typology 3).  

A10.5   Market-related factors 

Factors related to the market structure are cited mainly in the context of the energy 
sector, in countries such as Portugal, Romania and Lithuania. 

In Romania and Lithuania, although there are no major dissatisfactions with the sector, 
the overall belief is that there is only one supplier, and therefore switching is excluded 
as an option.  

“There is no one else except Enel, this is the only provider of electricity. I think 
the  electricity  was  chosen  way  before  I  was  born…maybe  my  parents  have 
chosen it or it was simply installed by the State” (man, Romania, 40, typology 
2). 

In  Portugal,  despite  the  increasing  number  of  players  on  the  market,  very  few 
consumers compared offers. Respondents with lower income and low level of education 
are  the  least  likely  to  compare  offers  or  to  aim  to  switch  to  another  provider.  All 
respondents  agree  is  that  there  are  very  few  differences  between  offers  in  terms  of 
prices.  Or,  price  is  the  main  reason  for  dissatisfaction  with  the  sector.  It  is  generally 
believed  that  energy  prices  –  gas  and,  especially,  electricity  –  are  very  high  and 

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disproportionate  to  the  Portuguese  average  standard  of  living,  and  also  much  higher 
than the prices charged in most other EU Member States. 

A10.6   Retailer-related factors 

These  types  of  difficulties  are  mainly  brought  up  when  talking  about  the  financial 
sector (particularly in Romania and Lithuania). 

In the two countries, the sector is regarded with a lot of scepticism. 

In  Romania,  this  is  due,  on  the  one  hand,  to  people’s  negative  experiences  and 
attitudes  towards  bank  employees,  and,  on  the  other  hand,  to  the  strong  belief  that 
banks follow their own interests, rather than consumer benefits. 

“That is a generally accepted truth: all banks want to take  our money and get 
rich.  They  never  care  about  the  customer,  they  only  care  about  our  money.” 
(man, Romania, 47, Typology 1) 

Romanian consumers’ frustration is increased by the fact that banks are services which 
consumers  have  to  pay  for,  and  still,  staffs’  attitudes  are  often  unhelpful  and 
disregardful. 

“I know that bank is also benefiting of my money and thus I can’t stand when 
they act superior with me: a lady simply avoided my eyes...it was just like she 
was not seeing me when in fact I was right there in front of her.  I really can’t 
accept  this.  If  I’m  there,  it  means  I  have  an  emergency  because  otherwise  I 
would have solved everything online” (woman, Romania, typology 3). 

In  Lithuania,  some  believe  that  the  bank  sector  lacks  transparency,  in  the  sense  that 
consumers  do  not  receive  enough  information  about  fees  and  other  terms  and 
conditions.  This  can  become  a  challenge  for  elderly  consumers,  who  find  it  more 
difficult  to  find  the  information  on  their  own,  and  who  rely  on  bank  employees  for 
information.  The  physical  problems  related  to  old  age  (weaker  hearing,  reaction  and 
concentration)  do  not  allow  these  respondents  to  understand  the  situation  quickly  or 
understand  complex  processes  or  terms.  It  is  felt  that  this  factor  is  not  sufficiently 
taken into account by banks and their employees when dealing with elderly consumers. 
These respondents feel uncomfortable when communicating with the banks. They feel 
that their questions are not taken seriously. In addition, they feel disappointed if they 
fail to understand the answers received from the banks’ employees. 

Internet use 
Although respondents who do not use the internet don’t see this as a direct reason for 
feeling  disadvantaged,  this  factor  does  limit  their  access  to  information  and 
communication when dealing with retailers. 

“I  do  not  get  physical  bills.  I  can  look  into  it  on  the  Internet,  I  do  not  have 
Internet,  but  I  drive  to  my  friend’s  and  she  helps  me  find  all  the  information 
that I need”. (Female, 61, Lithuania, typology 2) 

Some of the “offline” respondents would find it useful to have the internet at home, but 
cannot afford it. 

“I would like to have Internet at home, however, it is too expensive for me for 
now“. (Female, 69, Lithuania, Typology 3) 

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Others  use  the  internet  occasionally,  with  help  from  friends  and  relatives.  These 
respondents  assume  that  certain  situations  could  be  dealt  with  faster  and  more 
conveniently if they did have the internet. 

Other  respondents  “offline”  respondents  are  not  interested  in  the  internet,  and  claim 
that this does not affect them in their everyday lives. 

Attitudinal factors 
Factors  related  to  consumers’  attitudes  also  play  a  part  in  the  way  consumers  feel 
when dealing with retailers. 

In  some  cases,  respondents  admit  that  they  don’t  make  an  effort  to  read  or  fully 
understand contracts, terms and conditions, bills etc. This is mainly the case for more 
complex  services  (e.g.  the  financial  sector).  This  is  due,  on  the  one  hand,  to  lack  of 
interest  towards  the  topic,  or  to  lack  of  financial  literacy,  and,  on  the  other  hand,  to 
the  complexity  of  the  topics  and  to  the  prevalence  of  economic  and  technical  terms 
(e.g.  Portugal,  Lithuania).  In  such  cases,  respondents  tend  to  rely  on  the  account 
manager to provide all the information. In Lithuania, some of the difficulties are related 
to  the  fact  that  respondents  do  not  read  the  contracts.  On  the  other  hand,  they  feel 
that contracts are written in a manner which they would fail to understand anyway. 

In  Portugal,  on  the  one  hand,  respondents  expect  the  regulators  and  the  state  to 
minimise consumer disadvantages when dealing with retailers. On the other hand, they 
find  that  their  own  attitudes  lack  pro-activeness  when  it  comes  to  keeping  informed 
when it comes to market practices, offers, terms and conditions or mechanisms at their 
disposal. 

Some  of  the  above-mentioned  vulnerability  factors  are  sector-specific.  These  vary 
however from one country to another. 

Factors  related  to  the  market  apply  to  mainly  the  energy  sector.  In  Romania  and 
Lithuania,  there  is  only  one  supplier.  In  Portugal,  despite  the  fact  that  the  offer  is 
increasing,  there  is  a  strong  belief  that  there  are  very  few  differences  in  terms  of 
prices.  

When it comes to the internet sector, factors related to retention period (24 months in 
Portugal) may prevent people from being able to switch providers. 

Retailer-specific factors are brought up mainly in the context of the financial sector. 
On  the  one  hand,  in  countries  like  Romania,  Lithuania  and  Portugal,  people  are 
sceptical  with  regards  to  the  sector.  On  the  other  hand,  the  service  is  perceived  as 
complex,  and  the  different  aspects  (terms  and  conditions,  fees,  etc.)  are  not  always 
understood  by  the  consumers  (particularly  by  older  consumers,  or  by  those  who  lack 
financial literacy). 

Certain socio-demographic factors, along with attitudinal ones, tend to operate  across 
sectors. 

Respondents  with  low  financial  capacities  tend  to  feel  disadvantaged  when  dealing 
with  retailers  from  the  different  sectors,  on  the  one  hand,  due  to  the  fact  that  their 
access to certain goods and services is more limited (prices are perceived as too high), 
and  on  the  other  hand  due  to  a  lack  of  self-  confidence  associated  with  their  socio-
economic  status  (fear  of  losing  money,  as  well  as  of  not  being  taken  seriously  or 
considered as an “important” costumer). It is also the case for old age, along with the 

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other factors related to it (being retired, having health problems, being less up-
to  date  with  certain  technologies  etc.).  These  factors  play  an  important  part  in 
consumers’  attitudes  towards  certain  retailers  (particularly  from  the  financial  sector), 
but also in other situations (e.g. purchasing or returning products from stores). 

In  Lithuania,  for  Russian-speaking  respondents,  the  language  barrier  may  cause 
difficulties  to  consumers,  not  only  when  dealing  with  retailers  but  also  with  public 
services.  

Although  not  specifically  cited  as  a  disadvantage  by  the  respondents,  the  fact  of  not 
using  the  internet  also  limits  consumers’  possibilities  when  dealing  with  retailers 
from the different sectors. Consumers who do not use the internet are less able to look 
up  information  which  would  help  them  online,  but  also  need  to  rely  on  friends  or 
relatives  to  help  them  in  certain  situations  (e.g.  for  accessing  bills).  Furthermore,  in 
order to interact with the retailers or to benefit from certain services, respondents are 
constrained  to  comply  to  their  branch’s  opening  hours,  which  is  not  always  most 
convenient.  

Attitudinal  factors  also  tend  to  play  an  important  part  in  the  way  consumers  deal 
with retailers from the different sectors. In Portugal, consumers’ lack of pro-activeness 
in  terms  of  keeping 
informed  about  offers,  comparing  deals,  reading  and 
understanding terms and conditions may lead them to feel that they are not benefiting 
from  the  best  deals  on  the  market.  In  Romania,  consumers  who  lack  confidence  are 
less  likely  to  contact  the  seller  when  encountering  a  problem  with  goods  or  services 
and more reticent when it comes to defending their case. 

A10.7   Structural versus transitory nature of vulnerability 

The  qualitative  exercise  has  enabled  us  to  identify  various  types  of  coping  strategies 
employed by consumers when dealing with retailers. 

Some of these strategies consist of actions or attitudes employed by consumers in the 
idea of making the most appropriate choices, all while preventing possible difficulties. 

-  One  of  the  main  strategies  is  to  thoroughly  study  the  different  options  before 
selecting a supplier. This is however not possible in all countries/sectors, as in 
countries  such  as  Lithuania  and  Romania,  for  the  energy  sector,  the  general 
belief is that there is only one supplier. 

-  Systematically  comparing  offers  from  different  suppliers,  and  being  open  to 
switching in order to obtain a better deal (respondents in the UK are more pro-
active in this regard than those in other countries). 

-  Closely  monitoring  one’s  consumption,  and  keeping  track  of  the  amount  of 

money spent each time.  
-  Reading  communications 

(e.g.  energy  bills, 
communication  from  banks  etc.),  and  making sure  that  this is  well  understood 
(for example, by looking up the meaning of the different terms online, or asking 
the supplier to provide details). 

from  suppliers 

received 

“I simply started to learn the [energy bill]: I went on the internet and I 
googled  each  term  so  that  I  know  what  I’m  paying  for.  Moreover,  I 
started to write in my notebook how much I use and how much I pay for 
it. As to avoid being financially tricked, I started to call Enel each month 

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and  deliver  the  exact  amount  of  energy  that  I  have  consumed.  I  don’t 
like their estimations which are usually in customers’ disadvantage…so I 
simply eliminated this as well” (woman, Romania, 49, typology 3) 

-  Keeping informed through all available channels (online, telephone, face-to-face 

contact). 

-  Carefully selecting means of payment (in the context of dealing with the energy 
sector, one of the respondents in the UK preferred using prepaid meters, as this 
offered  her  more  control  over  her  weekly  budget  and  the  ability  to  alter 
consumption accordingly).  
In  the  context  of  avoiding  vulnerability  in  the  online  environment  –  keeping 
informed  about  security  issues,  installing  anti-virus  software  and  ad  blockers. 
One  respondent  mentioned  avoiding  posting  personal  information  on  social 
media,  due  to  security  concerns.  Others  take  basic  preventive  measures,  such 
as not opening emails from unknown addresses or clicking on suspicious links. 

- 

With  regards  to  the  financial  sector,  consumer  strategies  aimed  to  avoid  vulnerability 
differ.  On  the  one  hand,  regardless  of  their  attitudes  towards  the  sector,  some 
consumers  opt  for  a  pro-active  approach.  This  type  of  behaviour  is  common  for 
consumers  in  the  UK,  but  also  for  some  consumers  in  Romania.  These  respondents 
read  and  understand  communication  from  their  bank,  keep  in  contact  with  their 
account  manager,  are  used  to  handling  various  financial  products  and  develop 
strategies  in  order  to  maximise  their  benefit    (e.g.  receiving  their  salary  on  one 
account, transferring part of it towards another, with a better interest, and part on the 
credit  balance  etc.).  At  the  opposite  end,  avoidance  is  another  strategy,  as  some 
respondents do everything they can to have as little as possible to do with banks and 
bank employees. This type of attitude is typical for some of the consumers in Romania 
and Lithuania. Consumers who fall into this category adopt the following strategies: 

-  Conducting operations mainly through ATMs, rather than going to the branch 
-  Using  mainly  cash,  in  order  to  have  a  better  control  over  how  the  money  is 

spent 

-  Keeping one’s savings at home, rather than in a savings account 
-  Asking employers to pay them in cash. 

“I  don’t  want  to  have  to  do  anything  with  the  banks.  I  stay  away  as 
much as I can and I simply pray God that I would never have to interact 
with them. I know that they are not fair and I may lose the little that I 
have  right  now.  I  also  told  my  boss  that  I  want  to  receive  my  money 
cash, I don’t need a card” (man, 40, Romania, typology 2). 
“I do not have a bank account. I take my pensions in the post office, it is 
easier that way” (female, 73, Lithuania, typology 3). 

When  confronted  with  difficult  situations,  consumers  resort  to  various  strategies  in 
order to find solutions. 

In  cases  where  difficulties  consist  mainly  of  isolated  incidents  (as  for  example  in  the 
UK),  respondents  tend  to  solve  these  by  contacting  the  branch  and  asking  for 
compensation.    In  most  cases,  compensation  is  provided.  However,  it  is  up  to  the 
consumer to prepare a case and to gather evidence. If a solution is not found from the 
first  attempt,  the  consumer  takes  charge  of  escalating  the  matter,  or  referring  to  a 
third party (e.g. Citizens’ advice bureau).  

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In  Lithuania,  where  the  main  difficulties  encountered  are  with  the  financial  sector, 
respondents  tend  to  employ  the  same  strategy  (contacting  the  branch).  However, 
issues  are  not  always 
interpreted  as 
misunderstandings  of  the  contract  by  the  consumer.  In  some  of  these  cases, 
respondents  pay  the  fees  as  requested  (fearing  consequences),  and  then  close  their 
account, or switch to another bank.  

they  are  sometimes 

resolved,  as 

In  Portugal,  where  the  internet  sector  is  seen  as  problematic,  switching  providers 
happens  frequently.  However,  the  24  months  retention  period  is  a  major  barrier  to 
switching.  Therefore,  “threatening”  to  switch  providers  happens  more  often  than 
actually switching. However, just by manifesting their intention to switch to a different 
provider, consumers often succeed in obtaining better offers – in terms of services and 
price. 

In  Romania,  coping  strategies  come  across  as  being  strongly  related  to  consumer 
attitudes.  While  the  more  educated  or  determined  consumers  tend  to  compare  offers 
and  discuss  with  retailers,  for  the  more  submissive  profiles,  avoidance  is  their  main 
strategy. 

 “Why  would  I  go  back  [to  the  store],  it’s  my  fault  because  I  didn’t  read 
properly the label. I don’t want to get in any scandal, it does not suit me” (man, 
40, Romania, typology 2). 

Respondents  who  feel  disadvantaged  due  to  their  old  age  –  and  other  correlated 
factors (retirement, health problems) tend to adopt the following strategies: 

-  Asking for support from a friend or relative when making an important purchase  
„I  take  my  daughter  when  I  need  to  buy  something  more  serious.  She 
gives me advice and I feel braver, I know that I won‘t buy some kind of 
nonsense“. (Female, 71, Lithuania, typology 2) 

- 

If possible, trying to fix the problem themselves, without returning the product 
to the shop, or approaching the seller 

“Why  should  I  return  it,  I  can  fix  it  by  myself“.  (Male,  66,  Lithuania, 
typology 2) 

-  Going to the branch in person, rather than trying to solve the problem over the 

telephone. (This strategy is however more time consuming). 

“See how it is, sometimes I can’t hear properly, it’s more difficult for me 
to  concentrate  when  I’m  talking  on  the  phone.  But  when  I  see  the 
person I’m talking to, I perceive the information completely differently.” 
(Female, 69, Lithuania, Typology 3) 

-  Avoiding using  certain services  is  seen  as  another  way  of  minimising risks  (for 
example, deliberately not using computers or the internet, or not having a bank 
account). 
Following  up  on  payments  (for  example,  calling  certain  services  in  order  to 
check whether payments were received, and whether everything is in order).  

- 

753 

 
 
 
 
 
Annex 11│ Interview list 

Annex 11 Interview list 

Table 156:  Interview list 

Member 
State 

Organisation 

Name 

Organisation 
type 

Sector 

AT 

AT 

BE 

BE 

BE 

BU 

CY 
DE 

DE 

DK 

EE 

EL 

ES 
EU 
EU 

EU 
EU 
EU 

EU 
EU 

EU 

EU 

FI 

Austrian Regulatory 
Authority for Broadcasting 
and Telecommunications 
Chamber for Workers and 
Employees 
Reseau Financement 
Alternatif 

Dieter Staudacher 

Gabriele Zgubic 

Bernard Bayot 

Ligue de Familles 

Cecile Daron 

Test-Achats 
Bulgarian National 
Association "Active 
Consumers" 
The Cyprus Consumers 
Association 
BAGSO 
Verbraucherzentrale 
Bundesverband vzbv - 
Federation of German 
Consumer 
Organisations 

Danish Business Authority 
The Estonian Association of 
Consumer's Protection 
UGANDI  

KEPKA, Thessaloniki 
Organización de 
Consumidores y Usuarios 
(OCU) 
Eurofinas 
Age Platform 

Stephane Dochy 

Bogomil Nikolov 

Giorgos Stylianou 
Elke Tippelmann 

Anne Poder 

Evangelia Kekeleki 

David Ortega 
Alexandre Giraud 
Anne-Sophie Parent 

ANEC 
EuroFinuse 
European Disability Forum 

Chiara Giovannini 
Guillaume Prache 
Marie Denninghaus 

BEUC 
EBF 
European Financial Inclusion 
Network 

BEUC 
Competition and Consumer 
Authority 

Ms Anne Fily 
Noemie Papp 

Olivier Jerusalmy 

Ursula Pachl 

Regulator 
Consumer 
Organisation 

Other 
Consumer 
Organisation 
Consumer 
Organisation 

Consumer 
Organisation 
Consumer 
Organisation 
Other 

Online 

General 

Finance 

General 

Finance 

General 

General 
General 

Consumer 
Organisation 
Consumer 
Organisation 

Consumer 
Organisation 
Other 
Other 
Consumer 
Organisation 
Other 
Other 
Consumer 
Organisation 
Other 

Other 
Consumer 
Organisation 

General 

General 

General 
Finance 
General 

General 
Finance 
General 

Finance 
Finance 

Finance 

General 

Maren Osterloh 
Tore Kjelstrup 
Christensen 

Consumer 
Organisation 

General 

Consumer Agency  General 

Anja Peltonen 

Consumer Agency  General 

754 

 
 
Annex 11│ Interview list 

Table 156:  Interview list 

Member 
State 

Organisation 

Name 

Organisation 
type 

Sector 

Energy Authority 
Ministry of Justice 
Consumer Disputes Board 
Energy Branch Organisation 

Antti Paananen 
Karti Kummoinen 
Paivi Korpiola 
Riina Hainmaki 

Regulator 
Ministry 

Energy 
General 
Consumer  Agency   General 
Energy 

Maxime Chipoy 

Nicolas Revenu 

Regulator 
Consumer 
Organisation 
Consumer 
association 

Finance 

Online 

UFC - Que choisir 
Les Associations Familiales 
Catholiques 
Confédération de la 
Consommation, du 
Logement et du Cadre de 
Vie (CLCV) 
POTROŠAC: Croatian Union 
of Consumer Protection 
Organisations 
Hungarian Authority for 
Consumer Protection  

Consumer Association 
Associazione per la Difesa 
Utenti Servizi Bancari e 
Finanziari (ADUSBEF) 

Altroconsumo 
Banca d'Italia 
AGCOM 
Alliance of Lithuanian 
Consumer Organisations 
Institut Luxembourgeois de 
Régulation 
Union Luxembourgeoise des 
Consommateurs, nouvelle 
asbl (ULC) 
Maltese Competition and 
Consumer Authority  
Maltese Communication 
Authority  
Authority for Consumers and 
Markets 

Consumentenbond 
Netherlands Authority for 
the Financial Markets 

Sandrine Perrois 

Consumer 
Organisation 

General 

Igor Vujovic  

Consumer 
Organisation 

General 

Peter Aranyi 
Hildigunnur 
Hafsteinsdóttir 

Consumer Agency   General 

Consumer 
Organisation 

General 

Marco Novelli 

Marco Pierani 
Salvatore Accolla 
Sergio Del Grosso 

Egle Kybartiene 

Consumer 
Organisation 
Consumer 
Organisation 
Regulator 
Regulator 
Consumer 
Organisation 

Finance 

General 
Finance 
Online 

General 

Claude Hornick 

Regulator 

Energy 

Josiane Adams 

Consumer 
Organisation 

General 

Josefine Borg 
N/A (pooled written 
input) 

Consumer Agency   General 

Regulator 

Online  

Bob Boelema 

Michiel Karskens 

Consumer Agency 
Consumer 
Organisation 

Wijnand van de Beek 

Online  

General 

Finance 

General 

General 

Regulator 
Consumer 
Organisation 
Consumer 
Organisation 

Consumer Agency  General 

The Consumer Council 

Audun Skeidsvoll  

The Consumer Council 

UOKIK 

Toril Melander Stene 
Katarzyna 
Araczewska 

FI 
FI 
FI 
FI 

FR 

FR 

FR 

HR 

HU 

IS 

IT 

IT 
IT 
IT 

LT 

LU 

LU 

MT 

MT 

NL 

NL 

NL 

NO 

NO 

PL 

755 

 
 
 
Annex 11│ Interview list 

Table 156:  Interview list 

Member 
State 

Organisation 

Name 

Organisation 
type 

Sector 

Mateusz Ossowski 
Iris Pita 

Regulator 
Regulator 

Online 
Online 

Paulo Fonseca 

Consumer 
Organisation 

General 

Costel Stanciu 

Consumer 
Organisation 

General 

Gabrielle Feng-Krog 

Consumer Agency  General  

PL 
PT 

PT 

RO 

SE 

SE 

SE 

SI 

SK 
UK 

UK 

UK 

UK 

UK 

UK 

UK 
UK  

UKE 
ANACOM 
Associação Portuguesa para 
a Defesa do Consumidor 
(DECO) 
National Association for 
Consumers Protection (ANPC 
Romania) 
Swedish Consumer 
Authority  
The Swedish Consumers’ 
Association (Sveriges 
Konsumenter) 
Swedish Post and Telecom 
Authority (PTS) 
Slovene Consumers’ 
Association (SCA) 

SOS Poprad 
OFCOM 

Jens Henriksson 

Johan Rydberg 

Bostjan Krisper 
Petra Vargova 
Cakovska 
Benjamis Wallis  

Consumer Futures 

Heidi Ranscombe   

Which? 
Financial Conduct Authority 
(FCA) 

Louise Vergera 

Martin Coppack 

Which? 

Martin Saville 

Which? 
The Financial Inclusion 
Centre 
OFGEM 

Michelle Smyth 

Consumer 
Organisation 

Regulator 
Consumer 
Organisation 
Consumer 
Organisation 
Regulator 
Consumer 
Organisation 
Consumer 
Organisation 

Regulator 
Consumer 
Organisation 
Consumer 
Organisation 

General 

Online 

Finance 

General 
Online 

General 

Finance 

Finance 

Finance 

General 

Mick McAteer 
Zoe McLeod 

Other 
Regulator 

Finance 
Energy  

756 

 
 
Annex 12│ Task A2 Interview guide 

Annex 12 Task A2 Interview guide 

Introduction 

VVA  Europe  together  with  London  Economics  and  IPSOS  is  carrying  out  a  study  for 
European Commission’s DG JUST focusing on consumer vulnerability. The objective of 
the  study  is  to  identify  marketing  practices  that  are  especially  problematic  for 
consumers  and  to  identify  good  practice  measures  employed  in  Member  States  to 
mitigate  consumer  vulnerability.  These  interviews  are  therefore  a  key  element  of  the 
study and an opportunity to provide valuable input into the European Commission work 
in this field. 

We  will  use  the  information  from  the  interview  in  our  reports  to  the  European 
Commission.  We  will  however  not  quote  you  directly,  nor  attribute  any  statements  to 
you as an individual. The name of your organisation may be included in the report as 
part of a list of stakeholders consulted during the study. 

Do you have any questions?  

Questions 

Background information 

Answers 

1.  Please describe your role in your organisation 

2.  What activities related to protecting vulnerable 
consumers does your organisation perform? 

Problematic practices 

3.  What problematic market practices that exploit 
consumer vulnerability are you aware of in your 
country? Please provide concrete examples. 
  Are you aware of any problematic market 

practices in the online 
environment/telecommunications? 

  Are you aware of any problematic market 

practices in the energy sector? 

  Are you aware of any problematic market 

practices in the financial sector? 

Measures to address consumer vulnerability [to be 
adapted to interviewee expertise] 

4.  What are the specific measures in your country 

that address consumer vulnerability in the online 
environment and telecommunications sector?  
  Prompt: Legislation, non-legislative measures 
such as inspections, labelling schemes, digital 
literacy programmes, industry codes of 
conduct, campaigns, translation services, etc. 
  Do they target specific types of vulnerability? 

757 

 
 
 
 
  
 
 
 
 
 
 
 
 
 
Annex 12│ Task A2 Interview guide 

  Do they target specific consumer groups? 
  Do they target specific market practices? 
  What forms of sanctions are available? 
5.  What are the specific measures in your country 
that address consumer vulnerability in the 
energy sector?  
  Prompt: Legislation, non-legislative measures 

such as inspections, labelling schemes, 
industry codes of conduct, campaigns, 
translation services, social tariffs for energy, 
etc. 

  Do they target specific types of vulnerability? 
  Do they target specific consumer groups? 
  Do they target specific market practices? 
  What forms of sanctions are available? 
6.  What are the specific measures in your country 
that address consumer vulnerability in the 
financial sector?  
  Prompt: Legislation, non-legislative measures 

such as inspections, labelling schemes, 
financial education programmes, industry 
codes of conduct, campaigns, translation 
services, etc. 

  Do they target specific types of vulnerability? 
  Do they target specific consumer groups? 
  Do they target specific market practices? 
  What forms of sanctions are available? 
7.  Is there evidence of these measures being 

effective? 
  Prompt: studies, reports, evaluations, 

anecdotal evidence 

8.  Are you aware of any problems with the 

application of these measures? 

9.  Are you aware of recent case law in your country 
concerning problematic marketing practices in 
any of the sectors we have discussed 
(online/telecommunications/energy/financial 
services)?  

Overall  approach to consumer  vulnerability [if  not 
covered in previous questions] 

10. Is the term “vulnerable consumer” used in 

consumer protection legislation in your country? 
  How is it worded? 
 

Is it used in other legislation (e.g. sector 
specific)? 

11. How does the consumer protection legislation in 
your country refer to vulnerable consumers?  
  Are specific consumer groups singled out, 

 

which ones? 
Is there dedicated legislation addressing 
vulnerable consumers or specific consumer 
groups? 

  Are you aware of recent case law concerning 

758 

 
 
 
 
 
 
 
 
 
 
Annex 12│ Task A2 Interview guide 

consumer vulnerability in your country? 

“Average consumer” concept 

12. Is the concept of an “average consumer” used in 
consumer protection legislation in your country? 
  How is it worded? 
 

Is it used in other legislation (e.g. sector 
specific)? 

13. If yes, how is it defined?  

  Where is the definition set out? 
 

Is there a single definition or multiple 
definitions? 

14. Are you aware of any problems with the 

application of the “average consumer” concept in 
your country? 
 

 Prompt: Application of the concept to specific 
consumer groups 

15. Are you aware of case law in your country 

concerning the “average consumer” concept? 

Institutional landscape [optional, to be adapted to 
interviewee expertise] 

16. Which public institutions are engaged in 

protecting vulnerable consumers in your country? 
  Prompt: Ministries, consumer protection 

bodies, regulators 

17. Which other organisations are engaged in 

protecting vulnerable consumers in your country? 
  Prompt: NGOs, charities, help lines 
18. Is there collaboration in this area with other 
countries, or between different national 
institutions/organisations? 

Closing remarks 

19. Are you aware of recent articles, reports or 
studies on consumer vulnerability in your 
country? 

20. Which organisations or individuals in your 
country should we consult with regard to 
consumer vulnerability in the online 
environment, telecommunications, energy sector, 
or the financial sector? 

21. Do you have any additional remarks? Is there 

something that was not covered?  

759 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

Annex 13 Consumer 

questionnaire 

and 

behavioural 

experiment 

This section presents the final consumer questionnaire and experiment.  

Demographics  

Questions to set quota 

[ Qcountry: HIDDEN VARIABLE] 
[PROG: HIDDEN. SINGLE ANSWER] 

1 
2 
3 
4 
5 
6 
7 
8 
9 
10 
11 
12 
13 
14 
15 
16 
17 
18 
19 
20 
21 
22 
23 
24 
25 
26 
27 
28 
29 
30 

AUSTRIA 
BELGIUM 
BULGARIA 
CROATIA 
CYPRUS 
CZECH REPUBLIC 
DENMARK 
ESTONIA 
FINLAND 
FRANCE 
GERMANY 
GREECE 
HUNGARY 
ICELAND 
IRELAND 
ITALY 
LATVIA 
LITHUANIA 
LUXEMBOURG 
MALTA  
NORWAY 
POLAND 
PORTUGAL 
ROMANIA 
SLOVAKIA 
SLOVENIA 
SPAIN 
SWEDEN 
THE NETHERLANDS 
the UK 

760 

 
 
 
 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

[Qlanguage: HIDDEN VARIABLE] 
[PROG : HIDDEN. SINGLE ANSWER] 

1 
2 
3 
4 
5 
6 
7 
8 
9 
10 
11 
12 
13 
14 
15 
16 
17 
18 
19 
20 
21 
22 
23 
24 
25 
26 
27 
28 
29 
30 
31 

BULGARIAN 
CROATIAN 
CZECH 
DANISH 
DUTCH 
DUTCH BELGIUM 
ENGLISH 
ESTONIAN 
FINNISH  
FRENCH BELGIUM 
FRENCH FRANCE 
FRENCH LUXEMBOURG 
GERMAN 
GERMAN AUSTRIAN 
GREEK (CYPRUS) 
GREEK (GREECE) 
HUNGARIAN 
ICELANDIC 
ITALIAN 
LATVIAN 
LITHUANIAN 
LUXEMBOURGISH 
MALTESE 
NORWEGIAN 
POLISH 
PORTUGUESE 
ROMANIAN 
SLOVAKIAN 
SLOVENIAN 
SPANISH 
SWEDISH 

Q_STAGE. 
[PROG: HIDDEN, SINGLE ANSWER] 

1.  PILOT [ Q_COUNTRY= CODE 30 ] 
2.  MAIN [ Q_COUNTRY= CODE 1 TO 30 ] 

Q_METHODOLOGY. 
PROG: HIDDEN : SINGLE ANSWER 

1.  ONLINE 
2.  CAPI 
3.  CATI  

EXPERIMENT. 
[PROG:  MULTIPLE,  HIDDEN,  ONE  RESPONDENT  IS  ALLOCATED  TO  SEE  TWO 
EXPERIMENTS OUT OF 4 BASED ON THE Q_CELL] 
[PROG:  GENERIC  SECTOR  is  related  to  each  of  the  3  SECTORS  (energy/  online/ 
finance)  and  one  respondent  depending  on  the  allocation  cell  will  see  the  generic 
sector for energy or online or finance] 
[PROG:  [IF  Q_STAGE=  CODE  1  PILOT]  OR  [IF  F  Q_STAGE=  CODE  2  MAIN  AND 
Q_CUNTRY  =  CODE  30  (UK),  CODE  7  (Denmark),  CODE  18  (Lithuania),  CODE  23 

761 

 
 
 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

(Portugal), CODE 24 (Romania)    COUNTRIES where  EXPERIMENT PART is included 
FOR Q_STAGE= MAIN TO BE RECONFIRMED]] 

1.  ENERGY SECTOR 
2.  ONLINE SECTOR 
3.  FINANCE SECTOR 
4.  GENERIC SECTOR 

TREATMENT. 
PROG: SINGLE ANSWER; HIDDEN 
[RESPONDENT  IS  ALLOCATED  TO  ONE  TREATMENT  FOR  THE  1ST  EXPERIMENT  TO  BE 
SEEN AND TO THE OTHER TREATMENT FOR THE 2ND EXPERIMENT TO BE SEEN. THIS 
ALLOCATION DEPENDS ON THE CELL RESPONDENT IS SEEING AS LISTED BELOW IN 
Q_CELL] 
[PROG:  THE  FIELD  for  the  OFFERS  OF  THE  TWO  EXPERIMENTS/  RESPONDENT 
DEPENDS  ON  THE  TREATMENT  ACCORDING  TO  THE  CELL.  FOLLOW  THE 
INSTRUCTIONS within each experiment] 

1.  MARKETING PRACTICE 
2.  REMEDY TREATMENT  

Q_CELL. 

PROG:  HIDDEN  SINGLE  ANSWER;  ALLOCATE  RESPONDENTS  RANDOMLY  BASED  ON 
THE LEAST FULL QUOTA  

CELL 

1st 
experiment 

2nd 
experiment  Share 

Respondents 
per 1000 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

E/MP 

O/MP 

F/MP 

E/R 

O/R 

F/R 

G-E/MP 

G-O/MP 

G-F/MP 

G-E/R 

G-O/R 

G-F/R 

E/R 

O/R 

F/R 

E/MP 

O/MP 

F/MP 

O/R 

F/R 

E/R 

O/MP 

F/MP 

E/MP 

O/R 

F/R 

E/R 

O/MP 

F/MP 

E/MP 

G-O/MP 

G-F/MP 

G-E/MP 

G-O/R 

G-F/R 

G-E/R 

Key: 
E/MP = Energy sector  / Marketing practice 
O/MP = Online sector / Marketing practice 

8,33% 

8,33% 

8,33% 

8,33% 

8,33% 

8,33% 

4,17% 

4,17% 

4,17% 

4,17% 

4,17% 

4,17% 

4,17% 

4,17% 

4,17% 

4,17% 

4,17% 

4,17% 

83,3 

83,3 

83,3 

83,3 

83,3 

83,3 

41,7 

41,7 

41,7 

41,7 

41,7 

41,7 

41,7 

41,7 

41,7 

41,7 

41,7 

41,7 

762 

 
 
 
 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

F/MP = Finance sector / Marketing practice 
E/R = Energy sector / Remedy 
O/R = Online sector / Remedy 
F/R = Finance sector / Remedy 
G-E/MP = Generic-Energy / Marketing practice 
G-O/MP = Generic-Online / Marketing practice 
G-F/MP = Generic-Finance / Marketing practice 
G-E/R = Generic-Energy / Remedy 
G-O/R = Generic-Online / Remedy 
G-F/R = Generic-Finance / Remedy 

Q_DELAY_EXP. 

[PROG: SINGLE; HIDDEN VARIABLE TO BE USED FOR THE EXPERIMENTS (DELAYS IN 
SEEING  THE  OFFERS  –  IN  ROUND  2,  IF  RESPONDENT  CLICKS  TO  SEE  THE  2nd  NEW 
OFFER,  TIMER  OF  THE  2nd  NEW  OFFER  CAN  ONLY  START  ONCE  1st  NEW  OFFER  IS 
DISPLAYED  –  BLOCK  “NEXT”  BUTTON  UNTIL  ALL  REQUESTED  OFFER(S)  APPEAR(S) 
ON SCREEN) 
THE OPTION WILL BE PRES ERVED FOR ALL EXPERIMENT THE SAME] 

1.  10 sec 

f 
SUMMARY OF THE TIMERS TO BE SET : 

timer name 

TIMER 1(demo_part) 

TIMER8(exp_electricity) 

timer 
START 

q1intro 

E1 

TIMER12(exp_electricity_screen1) 

E3 

TIMER13(exp_electricity_screen2) 

E4 

TIMER9(exp_online) 

TIMER14(exp_online_round1) 

TIMER15(exp_online_round2) 

TIMER10(exp_finance) 

TIMER16(exp_finance_screen1) 

TIMER17(exp_finance_screen2 ) 

O1 

O1 

O4 

F1 

F1 

F2 

TIMER 11(EXP_generic) 

CC1 

TIMER18(exp_generic_screen1) 

CC1 

timer STOP 

q13c 

E4 
After  showing  "  Thank  you. 
The  deal  you  chose  is  now 
your current deal" 

 after  clicking  "choose  this 
deal " 

O5 

O3 

O5 

F4 
After  showing  "Thank  you. 
The  loan  you  chose  is  now 
your current loan" 

Before entering F3 

CC2FU 
After  showing  "  Thank  you. 
[deal/provider/account] 
The 
you  chose 
is  now  your 
‘current’ 
[deal/provider/account]." 

763 

 
 
 
 
  
  
  
  
  
  
Annex 13│ Consumer questionnaire and behavioural experiment 

TIMER19(exp_generic_screen2 ) 

cc2 

after  selecting  "  choose  this 
(deal/ provider/ account) 

TIMER2(identity_vulnerability) 

TIMER3(average_consumer) 

q14 

q17 

TIMER4(experience_electricity_sector)  A1_INTRO 

TIMER5(experience_online_sector) 

B1_INTRO 

TIMER6(experience_finance_sector) 

C1_INTRO 

TIMER 7(access_barriers) 

C21 

q16 

q19b 

A21 

B13 

C11 

C24 

[PROG: SHOW TO ALL] 

Thank you for taking part in this important study. The survey is about sharing the 
experiences you have with energy suppliers, financial services and home Internet 
providers. 

[PROG: [IF Q_STAGE= CODE 1 PILOT] OR [IF F Q_STAGE= CODE 2 MAIN AND 
Q_COUNTRY = CODE 30 (UK), CODE 7 (Denmark), CODE 18 (Lithuania), CODE 23 
(Portugal), CODE 24 (Romania)  SHOW THE FOLLOWING TEXT + LINK TO TERMS 
AND CONDITIONS] 

In  this  survey,  you  will  also  be  asked  to  complete  two  ‘experiments’.  In  these 
experiments  you  will  have  a  chance  to  win  extra  survey  points.  You  will  receive 
your  survey  points  for  participating  in  the  survey  as  usual  after  completing  it.  In 
each  experiment  you  will  be  shown  alternative  offers  which  you  need  to  choose 
between. Each time you manage to select the most advantageous offer you will be 
awarded with 25 additional survey points. In total during this survey, you have the 
chance  to  win  100  additional  survey  points  depending  on  the  choices  you 
make.   Any  additional  points  you  win  in  the  experiments  will  be  added  to  your 
account  after  the  survey  closes.  You  cannot  lose  any  of  your  initial  survey  points 
for participating in the survey. Once the survey is closed, you will be notified by e-
mail about the number of best offers you correctly selected and the total additional 
points you have won.  Please note that it can take up to 8 weeks for you to receive 
your additional survey points. 

Please refer to the terms and conditions for more details. 

 [PRG: SHOW LINK TO TERMS AND CONDITIONS] 

DEMOGRAPHICS 

[PROG: QUESTIONS ASKED TO ALL UNLESS SPECIFIED] 

[PROG:  PLEASE  RECORD  LENGTH  OF  INTERVIEW  STARTING  AT  Q1  EN  ENDING  AT 
QX] 

[PROG: Demographic questions to be used for quota: Q1, Q2, Q3, Q4, Q5, Q7a, Q7b] 

[TIMER1( demo part): START] 

764 

 
 
  
  
  
 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

Q1_intro. Firstly please tell us a few details about yourself. This is to ensure 
we are including a wide range of people in this research. 

[PROG: INFO TEXT] 

Q1. How old are you? 

[PROG: NUMERIC QUESTION – 2 DIGITS RANGE 0- 99; IF Q1 <16 END INTERVIEW] 

[PROG: RECODE INTO: HIDDEN VARIABLE] 
[SINGLE ANSWER] 

Q1_recode. 
[PROG: HIDDEN. SINGLE ANSWER] 

1.  16 - 24 
2.  25 - 34 
3.  35 - 44 
4.  45 - 54 
5.  55 – 64 
6.  65-74 
7.  75+ 

Q2. Are you a...  

[PROG: SINGLE ANSWER] 

1.  Woman 
2.  Man  

Q3. Please select the region where you live.  

[PROG: SINGLE ANSWER] 

[USE  STANDARD  REGION  LIST  FROM  PANEL  –  DO  NOT  ASK  IN  ICELAND  (IF 
qcountry=14)] 

Q4. Which best describes where you live?  

[PROG: SINGLE ANSWER] 

1.  Rural area or village 
2.  Small or medium sized town  
3.  Large town 

Q7a. What is the highest level of education you have achieved? 

[PROG: SINGLE ANSWER] [ASK ALL] 

PRG: INSERT LIST PER COUNTRY FROM EXCEL FILE “EDUCATION” 

RECODE INTO – HIDDEN VARIABLE PRG: SINGLE CODE  

1.  Low 
2.  Medium 
3.  High 

Q7b. How old were you when you stopped full-time education? 

[ASK ALL] 

765 

 
 
 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

1.  15 or less 
2.  16 to 19 
3.  20 or more 
4.  Still studying [ EXCUSIVE ] 
5.  No education [ EXCUSIVE ] 
6.  Don’t know [ EXCUSIVE ] 

[IF Q7b>Q1: DISPLAY FOLLOWING ERROR MESSAGE: 
“Answer provided is higher than your current age. “] 

/__/ 

[PROG: NUMERIC QUESTION – 2 DIGITS RANGE 0- 99][ASK IF Q7b=code4]: 

Q7c. Are you still studying… 

1.  In full time education 
2.  In combination with (part-time) job 
3.  Don’t know [ EXCLUSIVE ] 

Q5. Which of the following best describes your current work status? 

[ASK IF NOT CODE 4 AT Q7b] 

[PROG: SINGLE ANSWER] 

[RECODE  INTO:  HIDDEN  VARIABLE:  CODES  1-5  AS  ACTIVE  AND  CODES  6-10  AS 
INACTIVE] 

1.  Employed full-time 
2.  Employed part-time 
3.  Self-employed full-time 
4.  Self-employed part-time 
5.  Unemployed but looking for a job 
6.  Unemployed and not looking for a job 
7.  Long-term sick or disabled 
8.  Housewife / Homemaker 
9.  Retired 

Q6. For how long have you been in this situation? Please answer in years (if 
less than a year, please answer 0). 

[ASK IF CODES 5-9 AT Q5]   

[PROG: NUMERIC QUESTION – 2 DIGITS RANGE 0- 99]  

/__/ 

Q8. Which of the following best describes your situation? 

[PROG: SINGLE ANSWER] [ASK ALL] 

1.  Married 
2.  Remarried 
3.  Not married living with a partner 
4.  Single 
5.  Divorced or separated 
6.  Widowed 

766 

 
 
 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

7.  Other   

Q9.  Including  yourself,  how  many  people  live  in  your  household  at  the 
present time?  
Be  sure  to  count  all  people  living  in  your  home:  yourself,  your  partner,  your 
children, parents, friends and/or students. 

[PROG: SINGLE ANSWER] [ASK ALL] 

1 
2 
3 
4 
5 
6 or more 

Q10.  How  many  (if  any)  children  under  the  age  of  18  are  living  with  you  in 
your household? (If no persons under 18 in your household, please select 0) 
[ASK Q10 IF CODE 2 OR 3 OR 4 OR 5 OR 6 AT Q9] 

[PROG: SINGLE ANSWER] 

0 
1 
2 
3 
4 or more 

Q10a. Thinking about your household’s financial situation, would you say that 
making ends meet every month is…? 

[PROG: SINGLE ANSWER – ALLOW TO SKIP WITHOUT ANSWER] [ASK ALL] 

1.  Very easy 
2.  Fairly easy 
3.  Fairly difficult  
4.  Very difficult 

Q10b. Thinking about your household’s financial situation would you say that 
making ends meet every month is…? 

[PROG: ASK ONLY IF NO ANSWER IN Q10a] 

[PROG: SINGLE ANSWER] 

1.  Very easy 
2.  Fairly easy 
3.  Fairly difficult  
4.  Very difficult  
5.  Prefer not to say 

Q11. Is your mother tongue different from the official language(s) spoken in 
[INSERT Q_COUNTRY]? 

[PROG: SINGLE ANSWER] [ASK ALL] 

1.  No 
2.  Yes, but it does not cause me difficulty as a consumer 
3.  Yes, and it causes me difficulty as a consumer 

Q12 How frequently do you use the internet for the following activities? 

767 

 
 
 
 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

[PROG: SINGLE ANSWER PER ROW] [ASK ALL] 
[GRID ACROSS] 

1.  Several times a day 
2.  Every day or almost every day 
3.  2-3 times a week 
4.  Once a week 
5.  2-3 times a month 
6.  Once a month 
7.  A couple of times a year or less often 
8.  Never 

[GRID DOWN] 

1.  Searching for information 
2.  Comparing prices on products/services 
3.  Online Banking 
4.  Buying goods or services (holidays, books, music, etc.) 
5.  Selling goods or services 
6.  Using online social networks (Facebook, Twitter, Snapchat, etc.) 
7.  E-mail 

Q13  How  often  do  you  buy  goods  and  services  on  credit  (this  includes 
purchases made on credit cards, lines of credit and some loans)? 

[PROG: SINGLE ANSWER] [ASK ALL] 

1.  Very often 
2.  Sometimes 
3.  Seldom 
4.  Never 

Q13b Have you obtained or tried to obtain an unsecured loan or credit in the 
last 5 years?  

An  unsecured  loan  is  one  where  you  do  not  use  immovable  property  (e.g. 
your  residence),  as  security  or  another  kind  of  deposit  or  pledge,  to 
guarantee the loan (it can however include a car loan).  

Examples  of  unsecured  loans  are  loans  taken  by  credit  card,  store  card,  authorized 
overdraft, unsecured personal loan and revolving credit 

[ASK ONLY IN COUNTRIES WITH EXPERIMENTS: UK, RO, LT, DK, PT] 

[PROG: SINGLE ANSWER] [ASK ALL] 

1.  Yes, obtained  
2.  Yes, tried to obtain, but did not obtain it 
3.  Yes, I looked for it but decided not to take it 
4.  No, did not look for an unsecured loan or credit in the last 5 years 

Q13c From the point  of your household budget, have  you ever  had problems 
to repay your loans? 

[PROG: SINGLE ANSWER] [ASK ALL] 

1.  No 
2.  Yes, occasionally 
3.  Yes, all the time 

768 

 
 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

4.  Did never had loans 

A15 The term ‘kWh’ in an offer describes…? 

 [PROG: SINGLE ANSWER - RANDOMIZE] [ASK ALL] 

1: The unit of energy to measure electricity for billing purposes 
2: The unit of energy to measure gas for billing purposes 
3: The unit of energy to measure both electricity and gas for billing purposes 
4: Don’t know [KEEP POSITION] 

B11 The term ‘Mbps’ in an offer describes…? 

[PROG: SINGLE ANSWER - RANDOMIZE] [ASK ALL] 

1: The speed of the internet connection 
2: The maximum size of files I can download 
3: The speed of my computer 
4: Don’t know [KEEP POSITION] 
[TIMER1 (demo part ): STOP] 

[PROG: NEW SCREEN - TO BE DISPLAYED BEFORE 1st EXPERIMENT IS STARTING] 
[PROG: [IF Q_STAGE= CODE 1 PILOT] OR [IF F Q_STAGE= CODE 2 MAIN AND 
Q_COUNTRY = CODE 30 (UK), CODE 7 (Denmark), CODE 18 (Lithuania), CODE 23 
(Portugal), CODE 24 (Romania)  SHOW THE FOLLOWING TEXT BEFORE THE 1ST 
EXPERIMENT THAT RESPONDENT IS RECEIVING] 

Thank you for your answers so far.  

You are now going to take part in the first experiment where you will also have a 
chance to win additional survey points.  In total for this experiment, you can win 
up to 50 points (if you select the best offer in both rounds). 

You cannot lose any of your initial survey points for participating in the survey.   

 [PRG: SHOW LINK TO TERMS AND CONDITIONS] 

[PROG: NEW SCREEN – TO BE DISPLAYED BEFORE EXPERIMENT 2 IS STARTING] 
 [PROG: [IF Q_STAGE= CODE 1 PILOT] OR [IF F Q_STAGE= CODE 2 MAIN AND 
Q_COUNTRY = CODE 30 (UK), CODE 7 (Denmark), CODE 18 (Lithuania), CODE 23 
(Portugal), CODE 24 (Romania)  SHOW THE FOLLOWING TEXT BEFORE THE 2ND 
EXPERIMENT THAT RESPONDENT IS RECEIVING] 

Thank you for your answers so far.  

You are now going to take part in the second experiment where you will also have 
a chance to win additional survey points.  In total for this experiment, you can win 
up to 50 points (if you select the best offer in both rounds). 

You cannot lose any of your initial survey points for participating in the survey.   

 [PRG: SHOW LINK TO TERMS AND CONDITIONS] 

_____________________________________________________________
_________________ 

EXPERIMENT PART:  

769 

 
 
 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

ONE  RESPONDENT  IS  ALLOCATED  TO  SEE  2  EXPERIMENTS  OUT  OF  4  BASED 
ON THE CELL 

_____________________________________________________________
_________________ 
EXPERIMENT 1: ELECTRICITY  
[TIMER 8(exp_electricity): START] 

[REGISTER: 

Electricity sector experiment marketing practice  

1. 

2. 

3. 

EVERY CLICK THE RESPONDENT HAS DONE DURING THIS EXPERIMENT  

THE TIME SPENT ON THE EXPERIMENT  

EXACTLY THE OFFERS SEEN 

Electricity sector experiment remedy treatment 

1. 

2. 

3. 

EVERY CLICK THE RESPONDENT HAS DONE DURING THIS EXPERIMENT  

THE TIME SPENT ON THE EXPERIMENT  

EXACTLY THE OFFERS SEEN 

IF  THE  RESPONDENT  CLICKED  ON  THE  i-ICON  FOR  THE  PERSONAL 

4. 
PROJECTION 

IF  THE  RESPONDENT  CLICKeD  ON  THE  i-ICON  FOR  THE  TRAIFF 

5. 
COMPARISON RATE 

USE EXCEL “ELECTRICITY DEALS FOR EXPERIMENTS – FOR IPSOS”  

The  structure  of  the  experiment  and  the  MARKETING  PRACTICE  and  REMEDY 
TREATMENTS ARE THE SAME; DEPENDING ON THE TREATMENT THE FIELDS FOR 
THE OFFERS WILL SLIGHTLY DIFFER AS BELOW: 

PROG:  IF  MARKETING  PRACTICES  DON’T  SHOW  WITHIN  THE  DEAL  (OFFER): 
PERSONAL PROJECTION AND TARIFF COMPARISON RATE. 
PROG:  IF  REMEDY  TREATMENT  SHOW  WITHIN  THE  DEAL  (OFFER):    PERSONAL 
PROJECTION AND TARIFF COMPARISON RATE. 

E1.  How big is your home? 

[PROG: SINGLE ANSWER] 

1.  1 bedroom 
2.  2 bedrooms 
3.  3 bedrooms 
4.  4 bedrooms 
5.  5 bedrooms 
6.  More than 5 bedrooms 

770 

 
 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

E2.    Which  of  the  following  best  describes  how  often  you  use  electricity  at 
home? 

[PROG: SINGLE ANSWER] 

1.  Evenings and weekends (e.g. a working couple) 
2.  All day (e.g. a family home) 

[PROG:  Allocate  to  Profile  1,  2  or  3  according  to  the  table  below  BASED  ON  THE 
ANSWERS IN E1 AND E2 ] 

Answer at E2: 

Answer at E1: 

1. 

2. 

1. 

2. 

3. 

4. 

5. 

6. 

Profile 1 

Profile 1 

Profile 1 

Profile 1 

Profile 1 

Profile 2 

Profile 2 

Profile 2 

Profile 2 

Profile 3 

Profile 3 

Profile 3 

[PROG:  USE  THE  FOLLOWING  INSERT  TEXT  DEPENDING  ON  RESPONDENT  PROFILE 
FOR E3 AND E4] 

[Insert KWh according to profile] = 2000kWh if PROFILE 1 
[Insert KWh according to profile] = 3200kWh if PROFILE 2 
[Insert KWh according to profile] = 4900kWh if PROFILE 3 

[ TIMER 12(exp_electricity_screen1 ) : STARTS] 
E3. Assume you use  [Insert kWh according to profile] of electricity per year, 
manage  your  electricity  account  online,  and  pay  your  electricity  bill  by  fixed 
monthly direct debit. 

Please  imagine  you  are  trying  to  find  a  new  electricity  deal  for  your  home 
electricity. Please consider the electricity deals shown below. Which of these 
deals would you choose? 

[PROG: SINGLE ANSWER] 
[PROG:  FORESEE  A  BUTTON    THAT    ALLOWS  RESPONDENTS  TO  OPEN  A 
CALCULATOR  IF  THEY  WISH  TO;  REGISTER  IF  THE  CALCULATOR  HAS  BEEN 
OPENED OR NOT  

[PROG:  IF  THE  RESPONDENT  IS  ALLOCATED  TO  THE  MARKETING  PRACTICE 
TREATMENT, THEN PRESENT DEALS N AND P FROM THE SPREADSHEET, WITHOUT 
THE PERSONAL PROJECTION AND TARIFF COMPARISON RATE.] 

771 

 
 
 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

[IF THE RESPONDENT IS ALLOCATED TO THE REMEDY TREATMENT, THEN PRESENT 
DEALS N AND P FROM THE SPREADSHEET, WITH THE PERSONAL PROJECTION AND 
RATE.]
COMPARISON 
TARIFF 

[PROG: Interim screen should thank the respondent after selecting one deal:   “Thank 
you. The deal you chose is now your current deal. “ ] 

[TIMER 12(exp_electricity_screen1 ): STOP] 
[TIMER 13(exp_electricity_screen2 ): STARTS] 
E4. Assume you use [Insert kWh according to profile] of electricity per year, 
manage  your  electricity  account  online,  and  pay  your  electricity  bill  by  fixed 
monthly direct debit. 

The deal you just chose is now your current deal – this deal is shown below. 

Two  new  deals  are  also  available,  which  you  can  see  by  clicking  on  the 
buttons.  Would  you  change  to  one  of  these  new  deals,  or  stay  with  your 
current deal? 

PROG: SINGLE ANSWER 

[PROG:  FORESEE  A  BUTTON  THAT  ALLOWS  RESPONDENTS  TO  OPEN  A 
CALCULATOR  IF  THEY  WISH  TO;  REGISTER  IF  THE  CALCULATOR  HAS  BEEN 
OPENED OR NOT 

[PROG:  If  Deal  N  selected  at  E3  AND  MARKETING  PRACTICE  TREATMENT   present 
deals  N,M,O  (from  the  spreadsheet)  WITHOUT  the  PERSONAL  PROJECTION  and 
TARIFF COMPARISON RATE.  

772 

Deal NDeal PStanding charge: 4.3p/dayStanding charge: 11.9p/dayFirst 750kWh per year: 22.8p/kWhFirst 800kWh per year: 21.8p/kWhAdditional kWh: 11.9p/kWhAdditional kWh: 13.1p/kWhRates include direct debit discountDirect debit discount: 6%Choose this dealChoose this dealDeal NDeal PPersonal Projection: £369 per yearPersonal Projection: £388 per yearTariff Comparison Rate: 15.25p/kWhTariff Comparison Rate: 15.97p/kWhStanding charge: 4.3p/dayStanding charge: 11.9p/dayFirst 750kWh per year: 22.8p/kWhFirst 800kWh per year: 21.8p/kWhAdditional kWh: 11.9p/kWhAdditional kWh: 13.1p/kWhRates include direct debit discountDirect debit discount: 6%Choose this dealChoose this deal 
 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

Note that the deal labelled “Your current deal” is Deal N from the first round. ] 

[PROG: Respondents should have to click on a button to see  deal M and deal O, as 
illustrated  below.  Each  time  the  respondent  clicks  on  one  of  these  buttons  there 
should  be  a  TIME  DELAY  DEPENDING  ON  THE  Q_DELAY  ALLOCATED  AT  THE 
BEGINNING. For the experiment the time delay should be 10 seconds] 

[If Deal  N  selected at  E3 and REMEDY  TREATMENT  present deals  N,  M  and  O 
from  the  spreadsheet,  WITH  the  PERSONAL  PROJECTION  AND  TARIFF 
COMPARISON RATE.  

Note that the deal labelled “Your current deal” is Deal N from the first round. ] 

[PROG: Respondents should have to click on a button to see  Deal M and Deal O, as 
illustrated  below.  Each  time  the  respondent  clicks  on  one  of  these  buttons  there 
should  be  A  TIME  DELAY  DEPENDING  ON  THE  Q_DELAY  ALLOCATED  AT  THE 
BEGINNING. For the experiment the time delay should be 10 seconds.] 

773 

Your current dealStanding charge: 4.3p/dayFirst 750kWh per year: 22.8p/kWhView Deal MView Deal OAdditional kWh: 11.9p/kWhRates include direct debit discountStay with current dealYour current dealDeal MDeal OStanding charge: 4.3p/dayStanding charge: 11.9p/dayStanding charge: 7.8p/dayFirst 750kWh per year: 22.8p/kWhFirst 800kWh per year: 22.2p/kWhFirst 850kWh per year: 23.2p/kWhAdditional kWh: 11.9p/kWhAdditional kWh: 11.2p/kWhAdditional kWh: 10.3p/kWhRates include direct debit discountDirect debit discount: 8%Rates include direct debit discountStay with current dealSwitch to this dealSwitch to this deal 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

[PROG:  IF  THE  RESPONDENT  CHOSE  DEAL  P  AT  E3  PRESENT  DEALS  P,  O  AND  Q 
FROM  THE  SPREADSHEET,  WITH/WITHOUT  THE  PERSONAL  PROJECTION  AND 
TARIFF COMPARISON RATE DEPENDING ON THE RESPONDENT’S TREATMENT.] 

[PROG: ASK TO RESPONDENTS IN THE MARKETING PRACTICE TREATMENT] 

E5  Thinking  about  the  choices  you  made  between  the  electricity  deals  you 
just  saw,  which  of  the  following  features  was  the  most  important  when 
making your decision? 

PROG: SINGLE ANSWER 

1. 

2. 

3. 

4. 

5. 

The standing charge 

The price per kWh for the first batch of electricity 

The price per kWh for additional electricity 

Any discounts that applied 

The total cost of the deal over 12 months 

774 

Your current dealPersonal Projection: £369 per yearTariff Comparison Rate: 15.25p/kWhStanding charge: 4.3p/dayFirst 750kWh per year: 22.8p/kWhAdditional kWh: 11.9p/kWhView Deal MView Deal ORates include direct debit discountStay with current dealYour current dealDeal MDeal OPersonal Projection: £369 per yearPersonal Projection: £359 per yearPersonal Projection: £378 per yearTariff Comparison Rate: 15.25p/kWhTariff Comparison Rate: 14.45p/kWhTariff Comparison Rate: 15.04p/kWhStanding charge: 4.3p/dayStanding charge: 11.9p/dayStanding charge: 7.8p/dayFirst 750kWh per year: 22.8p/kWhFirst 800kWh per year: 22.2p/kWhFirst 850kWh per year: 23.2p/kWhAdditional kWh: 11.9p/kWhAdditional kWh: 11.2p/kWhAdditional kWh: 10.3p/kWhRates include direct debit discountDirect debit discount: 8%Rates include direct debit discountStay with current dealSwitch to this dealSwitch to this deal 
 
 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

6. 

Other features 

 [PROG: ASK TO RESPONDENTS IN THE REMEDY TREATMENT] 

E6  Thinking  about  the  choices  you  made  between  the  electricity  deals  you 
just  saw,  which  of  the  following  features  was  the  most  important  when 
making your decision? 

PROG: SINGLE ANSWER 

1. 

2. 

3. 

4. 

5. 

6. 

7. 

8. 

The standing charge 

The price per kWh for the first batch of electricity 

The price per kWh for additional electricity 

Any discounts that applied 

The total cost of the deal over 12 months 

The Personal Projection 

The Tariff Comparison Rate 

Other features 

[TIMER 13(exp_electricity_screen2 ) : STOP] 
[TIMER 8(exp_electricity): STOP] 

775 

 
 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

___________________________________________________
__________________ 
EXPERIMENT 2: ONLINE 
[TIMER 9(exp_online): START] 

[REGISTER: 

1.  EVERY CLICK THE RESPONDENT HAS DONE DURING THIS EXPERIMENT 
EXCEPT FOR BACK AND FORTH DURING THE SCREENS AND OFFERS  

2.   THE TIME SPENT ON THE EXPERIMENT  
3.   EXACTLY THE OFFERS SEEN 

REGISTER THE FOLLOWING : 
TOTAL  SCREEN  =  NB  OF  DIFFERENT  SCREENS  SAW  BY  RESPONDENTS 
DURING EXP. 

ROUND 1 

INFO TO BE REGISTERED IN PROGRAMMING AS IN THE TABLE BELOW : 

Person 

Saw 
Screen 1 

Saw 
Screen 2 

Saw 
Screen 3 

Saw 
Screen 4 

Saw 
Screen 5 

Total 
screens 
seen 

Reversed 
decision 

1 

2 

3 

4 

Yes 

Yes 

Yes 

Yes 

Yes 

Yes 

Yes 

No 

Yes 

Yes 

Yes 

No 

No 

Yes 

Yes 

Yes 

No 

No 

Yes 

Yes 

3 

4 

5 

3 

No 

Yes 

Yes 

No 

[Total screens seen= COUNTER (the number of different screens seen) 
Reversed decision= YES AT SCREEN 2 AND YES AT SCREEN 4] 

INDIVIDUAL  SCREEN=  REGISTER  EACH  SCREEN  SEEN  BY  THE  RESPONDENT 
CONSIDERING THE BACK AND FORTH . 

EXAMPLE: 

776 

 
 
 
 
 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

Person 

x 

y 

z 

t 

Saw 
Screen 
1 
1 

Saw 
Screen 
2 
1 

Saw 
Screen 
3 
1 

Saw 
Screen 
4 
0 

Saw 
Screen 
5 
0 

Total 
individual 
screens 
3 

1 

1 

1 

1 

1 

2 

2 

1 

1 

2 

2 

1 

0 

1 

1 

6 

6 

6 

0  respondent saw the screen 0 times 

1  respondent saw the screen 1 times 

2  respondent saw the screen 2 times 

3  respondent saw the screen 3 times 

ROUND 2 

INFO TO BE REGISTERED IN PROGRAMMING AS IN THE TABLE BELOW : 

Perso
n 

Saw 
Scree
n 1 

Saw 
Scree
n 2 

Saw 
Scree
n 3 

Saw 
Scree
n 4 

Saw 
Scree
n 5 

Saw 
Scree
n 6 

Saw 
Scree
n 7 

Total 
screen
s seen 

Reverse
d 
decision 

Yes 

Yes 

Yes 

No 

No 

No 

No 

Yes 

Yes 

Yes 

Yes 

No 

No 

No 

Yes 

Yes 

Yes 

Yes 

Yes 

Yes 

No 

Yes 

No 

No 

Yes 

Yes 

Yes 

Yes 

Yes 

Yes 

Yes 

No 

No 

Yes 

Yes 

3 

4 

6 

5 

5 

No 

Yes 

Yes 

Yes 

Yes 

1 

2 

3 

4 

5 

777 

 
 
 
 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

6 

Yes 

No 

No 

No 

No 

Yes 

Yes 

3 

No 

 [Total screens seen= COUNTER (the number of different screens seen) 

[Reversed decision=  

1st case: YES AT SCREEN 2 AND YES AT SCREEN 4 AND NO AT SCREEN 6 
2nd case: NO AT SCREEN 2 AND YES AT SCREEN 4 AND YES AT SCREEN 6  
3rd case: YES AT SCREEN 2 AND NO AT SCREEN 4 AND YES AT SCREEN 6  
4th case: YES AT SCREEN 2 AND YES AT SCREEN 6 AND YES AT SCREEN 
6 

Person 

x 

y 

z 

t 

w 

w 

Saw 
Screen 
1 
1 

Saw 
Screen 
2 
1 

Saw 
Screen 
3 
1 

Saw 
Screen 
4 
0 

Saw 
Screen 
5 
0 

Saw 
Screen 
6 
0 

Saw 
Screen 
7 
0 

Total 
individual 
screens 
3 

1 

1 

1 

1 

1 

1 

2 

0 

0 

0 

2 

1 

0 

0 

0 

2 

2 

1 

0 

0 

0 

1 

1 

0 

0 

0 

2 

1 

1 

1 

0 

0 

1 

1 

1 

6 

9 

6 

6 

3 

USE EXCEL FILE “ONLINE SECTOR EXPERIMENT FILE FOR IPSOS V2” 

The  structure  of  the  experiment  and  the  MARKETING  PRACTICE  and  REMEDY 
TREATMENTS ARE THE SAME; DEPENDING ON THE TREATMENT THE FIELDS FOR 
THE OFFERS WILL SLIGHTLY DIFFER AS BELOW: 

PROG: IF MARKETING PRACTICES DON’T SHOW WITHIN THE DEAL (OFFER): THE 
TOTAL COST FOR 12 MONTHS  
PROG:  IF  REMEDY  TREATMENT  SHOW  WITHIN  THE  DEAL  (OFFER  THE  TOTAL 
COST FOR 12 MONTHS 
[PROG  :  In 
TELEPHONE; TV PACKAGES 

this  experiment,  respondents  choose  between:  BROADBAND; 

From 5 providers: Provider N, Provider P, Provider M, Provider O, and Provider Q. 
The  Excel  file  specifies  the  characteristics  of  each  broadband  package  in  the  tabs 
called “Full text” and “Numeric”. (The Full text tab gives the full text that should 
be  shown  to  respondents,  whereas  Numeric  tab  gives  just  the  relevant  values 
for  characteristics  that  vary  across  packages  or  countries.  The  programmers  can 
use whichever of these tabs is most convenient.) 

There are 2 rounds in the experiment.  

  Respondents choose between Providers N and P in Round 1.  
 

If they choose Provider N in Round 1 they choose between Providers N, M 
and O in Round 2.  
If they choose Provider P in Round 1  they choose between Providers P, O 
and Q in Round 2. 

 

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Annex 13│ Consumer questionnaire and behavioural experiment 

Each  round  has  3  screens  –  i.e.  there  are  6  screens  in  total.  The  tab  called 
“Presentation”  in  the  Excel  file  specifies  what  information  should  be  shown  to 
respondents on each screen.  

  Columns D, E and F specify what information should be shown if the 

respondent is allocated to the MARKETING PRACTICE.  

  Columns H, I and J specify what information should be shown if the 

respondent is allocated to the REMEDY TREATMENT 

The Presentation tab also specifies how information should be presented – i.e. which 
line and what font. There are also mock-ups in the document below. In terms of how 
information should be presented, the programmers can follow the Excel file and/or the 
mock-ups. 

The  rest  of  this  document  specifies  what  should  be  shown  to  respondents  and  what 
respondents  need  to  do  at  Round  1/Screen  1;  Round  1/Screen  2;  Round 
1/Screen  3;  Round  2/Screen  1;  Round  2/Screen  2;  and  Round  2/Screen  3. 
Text that should be shown to respondents is BLACK. 

IMPORTANT NOTE: If the respondent chose Provider P in Round 1, then in Round 2 
the  set-up  fee  does  not  apply  for  Provider  P.  This  means  that  in  Round  2  for 
Provider P: 

It needs to say “No set-up fee” 

 
  The total cost for 12 months is lower (by the amount of the set-up fee). 

This  is  dealt  with  in  the  Excel  file  by  having  two  columns  for  each  of  these 
characteristics – one column for Round 1 and one column for Round 2. ]  

[TIMER 14(exp_ONLINE_ROUND1): STARTS] 

ROUND 1 / SCREEN 1 

O1. Suppose you are looking for a new broadband, telephone and TV package 
for  your  home.  Please  consider  the  offers  from  two  providers  shown  below 
(these offers do not include mobile telephone). 

Which of these offers would you choose? Please click to see more information 
about one offer. 

[PROG: SINGLE ANSWER] 
[PROG: respondents choose between Provider N and Provider P. ] 
[PROG: FORSEE a button below each offer which says “Click to see more”.] 
[The  tab  called  “Presentation”  in  the  Excel  file  specifies  what  information  should  be 
shown to respondents.] 
[Respondents  are  only  shown  THE  TOTAL  COST  FOR  12  MONTHS  if  they  are 
allocated to the REMEDY TREATMENT] 

[PROG:  FORESEE  A  BUTTON  THAT  ALLOWS  RESPONDENTS  TO  OPEN  A 
CALCULATOR  IF  THEY  WISH  TO;  REGISTER  IF  THE  CALCULATOR  HAS  BEEN 
OPENED OR NOT  

779 

 
 
 
 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

MARKETING PRACTICE TREATMENT: 

REMEDY TREATMENT: 

ROUND 1 / SCREEN 2 

O2. More information on the offer you just chose is shown below.  

You can click to  see more  information  on  your chosen  offer,  or return to the 
previous screen to change your selection. 

[PROG: INFO] 
[PROG:  At  Round  1/Screen  2,  respondents  are  shown  more  information  about  the 
offer they chose at Round 1/Screen 1. For example, if the respondent chose Provider 
N at Screen 1, then at Screen 2 they would see the information shown below. ] 

[FORESEE two buttons below the offer, which say “Click to see more” and “Return 
to previous screen”.] 
[The  tab  called  “Presentation”  in  the  Excel  file  specifies  what  information  should 
be shown to respondents.] 

[THIS  SCREEN  IS  THE  SAME  FOR  ALL  RESPONDENTS  (I.E.  THE  TREATMENT 
DOES NOT MAKE A DIFFERENCE).] 

[PROG:  FORESEE  A  BUTTON  THAT  ALLOWS  RESPONDENTS  TO  OPEN  A 
CALCULATOR  IF  THEY  WISH  TO;  REGISTER  IF  THE  CALCULATOR  HAS  BEEN 
OPENED OR NOT 

780 

Provider NProvider PBroadband+Telephone+TVBroadband+Telephone+TV12 month contract with renewal option12 month contract with renewal optionNo set-up fee!5% Discount!Up to 30Mb per secondUp to 30Mb per secondUnlimited downloadsUnlimited downloadsUsed to be £45 per monthUsed to be £40 per monthYou get a discount of 2% on your monthly subscriptionYou get a discount of 5% on your monthly subscriptionNow £44 per month with discountNow £38  per month with discountClick to see moreClick to see moreProvider NProvider PBroadband+Telephone+TVBroadband+Telephone+TV12 month contract with renewal option12 month contract with renewal optionNo set-up fee!5% Discount!Up to 30Mb per secondUp to 30Mb per secondUnlimited downloadsUnlimited downloadsUsed to be £45 per monthUsed to be £40 per monthYou get a discount of 2% on your monthly subscriptionYou get a discount of 5% on your monthly subscriptionNow £44 per month with discountNow £38  per month with discountTotal cost for 12 months £529Total cost for 12 months £656Click to see moreClick to see more 
 
 
 
 
 
 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

ROUND 1 / SCREEN 3 

O3. More information on your chosen offer is shown below. 

You  can  either  confirm  your  selection  by  clicking  “Choose  this  offer”,  or 
return to the previous screen. 

[PROG: SINGLE ANSWER] 
[PROG:  At  Round  1/Screen  3,  respondents  are  shown  full  information  about  the 
offer  they  chose  at  Round  1/Screen  2.  For  example,  if  the  respondent  chose 
Provider  N  at  Screen  2,  then  at  Screen  3  they  would  see  the  information  shown 
below. 
[FORESEE two buttons below each offer, which say “Choose this offer” and “Return 
to previous screen”.] 
The tab called “Presentation” in the Excel file specifies what information should be 
shown to respondents.  

[THIS  SCREEN  IS  THE  SAME  FOR  ALL  RESPONDENTS  (I.E.  THE  TREATMENT 
DOES NOT MAKE A DIFFERENCE).] 
[PROG:  FORESEE  A  BUTTON  THAT  ALLOWS  RESPONDENTS  TO  OPEN  A 
CALCULATOR  IF  THEY  WISH  TO;  REGISTER  IF  THE  CALCULATOR  HAS  BEEN 
OPENED OR NOT 

781 

Provider NBroadband+Telephone+TV12 month contract with renewal optionNo set-up fee!200 free minutes to national fixed linesFree night time & weekend calls to any fixed line unlimited10 TV channelsNo set-up feeClick to see moreReturn to previous screen 
 
 
 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

Interim screen between Round 1 and Round 2: “Thank you. The provider you chose is 
now your current provider.” 

[TIMER 14(exp_ONLINE_ROUND1 ) : STOP] 
 [TIMER 15(exp_ONLINE_ROUND2 ) : STARTS] 
ROUND 2 / SCREEN 1 

O4. Now suppose you are deciding whether to renew your contract with your 
current provider. The offer from your current provider is shown below. 

Offers are also available from two new providers. You can see these offers by 
clicking on the buttons. (None of these offers includes mobile telephone.) 

Which of these offers would you choose? Please click to see more information 
about one offer. 

[PROG: SINGLE ANSWER] 

[PROG:  At  Round  2/Screen  1,  respondents  choose  between  the  provider  they 
chose in Round 1 and two other providers. 
PROG:If they chose Provider N in Round 1they choose between Providers N,M and 
O in Round2 
PROG:If they chose Provider Pin Round 1 they choose between Providers P,O and Q 
in Round2] 

[PROG: Respondents should have to click on buttons  to see Providers M and O, as 
illustrated  below.  Each  time  the  respondent  clicks  on  one  of  these  buttons  there 
should  be  a  time  delay  DEPENDING  ON  THE  Q_DELAY  ALLOCATED  AT  THE 
BEGINNING. For the main study , the time delay should be 10 seconds . ] 

782 

Provider NBroadband+Telephone+TV12 month contract with renewal optionNo set-up fee!Up to 30Mb per secondUnlimited downloadsUsed to be £45 per monthYou get a discount of 2% on your monthly subscriptionNow £44 per month with discount200 free minutes to national fixed linesFree night time & weekend calls to any fixed line unlimited10 TV channelsNo set-up feeTotal cost for 12 months £529Choose this offerReturn to previous screen 
 
 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

[PROG: Once a respondent can see an offer, there should be a button below it which 
says  “Click  to  see more”.]  [PROG:  The  tab  called  “Presentation”  in  the  Excel  file 
specifies what information should be shown to respondents.] 

[Respondents  are  only  shown  THE  TOTAL  COST  FOR  12  MONTHS  if  they  are 
allocated to the REMEDY TREATMENT] 
[PROG:  FORESEE  A  BUTTON  THAT  ALLOWS  RESPONDENTS  TO  OPEN  A 
CALCULATOR  IF  THEY  WISH  TO;  REGISTER  IF  THE  CALCULATOR  HAS  BEEN 
OPENED OR NOT 

EXAMPLE: 
Marketing practice treatment, before respondents click buttons to see Providers M and 

Marketing  practice  treatment,  after  respondents  click  buttons  to  see  Providers  M  and 
O: 

783 

Renew with your current providerProvider NBroadband+Telephone+TV12 month contract with renewal optionNo set-up fee!Up to 30Mb per secondUnlimited downloadsUsed to be £45 per monthYou get a discount of 2% on your monthly subscriptionNow £44 per month with discountClick to see moreView offer from Provider MView offer from Provider ORenew with your current providerProvider NProvider MProvider OBroadband+Telephone+TVBroadband+Telephone+TVBroadband+Telephone+TV12 month contract with renewal option12 month contract with renewal option12 month contract with renewal optionNo set-up fee!10% Discount!Only £30 per month!Up to 30Mb per secondUp to 30Mb per secondUp to 30Mb per secondUnlimited downloadsUnlimited downloadsUnlimited downloadsUsed to be £45 per monthUsed to be £40 per month£30 per monthYou get a discount of 2% on your monthly subscriptionYou get a discount of 10% on your monthly subscriptionNow £44 per month with discountNow £36 per month with discountClick to see moreClick to see moreClick to see more 
 
 
 
 
 
 
 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

Remedy treatment, before respondents click buttons to see Providers M and O: 

Remedy treatment, after respondents click buttons to see Providers M and O: 

ROUND 2 / SCREEN 2 & ROUND 2 / SCREEN 3 

[PROG: Round 2/Screen 2 and Round 2/Screen 3 would then proceed in exactly 
the  same  way  as  Round  1/Screen  2  and  Round  1/Screen  3  above.  That  is, 
respondents  are  shown  more  information  about  the  offer  they  chose  at  Round 
2/Screen 1, and can either proceed: 

 To choose that deal  
 To go back and change their selection. 

784 

Renew with your current providerProvider NBroadband+Telephone+TV12 month contract with renewal optionNo set-up fee!Up to 30Mb per secondUnlimited downloadsUsed to be £45 per monthYou get a discount of 2% on your monthly subscriptionNow £44 per month with discountTotal cost for 12 months £529Click to see moreView offer from Provider MView offer from Provider ORenew with your current providerProvider NProvider MProvider OBroadband+Telephone+TVBroadband+Telephone+TVBroadband+Telephone+TV12 month contract with renewal option12 month contract with renewal option12 month contract with renewal optionNo set-up fee!10% Discount!Only £30 per month!Up to 30Mb per secondUp to 30Mb per secondUp to 30Mb per secondUnlimited downloadsUnlimited downloadsUnlimited downloadsUsed to be £45 per monthUsed to be £40 per month£30 per monthYou get a discount of 2% on your monthly subscriptionYou get a discount of 10% on your monthly subscriptionNow £44 per month with discountNow £36 per month with discountTotal cost for 12 months £529Total cost for 12 months £512Total cost for 12 months £420Click to see moreClick to see moreClick to see more 
 
 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

IMPORTANT NOTE: If the respondent chose Provider P in Round 1, then in Round 2 
the  set-up  fee  does  not  apply  for  Provider  P.  This  means  that  in  Round  2  for 
Provider P: 

It needs to say “No set-up fee” 

 
  The total cost for 12 months is lower (by the amount of the set-up fee). 

This  is  dealt  with  in  the  Excel  file  by  having  two  columns  for  each  of  these 
characteristics – one column for Round 1 and one column for Round 2. 

For example, if the respondent chose Provider P in Round 1 and is allocated to the 
REMEDY  TREATMENT  the  offers  they  will  be  shown  in  Round  2  are  presented 
below  (note  that  for  Provider  P  the  total  cost  for  12  months  is  £456,  rather  than 
£656). 

Use for both ROUND  2 /SCREEN 2 and ROUND 2/SCREEN 3 THE FOLLOWING 
TEXT 

Q5. More information on the offer you just chose is shown below.  

You can click to  see more  information  on  your chosen offer,  or return to the 
previous screen to change your selection. 

[PROG: INFO SCREEN] 

Round 2 / Screen 1 

785 

Renew with your current providerProvider PProvider OProvider QBroadband+Telephone+TVBroadband+Telephone+TVBroadband+Telephone+TV12 month contract with renewal option12 month contract with renewal option12 month contract with renewal option5% Discount!Only £30 per month!5% Discount!Up to 30Mb per secondUp to 30Mb per secondUp to 30Mb per secondUnlimited downloadsUnlimited downloadsUnlimited downloadsUsed to be £40 per month£30 per monthUsed to be £35 per monthYou get a discount of 5% on your monthly subscriptionYou get a discount of 5% on your monthly subscriptionNow £38  per month with discountNow £33 per month with discountTotal cost for 12 months £456Total cost for 12 months £420Total cost for 12 months £479Click to see moreClick to see moreClick to see more 
 
 
 
 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

Round 2 / Screen 2 (if chose Provider P) 

Round 2 / Screen 3 (if chose Provider P) 

O6  Thinking  about  the  choices  you  made  between  the  broadband, 
telephone  and TV packages you  just saw, which  of the following features 
was the most important when making your decision? 

PROG: SINGLE ANSWER 

786 

Provider PBroadband+Telephone+TV12 month contract with renewal option5% Discount!200 free minutes to national fixed linesFree night time & weekend calls to any fixed line up to 60 10 TV channelsNo set-up feeClick to see moreReturn to previous screenProvider PBroadband+Telephone+TV12 month contract with renewal option5% Discount!Up to 30Mb per secondUnlimited downloadsUsed to be £40 per monthYou get a discount of 5% on your monthly subscriptionNow £38  per month with discount200 free minutes to national fixed linesFree night time & weekend calls to any fixed line up to 60 min, then charged at standard network rate10 TV channelsNo set-up feeTotal cost for 12 months £456Choose this offerReturn to previous screen 
 
 
 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

1.  The price per month 
2.  The discount 
3.  The set-up fee 
4.  The free calls 
5.  The total cost for 12 months 
6.  Other features 

[TIMER 15(exp_ONLINE_ROUND2): STOP] 
[TIMER 9(exp_online): STOP] 

787 

 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

_____________________________________________________________
_________________ 
EXPERIMENT 3: FINANCE SECTOR 
_____________________________________________________________
__________________ 
[TIMER 10(exp_finance): START] 
[REGISTER: 

Finance sector experiment marketing practice  

1.  EVERY CLICK THE RESPONDENT HAS DONE DURING THIS EXPERIMENT  
2.  THE TIME SPENT ON THE EXPERIMENT  
3.  EXACTLY THE OFFERS SEEN 

Finance sector experiment remedy treatment 

1.  EVERY CLICK THE RESPONDENT HAS DONE DURING THIS EXPERIMENT  
2.  THE TIME SPENT ON THE EXPERIMENT  
3.  EXACTLY THE OFFERS SEEN 
4.  IF THE RESPONDENT CLICKED ON THE i-ICON FOR THE APR 

The  structure  of  the  experiment  and  the  MARKETING  PRACTICE  and  REMEDY 
TREATMENTS ARE THE SAME; DEPENDING ON THE TREATMENT THE FIELDS FOR 
THE OFFERS WILL SLIGHTLY DIFER AS BELOW: 

PROG: IF MARKETING PRACTICES DON’T SHOW WITHIN THE DEAL (OFFER): THE 
APR IS ALSO SHOWN IN LARGE FONT AT THE TOP  
PROG:  IF  REMEDY  TREATMENT  SHOW  WITHIN  THE  DEAL  (OFFER):  THE  APR  IS 
ALSO SHOWN IN LARGE FONT AT THE TOP 
[PROG: USE EXCEL FILE “FINANCE SECTOR EXPERIMENT FOR IPSOS”] 

[PROG:  Respondents  choose  between  5  loans:  Loan  N,  Loan  P,  Loan  M,  Loan  O, 
and Loan Q. The Excel file specifies the characteristics of  each loan in the tab called 
“Loan characteristics”.] 

The loan amount (“Loan amount: .....”), duration (“12 month personal loan”) and 
interest rate (“...% fixed”) are always shown in large font at the top, as illustrated in 
the mock-ups below. 

[THE APR  IS ALSO SHOWN  IN  LARGE FONT AT THE TOP  ONLY  IF THE RESPONDENT 
IS ALLOCATED TO THE REMEDY TREATMENT.] 

[PROG:  Full  details  of  each  loan  are  provided  in  a  single  sentence  at  the  bottom,  as 
illustrated in the mock-ups.] 

[PROG: If the LOAN has a (non-zero) fee then this sentence should read: 

  Representative example: Assumed borrowing of [Loan amount] over 12 

months at a fixed rate of [Interest rate] per annum, with [Fee] arrangement 
and credit fee would result in an APR of [APR]%, monthly repayments of 

788 

 
 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

[Monthly payment], equal repayments each months, total amount repayable 
[Total amount repayable]. ] 

[PROG: If the LOAN does not have a fee (i.e. in the Excel file there is a 0 in the Fee 
column) then this sentence should read: 

  Representative example: Assumed borrowing of [Loan amount] over 12 

months at a fixed rate of [Interest rate] per annum, with no arrangement and 
credit fee would result in an APR of [APR]%, monthly repayments of [Monthly 
payment], equal repayments each months, total amount repayable [Total 
amount repayable]. 

The red text is completed by drawing the relevant value from the Excel file. ] 

[TIMER 16(exp_FINANCE_SCREEN1) : START] 
F1. Please imagine you want to borrow [Loan amount] for 12 months in order 
to buy a good, such as a holiday, a television, or some other product for your 
home. 

Please consider the loan offers shown below. Which of these loans would you 
choose? 

(Please note that these loans are unsecured loans which are not linked to the 
purchase of a certain good or service.) 

[PROG: SINGLE ANSWER] 

[PROG: Respondents choose between Loan N and Loan P from the spreadsheet.] 

[PROG:  FORESEE  A  BUTTON  THAT  ALLOWS  RESPONDENTS  TO  OPEN  A 
CALCULATOR  IF  THEY  WISH  TO;  REGISTER  IF  THE  CALCULATOR  HAS  BEEN 
OPENED OR NOT  

[PROG:  If  the  respondent  is  allocated  to  the  MARKETING  PRACTICE  TREATMENT 
then  the  loans  are  presented  with  the  APR  shown  in  the  small  text  but  not  in 
larger text directly below the interest rate, as illustrated below.] 

[PROG: If the respondent is allocated to the REMEDY TREATMENT then the loans are 
presented  with  the  APR  shown  in  the  small  text  and  also  in  larger  text  directly 
below  the  interest  rate,  as  illustrated  below.  There  should  be  an  ‘i’  icon  which 
respondents can click on to see an explanation of APR.] 

789 

Loan NLoan amount: £93012 month personal loan3.7% fixedRepresentative example: Assumed borrowing of £930 over 12 months at a fixed rate of 3.70% per annum, with no arrangement and credit fee would result in an APR of 3.8%, monthly repayments of £79.06, equal repayments each months, total amount repayable £948.72.Loan PLoan amount: £93012 month personal loan2.7% fixedRepresentative example: Assumed borrowing of £930 over 12 months at a fixed rate of 2.70% per annum, with £67.10 arrangement and credit fee would result in an APR of 17.0%, monthly repayments of £84.31, equal repayments each months, total amount repayable £1,011.72.Choose this loanChoose this loan 
 
 
  
 
  
 
Annex 13│ Consumer questionnaire and behavioural experiment 

INTERIM  SCREEN  should  thank  the  respondent  saying:  Thank  you.  The  loan  you 
chose is now your ‘current loan’. 

[TIMER 16(exp_FINANCE_SCREEN1): STOP] 
[TIMER 17(exp_FINANCE_SCREEN2): START] 
F2.  Now  suppose  you  wish  to  take  out  another  12  month  loan  for  the  same 
amount.  

Your current loan is on offer  again on  the same terms. This is  loan is  shown 
below. 

Two  new  loans  are  also  available.  You  can  see  the  details  of  these  loans  by 
clicking on the buttons. 

Which loan would you choose? 

[PROG: SINGLE ANSWER] 

[If the respondent chose Loan N at F1 they choose between  Loans N, M and O at 
F2.] 
[If the respondent chose Loan P at F1, then they choose between Loans P, O and Q 
at F2.] 

[Mock-ups  are  presented  below  for  the  case  where  the  respondent  chose  Loan  N  at 
F1.] 
[Respondents should have to click on a button to see  Loans M, O and Q (whichever 
two  are  relevant).  Each  time  the  respondent  clicks  on  one  of  these  buttons  there 
should  be  a  TIME  DELAY  DEPENDING  ON  THE  Q_DELAY  ALLOCATED  AT  THE 
BEGINNING .For the experiment the time delay should be 10 seconds .] 

[PROG:  FORESEE  A  BUTTON  THAT  ALLOWS  RESPONDENTS  TO  OPEN  A 
CALCULATOR IF THEY WISH TO;] 

[If  the  respondent  is  allocated  to  the  MARKETING  PRACTICE  TREATMENT  then  the 
loans  are  presented  with  the  APR  shown  in  the  small  text  but  not  in  larger  text 
directly below the interest rate, as illustrated below.] 

INCLUDE  “  Your  current  “  (in  bold)  IN  FRONT  OF  “  LOAN  N”  [  VALID  FOR  ALL 

DIFFERENT LOANS : N or P]  

790 

Loan NLoan amount: £93012 month personal loan3.7% fixedAPR 3.8%Representative example: Assumed borrowing of £930 over 12 months at a fixed rate of 3.70% per annum, with no arrangement and credit fee would result in an APR of 3.8%, monthly repayments of £79.06, equal repayments each months, total amount repayable £948.72.Loan PLoan amount: £93012 month personal loan2.7% fixedAPR 17.0%Representative example: Assumed borrowing of £930 over 12 months at a fixed rate of 2.70% per annum, with £67.10 arrangement and credit fee would result in an APR of 17.0%, monthly repayments of £84.31, equal repayments each months, total amount repayable £1,011.72.Choose this loanChoose this loan 
 
  
 
  
 
 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

[PROG:  If the  respondent is allocated to the  REMEDY  TREATMENT then the loans are 
presented  with  the  APR  shown  in  the  small  text  and  also  in  larger  text  directly 
below  the  interest  rate,  as  illustrated  below.  There  should  be  an  ‘i’  icon  which 
respondents can click on to see an explanation of APR.] 

791 

                            Your current loanLoan NLoan amount: £93012 month personal loan3.7% fixedRepresentative example: Assumed borrowing of £930 over 12 months at a fixed rate of 3.70% per annum, with no arrangement and credit fee would result in an APR of 3.8%, monthly repayments of £79.06, equal repayments each months, total amount repayable £948.72.Loan OLoan amount: £93012 month personal loan2.8% fixedRepresentative example: Assumed borrowing of £930 over 12 months at a fixed rate of 2.80% per annum, with £30.50 arrangement and credit fee would result in an APR of 9.2%, monthly repayments of £81.26, equal repayments each months, total amount repayable £975.12.Loan OLoan amount: £93012 month personal loan2.8% fixedRepresentative example: Assumed borrowing of £930 over 12 months at a fixed rate of 2.80% per annum, with £30.50 arrangement and credit fee would result in an APR of 9.2%, monthly repayments of £81.26, equal repayments each months, total amount repayable £975.12.Choose this loanView Loan MView Loan OYour current loanLoan NLoan amount: £93012 month personal loan3.7% fixedRepresentative example: Assumed borrowing of £930 over 12 months at a fixed rate of 3.70% per annum, with no arrangement and credit fee would result in an APR of 3.8%, monthly repayments of £79.06, equal repayments each months, total amount repayable £948.72.Loan MLoan amount: £93012 month personal loan3.0% fixedRepresentative example: Assumed borrowing of £930 over 12 months at a fixed rate of 3.00% per annum, with £54.20 arrangement and credit fee would result in an APR of 14.6%, monthly repayments of £83.36, equal repayments each months, total amount repayable £1,000.32.Loan OLoan amount: £93012 month personal loan2.8% fixedRepresentative example: Assumed borrowing of £930 over 12 months at a fixed rate of 2.80% per annum, with £30.50 arrangement and credit fee would result in an APR of 9.2%, monthly repayments of £81.26, equal repayments each months, total amount repayable £975.12.Choose this loanChoose this loanChoose this loan 
 
 
  
  
 
     
     
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

INCLUDE  “  Your  current  “  (in  bold)  IN  FRONT  OF  “  LOAN  N”  [  VALID  FOR  ALL 
DIFFERENT LOANS : N or P]  

    [TIMER 17(exp_FINANCE_SCREEN2) : STOP] 

F3. Thinking of the choices you made between the loans you just saw, which 
of the following features was the most important when making your decision: 

PROG: SINGLE ANSWER 

1.  The interest rate 

792 

                            Your current loanLoan NLoan amount: £93012 month personal loan3.7% fixedAPR 3.8%Representative example: Assumed borrowing of £930 over 12 months at a fixed rate of 3.70% per annum, with no arrangement and credit fee would result in an APR of 3.8%, monthly repayments of £79.06, equal repayments each months, total amount repayable £948.72.Loan OLoan amount: £93012 month personal loan2.8% fixedRepresentative example: Assumed borrowing of £930 over 12 months at a fixed rate of 2.80% per annum, with £30.50 arrangement and credit fee would result in an APR of 9.2%, monthly repayments of £81.26, equal repayments each months, total amount repayable £975.12.Loan OLoan amount: £93012 month personal loan2.8% fixedRepresentative example: Assumed borrowing of £930 over 12 months at a fixed rate of 2.80% per annum, with £30.50 arrangement and credit fee would result in an APR of 9.2%, monthly repayments of £81.26, equal repayments each months, total amount repayable £975.12.Choose this loanView Loan MView Loan OYour current loanLoan NLoan amount: £93012 month personal loan3.7% fixedAPR 3.8%Representative example: Assumed borrowing of £930 over 12 months at a fixed rate of 3.70% per annum, with no arrangement and credit fee would result in an APR of 3.8%, monthly repayments of £79.06, equal repayments each months, total amount repayable £948.72.Loan MLoan amount: £93012 month personal loan3.0% fixedAPR 14.6%Representative example: Assumed borrowing of £930 over 12 months at a fixed rate of 3.00% per annum, with £54.20 arrangement and credit fee would result in an APR of 14.6%, monthly repayments of £83.36, equal repayments each months, total amount repayable £1,000.32.Loan OLoan amount: £93012 month personal loan2.8% fixedAPR 9.2%Representative example: Assumed borrowing of £930 over 12 months at a fixed rate of 2.80% per annum, with £30.50 arrangement and credit fee would result in an APR of 9.2%, monthly repayments of £81.26, equal repayments each months, total amount repayable £975.12.Choose this loanChoose this loanChoose this loan 
 
 
 
 
  
 
     
 
Annex 13│ Consumer questionnaire and behavioural experiment 

2.  The APR 
3.  The monthly payments 
4.  The total amount repayable 
5.  Other features 

F4.  You  are  comparing  two  offers  of  a  personal  loan  of  1000  £  for  one year. 
In  both  cases,  the  loan  is  repaid  through  equal  monthly  repayments.  In  the 
first offer, the interest rate  is  5% and the APR is 8.0%. In the second  offer, 
the interest  rate is 6% and the  APR is 7.0%. Which offer will  cost you more 
at the end of the one year term? 

PROG: SINGLE ANSWER 

1.  First offer 
2.  Second offer 
3.  Both the same [volunteered] 
4.  Don’t know 

[TIMER 10(exp_finance): STOP] 

793 

 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

_____________________________________________________________
__________________ 
EXPERIMENT 4: GENERIC 
_____________________________________________________________
__________________ 
[TIMER 11(exp_generic): START] 
[REGISTER: 

1.  EVERY CLICK THE RESPONDENT HAS DONE DURING THIS EXPERIMENT  
2.   THE TIME SPENT ON THE EXPERIMENT  
3.   EXACTLY THE OFFERS SEEN 

[PROG: USE EXCEL FILE “CROSS CUTTING EXPERIMENT SCRIPTING FILE”] 

[PROG:  The  marketing  practice  examined  in  the  cross-cutting  experiment  is  “teaser 
rates”  –  i.e.  where  an  initial,  more  favourable  price  or  rate  is  attached  to  a 
product for a certain period of time, after which the price/rate changes to another 
price/rate which is less favourable for the consumer. 

The cross-cutting experiment is ‘framed’ using products from the: 

  ENERGY SECTOR (ELECTRICITY DEALS) 
  ONLINE ENVIRONMENT (BROADBAND PACKAGES)  
  FINANCE SECTOR (SAVINGS ACCOUNTS). 

 In this document, the term “product” is used to refer generally to an: 

  Electricity deal 
  Broadband package  
  Savings account 

[PROG: The structure of the experiment and the MARKETING PRACTICE and REMEDY 
TREATMENTS  ARE  THE  SAME  irrespective  of  which  product  is  used  to  frame  the 
experiment (so that the data can be pooled).]  

[PROG:  Like  the  sector-specific  experiments,  the  cross-cutting  experiment  has  two 
rounds (CC1 and CC2 below): 

STEP 1: 
Respondents are first asked to choose between two products: 

1) One which has an initial teaser rate and a subsequent flat rate 
2) One which only has a single flat rate.  

In  a  choice  only  one  of  these  will  be  the  cheapest  over  12  months  and  this  will 
vary between the teaser and the flat rate deal for different respondents.  

o  Respondents are told that they will stay with the product they choose 

for 12 months (hence only one of the products on offer is the correct 
choice). 

STEP 2: 
Then respondents can either choose: 
  To  stay  with  their  ‘current  product’  (i.e.  the  product  they  chose  in  the  first 
round), 
 Or change to one of two ‘new’ products: one new product has a teaser rate, 
and the other new product only has a single flat rate. 

794 

 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

o  However, if the respondent chose the teaser rate product in the first 
round, the teaser rate of this product will no longer apply in the 
second round. 

o  Hence for this product only the flat rate is quoted and the total 

cost/interest payable over 6, 12 and 24 months are calculated 
based on the flat rate only – see mock-ups for CC2 below. 

o  Again respondents are told that they will stay with the product they 

choose for 12 months (hence only one of the products on offer is the 
correct choice). ] 

[PROG:  The teaser  and  flat  rates  on  teaser  rate  products  will  be  varied  (across 
respondents)  between  three  levels,  namely  90%,  100%  or  110%  of  the  median 
(see  table  below).  This  implies  that  8  teaser  rate  products  are  included  in  the 
experiment in total. 

The  rate  on  flat  rate  products  will  take  one  of  two  levels.  These  levels  are  very 
close  to  one  another  (the  only  reason  for  having  two  levels  is  so  that  respondents 
don’t see exactly the same product twice), and are also very close to the median. 

Therefore: 

 In 50% of cases the teaser rate product will be the cheapest over 12 months 
And  in 50% of cases the flat rate product will be the cheapest over 12 months 
 – see the table below. ] 
[PROG:  The  performance  measure  we  will  examine  is  whether  respondents 
CORRECTLY chose the product that is cheapest over 12 months. 

  In the energy sector and online environment frames, the rates (teaser 

and flat) are quoted as a monthly cost.  

  In the finance sector frame, the rates are quoted as annual interest rates, 

where interest is paid monthly. ] 

[PROG: In addition, since the teaser and flat rates on the teaser rate products will 
be  varied,  the  analysis  will  also  be  able  to  examine  whether  respondents’  propensity 
to  choose  the  teaser  rate  product  is  more  influenced  by  changes  to  the  teaser 
rate or the flat rate.] 

[PROG : The treatments are: 

  Under THE MARKETING PRACTICE TREATMENT, the teaser rate and the 

flat rate are displayed equally prominently, without any further information 
regarding the total cost or interest payable over any time period.  

  Under the REMEDY TREATMENT, respondents are also shown: 

 the total cost or interest payable for each product over 6 months, 12 
months and 24 months. ] 

Electricity deals used in the cross-cutting experiment in the UK, ordered by 
cost over 12 months 

Product 

Type 

(monthly) 

(monthly) 

Cost over 12 
months 

Teaser rate 

Flat rate  

795 

 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

O 

P 

Q 

R 

M 

N 

S 

T 

U 

V 

Teaser 

Teaser 

Teaser 

Teaser 

Flat 

Flat 

Teaser 

Teaser 

Teaser 

Teaser 

6.75 

7.50 

8.25 

6.75 

: 

: 

8.25 

6.75 

7.50 

8.25 

54.00 

54.00 

54.00 

60.00 

42.40 

42.60 

60.00 

66.00 

66.00 

66.00 

459 

462 

465 

507 

509 

511 

513 

555 

558 

561 

[PROG: The following tables present the text that respondents will be shown for each 
of the frames (electricity, online, finance) 

[TIMER 18(exp_GENERIC_SCREEN1) : START] 

Suppose  you  are  looking  for  a  new  electricity  deal  for  your  home 
electricity. 

You will stay with the deal you choose for 12 months. 

Please  consider the  electricity deals shown below.  Which of these  offers 
would you choose? 

Suppose  you  are  looking  for  a  new  broadband,  telephone  and  TV 
package for your home. 

You will stay with the provider you choose for 12 months. 

Please  consider  the  offers  from  providers  shown  below.  Which  of  these 
offers would you choose? 

Suppose you are looking for a new savings account for your money.  

You will keep your money in the account you choose for 12 months. 

Please  consider  the  account  offers  shown  below.  Which  of  these  offers 
would you choose? 

CC1_E 

(Electricity 
frame) 

SINGLE 
ANSWER 

CC1_O 

(Online 
frame) 

SINGLE 
ANSWER 

CC1_F 

(Finance 
frame) 

SINGLE 
ANSWER 

Respondents choose between two products: 

  Flat rate product drawn randomly from Products M and N in the Excel file 
  Teaser rate product drawn randomly from Products O to V in the Excel file 

796 

 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

  Hence  for  50%  of  respondents  the  flat  rate  product  is  the  cheapest  over  12 
months, 
 Whereas for 50% of respondents the teaser rate product is the cheapest over 
12 months.)  ] 

[PROG:  FORESEE  A  BUTTON  THAT  ALLOWS  RESPONDENTS  TO  OPEN  A 
CALCULATOR  IF  THEY  WISH  TO;  REGISTER  IF  THE  CALCULATOR  HAS  BEEN 
OPENED OR NOT] 

[PROG: Respondents allocated to THE REMEDY TREATMENT are shown the total cost 
of  each  product  (energy  and  online  sector  frames)  or  total  interest  payable 
(finance sector frame) over 6, 12 and 24 months. ] 

[PROG:  Respondents  allocated  to  the  MARKETING  PRACTICE  TREATMENT  are  not 
shown this information.] 

[PROG:  Mock-ups  of  the  options  to  be  shown  to  respondents  in  each  frame  /  under 
each treatment are presented below, using Products R and M as examples.] 

Energy sector frame 

Marketing practice: 

Remedy: 

Online environment frame 

Marketing practice: 

Remedy: 

797 

Electricity Deal RElectricity Deal M£6.75 per month for the first 4 monthsFlat rate: £42.4 per month£60 per month thereafterChoose this dealChoose this dealElectricity Deal RElectricity Deal MTotal cost over:Total cost over:    - 6 months: £147    - 6 months: £254    - 12 months: £507    - 12 months: £509    - 24 months: £1227    - 24 months: £1018£6.75 per month for the first 4 monthsFlat rate: £42.4 per month£60 per month thereafterChoose this dealChoose this dealProvider RProvider MBroadband+Telephone+TVBroadband+Telephone+TV£6.75 per month for the first 4 monthsFlat rate: £42.4 per month£60 per month thereafterChoose this offerChoose this offerProvider RProvider MBroadband+Telephone+TVBroadband+Telephone+TVTotal cost over:Total cost over:    - 6 months: £147    - 6 months: £254    - 12 months: £507    - 12 months: £509    - 24 months: £1227    - 24 months: £1018£6.75 per month for the first 4 monthsFlat rate: £42.4 per month£60 per month thereafterChoose this offerChoose this offer 
 
 
 
 
 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

Finance sector frame 

Marketing practice: 

Remedy: 

Interim 
[deal/provider/account] you chose is now your ‘current’ [deal/provider/account]. 

saying:  Thank  you.  The 

respondent 

should 

screen 

thank 

the 

[deal/provider/account] = “ deal “ if respondent is seeing GENERIC EXPERIMENT FOR  
Electricity deal 
[deal/provider/account]  =”provider”  if  respondent  is  seeing  GENERIC  EXPERIMENT 
FOR Broadband package , ONLINE ENVIRONMENT  
[deal/provider/account]= “account” if respondent is seeing GENERIC EXPERIMENT FOR 
savings account / finance sector  

[TIMER 18(exp_GENERIC_SCREEN1): STOP] 
[TIMER 19(exp_GENERIC_SCREEN2): START] 

CC2_E 

(Electricity 
frame) 

Single 
Answer 

CC2_O 

(Online 
frame) 

Single 
Answer 

Suppose it is 12 months later and you are deciding whether to stay with 
your current electricity deal for a further 12 months. 

Your current deal is shown below. 

Two new deals are also available. You can see the details of these deals 
by clicking on the buttons. 

[INSERT BLANK LINE] 

You will stay with the deal you choose for another 12 months. 

Which deal would you choose? 

Suppose it is 12 months later and you are deciding whether to stay with 
your current broadband provider for a further 12 months. 

The offer from your current provider is shown below. 

Two new offers are also available. You can see the details of these offers 
by clicking on the buttons. 

[INSERT BLANK LINE] 

You will stay with the deal you choose for another 12 months. 

Which offer would you choose? 

798 

 
 
 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

CC2_F 

(Finance 
frame) 

Single 
Answer 

Suppose it is 12 months later and you are deciding whether to keep your 
money in your current saving account. 

Your current saving account is shown below. 

Two  new  accounts  are  also  available.  You  can  see  the  details  of  these 
accounts by clicking on the buttons. 

[INSERT BLANK LINE] 

You will stay with the deal you choose for another 12 months. 

Which account would you choose? 

[PROG: If the respondent chose the flat rate product (i.e. Product  M or N) at CC1, 
then that at CC2 they choose between the following three: 

  Flat rate product chosen at CC1 (i.e. Product M or N) 
  Flat rate product not offered at CC1 (i.e. Product M or N) 
  Teaser rate product drawn randomly from Products O to V, excluding the one 

offered at CC1 ] 

[PROG:  If  the  respondent  chose  the  teaser  rate  product  at  CC1,  then  that  at  CC2 
they choose between the following three: 

  Teaser rate product chosen at CC1, where the teaser rate no longer applies 

o  For this product only the flat rate is quoted and the total cost/interest 

payable over 6, 12 and 24 months are calculated based on the flat rate 
only – see mock-ups below 

  Flat rate product not offered at CC1 (i.e. Product M or N) 
  Teaser rate product drawn randomly from Products O to V, excluding the 

one offered at CC1 ] 

[PROG: Respondents should have to click on a button to see the new products that are 
on offer. Each time a respondent clicks on one of these buttons there should be a time 
delay depending on q_delay allocated at the beginning of the questionnaire.  The time 
delay should be 10 seconds .] 

[PROG:  FORESEE  A  BUTTON  THAT  ALLOWS  RESPONDENTS  TO  OPEN  A 
CALCULATOR  IF  THEY  WISH  TO;  REGISTER  IF  THE  CALCULATOR  HAS  BEEN 
OPENED OR NOT] 
[PROG: Respondents  allocated to the  REMEDY TREATMENT are  shown the  total cost 
of  each  product  (energy  and  online  sector  frames)  or  total  interest  payable 
(finance sector frame) over 6, 12 and 24 months.] 

[PROG:  Respondents  allocated  to  the  MARKETING  PRACTICE  TREATMENT  are  not 
shown this information.] 

[Mock-ups  of  the  options  to  be  shown  to  respondents  in  each  frame  /  under  each 
treatment are presented below, assuming for example that the new products on offer 
are Products N and T.] 

Finance sector frame 
Choose M at CC1/ Products N and T offered at CC2/ Marketing practice treatment 
INCLUDE  “Your  current“  (in  bold)  IN  FRONT  OF  “  saving  account  M”  [  VALID  FOR 
ALL DIFFERENT SAVING ACCOUNTS : M,N, O, P, Q, R, S, T, U or V]  

799 

 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

Choose M at CC1/ Products N and T offered at CC2/ Remedy treatment 
INCLUDE  “Your  current“  (in  bold)  IN  FRONT  OF  “  saving  account  M”  [  VALID  FOR 
ALL DIFFERENT SAVING ACCOUNTS : M,N, O, P, Q, R, S, T, U or V]  

800 

Your current saving account:Saving account MSaving account NSaving account T1.91% annual interest1.95% annual interest5.16% annual interest rate for the first 4 monthsInterest is paid monthlyInterest is paid monthly0.52% annual interest thereafterInterest is paid monthlyChoose this accountView account NView account TYour current saving account:Saving account MSaving account NSaving account TInterest paid for every £1,000 initially deposited:Interest paid for every £1,000 initially deposited:Interest paid for every £1,000 initially deposited:    - Over 6 months: £9.5    - Over 6 months: £9.7    - Over 6 months: £17.79    - Over 12 months: £19.1    - Over 12 months: £19.5    - Over 12 months: £20.43    - Over 24 months: £38.56    - Over 24 months: £39.38    - Over 24 months: £25.741.91% annual interest1.95% annual interest5.16% annual interest rate for the first 4 monthsInterest is paid monthlyInterest is paid monthly0.52% annual interest thereafterInterest is paid monthlyChoose this accountView account NView account T 
 
 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

Choose R at CC1/ Products N and T offered at CC2/ Marketing practice treatment 
INCLUDE “Your current“ (in bold) IN FRONT OF “ saving account R” [ VALID FOR ALL 
DIFFERENT SAVING ACCOUNTS : M,N, O, P, Q, R, S, T, U or V]  

Choose R at CC1/ Products N and T offered at CC2/ Remedy treatment 
INCLUDE “Your current“ (in bold) IN FRONT OF “ saving account R” [ VALID FOR ALL 
DIFFERENT SAVING ACCOUNTS : M,N, O, P, Q, R, S, T, U or V]  

801 

 
 
 
 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

Energy sector frame 
Choose M at CC1/ Products N and T offered at CC2/ Marketing practice treatment 
INCLUDE  “Your  current“  (in  bold)    IN  FRONT  OF  “  Electricity  Deal  M”  [  VALID  FOR 
ALL DIFFERENT ELECTRICITY DEALS : M,N, O, P, Q, R, S, T, U or V]  

Choose M at CC1/ Products N and T offered at CC2/ Remedy treatment 
INCLUDE  “Your  current“  (in  bold)  IN  FRONT  OF  “  Electricity  Deal  M”  [  VALID  FOR 
ALL DIFFERENT ELECTRICITY DEALS : M,N, O, P, Q, R, S, T, U or V]  

Choose R at CC1/ Products N and T offered at CC2/ Marketing practice treatment 

802 

Your current deal:Electricity Deal MElectricity Deal NElectricity Deal TFlat rate: £42.4 per monthFlat rate: £42.6 per month£6.75 per month for the first 4 months£66 per month thereafterChoose this dealView deal NView deal TYour current deal:Electricity Deal MElectricity Deal NElectricity Deal TFlat rate: £42.4 per monthFlat rate: £42.6 per month£6.75 per month for the first 4 months£66 per month thereafterChoose this dealChoose this dealChoose this dealYour current deal:Electricity Deal MElectricity Deal NElectricity Deal TTotal cost over:Total cost over:Total cost over:    - 6 months: £254    - 6 months: £256    - 6 months: £159    - 12 months: £509    - 12 months: £511    - 12 months: £555    - 24 months: £1018    - 24 months: £1022    - 24 months: £1347Flat rate: £42.4 per monthFlat rate: £42.6 per month£6.75 per month for the first 4 months£66 per month thereafterChoose this dealView deal NView deal TYour current deal:Electricity Deal MElectricity Deal NElectricity Deal TTotal cost over:Total cost over:Total cost over:    - 6 months: £254    - 6 months: £256    - 6 months: £159    - 12 months: £509    - 12 months: £511    - 12 months: £555    - 24 months: £1018    - 24 months: £1022    - 24 months: £1347Flat rate: £42.4 per monthFlat rate: £42.6 per month£6.75 per month for the first 4 months£66 per month thereafterChoose this dealChoose this dealChoose this deal 
 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

INCLUDE “Your current“ (in bold) IN FRONT OF “ Electricity Deal R” [ VALID FOR ALL 
DIFFERENT ELECTRICITY DEALS : M,N, O, P, Q, R, S, T, U or V]  

Choose R at CC1/ Products N and T offered at CC2/ Remedy treatment 
INCLUDE  “Your  current“  (in  bold)    IN  FRONT  OF  “  Electricity  Deal  R”  [  VALID  FOR 
ALL DIFFERENT ELECTRICITY DEALS : M,N, O, P, Q, R, S, T, U or V]  

Online environment frame 
Choose M at CC1/ Products N and T offered at CC2/ Marketing practice treatment 
INCLUDE  “Your  current“  (in  bold)    IN  FRONT  OF  “  Provider  M”  [  VALID  FOR  ALL 
DIFFERENT PROVIDERS: M,N, O, P, Q, R, S, T, U or V]  

803 

Your current deal:Electricity Deal RElectricity Deal NElectricity Deal TFlat rate: £60 per monthFlat rate: £42.6 per month£6.75 per month for the first 4 months£66 per month thereafterChoose this dealView deal NView deal TYour current deal:Electricity Deal RElectricity Deal NElectricity Deal TFlat rate: £60 per monthFlat rate: £42.6 per month£6.75 per month for the first 4 months£66 per month thereafterChoose this dealChoose this dealChoose this dealYour current deal:Electricity Deal RElectricity Deal NElectricity Deal TTotal cost over:Total cost over:Total cost over:    - 6 months: £360    - 6 months: £256    - 6 months: £159    - 12 months: £720    - 12 months: £511    - 12 months: £555    - 24 months: £1440    - 24 months: £1022    - 24 months: £1347Flat rate: £60 per monthFlat rate: £42.6 per month£6.75 per month for the first 4 months£66 per month thereafterChoose this dealView deal NView deal TYour current deal:Electricity Deal RElectricity Deal NElectricity Deal TTotal cost over:Total cost over:Total cost over:    - 6 months: £360    - 6 months: £256    - 6 months: £159    - 12 months: £720    - 12 months: £511    - 12 months: £555    - 24 months: £1440    - 24 months: £1022    - 24 months: £1347Flat rate: £60 per monthFlat rate: £42.6 per month£6.75 per month for the first 4 months£66 per month thereafterChoose this dealChoose this dealChoose this deal 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

Choose M at CC1/ Products N and T offered at CC2/ Remedy treatment 
INCLUDE  “Your  current“  (in  bold)    IN  FRONT  OF  “  Provider  M”  [VALID  FOR  ALL 
DIFFERENT PROVIDERS : M,N, O, P, Q, R, S, T, U or V]  

Choose R at CC1/ Products N and T offered at CC2/ Marketing practice treatment 
INCLUDE  “Your  current“  (in  bold)  IN  FRONT  OF  “  Provider  R”  [  VALID  FOR  ALL 
DIFFERENT PROVIDERS : M,N, O, P, Q, R, S, T, U or V]  

804 

Your current provider:Provider MProvider NProvider TBroadband+Telephone+TVBroadband+Telephone+TVBroadband+Telephone+TVFlat rate: £42.4 per monthFlat rate: £42.6 per month£6.75 per month for the first 4 months£66 per month thereafterChoose this offerView offer NView offer TYour current provider:Provider MProvider NProvider TBroadband+Telephone+TVBroadband+Telephone+TVBroadband+Telephone+TVFlat rate: £42.4 per monthFlat rate: £42.6 per month£6.75 per month for the first 4 months£66 per month thereafterChoose this offerChoose this offerChoose this offerYour current provider:Provider MProvider NProvider TBroadband+Telephone+TVBroadband+Telephone+TVBroadband+Telephone+TVTotal cost over:Total cost over:Total cost over:    - 6 months: £254    - 6 months: £256    - 6 months: £159    - 12 months: £509    - 12 months: £511    - 12 months: £555    - 24 months: £1018    - 24 months: £1022    - 24 months: £1347Flat rate: £42.4 per monthFlat rate: £42.6 per month£6.75 per month for the first 4 months£66 per month thereafterChoose this offerView offer NView offer TYour current provider:Provider MProvider NProvider TBroadband+Telephone+TVBroadband+Telephone+TVBroadband+Telephone+TVTotal cost over:Total cost over:Total cost over:    - 6 months: £254    - 6 months: £256    - 6 months: £159    - 12 months: £509    - 12 months: £511    - 12 months: £555    - 24 months: £1018    - 24 months: £1022    - 24 months: £1347Flat rate: £42.4 per monthFlat rate: £42.6 per month£6.75 per month for the first 4 months£66 per month thereafterChoose this offerChoose this offerChoose this offerYour current provider:Provider RProvider NProvider TBroadband+Telephone+TVBroadband+Telephone+TVBroadband+Telephone+TVFlat rate: £60 per monthFlat rate: £42.6 per month£6.75 per month for the first 4 months£66 per month thereafterChoose this offerView offer NView offer TYour current provider:Provider RProvider NProvider TBroadband+Telephone+TVBroadband+Telephone+TVBroadband+Telephone+TVFlat rate: £60 per monthFlat rate: £42.6 per month£6.75 per month for the first 4 months£66 per month thereafterChoose this offerChoose this offerChoose this offer 
 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

Choose R at CC1/ Products N and T offered at CC2/ Remedy treatment 
INCLUDE  “Your  current“  (in  bold)  IN  FRONT  OF  “  Provider  R”  [VALID  FOR  ALL 
DIFFERENT PROVIDERS : M,N, O, P, Q, R, S, T, U or V]  

[TIMER 19(exp_GENERIC_SCREEN2): STOP] 

Thinking 

 CC3 
the 
choices 
[deal/provider/account]  you  just  saw,  which  of  the  following  features 
was the most important when making your decision? 

you  made  between 

about 

the 

[PROG: ASK ALL RESPONDENTS] 

1.  The monthly [cost/ cost/ interest] of the deals 

2.  The monthly [cost/ cost/ interest] over the first 4 months 

3.  The monthly [cost/ cost/ interest] after the fourth month 

4.  The [total cost/ total cost/ total interest] over 6 months 

5.  The [total cost/ total cost/ total interest] over 12 months 

6.  The [total cost/ total cost/ total interest] over 24 months 

7.  Other features 

[deal/provider/account]  =  “  deal  “  if  respondent  is  seeing  GENERIC  EXPERIMENT 
FOR  Electricity deal 

[deal/provider/account] =”provider” if respondent is seeing GENERIC EXPERIMENT 
FOR Broadband package , ONLINE ENVIRONMENT  

805 

Your current provider:Provider RProvider NProvider TBroadband+Telephone+TVBroadband+Telephone+TVBroadband+Telephone+TVTotal cost over:Total cost over:Total cost over:    - 6 months: £360    - 6 months: £256    - 6 months: £159    - 12 months: £720    - 12 months: £511    - 12 months: £555    - 24 months: £1440    - 24 months: £1022    - 24 months: £1347Flat rate: £60 per monthFlat rate: £42.6 per month£6.75 per month for the first 4 months£66 per month thereafterChoose this offerView offer NView offer TYour current provider:Provider RProvider NProvider TBroadband+Telephone+TVBroadband+Telephone+TVBroadband+Telephone+TVTotal cost over:Total cost over:Total cost over:    - 6 months: £360    - 6 months: £256    - 6 months: £159    - 12 months: £720    - 12 months: £511    - 12 months: £555    - 24 months: £1440    - 24 months: £1022    - 24 months: £1347Flat rate: £60 per monthFlat rate: £42.6 per month£6.75 per month for the first 4 months£66 per month thereafterChoose this offerChoose this offerChoose this offer 
 
 
 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

[deal/provider/account]=  “account” if respondent is seeing GENERIC EXPERIMENT 
FOR savings account / finance sector  

[cost/  cost/  interest]   =  “cost”  if  respondent  is  seeing  GENERIC  EXPERIMENT 
FOR  Electricity deal 

[cost/  cost/  interest]   =”cost”  if  respondent  is  seeing  GENERIC  EXPERIMENT  FOR 
Broadband package , ONLINE ENVIRONMENT  

[cost/  cost/  interest]  =  “interest”  if  respondent  is  seeing  GENERIC  EXPERIMENT 
FOR savings account / finance sector  

[total  cost/  total  cost/  total  interest]    =  “total  cost”  if  respondent  is  seeing 
GENERIC EXPERIMENT FOR  Electricity deal 

[total cost/ total cost/ total interest]  =”total cost” if respondent is seeing GENERIC 
EXPERIMENT FOR Broadband package , ONLINE ENVIRONMENT  

[total  cost/  total  cost/  total  interest]  =  “  total  interest”  if  respondent  is  seeing 
GENERIC EXPERIMENT FOR savings account / finance sector  

[TIMER11(exp_generic): STOP] 

11.4.1. 

Questions to identify vulnerability 

_____________________________________________________________________
__________ 

[TIMER2(questions_id_vulnerability_1.1.2): START] 

_____________________________________________________________________
___________ 

[Q14: SHOW ONLY WHEN Q_METHODOLOGY= CODE 2 OR 3; CATI OR CAPI] 

Q14 Imagine you hear the following advertisement on the radio: “Sign-up to 
BEACH  BREAKS  and  receive  a  free  pair  of  sunglasses.  Sunglasses  delivered 
when you purchase one of our beach holidays” . 

Does this advertisement in your opinion … 

[PROG: SINGLE ANSWER] 

1.  Offer free sunglasses to all who sign-up to beach breaks 
2.  Offer sunglasses only to those who buy a holiday from beach breaks 
3.  Don’t know 

[PROG:  BEFORE  Q14  INSERT  AUDIO  CHECK  –  IF  RESPONDENT  DID  NOT  SUCCEED 
WITH  THE  AUDIO  CHECK,  HE  SHOULD  CONTINUE  WITH  QUESTIONNAIRE  AND  NOT 
BE TERMINATED] 

[Q14: SHOW ONLY WHEN Q_METHODOLOGY= CODE 1; ONLINE] 

806 

 
 
 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

Q14. We’re now going to let you hear a commercial radio ad. Please click on 
the icon below and listen to it attentively. The speed at which the ad will load 
depends on your internet connection.   

Don’t forget to put on your speakers. 

[PROG: Q14: SHOW ONLY IF Q_METHODOLOGY= CODE 1 ONLINE] 

[PROG: INFO TEXT]   

[INSERT AD] 

Q14a Could you hear the radio commercial in good quality on your computer? 
[PROG: SINGLE ANSWER] 

1.  Yes 
2.  No 

Q14b Does this advertisement in your opinion … 

[PROG: SINGLE ANSWER] 

1.  Offer free sunglasses to all who sign-up to beach breaks 
2.  Offer sunglasses only to those who buy a holiday from beach breaks 
3.  Don’t know 

 [ASK ALL] 

Q15 And which of the following three statements corresponds the most with 
your opinion? 

[PROG: SINGLE ANSWER– ROTATE] 

1.  Most advertisements report objective fact, I trust most of the information 

provided in advertisements 

2.  I take some precautions in regards to information provided in advertisements, 

some information is true whereas some is misleading 

3.  Most advertisements do not provide the truth, I do not trust most of the 

information provided in advertisements 

Q16  The  following  statements  are  about  disadvantages  that  consumers  may 
have  when  dealing  with  retailers.  To  what  extent  do  they  apply  to  you 
personally? 

You  feel  vulnerable  or  disadvantaged  when  choosing  and  buying  goods  or 
services… 

PROG: SINGLE ANSWER PER ROW  

[GRID ACROSS] 

1.  To a great extent 
2.  To some extent 
3.  Hardly at all 
4.  Not at all 

[GRID DOWN – ROTATE CODES 1 TO 7] 

9.  Because of your health problems  
10. Because of your poor financial circumstances 

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Annex 13│ Consumer questionnaire and behavioural experiment 

11. Because of your current employment situation 
12. Because offers, terms or conditions are too complex  
13. Because of your age  
14. Because you belong to a minority group  
15. Because of other personal issues [KEEP POSITION] 
16. Because of other reasons [KEEP POSITION] 

_____________________________________________________________________
___ 
[TIMER 2(questions_id_vulnerability_1.1.2): STOP] 
_____________________________________________________________
___________ 

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Annex 13│ Consumer questionnaire and behavioural experiment 

11.4.2. 

Questions to identify the average consumer 

[TIMER 3(questions_average_consumer): START] 

__________________________________________________________________ 
Q17. In general, when choosing and buying goods and services, how…? 

PROG: SINGLE ANSWER PER ROW 

[GRID ACROSS] 

1.  Very 
2.  Quite 
3.  Not very 
4.  Not at all 

[GRID DOWN - RANDOMIZE] 

1.  Informed do you feel about price, quality, conditions, etc.…? 
2.  Careful are you in making purchasing decisions? 

Q18.  To  what  extent  do  you  agree,  or  disagree,  with  the  following 
statements? 

PROG: SINGLE ANSWER PER ROW 

[GRID ACROSS] 

1.  Strongly agree 
2.  Tend to agree 
3.  Tend to disagree 
4.  Strongly disagree 

[GRID DOWN - RANDOMIZE] 

1.  You need to be very careful in dealing with people 
2.  Most of my friends and relatives buy goods and services online 
3.  Most of my friends and relatives buy goods and services on credit (this includes 

purchases made on credit cards, lines of credit and some loans) 

4.  Most of my friends and relatives find it difficult to make ends meet every 

month 

Q19. And how do you see yourself. Are you generally a person who is … 

[PROG: SINGLE ANSWER] 

1. Not at all willing to take risks 
2. Not very willing to take risks 
3. Fairly willing to take risks 
4. Very willing to take risks 

________________________________________________________ 
[TIMER 3(questions_average_consumer): STOP] 
_____________________________________________________________
____ 
Experience in markets  

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Annex 13│ Consumer questionnaire and behavioural experiment 

Energy sector 

_____________________________________________________________
___ 
[TIMER 4(experience_energy_sector): START] 
_____________________________________________________________
___ 

[PROG: INFO TEXT] 

Now some questions about your gas and electricity services.A0 Is your home 
connected to electricity and/or gas services? 

Tick all that applies 

[PROG: MULTIPLE ANSWERS - CODE 3 EXCLUSIVE] 

1.  Yes, electricity 
2.  Yes, gas (pipeline) 
3.  No, neither 

A1 Does your household have a contract with an electricity or gas supplier? 

Tick all that applies 

[PROG: MULTIPLE ANSWERS - CODE 3 EXCLUSIVE] 

1.  Yes, electricity  
2.  Yes, gas (pipeline) 
3.  No, neither 

A2  Are  you  responsible  or  jointly  responsible  for  payment  of  the  electricity 
and/or gas bills in your household? 

[PROG: ASK IF CODE 1 OR 2 AT A0] 

[PROG: SINGLE ANSWER] 

1.  Yes  
2.  No 

A3 Are you entitled to receive social support (benefits, special rates or other 
type of support) to help you pay your energy bills?  

[PROG: ASK IF CODE 1 OR 2 AT A1] 

[PROG: SINGLE ANSWER] 

1.  Yes  
2.  No, I’m not entitled 
3.  No, I do not know whether I’m entitled 

A4 To what extent do they help you? 

[PROG: SINGLE ANSWER] 

[PROG: ASK IF CODE 1 IN A3] 

1. A lot 

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Annex 13│ Consumer questionnaire and behavioural experiment 

2. A fair amount 
3. Not very much 
4. Not at all 
A5  To  what  extent  do  you  know  the  contract  conditions  (type  of  contract, 
conditions for termination, etc.) with your electricity and/or gas supplier? 

[PROG: ASK IF CODE 1 OR 2 AT A1] 

[SINGLE ANSWER] 

1.  Completely 
2.  A fair amount 
3.  Not very much 
4.  Not at all 

 [ASK ALL] 

A6  Do  you  compare  the  deals  offered  by  different  gas  and/or  electricity 
providers? 

[PROG: SINGLE ANSWER] 

1.  Yes, from time to time to check if there are better deals for me 
2.  Yes, but only when I need to renew my contract 
3.  Yes, but only sporadically (because I saw an interesting ad, etc.) 
4.  No, I only compared offers the first time I needed to choose a provider 
5.  No, I have never compared 

A7  Where  do  you  get  information  to  compare  deals  offered  by  different  gas 
and/or electricity providers? 

Tick all that applies 

[PROG: ASK IF CODE 1-4 IN A6] 

[MULTIPLE ANSWERS - RANDOMIZE] 

1.  I search on-line 
2.  I use comparison websites 
3.  Advertisements (print, radio, television, door-step selling, online) 
4.  Assistance from national authorities 
5.  Assistance from consumer organisations 
6.  Friends/family 
7.  Other [KEEP POSITION] 

 [ASK ALL] 

A8 How easy or difficult is it for you to compare the deals offered by different 
gas and/or electricity suppliers?  

[PROG: SINGLE ANSWER] 

1.  Very easy 
2.  Fairly easy 
3.  Fairly difficult 
4.  Very difficult 

A9a  What  makes  it  difficult  to  compare  the  deals  offered  by  different  gas 
and/or electricity suppliers? 

Tick all that applies 

[ASK ALL ANSWERING CODES 3 TO 4 AT A8] 

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Annex 13│ Consumer questionnaire and behavioural experiment 

[PROG: MULTIPLE ANSWERS- RANDOMIZE] 

1: I do not know where I can find this information 
2: There are too many offers to choose from 
3: I do not have enough time  
4: Hard to compare like for like, prices are provided in different formats, deals include 
different offers (bundles/ packages of services) 
5: The wording used by suppliers is difficult to understand 
6: Suppliers do not provide enough information 
7: I do not know how to compare 
8: I am not interested 
9: I am not financially minded 
10: I am not technically minded (good at technical things) 
11: There is only one provider in my area 
12: My partner / someone else takes care of comparing deals 
13: Offers are time limited/ a better offer may come out next month 
16. I live in a housing cooperative where such services are collectively negotiated 
17. I live in rented accommodation and my landlord does not allow me to switch 

18. I have an electricity prepayment meter 
19. Other [KEEP POSITION] 

A9b  Why  do  you  never  compare  the  deals  offered  by  different  gas  and/or 
electricity suppliers? 

Tick all that applies 

 [PROG:  ASK  IF  CODE  CODES  4  OR  5  SELECTED  AT  QA6  AND  CODES  1  TO  2 
SELECTED AT A8] 

[PROG: MULTIPLE ANSWERS - randomize] 

1: I do not know where I can find this information 
2: There are too many offers to choose from 
3: I do not have enough time  
4: Hard to compare like for like, prices are provided in different formats, deals include 
different offers (bundles/ packages of services) 
5: The wording used by suppliers is difficult to understand 
6: Suppliers do not provide enough information 
7: I do not know how to compare 
8: I am not interested 
9: I am not financially minded 
10: I am not technically minded (good at technical things) 
11: There is only one provider in my area 
12: My partner / someone else takes care of comparing deals 
13: Offers are time limited/ a better offer may come out next month 
14: Switching is a hassle 
15: I am happy with the service provided by my provider 
16: I live in a housing cooperative where such services are collectively negotiated 
17: I live in rented accommodation and my landlord does not allow me to switch 
18: I have an electricity prepayment 19: Other [KEEP POSITION]  

A10 How does your household pay its energy? 

[PROG: ASK IF CODE 1 OR 2 AT A1] 

[PROG: SINGLE ANSWERS] 

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Annex 13│ Consumer questionnaire and behavioural experiment 

1.  You pay a variable amount that is based on actual usage 
2.  You pay a fixed amount over a number of periods and this amount is revised 

once your actual usage is known 

3.  You have a prepayment meter, meaning that you pay for your energy before 

you use it 

A10a  Did  you  read  the  most  recent  energy  bill  sent  to  you  by  your  gas  or 
electricity supplier?  

[PROG: ASK IF CODE 1 OR 3 AT A10] 

[PROG: SINGLE ANSWER] 

1.  Read it in detail 
2.  Glanced over it or skim read it 
3.  Only looked at the total price 
4.  Did not read it at all 
5.  Don’t know/ don’t remember 

A10b Did you read the most recent energy settlement bill sent to you by your 
gas or electricity supplier?  

[PROG: ASK IF CODE 2 AT A10] 

[PROG: SINGLE ANSWER] 

1.  Read it in detail 
2.  Glanced over it or skim read it 
3.  Only looked at the total price 
4.  Did not read it at all 
5.  Don’t know/ don’t remember 

A10c What is the main reason for not reading the energy bill? 

[PROG:  TO THOSE  WHO  HAVE  NOT READ THE BILL OR CAN  NOT REMEMBER (=code 
4,5 at A10a or code 4,5 at A10b)] 

[PROG: SINGLE ANSWER] 

1.  I do not understand my energy bill 
2.  My partner / someone else takes care of paying the bill 
3.  I have a rent contract, it is the owner who receives the bill 
4.  Other 

A11  Thinking  about  this  bill,  how  easy  or  difficult  was  it  for  you  to 
understand? 

[PROG: ASK IF CODE 1 OR 2 OR 3 AT A10a AND A10b] 

[SINGLE ANSWER] 

1.  Very easy 
2.  Fairly easy 
3.  Fairly difficult 
4.  Very difficult 

[ASK ALL]  

A16 Approximately how much electricity do you use per year? 

[ASK ONLY IN COUNTRIES WITH EXPERIMENTS: UK, RO, LT, DK, PT] 

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Annex 13│ Consumer questionnaire and behavioural experiment 

[PROG: ASK IF CODE 1 AT A1] 

 [PROG:  NUMERIC  QUESTION  –  5  DIGITS  RANGE  0-  99999  +  1  ANSWER  CODE 
(EXCLUSIVE)]  

/__/  kWh 

Don’t know [ Exclusive] 

A17 Approximately how much do you spend on electricity per year? 

[ASK ONLY IN COUNTRIES WITH EXPERIMENTS: UK, RO, LT, DK, PT] 

[PROG: ASK IF CODE 1 AT A1] 

 [PROG:  NUMERIC  QUESTION  –  5  DIGITS  RANGE  0-  99999  +  1  ANSWER  CODE 
(EXCLUSIVE)]  

/__/  [INSERT NATIONAL CURRENCY SYMBOL] 

Don’t know [ Exclusive] 

A18 Do you know the following about your current energy supply? 

[ASK ONLY IN COUNTRIES WITH EXPERIMENTS: UK, RO, LT, DK, PT] 

[PROG: ASK IF CODE 1 OR 2 AT A1] 

[PROG: SINGLE ANSWER PER ROW] 

[GRID ACROSS] 

1.  Yes 
2.  No 

[GRID DOWN  - RANDOMIZE] 

1.  The name of your current energy supplier 

2.  The name of your current energy tariff 

3.  Your meter type (i.e. single rate or multi-rate) 

A21 Have you switched your electricity supplier or electricity tariff scheme in 
the last 5 years? 

[ASK ONLY IN COUNTRIES WITH EXPERIMENTS: UK, RO, LT, DK, PT] 

[PROG: ASK IF CODE 1 AT A1] 

[PROG: SINGLE ANSWER] 

1.  Yes, once 

2.  Yes, twice 

3.  Yes, three times or more 

4.  Have not switched my electricity supplier or electricity tariff scheme in the last 

5 years 

A22  Why  have  you  switched  your  electricity  supplier?  Please  select  the 
answer below that best describes your main reason.  

814 

 
 
 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

[PROG: TO THOSE WHO HAVE SWITCHED (=not code 4 at A21)] 

[PROG: SINGLE ANSWER] 

1.  I found a better deal 
2.  I moved house 
3.  I wanted a more environmentally friendly provider 
4.  Because of the poor customer service 
5.  I have experienced billing problems 
6.  There were too many interruptions in supply 
7.  Friends/family advised me to switch 
8.  Other 

A23 Why have you never switched your electricity supplier? Please select the 
answer below that best describes your main reason.  

[PROG: TO THOSE WHO HAVE NOT SWITCHED (=code 4 at A21)] 

[PROG: SINGLE ANSWER] 

1.  I’m happy with my current supplier 
2.  I’ve checked prices of other suppliers and I think I’m on the best deal 
3.  I don’t think there is any difference between the suppliers to make 

switching worthwhile 
4.  Switching is a hassle  
5.  I am unsure about where to get information to help me make a good choice 
6.  I am in debt with my current supplier/s so don’t think I can switch 
7.  I live in rented accommodation and don’t think my landlord will allow me to 

switch 

8.  I live in a housing cooperative where such services are collectively 

negotiated 

9.  I wouldn’t know how to switch even if I wanted to 
10.  I did not know it was possible to switch 
11.  No other supplier is available in the area where I live 
12.  It is difficult to compare the offers of different electricity providers 
13.  I never thought about the issue 
14.  Other 

_____________________________________________________________________
___ 
[TIMER 4(experience_energy_sector):: STOP] 
_____________________________________________________________
__________ 

815 

 
 
 
 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

11.4.3. 

Online 

_____________________________________________________________
___________________ 
[TIMER 5((experience_online_sector):: START] 
_____________________________________________________________
____ 

[INFO TEXT] 

Now some questions about the Internet. 
B1 Do you have an Internet connection at home? 

[PROG: SINGLE ANSWER] 

1.  Yes 
2.  No 

B2 Are you responsible or jointly responsible for payment of the Internet bills 
in your household? 

[PROG: ASK IF CODE 1 AT B1] 

[PROG: SINGLE ANSWER] 

1.  Yes 
2.  No 

B3 Do you receive this Internet connection as part of a bundle? 

By  bundle,  we  mean  a  combined  package  offering  internet  connection 
together with TV, fixed telephone or mobile telephone at an overall price. 

Tick all that applies 

[PROG: ASK IF CODE 1 AT B1] 

[MULTIPLE ANSWERS; CODE 1, 6 EXCLUSIVES] 

1.  No, it is not part part of a bundle 
2.  Yes, together with television channels 
3.  Yes, together with landline telephone 
4.  Yes, together with mobile telephone 
5.  Yes, together with mobile internet 
6.  Don’t know 

B4  To  what  extent  would  you  say  you  know  the  contract  conditions  (type  of 
contract, conditions for termination, etc.) of your internet subscription? 

[PROG: ASK IF CODE 1 AT B1] 

[PROG: SINGLE ANSWER] 

1.  Completely 
2.  A fair amount 
3.  Not very much 
4.  Not at all 

 [ASK ALL] 

B5  Do  you  compare  the  services  offered  by  different  internet  service 
providers? 

[PROG: SINGLE ANSWER] 

816 

 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

1.  Yes, from time to time to check if there are better deals 
2.  Yes, but only when I need to renew my contract 
3.  Yes, but only sporadically (because I saw an interesting advertisement, etc.) 
4.  No, I only compared offers the first time I needed to choose a provider 
5.  No, I have never compared 

B6  Where  do  you  get  information  to  compare  deals  offered  by  different 
internet service providers? 

Tick all that applies 

[PROG: ASK IF CODE 1-4 IN B5] 

[MULTIPLE ANSWERS - RANDOMIZE] 

1.  I search on-line 
2.  I use comparison websites 
3.  Advertisements (print, radio, television, online) 
4.  Assistance from national authorities 
5.  Assistance from consumer organisation 
6.  Friends/family 
7.  Other [KEEP POSITION] 

 [ASK ALL] 

B7 How easy or difficult do you think it is to compare the services offered by 
different Internet providers?  

[PROG: SINGLE ANSWER] 

1.  Very easy 
2.  Fairly easy 
3.  Fairly difficult 
4.  Very difficult 

B8a  What  makes  it  difficult  to  compare  the  services  offered  by  different 
Internet providers? 

Tick all that applies 

[PROG: ASK ALL ANSWERING CODES 3 OR 4 AT B7] 

[PROG: MULTIPLE ANSWERS - randomize] 

1: I do not know where I can find this information 
2: There are too many offers to choose from 
3: I do not have enough time 
4: Hard to compare like for like, prices are provided in different formats, deals include 

different offers (bundles/ packages of services) 

5: The wording used by suppliers is difficult to understand 
6: Suppliers do not provide enough information 
7: I do not know how to compare 
8: I am not interested 
9: I am not financially minded 
10: I am not technically minded (good at technical things) 
11: There is only one provider in my area 
12: My partner / someone else takes care of comparing deals 
13: Offers are time limited/ a better offer may come out next month 
16: I live in a housing cooperative where such services are collectively negotiated 
17: I live in rented accommodation and my landlord does not allow me to switch 

19:. Other [KEEP POSITION] 

817 

 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

B8b  Why  do  you  never  compare  the  services  offered  by  different  Internet 
providers? 

Tick all that applies 

[PROG: ASK ALL ANSWERING CODES 4 OR 5 AT B5 AND CODES 1 OR 2 AT B7] 

[PROG: MULTIPLE ANSWERS - randomize] 

1: I do not know where I can find this information 
2: There are too many offers to choose from 
3: I do not have enough time 
4: Hard to compare like for like, prices are provided in different formats, deals include 

different offers (bundles/ packages of services) 

5: The wording used by suppliers is difficult to understand 
6: Suppliers do not provide enough information 
7: I do not know how to compare 
8: I am not interested 
9: I am not financially minded 
10: I am not technically minded (good at technical things) 
11: There is only one provider in my area 
12: My partner / someone else takes care of comparing deals 
13: Offers are time limited/ a better offer may come out next month 
14: Switching is a hassle 
15: I am happy with the service provided by my provider 
16: I live in a housing cooperative where such services are collectively negotiated 
17: I live in rented accommodation and my landlord does not allow me to switch 
19: Other [KEEP POSITION]  

B9 Did you read the most recent communication sent to you by your Internet 
service provider?  

[PROG: SINGLE ANSWER; ASK IF CODE 1 AT B1] 

1.  Read it in detail 
2.  Glanced over it or skim read it 
3.  Only saw what it was (i.e. looked at the numbers), but did not read it 
4.  Did not read it at all 
5.  Don’t know/ don’t remember 

B10  Thinking  about  the  most  recent  communication  sent  to  you  by  your 
Internet service provider, how easy or difficult was it for you to understand? 

 [PROG: SINGLE ANSWER; ASK IF CODE 1 AT B1 AND IF CODES 1 OR 2 AT B9] 

1.  Very easy 
2.  Fairly easy 
3.  Fairly difficult 
4.  Very difficult 

 [ASK ALL] 

B10a Which of the following reasons best describes why you did not read this 
communication? 

[PROG: SINGLE ANSWER] 

[PROG:  TO  THOSE  WHO  HAVE  NOT  READ  THE  COMMUNICATION  OR  CAN  NOT 
REMEMBER (=code 3, 4,5 at B9)] 

818 

 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

1.  lack of time 
2.  I do not read / open letters/communications from my Internet services 

provider 

3.  I do not read / open letters/ communications from any Internet service 

provider 

4.  The communication looked like advertising 
5.  Other 

B12 For how long have you had Internet access at home? 

[ASK ONLY IN COUNTRIES WITH EXPERIMENTS: UK, RO, LT, DK, PT] 

PROG: SINGLE ANSWER; ASK IF CODE 1 AT B1] 

1.  Less than 5 years 
2.  More than 5 years 

B13 Have you switched your home Internet provider or Internet tariff scheme 
in the last 5 years? 

[ASK ONLY IN COUNTRIES WITH EXPERIMENTS: UK, RO, LT, DK, PT] 

PROG: SINGLE ANSWER; ASK IF CODE 1 AT B1] 

1.  Yes, once 
2.  Yes, twice 
3.  Yes, three times or more 
4.  Have not switched my internet provider or Internet tariff scheme in the last 5 

years 

B14  Why  have  you  switched  your  home  Internet  provider  or  Internet  tariff 
scheme?  Please  select  the  answer  below  that  best  describes  your  main 
reason.  

[PROG: TO THOSE WHO HAVE SWITCHED (=not code 4 at B13)] 

[PROG: SINGLE ANSWER] 

1.  I found a better deal 
2.  I moved house 
3.  I wanted faster Internet 
4.  Because of the poor customer service 
5.  I have experienced billing problems 
6.  There were too many interruptions in supply 
7.  Friends/family advised me to switch 
8.  Other 

B15  Why  have  you  never  switched  your  home  Internet  provider  or  Internet 
tariff  scheme  in  the  last  5  years?  Please  select  the  answer  below  that  best 
describes your main reason.  

[PROG: TO THOSE WHO HAVE NOT SWITCHED (=code 4 at B13)] 

[PROG: SINGLE ANSWER] 

1.  I’m happy with my current provider  
2.  I’ve checked prices of other providers and I think I’m on the best deal 

819 

 
 
 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

3.  I don’t think there is any difference between the providers to make 

switching worthwhile 
4.  Switching is a hassle  
5.  I am unsure about where to get information to help me make a good choice 
6.  I am in debt with my current provider/s so don’t think I can switch 
7.  I live in rented accommodation and don’t think my landlord will allow me to 

switch 

8.  I live in a housing cooperative where such services are collectively 

negotiated 

9.  I wouldn’t know how to switch even if I wanted to 
10.  I did not know it was possible to switch 
11.  No other provider is available in the area where I live 
12.  It is difficult to compare the offers of different Internet service providers 
13.  I never thought about the issue 
14.  Other 

_____________________________________________________________
___________ 
[TIMER 5(experience_online_sector): STOP] 
_____________________________________________________________
__________ 

11.4.4. 

Finance 

_____________________________________________________________
__________________ 
[TIMER 6(experience_finance_sector): START] 
_____________________________________________________________________
__________ 

[INFO TEXT] 

Now some questions about the banking services you use. 

[ASK ALL] 

C1 Do you have a bank account (personally or together with your partner)? 

[PROG: SINGLE ANSWER] 

1.  Yes 
2.  No 

C2  To  what  extent  would  you  say  you  know  the  contract  conditions  of  this 
bank account? 

[PROG: SINGLE ANSWER; ASK IF CODE 1 AT C1] 

1: Completely 
2: A fair amount 
3: Not very much 
4: Not at all 

820 

 
 
 
 
 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

[ASK ALL] 

C3 Do you compare the services offered by different banks? 

With  services  we  mean  all  types  of  products  or  services  offered  by  banks, 
such as current accounts, savings accounts, credits, mortgages, etc. 

[PROG: SINGLE ANSWER] 

1.  Yes, from time to time to check if there are better deals for me 
2.  Yes, but only when I am notified that the conditions of my contract will be 

changed  

3.  Yes, but only sporadically (because I saw an interesting ad, etc.) 
4.  No, I only compared offers the first time I needed to choose a bank 
5.  No, I have never compared 

C4  Where  do  you  get  information  to  compare  deals  offered  by  different 
banks? 

Tick all that applies 

[PROG: ASK IF CODE 1-4 IN C3] 

[PROG: MULTIPLE ANSWERS - RANDOMIZE] 

1.  I search on-line 
2.  I use comparison websites 
3.  Advertisements (print, radio, television, online) 
4.  Assistance from national authorities 
5.  Assistance from consumer organisation 
6.  Friends/family 
7.  I go to the bank 
8.  Other [KEEP POSITION] 

[ASK ALL] 

C5  How  easy  or  difficult  do  you  believe  it  is to  compare  the  services offered 
by different banks?  

[PROG: SINGLE ANSWER] 

1.  Very easy 
2.  Fairly easy 
3.  Fairly difficult 
4.  Very difficult 

C6  What  makes  it  difficult  to  compare  the  services  offered  by  different 
banks? 

Tick all that applies 

[PROG:  MULTIPLE  ANSWERS;  ASK  ALL  ANSWERING  CODES  3  OR  4  AT  C5  - 
RANDOMIZE] 

1: I do not know where I can find this information 
2: There are too many offers to choose from 
3: I do not have enough time  
4: Hard to compare like for like, prices are provided in different formats, deals include 

different offers (bundles/ packages of services) 

5: The wording used by banks is difficult to understand 
6: Suppliers do not provide enough information 
7: I do not know how to compare 

821 

 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

8: I am not interested 
9: I am not financially minded 
10: I am not technically minded (good at technical things) 
11: There is only one provider in my area 
12: My partner / someone else takes care of comparing deals 
13: Offers are time limited/ a better offer may come out next month 

19: Other [KEEP POSITION] 

C6b Why do you never compare the services offered by different banks? 

Tick all that applies 

[PROG:  MULTIPLE  ANSWERS  ;  ASK  ALL  ANSWERING  CODES  4  OR  5  AT  C3  AND 
CODES 1 TO 2 AT C5 - RANDOMIZE] 

1: I do not know where I can find this information 
2: There are too many offers to choose from 
3: I do not have enough time  
4: Hard to compare like for like, prices are provided in different formats, deals include 

different offers (bundles/ packages of services) 

5: The wording used by banks is difficult to understand 
6: Suppliers do not provide enough information 
7: I do not know how to compare 
8: I'm not interested 
9: I am not financially minded 
10: I am not technically minded (good at technical things) 
11: There is only one provider in my area 
12: My partner / someone else takes care of comparing deals 
13: Offers are time limited/ a better offer may come out next month 
14: Switching is a hassle 
15: I am happy with the service provided by my provider 
20: I am currently in in a poor financial situation ( bankaccount  overdraft) 

19: Other [KEEP POSITION]  

C8  Did  you  read  the  most  recent  communication  (other  than  a  statement) 
sent to you by your bank?  

[PROG: SINGLE ANSWER; ASK IF CODE 1 AT C1] 

1.  Read it in detail 
2.  Glanced over it or skim read it 
3.  Only saw what it was (i.e. looked at the numbers), but did not read it 
4.  Did not read it at all 
5.  Don’t know/ don’t remember 

C9 Thinking about this most recent communication sent to you by your bank, 
how easy or difficult was it for you to understand? 

[PROG: SINGLE ANSWER; ASK IF CODE 1 AT C1 AND CODES 1 OR 2 AT C8] 

1.  Very easy 
2.  Fairly easy 
3.  Fairly difficult 
4.  Very difficult 

 [ASK ALL] 

C9a Which of the following reasons best describes why you did not read this 
communication? 

822 

 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

[PROG: SINGLE ANSWER] 

[PROG:  TO  THOSE  WHO  HAVE  NOT  READ  THE  COMMUNICATION  OR  CAN  NOT 
REMEMBER (=code 3, 4,5 at B9)] 

1.  lack of time 
2.  I do not read / open letters/communications from my bank 
3.  I do not read / open letters/ communications from any bank  
4.  The communication looked like advertising 
5.  Other 

C10  Thinking  now  about  savings  or  deposit  accounts,  which  of  the  following 
would be the best interest rate?  

[PROG: SINGLE ANSWER] 

1: 1% 
2: 2% 
3: 3% 
4: 4% 
5: Don’t know 
C11 Have you switched your main bank account in the last 5 years? 

[ASK ONLY IN COUNTRIES WITH EXPERIMENTS: UK, RO, LT, DK, PT] 

[PROG: ASK IF CODE 1 AT C1] 

[PROG: SINGLE ANSWER] 

1.  Yes, once 

2.  Yes, twice 

3.  Yes, three times or more 

4.  Have not switched my main bank account in the last 5 years 

C12  Why  have  you  switched  your  main  bank  account?  Please  select  the 
answer below that best describes your main reason.  

[PROG: TO THOSE WHO HAVE SWITCHED (=not code 4 at C11)] 

[PROG: SINGLE ANSWER] 

1.  I found a better deal 
2.  I moved house 
3.  Because of the poor customer service 
4.  Friends/family advised me to switch 
5.  Fear of loosing money 
6.  Other 

C13  Why  have  you  never  switched  your  main  bank  account  in  the  last  5 
years? Please select the answer below that best describes your main reason.  

[PROG: TO THOSE WHO HAVE NOT SWITCHED (=code 4 at C11)] 

[PROG: SINGLE ANSWER] 

823 

 
 
 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

1.  I’m happy with my current provider 
2.  I’ve checked prices of other providers and I think I’m on the best deal 
3.  I don’t think there is any difference between the providers to make 

switching worthwhile 
4.  Switching is a hassle  
5.  I am unsure about where to get information to help me make a good choice 
6.  I am in debt with my current provider/s so don’t think I can switch 
7.  I wouldn’t know how to switch even if I wanted to 
8.  I did not know it was possible to switch 
9.  No other provider is available in the area where I live 
10.  It is difficult to compare the offers of different banks 
11.  I never thought about the issue 
12.  Other 

_____________________________________________________________________
___ 
[TIMER 6(experience_finance_sector): STOP] 
_____________________________________________________________________
___ 

824 

 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

Access Barriers 

_____________________________________________________________________
_________ 
[TIMER7(access_barriers): START] 
_____________________________________________________________________
_________ 
Q21  Have  you  encountered  any  of  the  following  situations  in  the  past  12 
months? 

[PROG: SINGLE ANSWER PER ROW] 

[PROG: SHOW IN COLUMNS] 

1.  Yes 
2.  No 

[SHOW IN ROWS AS ITEMS - RANDOMIZE] 

1:  You  had  to  pay  more  for  your  energy,  telecommunication  or  banking  services 
because  you  were  not  able  to  use  certain  payment  methods  such  as  online  billing  or 
direct debit 
2:  You  were  unable  to  read  your  energy,  telecommunication  or  banking  contract’s 
terms and conditions due to overly small print 
3: You did not switch your energy, telecommunication or banking supplier because of 
additional costs for termination of the contract (eg extra fees for returning the internet 
modem) 
4:  You  decided  not  to  purchase  a  product  or  service  online  because  you  found  it  too 
difficult to complete the purchase. 
5:  You  were  unable  to  buy  a  product  or  service  online  because  you  didn't  have  a 
payment card allowing you to pay over the internet 
6. You decided not to switch provider because the offer was bundled or because your 
current product is part of a bundle. 
Q22 In the past 12 months, have you experienced any problem when buying 
or  using  any  goods  or  services  in  (INSERT  Q_COUNTRY)  where  you  thought 
you had a legitimate cause for complaint? 

[PROG: SINGLE ANSWER] 
1: Yes, and you took action to solve the problem 
2: Yes, but you did not do anything 
3: No 
Behavioural questions  

Computational abilities 

Q23  Suppose  that  the  exact  same  product  is  on  sale  in  shop  A  and  shop  B. 
Which shop is cheaper if…? 

[GRID ACROSS] 

1.  Shop A  
2.  Shop B  
3.  Don’t know 

[GRID DOWN; SINGLE ANSWER PER ROW] 

1.  Shop A offers a TV set for 440 £. Shop B offers the exact same type of TV set 

at 500 £, but with a discount of 10% 

825 

 
 
 
 
Annex 13│ Consumer questionnaire and behavioural experiment 

2.  Shop A offers a TV set for 890 £. Shop B offers the exact same type of TV set 

at 940 £, but with a reduction of 60 £ 

11.4.5. 

Self-control & others 

Q24  To  what  extent  do  you  agree,  or  disagree,  with  the  following 
statements? 

[PROG: GRID ACROSS] 

1.  Strongly agree 
2.  Tend to agree 
3.  Tend to disagree 
4.  Strongly disagree 

[PROG: GRID DOWN; SINGLE ANSWER PER ROW - RANDOMIZE] 

1.  I believe most people can be trusted 
2.  I have a hard time breaking bad habits. 
3.  I’m good at resisting temptation. 
4.  People would say that I have very strong self-discipline. 
5.  I’m impulsive in the purchase decisions I take. 

_____________________________________________________________________
___ 
[TIMER 7(access_barriers): STOP] 
_____________________________________________________________
__________ 

Thank you very much for taking part in this survey.  

We may wish to carry out some further research on this subject(s) in the 
future. 

Would you be willing to be re-contacted within the next year, should we wish 
to do so? There would be no obligation to take part. 

[PROG: SINGLE ANSWER] 
1: Yes 
2: No 

[PRG: SHOW LINK TO TERMS AND CONDITIONS] 

826 

 
 
 
 
 
 
 
 
 
HOW TO OBTAIN EU PUBLICATIONS 

Free publications: 

•  one copy: 

via EU Bookshop (http://bookshop.europa.eu); 

•  more than one copy or posters/maps: 

from the European Union’s representations (http://ec.europa.eu/represent_en.htm);  
from the delegations in non-EU countries 
(http://eeas.europa.eu/delegations/index_en.htm);  
by contacting the Europe Direct service (http://europa.eu/europedirect/index_en.htm) 
or calling 00 800 6 7 8 9 10 11 (freephone number from anywhere in the EU) (*). 

(*)  The information given is free, as are most calls (though some operators, phone boxes or hotels may 
charge you). 

Priced publications: 

•  via EU Bookshop (http://bookshop.europa.eu). 

 
 
 
 
 
 
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doi: 10.2818/056024