Measuring the quality of government at the subnational level and comparing results with previous studies.
Date: 2019-03-21
Institutions: Directorate-General for Regional and Urban Policy
Subjects: EU regional policy, Poland, Spain, governance, government, local government, research method, sample survey

Measuring the quality of 
Government at the 
subnational level and 
comparing results with 
previous studies 

№ 2016.СЕ.16.ВАТ.066 

Nicholas Charron, Victor Lapuente and Bo Rothstein 
March 2019 

EUROPEAN COMMISSION 

Directorate-General Regional and Urban Policy 
Directorate B - Policy 
Unit B.1 - Policy Development and Economic Analysis 

Contact: Paola Annoni 
E-mail: Paola.ANNONI@ec.europa.eu

European Commission 
B-1049 Brussels

EUROPEAN COMMISSION 

Measuring the quality of Government at the 
subnational level and comparing results 
with previous studies 

№ 2016СЕ16ВАТ066 

Directorate-General for Regional and Urban Policy 

2019 

EN 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Europe Direct is a service to help you find answers  
to your questions about the European Union. 

Freephone number (*): 

00 800 6 7 8 9 10 11 

(*) 

The information given is free, as are most calls (though some operators, phone 

boxes or hotels may charge you). 

LEGAL NOTICE 

This document has been prepared for the European Commission however it reflects the views only of the 
authors, and the Commission cannot be held responsible for any use which may be made of the information 
contained therein. 

More information on the European Union is available on the Internet (http://www.europa.eu). 

Luxembourg: Publications Office of the European Union, 2019 

ISBN: 978-92-76-01746-2 
doi: 10.2776/868107 

© European Union, 2019 
Reproduction is authorised provided the source is acknowledged. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

TABLE OF CONTENTS 

Introduction…………………………………………………………………………..……………………….…..    7 

PART I: Background, Methodology and Sample……………………………………..………..   9 

2017 EQI Survey Questions…………………………………………..…………………………………  13 

Construction of the EQI…………………………………………………….……………………….……..  15 

Margins of Error for the EQI 2017…………………………..………………………….………….…  20 

Testing the Uncertainty of the Estimates……………………………………………….….…….  21 

Final Index: Regional variation of EQI and External Valididty Checks….......….  31 

General Observable patters over time………………………………………………………….....  35 

Systematic Tests of Time Trends………………………………………………………………..….…  37 

Exploring potential case study regions…………………………………………………….……...  47 

PART II : Case Study analysis………………………………………………………………………...  57 

Polish regions……………………………………………………………………………………………………..  59 

Spanish regions………………………………………………………………………………………………...  84 

Conclusions……………………………………………………………………………………………………...  95 

Sources………………………………………………………………………………………………………..…..  97 

Appendix……………………………………………………………………………………………………..…...  99 

5 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

1. Introduction 

This  document  presents  the  latest  of  three  rounds  of  the  EQI  data  on  regional 
governance  in  EU  countries  (Charron,  Dijkstra  and  Lapuente  2014,  2015;  Charron, 
Lapuente and Rothstein 2013).  While this round of data largely builds on the work of 
previous  rounds,  there  are  several  alterations  based  on  suggestions  from  a  Rauch 
analysis of the 2010, and 2013 rounds of the EQI data (Annoni and Charron 2017).  In 
this  document,  we  highlight  the  sample,  summary  statistics  and  question  items  that 
are included in the 2017 round of the EQI.  Together with national estimates from the 
World Bank Governance Indicators (Kaufmann, Kraay and Mastruzzi 2009), we report 
data  on  Quality  of  Government  (‘QoG’)  for  all  EU  28  countries  and  for  NUTS  1  and 
NUTS  2  regions  for  21  EU  countries,  totaling  185  regions12.    The  QoG  questions  are 
aimed at capturing average citizens’ perceptions and experiences with corruption, and 
the extent to which they rate their public services as impartial and of good quality. 

In  addition,  we  highlight  broad  patterns  as  we  see  them  in  the  data  and  more 
specifically analyze trend in the EQI over time within regions.  Using several statistical 
and  observational  techniques,  we  elucidated  four  interesting  case  studies  from  Spain 
and  Poland,  which  were    undertaken  to  better  draw  out  ‘best  practices’  to  improve 
governance at the sub-national level in other EU regions. 

1   The  2017  round  of  survey  data  and  research  was  funded  by  the  EU  Commission  via  an  EU  Tender 

“Measuring Quality of Government and Sub-National Variation” 

2   NUTS  stands  for  ‘Nomenclature  of  territorial  units  for  statistics’  and  more  can  be  read  about  this  at: 

http://epp.eurostat.ec.europa.eu/portal/page/portal/nuts_nomenclature/introduction   

Kosovo is included, and because it is technically still a region in Serbia according to the EU, it is coded 
as such here as well. 

7 

 
 
 
 
 
 
                                                 
8 

 
 
 
 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

PART I: EQI survey, methodology & results 

2. Background, Methodology and Sample 

The field work began during the month of May, 2017 and were conducted in the local 
majority language in each country/region.  The results were returned to the Quality of 
Government Institute in August, 2017.   

The  E.U.  regional  survey  was  undertaken  by  Efficience  3  (E3),  a  French  market-
research,  Survey  Company  specializing  in  public  opinion  throughout  Europe  for 
researchers,  politicians  and  advertising  firms.    E3  has  also  conducted  the  2010  and 
2013 rounds of the EQI and were thus familiar with the question format and goals of 
the  survey.    E3  conducted  the  interviews  themselves  in  several  countries  and  used 
sub-contracting partners in others3.  The respondents, from 18 years of age or older, 
were  contacted  randomly  via  telephone  in  the  local  language.  Telephone  interviews 
were conducted via both landlines and mobile phones, with both methods being used 
in most countries.   Decisions about whether to contact  residents more often  via land 
or mobile lines was based on local expertise of market research firms in each country.  
For  purposes  of  regional  placement,  respondents  were  asked  the  post  code  of  their 
address to verify the area/ region of residence if mobile phones were used.   

Ideally,  a  survey  would  be  a  mirror  image  of  actual  societal  demographics  –  gender, 
income,  education,  rural-urban,  ethnicity,  etc.    However,  we  are  not  privy  to  exact 
demographic  distributions;  in  particular  at  the  regional  level  in  most  cases,  thus 
imposing artificial demographic lines might lead to even more problems than benefits.   
We  thus  sought  the  next  best  solution.  Based  on  their  expert  advice,  to  achieve  a 
random  sample,  we  used  what  was  known  in  survey-research  as  the  ‘next  birthday 
method’.  The next birthday method is an alternative to the so-called quotas method.  
When  using  the  quota  method  for  instance,  one  obtains  a  (near)  perfectly 
representative sample  – e.g. a near exact proportion of the  amount  of men, women, 
certain  minority  groups,  people  of  a  certain  age,  income,  etc.  However,  as  one 
searches for  certain demographics within the population, one might end up with only 
‘available’  respondents,  or  those  that  are  more  ‘eager’  to  respond  to  surveys,  which 
can  lead  to  less  variation  in  the  responses,  or  even  bias  in  the  results.    The  ‘next-
birthday’  method,  which  simply  requires  the  interviewer  to  ask  the  person  who 
answers the phone who in their household will have the next birthday, still obtains a 
reasonably  representative  sample  of  the  population.    The  interviewer  must  take  the 
person  who  has  the  next  coming  birthday  in  the  household  (if  this  person  is  not 
available,  the  interviewer  makes  an  appointment),  thus  not  relying  on  whomever 
might simply be available to respond in the household.  So, where the quota method 
is  stronger  in  terms  of  a  more  even  demographic  spread  in  the  sample,  the  next-
birthday method is stronger at ensuring a better range of opinion.  The next-birthday 
method was thus chosen because we felt that what we might have lost in demographic 
representation in the sample would be made up for by a better distribution of opinion.  
In  attempt  to  compensate  for  some  key  demographic  over/under-representation,  E3 
provides  weights  based  on  age  and  gender  for  each  region,  comparing  the  sample 
drawn to actual demographic statistics from Eurostat.  In the end, we find variation in 
response  and  refusal  rates  by  country,  which  could  have  to  do  with  many  factors 
including  the  sensitivity  of  one  of  the  primary  the  topics  at  hand  –  corruption.  A 
breakdown of the sample response rate, land line vs. mobile phone use, etc.  is listed 
in the table below by country.  

3   http://www.efficience3.com/en/accueil/index.html. For names of the specific firms to which Efficience 3 

sub-contracted in individual countries, please write cati@efficience3.com 

9 

 
 
                                                 
Table 1: Sample by Country 

Country 

1 France 

2 Belgium 

3 Bulgaria 

4 Czech Republic 

5 Slovakia 

6 Hungary 

7 Croatia 

8 Romania 

9 Finland 

10 Italy 

11 Greece 

12 Portugal 

13 Denmark 

14 Sweden 

15 Germany 

16 UK 

17 Ireland 

18 Austria 

19 Netherlands 

20 Poland 

21 Spain 

Total 

NUTS lvl  # regions 

n per 
NUTS 

2 

1 

2 

2 

2 

2 

2 

2 

2 

2 

1 

2 

2 

1 

1 

1 

2 

2 

1 

2 

2 

26 

3 

6 

8 

4 

7* 

2 

8 

5 

21 

4 

7 

5 

3 

16 

12 

2 

9 

4* 

16 

17 

401 

450 

400 

450 

450 

400 

450 

450 

400 

400 

405 

400 

450 

400 

450 

450 

450 

450 

460 

403 

411 

total n 

10422 

% 
sample 

13.4 

1350 

2400 

3600 

1800 

2800 

900 

3600 

2000 

8400 

1620 

2800 

2250 

1200 

7200 

5400 

900 

4050 

1840 

6442 

6992 

1.7 

3.1 

4.6 

2.3 

3.6 

1.2 

4.6 

2.6 

10.8 

2.1 

3.6 

2.9 

1.5 

9.2 

6.9 

1.2 

5.2 

2.4 

8.3 

9 

185 

77966 

100 

Note: *Hungary was a NUTS 1 country in 2010 and 2013 and is now at NUTS 2.  Netherlands 
was  a  NUTS  1  and  NUTS  2  country  in  2010  and  2013  respectively  and  is  now  at  NUTS  1,  yet 
NUTS 2 regions are calculated so as to make comparisons with 2013 data. 

Two issues in the preparation of this study are worthy of mention here.  First, in some 
areas, such as immigration, customs, defence or the judicial arena, we do not expect 
much  variation  from  region  to  region  within  countries  at  all.    Thus  to  maximize 
regional variation on the QoG-oriented question in the survey, we elected to limit the 
questions in the survey to only those policy areas that are most often either governed 
or administered by sub-national bodies.  In the end, three policy areas were selected – 
health care, education and law enforcement.  In addition to these three policy areas, 
we  also  inquire  about  the  integrity  of  regional elections  as  well  as  the  impartiality  of 
the tax authorities. 

The  second  issue  to  deal  with is  the  fact  that  in  some  countries  –  such  as  Germany, 
Belgium, Italy or Spain – the regions that we are targeting in the questions are both 
politically  and  administratively  meaningful.    That  is  to  say  that  these  regional 
governments are elected by their local constituents, and that these governments have 
their  own  autonomous  revenues  (either  from  directly  taxing  citizens,  or  central 
government  transfers  or  both)  and  have  a  degree  of  autonomy  with  which  to 
redistribute  resources  in  the  form  of  public  services.    However,  in  more  politically 
centralized  countries,  such  as  Bulgaria,  Romania,  Slovakia  or  Portugal,  this  issue 
becomes  more  challenging.    The  regions  that  we  are  targeting  (NUTS  1  or  NUTS  2) 
while  meaningful  in  the  sense  that  EU  development  funds  are  targeted  directly  to 

10 

 
 
  
  
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

them  and  that  Eurostat  reports  annual  data  on  them,  they  have  in  some  cases  been 
mainly  an  invention  for  EU  statistical  purposes,  yet  not  politically  meaningful.  
Therefore  asking  a  respondent  in  some  cases  ‘how  would  you  rate  the  quality  ‘X’ 
service  in  your  region  of  ‘Y’’  might  be  very  confusing,  since  respondents  from 
countries  like  Hungary  or  Romania  might  not  recognize  that  they  are  even  living  in 
region  ‘Y’.    It  can  therefore  be  argued  that  the  administrative  and  political 
responsibility of the regions in these three public services varies in different countries 
and thus this may be problematic for this data gathering.  However this study argues 
otherwise, in that we attempt to capture all regional variation within a country and, as 
several other scholars have noted (e.g. Tabellini 2005; Charron and Lapuente 2013), 
there are numerous empirical indications and anecdotal evidence pointing out that the 
provision  and  quality  of  public  services  controlled  by  a  powerful  central  government 
can nonetheless largely vary across different regions. 

In the 2010 and 2013 rounds, in order to synthesize the survey and make the results 
as comparable between and within countries as possible, we asked respondents about 
questions  focusing  around  three  key  concepts  of  QoG  –  the  ‘quality’  of  the  services 
themselves,  the  extent  to  which  they  are  administered  ‘impartiality’  and  extent  to 
which  ‘corruption’  exists  in  their  area.    In  countries  where  the  NUTS  region  is  not 
recognizable,  we  continue  with  this  approach.    However,  in  2017,  for  countries  with 
politically  relevant  regions,  we  elected  to  attempt  to  maximize  validity  and  regional 
variation  at  the  regional  level  by  substituting  the  local  word  for  the  regional  level  in 
question in lieu of ‘in your area’.  For example, in Germany, a respondent would hear 
the phrase ‘in your Bundesland’. 

11 

 
 
 
Table 2: Demographic summary of Respondents 2017 survey 

category 

Gender 

Education 

Age 

Income 

Employment 

Population 

% respondents 

48.6 

51.4 

10.1 

17.6 

34.2 

27.8 

10 

0.3 

18 

35.8 

26.9 

19.3 

0.1 

26.2 

31.6 

28.8 

13.4 

19.8 

39.8 

4.2 

6 

3.7 

24.6 

1.3 

0.6 

32.9 

37.5 

20.9 

6.8 

2.1 

male 

female 

<Primary 

some secondary 

secondary 

college/university 

post-grad degree 

n/a 

18-29 

30-44 

45-64 

>65 

n/a 

Low 

Medium 

High 

n/a 

Public sector 

private sector 

student 

unemployed 

Housewife/man 

retired 

other 

n/a 

<10k 

10k-100k 

100k-1m 

>1m 

n/a 

12 

 
 
  
  
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
   
 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

3. 2017 Survey Question Items 

Several empirical (based on Annoni and Charron 2017) and conceptual improvements 
are made to the question items that make up the  EQI index in 2017.   In  sum, three 
key changes have been made. First, the scale of the questions has been changed.  In 
previous years we used an odd-numbered 11 point scale.  However, we found that the 
‘5’  response  (mid-point)  was  overused  and  might  be  lead  to  misleading  results.    An 
even  ’10  point’  scale  is  now  employed  to  keep  the  variation  of  a  larger  scale  but  to 
eliminate  the  middle  category  which  may  have  been  representing  ‘don’t  know’  at 
times.      Second,  two  questions  have  been  removed  due  to  poor  performance,  and 
three others have been added, for a total of 17 question items (compared with 16 in 
the  previous  two  rounds).    Third,  as  noted,  we  emphasize  the  regional  level  in 
question in the local langue by country when relevant (as opposed to ‘in your area’ in 
previous years in all cases)4.   

We begin however by highlighting the ‘core’ questions that have remained in the three 
rounds of the survey over time. 

First, in question 4-6 in the current survey, respondents rate the quality of their three 
public services in question on a scale of ‘1’ (extremely poor quality) to ‘10’ (extremely 
high quality): 

4. ‘How would you rate the quality of public education in your area5?’ (edqual) 

5.  ‘How  would  you  rate  the  quality  of  the  public  health  care  system  in  your  area?’ 
(helqual) 

6. ‘How would you rate the quality of the police force in your area?’ (lawqual) 

The next six questions try to capture the extent to which public services are delivered 
impartially  in  the  regions  of  Europe.    ‘Impartiality’  is  admittedly  a  more  complicated 
concept to put  forth to  respondents than ‘quality’,  so we  framed this question in two 
ways  –with  a  more  negative  tone,  and  a  more  positive  tone.    In  the  first  three 
questions  (7-9),  we  asked  citizens  to  rate  whether  they  agreed  that  ‘certain  people’ 
get  special  advantages  when  dealing  with  the  public  service  in  question  from  1 
(strongly disagree) to 10 (strongly agree).  The second set of questions (10-12) asks 
respondents  whether  all  people  in  their  region  are  ‘treated  equally’  by  the  service  in 
question  on  a  four  point  scale  (1.  Agree,  2.  rather  agree,  3.  rather  disagree  or  4. 
Disagree).  We use all six questions in the final index to allow for as much variation as 
possible  while  not  letting  either  the  ‘positively’  or  ‘negatively’  framed  question 
determine the impartiality data alone. 

7. “Certain people are given special advantages in the public education system in my 
area.” (edimpart1) 

8. “Certain people are given special advantages in the public health care system in my 
area.” (helimpart1) 

4  

In  cases  where  countries  have  politically  relevant  or  recognizable  regions  at  the  NUTS  1  or  NUTS  2 
level in question, we substitute this phrase with the regional name. 

13 

 
 
 
 
                                                 
 
9.  “The  police  force  gives  special  advantages  to  certain  people  in  my  area.” 
(lawimpart1) 

10.  “All  citizens  are  treated  equally  in  the  public  education  system  in  my  area” 
(edimpart2) 

11.  “All  citizens  are  treated  equally  in  the  public  health  care  system  in  my  area” 
(helimpart2) 

12. “All citizens are treated equally by the police force in my area” (lawimpart2) 

The  next  three  questions  deal  with  respondents’  perception  of  the  extent  to  which 
corruption  is  present  in  their  public  services,  along  with  a  general  question  of  how 
often  they  believe  that  ‘others  in  their  area’  use  corruption  to  obtain  public  services.  
Again, perceptions may not capture the full story, however, as Kaufman et al (2009:3) 
argue  “perceptions  matter  because  agents  base  their  actions  on  their  perceptions, 
impression,  and  views”,  thus  if  citizens  believe  their  public  services  are  inefficient  or 
corruption,  they  are  less  likely  to  use  their  services,  likewise  with  foreign  firms  and 
investment  in  countries  perceived  to  be  plagued  with  problems  of  rent-seeking  and 
public  sector  mismanagement.    However,  we  complement  these  questions  with 
additional  questions  about  respondents’  actual  experience  with  bribery  later  on.    The 
first  three  questions  are  scaled  as  1-10,  with  ‘1’  being  “strongly  disagree”  and  ‘10’ 
being “strongly agree”.  

13. “Corruption is prevalent in my area’s local public school system” (edcorr) 

14. “Corruption is prevalent in the public health care system in my area” (helcorr) 

15. “Corruption is prevalent in the police force in my area” (lawcorr) 

The  following  two  question  constitute  a  slight  change  from  the  previous  2010  and 
2013  rounds,  whereby  instead  of  asking  citizens  about  either  ‘how  often  others 
engage  in  bribery  to  obtain  public  services’  (2010),  or  asking  respondents  about 
corruption for ‘special advantages’ (2013), we split these ideas of so called ‘need’ and 
‘greed’ corruption (Bauhr 2014) into the following two questions (1-10, with ‘1’ being 
“strongly disagree” and ‘10’ being “strongly agree”) 

16a.  People  in  my  area  must  use  some  form  of  corruption  to  just  to  get  some  basic 
public services 

16b.  Corruption  in  my  area  is  used  to  get  access  to  special  unfair  privileges  and 
wealth. 

In  addition  to  corruption  perceptions  questions,  we  ask  about  citizens’  direct 
experience  with  corruption.    In  contrast  to  2010  and  2013,  where  we  only  inquired 
about whether a respondent paid a bribe for one of the public service in question, we 
add whether the respondent was asked to pay a bribe by a public sector employee at 
one of the services in question so as to attempt to capture the direction of who is the 
‘initiator’.    For  the  final  index,  we  code  a  respondent  as  ‘1’  for  Q17  or  18  if  they 
answered ‘yes’ to any of the four sub-questions. 

17. In the last 12 months, have you or anyone in your family been asked by a public 
official  to  give  an  informal  gift  or  bribe  in:  (a):  Education  services?  (b):  Health  or 
medical services? (c): Police? d) any other public service? ‘(yes/no)’ (bribe) 

14 

 
 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

18. ‘In the past 12 months have you or anyone living in your household paid a bribe in 
any form to: (a): Education services? (b): Health or medical services? (c): Police? d) 
any other public service? ‘(yes/no)’ (bribe) 

Finally, we ask about two other relevant regional aspects of QoG, namely the extent to 
which corruption is present in their area’s elections and the respondents’ view of how 
fair the tax authorities are.  In previous rounds, we inquired about one’s trust in their 
area’s  media  in  reporting  on  matters  of  corruption  in  the  public  sector  and  among 
politicians.   

Q19-20:  Please  respond  to  the  following  2  questions  with  the  following  ('0'  strongly 
disagree - '10' strongly agree) 

Q18: “Elections in my area are clean from corruption” (elections) 

Q20: The tax authorities in my area treat all people equally (tax) 

4. Construction of the EQI 

We  begin  by  taking  the  country  average  from  the  World  Bank’s  WGI  data  for  four 
indicators:  ‘control  of  corruption’,  ‘government  effectiveness’,  ‘rule  of  law’  and  ‘voice 
and accountability’ and combine the four into one composite index (equal weighting)6 
The data is taken for the most recent year of publication (in this case 2015). Then, the 
combined WGI data is standardized for the EU sample. This figure is used as country’s 
mean score in the EQI for all countries in the sample so as to combine those countries 
outside the survey with those in it as well as to ‘anchor’ the regional QoG estimates in 
a  national  context  that  is  not  captured  by  the  regionally-based  survey  questions7   
Table  3  shows  the  results  of  the  latest  national  level  WGI  scores  by  country  and 
indicator.  The countries are in rank order and grouped together based on the result of 
a  cluster  analysis8  of  that  grouped  together  countries  that  were  most  similar  on  the 
four individual WGI indicators.  The scores are then added together (equal weighting) 
and  then  standardized  within  the  sample  of  30  European  countries.    As  a  point  of 
reference, we also provide the rank-change from the 2013 EQI (which used 2011 WGI 
data) 

We  see  six  cluster  groups  in  the  data.    The  most  difficult  states  to  place  were  Malta 
and Cyprus, as they could also belong to group 4, yet in the end were placed in group 
3.  Moreover, when changing some assumptions of the cluster analysis, Slovakia could 
be placed in the higher group 4.  We observe that the rank order of countries has not 
changed for most of the states in the sample, and most changes are only 1-2 places.  
Notable  exceptions  are  Cyprus,  Austria,  and  Hungary,  which  fell  three  places,  and 
Spain  which  fell  five  places  respectively  since  the  EQI  2013  (which  used  the  latest 
published WGI data at that time, which was from 2011).  Notable improvements were 
Estonia  and  Portugal,  which  climbed  three  and  Lithuania,  which  made  the  largest 
changes at plus 6 in the rankings within the EU28.  In addition, Greece now enters the 

6  

In addition, we underwent extensive sensitivity testing of each of these 4 pillars of QoG from the World 
Bank and found the data to be highly robust. For a closer look at the sensitivity tests and results for the 
EU  sample  of  countries  see  Charron,  Nicholas.  2010.  “Assessing  The  Quality  of  the  Quality  of 
Government Data: A Sensitivity Test of the World Bank Government Indicators.” QoG Working paper. 

7   Charron 2013 provides more on this point. 

8   Wards linkage and squared Euclidean distancing 

15 

 
 
 
                                                 
bottom group, where Romania and Bulgaria had been the  only EU28  countries in the 
first two rounds.   

Table 3: Country Level Governance Indicators and Rankings 

2017 
rank 

country 

CoC 

RoL 

GEE 

VAA 

total 
ave. 

EU z-score 

2013 
rank 

rank change 

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 

FINLAND 

SWEDEN 

2.28 

2.07 

1.82 

1.56 

1.93 

1.446 

2.25 

2.04 

1.81 

1.6 

1.92 

1.428 

DENMARK 

2.23 

2.04 

1.85 

1.57 

1.92 

1.425 

NETHERLANDS 

1.89 

1.93 

1.84 

1.57 

1.81 

1.232 

LUXEMBOURG 

2.12 

1.86 

1.72 

1.52 

1.81 

1.226 

GERMANY 

1.82 

1.78 

1.74 

1.43 

1.69 

1.034 

UK 

IRELAND 

AUSTRIA 

BELGIUM 

FRANCE 

1.87 

1.81 

1.74 

1.27 

1.67 

0.995 

1.64 

1.79 

1.54 

1.35 

1.58 

0.839 

1.49 

1.58 

1.85 

1.47 

1.4 

1.55 

0.796 

1.42 

1.44 

1.39 

1.46 

0.636 

1.28 

1.41 

1.44 

1.18 

1.33 

0.413 

ESTONIA 

1.25 

1.33 

1.09 

1.17 

1.21 

0.213 

PORTUGAL 

0.92 

1.14 

1.23 

1.12 

1.1 

0.031 

MALTA 

CYPRUS 

LITHUANIA 

0.92 

1.15 

0.85 

1.18 

1.03 

-0.101 

0.98 

0.56 

1.01 

1.04 

1.04 

1.02 

-0.116 

0.98 

1.2 

0.97 

0.93 

-0.27 

SLOVENIA 

0.73 

0.95 

0.97 

0.95 

0.9 

-0.317 

CZECH REP. 

0.39 

1.12 

1.05 

1.02 

0.9 

-0.323 

SPAIN 

POLAND 

LATVIA 

0.49 

0.58 

0.9 

0.8 

1.18 

1.02 

0.89 

-0.325 

0.8 

1.04 

0.81 

-0.478 

0.4 

0.79 

1.1 

0.82 

0.78 

-0.527 

SLOVAK REP. 

0.15 

0.48 

0.84 

0.97 

0.61 

-0.812 

ITALY 

-0.05 

0.25 

0.45 

1.01 

0.42 

-1.138 

HUNGARY 

CROATIA 

GREECE 

0.1 

0.2 

0.4 

0.2 

0.49 

0.52 

0.38 

-1.203 

0.51 

0.5 

0.35 

-1.248 

-0.13 

0.24 

0.25 

0.59 

0.24 

-1.444 

ROMANIA 

-0.05 

0.15 

-0.04  0.43 

0.12 

-1.637 

BULGARIA 

-0.31 

-0.12  0.22 

0.39 

0.04 

-1.777 

2 

3 

1 

4 

5 

7 

8 

9 

6 

10 

11 

15 

16 

13 

12 

22 

17 

18 

14 

19 

23 

20 

24 

21 

26 

25 

28 

27 

1 

1 

-2 

0 

0 

1 

1 

1 

-3 

0 

0 

3 

3 

-1 

-3 

6 

0 

0 

-5 

-1 

2 

-2 

1 

-3 

1 

-1 

1 

-1 

16 

 
 
 
 
 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

Table 4: Summary of Regional EQI Indicators 

Pillar 

Variable Description 

Variable name 

Corruption Items 

perceptions 

corruption in education 

corruption in health care 

corruption in law enforcement 

need corruption 

greed corruption 

elections clean from corruption 

stEdCorr 

stHelCorr 

stLawCorr 

stNeedCorr 

stGreedCorr 

stElecCorr 

experiences 

asked to pay a bribe for public service 

stnoAskB_any1 

paid a bribe for public service 

stnopayB_any1 

Impartiality Items 

some  get special advantages in education 

stEdImpart1 

some  get special advantages in health care 

stHelImpart1 

some get special advantages in law enforcement 

stLawImpart1 

all treated equally in education 

all treated equally in health care 

stEdImpart2 

stHelImpart2 

all treated equally in law enforcement 

stLawImpart2 

 All treated fairly by tax authorities 

stTaxImpart 

Quality Items 

quality of education 

quality of health care 

quality of law enforcement 

stEdQual 

stHelQual 

stLawQual 

17 

 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
In  previous  rounds,  we  then  took  the  standardized  sample  mean  for  2015  WGI  data 
and  set  each  country’s  national  average  as  such.      A  key  difference  in  this  round 
(and  retrospectively  in  other  two  rounds)  we  now  aggregate  to  the  WGI  at 
the pillar levels of corruption impartiality and quality in order to better make 
use of these three distinct concepts empirically (as shown in Figure 1).  This also 
allows for the added advantage of more valid comparison of unit changes in each pillar 
over  time.    The  regional  data  itself  combines  18  survey  questions  about  QoG  in  the 
region,  which  are  shown  in  Table  3.  As  noted,  the  questions  are  centered  on  three 
QoG  concepts:  ‘quality’,  ‘impartiality’  and  ‘corruption’.  In  building  the  regional  index, 
we re-score each variable so that higher numbers equate to higher QoG and then the 
18  questions/indicators  to  three  pillars  based  on  factor  analysis9  then  we  averaged 
these  three  pillars  together  to  form  the  final  index  figure  for  each  region.  After  each 
stage of aggregation, the data are standardized. For the seven EU28 countries outside 
of the regional survey, there is nothing to add to the WGI Country score, thus the WGI 
data  is  used  as  the  QoG  estimate  alone,  as  regional  variation  is  unobserved.  With 
respect to countries with the regional data, we set the national average as the WGI for 
each  of the three pillars10 and  explain the within‐country variance using the  regional‐
level data. The ‘roadmap’ so to speak of the aggregation process can be seen in Figure 
1. 

To begin, we aggregate the individual scores (‘survey question’) to the corresponding 
regional  level,  so  that  each  of  the  17  questions  in  the  index  is  now  a  regional 
‘indicator’.    We  test  the  overall  consistency  of  the  17  indicators  with  the  Cronbach’s 
Alpha,  which  was  0.934,  showing  high  levels  of  association.    In  addition,  of  the  153 
pairwise correlations, all but two are positive when items are re-scaled so that higher 
scores  equal  higher  QoG  (see  appendix,  table  A2).    The  two  are  between 
stnopayB_any1  and  stEdImpart1,  and  stnopayB_any1  and  stTaxImpart;  yet  neither 
negative  pairwise  correlation  is  significant  (p  value  =  0.39  and  0.14  respectively).  
Next,  factor  analysis  then  groups  the  18  indictors  into  more  similar  groupings,  of 
which  we  find  three.    After  normalizing  each  of  the  18  indicators  (through  z-score 
standardization) so that they share a common range, the 18 indicators are aggregated 
into  the  three  groupings  ‘pillars’.    The  one  exception  is  the  corruption  pillar  that  has 
one  additional  step  –  which  contains  two  sub-pillars  called  ‘experience’  and 
‘perceptions’ which represent question items reflecting personal experience with petty 
corruption  versus  perception  of  corruption  in  various  other  areas.    These  two  sub-
pillars are aggregated using equal weighting.   The pillars are then aggregated into the 
regional index11. After each step of aggregation, the data is standardized.  

9   Results of the factor analysis can factor weights are found in the appendix 2, Table A.3 of this paper.  In 
previous years, the underlying pillars were determined by the concepts, while in 2017, there were three 
clusters that were determined soley by the principle component analysis 

10   For  corruption  pillar,  the  regional  estimates  are  centered  round  the  ’control  of  corruption  WGI  score.  
For  impartiality,  the  estimates  are  set  around  ‘government  effectiveness’  WGI  score.    The  regional 
impartiality indicators are centered on the ‘rule of law’ and the regional ‘quality’ indicators are centered 
on the ‘voice and accountability’ and ‘government effectiveness’ WGI national scores.   

11   Nardo et al. (2008) point out that when combining multiple indicators into a single index, the underlying 
data  should  be  significantly  correlated.    We  find  that  98.5%  of  the  pairwise  correlations  among  the 
variables  are  significant  and  in  the  expected  direction  at  the  99%  level  of  confidence.    We  show  the 
results in Appendix 2, Table A.2. 

18 

 
 
 
 
 
                                                 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

Figure 1: EQI 2017 Roadmap 

Note: * represents the  stage  at which  the  regional  data is centered on  the  national level WGI 
data. 

For  data  for  the  regional  pillars’  score  for  each  of  the  countries  included  in  the  2017 
regional  survey,  weighting  each  region’s  score  by  their  share  of  the  national 
population.  This  figure  is  thus  used  to  explain  regional  variation  only  within  each 
country included (not absolute levels of QoG).  We then subtract this mean score from 
each region’s individual pillar score from the regional study, which shows if the region 
is above or below its national average and by how much. This figure is then added to 
the  national  level,  WGI  data,  so  each  region  has  an  adjusted  score  for  each  of  the 
three  pillars,  centered  on  the  respective  WGI  indicators.  It  is  worth  mentioning  that 
none  of  the  regional  variation  from  the  regional  index  is  lost  during  this  merging 
process;  the  country  mean  of  all  regional  scores  is  simply  adjusted.    The  formula 
employed is the following:  

𝐸𝑄𝐼𝑝𝑖𝑙𝑙𝑎𝑟𝑟𝑒𝑔𝑖𝑜𝑛𝑋 𝑖𝑛 𝑐𝑜𝑢𝑛𝑡𝑟𝑦𝑌 =   𝑊𝐺𝐼𝑐𝑜𝑢𝑛𝑡𝑟𝑦𝑌 + (𝑅𝑞𝑜𝑔𝑟𝑒𝑔𝑖𝑜𝑛𝑋 𝑖𝑛 𝑐𝑜𝑢𝑛𝑡𝑟𝑦𝑌 −   𝐶𝑅𝑞𝑜𝑔𝑐𝑜𝑢𝑛𝑡𝑟𝑦𝑌) 

19 

Individual levelregional level survey questionsregional indicatorssub-pillarspillars*final IndexstEdCorrstEdCorrstHelCorrstHelCorrstLawCorrstLawCorrperceptionsstNeedCorrstNeedCorrsub-pillarstGreedCorrstGreedCorrstElecCorrstElecCorrcorruption pillarstnoAskB_any1stnoAskB_any1experiencestnopayB_any1stnopayB_any1sub-pillarstEdImpart1stEdImpart1stHelImpart1stHelImpart1stLawImpart1stLawImpart1stEdImpart2stEdImpart2impartilaityEQI IndexstHelImpart2stHelImpart2pillarstLawImpart2stLawImpart2stTaxImpartstTaxImpartstEdQualstEdQualstHelQualstHelQualqualitystLawQualstLawQualpillar 
 
 
 
where ‘EQI’ is the final score from each region or country  in each pillar –corruption, 
impartiality  and  quality  -  of  the  EQI.  ‘WGI’  is  the  World  Bank’s  national  average  for 
each  country  for  each  pillar,  while  ‘Rqog’  is  each  region’s  score  from  the  regional 
survey  and  ‘CRqog’  is  the  country  average  (weighted  by  regional  population)  of  all 
regions within the country from the regional survey for each pillar. The EQI pillars are 
standardized so that the mean is ‘0’ with a standard deviation of ‘1’.  The three pillar 
scores are then aggregated using equal weighting. 

A full list of the EQI for 2017 for all countries and regions is located in Appendix 1.  As 
in  the  results  for  2010  and  2013,  we  find  that  in  several  cases,  the  data  show 
significant  and  wide  variations  in  QoG  within  countries  (Italy,  Belgium,  Spain  and 
Bulgaria  for  example),  while  others  show  little  to  no  variation  in  regional  QoG 
(Denmark, Sweden, Netherlands, Slovakia).   

5. Margins of Error for the EQI 2017 

As  we  reported  for  2010,  we  construct  margins  of  error  for  the  regional  estimates, 
similar  to  the  authors  of  the  WGI  report  ‘margins  of  error’  around  each  of  the  QoG 
variables  that  they  publish  annually.      The  idea  is  to  construct  a  type  of  margin  of 
error around the regional estimates so that we can say with some degree of certainty 
that region ‘x’s higher QoG score is in fact ‘significantly’ higher than region ‘y’s score.   

As noted, the regional QoG index is based on data from a randomly selected group of 
respondents  in  each  of  the  185  regions.    Since  this  is  an  estimate  of  the  total 
population,  we  provide  some  sence  of  the  uncertainaty  around  the  data  point.  
Although,  in  theory,  any  number  can  be  chosen,  we  select  a  margin  of  error  at  the 
95% confidence level.   After obtaining the margin of error based on our sample size, 
we then can calculate the distance around the estimates of QoG for each region.   

To  be  precise,  there  are  two  ways  to  go  about  calculating  the  margin  of  error  for 
survey data – an ‘exact’ confidence interval and an ‘approximate’ confidence interval.  
The former takes into account both sampling and non-sampling errors, while the latter 
only random sampling errors.  While the ‘exact’ interval may be more precise, we find 
the  advantages  of  the  ‘approximate’  confidence  interval  to  far  outweigh  the 
drawbacks,  in  particular  with  respect  to  the  efficiency  and  time  saved  in  the 
calculation.  Moreover, we have no reason to suspect that there is any bias in certain 
groups being excluded or not being forthright in their responses, so compensating for 
such  error  is  simply  beyond  our  reach.    Thus  we  report  an  ‘approximate’  confidence 
interval for each region’s QoG estimate.   

We  begin  by  assuming  a  normal  distribution  of  the  sample  so  that  we  may  use  the 
Central Limit Theorem.  We know from basic statistical probability that in a sample ‘x’, 
95% of the area of a basic normal Bell curve are between our estimates (µ) 1.96+/- 

the standard  error around µ.   We calculate the standard  error as: S.E. = 
. The 
margin  of  error  for  each  individual  region  is  based  around  the  QoG  estimate: 

have been aggregated from the survey data.   

 with N = 18, because there are 18 indicators in the QoG index which 

As shown in Figure 5, each region will have their own individual margin of error based 
on  the  consistency  of  the  estimates  for  each  of  the  18  aggregated  questions  in  the 
survey.    Regions  where  aggregate  responses  to  the  QoG  questions  are  inconsistent 
(e.g. citizens feel that that the services are impartial, but lack good quality) will have 
higher  margins  of  error  than  those  regions  where  citizens  rated  the  quality, 
impartiality and corruption at a consistently high (or moderate or low) level.   

20 

nn/96.1 
 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

Figure 2: EQI in Rank order with Regional Margins of Error 

The  mean  margin  of  error  by  region  is  0.31  with  a  standard  deviation  of  0.10.    The 
three  regions  with  the  greatest  level  of  consistency  are  Burenland  (AT11),  Voralberg 
(AT32)  and  East  Midland  (UKF)  with  0.14,  0.15  and  0.16  respectively.    The  three 
regions with the margins of error around their estimates are the two Croatian regions 
(HR03  and  HR04)  and  Southern  Transdanubia  (HU22)  with  0.57,  0.55  and  0.54 
respectively.    Figure  2  shows  the  full  range  of  countries  and  regions  with  confidence 
intervals  around  the  estimates  of  the  EQI  201312  The  highest  ranked  region  is  the 
small,  island,  Swedish  speaking  Finish  region  of  Åland,  which  shows  to  be  a  positive 
outlier; while the region of Severozapad (BG31) is ranked lowest.   

6. Testing the Uncertainty of the Estimates 

In this section, we  summarize a number of alternative simulations that were done to 
the index in order to test how sensitive the results are to our model assumptions and 
specifications.  Specifically, we examine the effects of alternative weighting schemes, 
aggregation  methods,  standardization  and  exclusion  of  individual  indicators  in  the 
index to test how close the results resemble the final ones reported.   

In  the  2017  version  of  the  EQI,  the  method  is  now  to  center  the  regional  indicators 
around  national  estimates  for  each  of  the  three  pillars  –  corruption,  impartiality  and 
quality  –  and  thus  to  elucidate  as  clear  results  as  possible,  we  undergo  sensitivity 

12   Due to the fact that the margins of error are constructed using the regional data, there are no confidence 

intervals for the national level estimates, thus countries like Estonia or Malta do not have them. 

21 

FI20BG31-3-2-10123501001502000EQI 2017 rank orderEQI estimate95% margin of error 
 
 
 
 
                                                 
analyses  for  each  of  the  three  pillars.    In  sum,  the  following  adjustments  are 
considered – testing the sensitivity with all possible combinations 

Table 4 : Alternative Construction Scenarios to Test Uncertainty 

model 

weighting  aggregation 

excluded 
indicators 

standardization 

micro to macro adjustments 

original EQI  equal 

arithmetic 

none 

z-score 

none 

alterantives  factor 

geometric 

all - one at a 
time 

min-max 

aggregate using gender & age 
weights 

aggregate exclusing high 
education 

aggregate exclusing low education 

6.1 Corruption pillar 

Table 5 shows the top 10 most divergent scenarios from the final EQI corruption pillar 
estimates.  The tables shows the various ways in which the original EQI roadmap has 
been  altered,  along  with  the  median  shift  in  regional  rank  and  the  region  with  the 
greatest shift in rank due to the alteration and the direction of that shift.   

In  general,  we  observe  that  the  corruption  pillar  is  quite  stable  and  robust  to 
alterations. The Spearman rank coefficient is above 0.95 in all cases, and only in the 
simulation least like the original corruption pillar results do we see the Spearman rank 
drop below 0.96.  The  median shift in rank ranges between five and eight for the 10 
most  deviant  scenarios  in  Table  5,  which  is  relatively  small  given  that  there  are  193 
regions in the sample.   

22 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

Table  5:  Results  of  Sensitivity  Testing  for  Regional  Level  Data  in  Corruption 
Pillar 

Scenario  Aggregation  Weighting  Excluded   micro-macro  Normalization  Median  Max 

Max 

Spearman 

Rank 

Indicator  adjustment  Method 

shift 

shift 

Region 

Rank 
Coefficient 

Reg. 
Corruption 
Pillar 

Arithmetic 

Equal 

none 

none 

Standardized  0 

0 

0 

1 

1 

2 

3 

4 

5 

6 

7 

8 

9 

Arithmetic 

Equal 

Pay bribe 

Gender 
age weights 

& 

Standardized  5 

102 (-) 

Arithmetic 

Factor 

none 

none 

Standardized  8 

44 (-) 

Arithmetic 

Equal 

Pay bribe  none 

min-max 

5 

102(-) 

Arithmetic 

Factor 

none 

drop 
ed. 

high 

Standardized  8 

88(-) 

Arithmetic 

Factor 

none 

drop low ed.  Standardized  6 

108(-) 

Arithmetic 

Factor 

none 

Arithmetic 

Equal 

Ask bribe 

Geometric 

Factor 

none 

Gender 
age weights 

& 

Gender 
age weights 

& 

drop 
ed. 

high 

Standardized  7 

87(-) 

Standardized  6 

104(+) 

Standardized  7 

108 (-) 

Valle d’Aosta 
(IT) 

0.958 

Haute-
Normandie 
(FR) 

0.960 

Valle d’Aosta 
(IT) 

0.961 

Valle d’Aosta 
(IT) 

0.962 

Valle d’Aosta 
(IT) 

0.962 

Valle d’Aosta 
(IT) 

0.965 

Valle d’Aosta 
(IT) 

0.966 

Valle d’Aosta 
(IT) 

0.967 

Arithmetic 

Equal 

Ask bribe  none 

min-max 

6 

104 
(+) 

Valle d’Aosta 
(IT) 

0.967 

10 

Geometric 

Equal 

Ask bribe  none 

Standardized  6 

70 (+) 

Strední Cechy 
(CZ) 

0.969 

Note:  total  of  193  regions,  with  1st  scenario  representing  the  final  index.    These  are  the  10 
scenarios  LEAST  like  the  aggregated  regional  Corruption  index  used  to  build  the  EQI.    Median 
shift is absolute median shift.  

23 

 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
  
  
  
  
  
  
 
 
 
There  are  a  few  outlying  regions  that  are  highly  affected  by  alterations  to  the  EQI 
index  assumptions  however.    Namely,  the  region  of  Valle  d’Aosta  (ITC2)  is  highly 
sensitive  to  several  of  the  alterations,  moving  at  times  over  100  places  in  the 
rankings.    For  example  removing  certain  experience  indicators  of  corruption,  and 
making  changes  in  the  aggregation  process  from  the  micro  to  macro  level.  Other 
regions  that  make  substantial  shifts  in  ranks  (between  35  and  70  places)  in  certain 
alternative simulations are Strední Cechy (CZ02), Jihozapad (CZ06), Haute-Normandie 
(FRD2),  and  Nyugat-Dunántúl  (HU22).    Figure  3  shows  the  scatterplot  of  the 
simulation  with  the  lowest  Spearman  Rank  coefficient  compared  with  the  original 
corruption pillar, highlighting regions that moved 50 places or more in the rankings. 

Figure 3 : Most Deviant Scenario in Corruption Pillar 

24 

 
 
 
 
 
 
 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

6.2 Impartiality  

Table 6: Results of Sensitivity Testing for Regional Level Data in Impartiality 
Pillar 

Rank 

Aggregation 
method 

Weighting 

Removed 
Indicator 

Micro-macro 
adjustment 

Standardization 
Method 

Median 
shift 

Max shift 

Max Region 

Spearman 
Rank 
Coefficient 

Reg. 
Corruption 
Pillar 

Arithmetic 

Equal 

none 

none 

z-score 

0 

0 

0 

1 

1 

2 

3 

4 

5 

6 

7 

8 

9 

Arithmetic 

Factor 

Edimpart2 

none 

min-max 

5  

27 (-) 

Arithmetic 

Equal 

TaxImpart 

none 

min-max 

 5 

31(-) 

Arithmetic 

Equal 

Edimpart2 

none 

min-max 

 5 

17 (-) 

Arithmetic 

Equal 

LawImpart1 

Gender  &  age 
weights 

z-score 

 4 

24 (+) 

Arithmetic 

Equal 

Edimpart2 

none 

z-score 

 5 

20 (+) 

Arithmetic 

Equal 

TaxImpart 

none 

z-score 

 4 

28 (-) 

Arithmetic 

Factor 

TaxImpart 

none 

min-max 

4  

30 (-) 

Arithmetic 

Equal 

LawImpart1 

none 

min-max 

 4 

21 (+) 

Arithmetic 

Equal 

LawImpart1 

none 

min-max 

 5 

22 (+) 

Strední  Cechy 
(CZ) 

0.975 

Severoiztochen 
(BG) 

0.981 

Nisia  Aigaiou, 
Kriti (EL) 

0.982 

Nord 
(RO) 

Vest 

0.982 

Észak-Alföld 
(HU) 

0.982 

Yugoiztochen 
(BG) 

0.982 

Yugoiztochen 
(BG) 

0.982 

Nord 
(RO) 

Nord 
(RO) 

Vest 

Vest 

0.984 

0.984 

10 

Arithmetic 

Factor 

LawImpart2 

none 

min-max 

 4 

22 (+) 

Brussels (BE) 

0.985 

Note: total  of 193  regions, with 1st scenario representing the final index.   These are 
the  10  scenarios  LEAST  like  the  aggregated  regional  Impartiality  index  used  to  build 
the EQI.  Median shift is absolute median shift.  

Table 6 reports a similar rank table of the ten most divergent cases from the original 
impartiality  pillar.    In  this  pillar,  we  observe  much  more  stability  than  in  corruption.  
All  Spearman  rank  coefficient  are  0.975  or  above,  and  the  median  shifts  are  all 
between four and five places for the ten most divergent cases. The most drastic shift 
occurs  for  the  region  Severoiztochen  (BG33),  which  drops  31  places  when  dropping 
the  tax  authority  impartiality  item  (TaxImpart)  and  using  min-max  standardization.  
Otherwise, even the max shift does not exceed 30 places in most scenarios.  Figure X 
shows  the  scatterplot  with  original  and  most  divergent  impartiality  scenario, 
highlighting the two regions which shifted 25 places or more in the rankings. 

25 

 
  
  
  
  
  
  
  
  
 
 
 
 
 
Figure 4 : Most Deviant Scenario : Impartiality Pillar 

26 

 
 
 
 
 
 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

6.3 Quality 

Table 7: Results of Sensitivity Testing for Regional Level Data in Quality Pillar 

Scenario 

Aggregation  Weighting 

Excluded   micro-macro 

Normalization  Median  Max 

Max 

Spearman 

Rank 

Indicator 

adjustment 

Method 

shift 

shift 

Region 

Rank 
Coefficient 

Reg. 
Quality 
Pillar 

Arithmetic 

Equal 

none 

none 

z-score 

1 

2 

3 

4 

5 

6 

7 

8 

9 

Arithmetic 

Equal 

HelQual 

gender  &  age 
weights 

z-score 

Arithmetic 

Factor 

HelQual 

none 

min-max 

Arithmetic 

Equal 

EdQual 

gender  &  age 
weights 

z-score 

Arithmetic 

Equal 

HelQual 

none 

min-max 

Arithmetic 

Equal 

HelQual 

none 

z-score 

Arithmetic 

Equal 

EdQual 

none 

min-max 

Arithmetic 

Factor 

EdQual 

none 

min-max 

Arithmetic 

Factor 

EdQual 

none 

z-score 

Arithmetic 

Factor 

LawQual 

none 

min-max 

10 

Arithmetic 

Equal 

LawQual 

gender  &  age 
weights 

z-score 

0 

7 

9 

7 

8 

8 

8 

8 

8 

8 

8 

0 

0 

1 

56 (+) 

Dél-Dunántúl (HU) 

0.930 

50 (+) 

Dél-Alföld (HU) 

0.930 

96 (-) 

Border, Midland & 
Western (IE) 

0.931 

50 (+) 

Dél-Alföld (HU) 

0.931 

48 (+) 

Southern & Eastern 
(IE) 

0.934 

94 (-) 

Border, Midland & 
Western (IE) 

0.941 

94 (-) 

Border, Midland & 
Western (IE) 

0.941 

93 (-) 

Border, Midland & 
Western (IE) 

0.947 

42 (-) 

Dél-Alföld (HU) 

0.949 

41 (-) 

Dél-Alföld (HU) 

0.951 

Note:  total  of  193  regions,  with  1st  scenario  representing  the  final  index.    These  are  the  10 
scenarios LEAST like the aggregated regional Quality index used to build the EQI.  Median shift 
is absolute median shift.  

Table 7 shows the summary of the ten most divergent cases for the quality pillar.  On 
whole the quality pillar is slightly more sensitive to the alterations, most likely due to 
the  fact  there  are  only  three  items  that  make  up  the  pillar,  compared  with  seven 
impartiality  and  eight  corruption  items  respectively.    Thus  the  removal  of  one  of  the 
indicators  represents  a  larger  proportional  changes  in  the  underlying  data  than  any 
removal of a single item from the previous two pillars.   

On whole the Spearman Rank coefficient shows quite similar rankings compared with 
the  original  EQI  pillar,  with  the  two  most  divergent  scenarios  dropping  to  roughly 
0.93.    All  others  are  above  0.93  however.    The  median  shifts  range  between  7-9 
places for these ten most diverging scenarios, with the two Irish regions showing the 
most  sensitivity  to  the  alterations,  along  with  the  Hungarian  region  of  Dél-Alföld 
(HU33)  and  the  capital  region  of  Madrid  making  significant  shifts  at  times  as  well.  
Figure  X  shows  the  most  divergent  scenario  and  labels  regions  with  a  shift  of  40  or 
more places from the original ranking.   

27 

 
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
Figure 5 : Most Deviant Scenario : Quality Pillar 

Finally, as respondents were contact via two forms of telephone – landlines and mobile 
phones,  we  examine  the  extent  to  which  one’s  type  of  telephone  contact  has  any 
systematic relationship with higher or lower repsonses to the main questions. 

Table  8  shows  the  proportion  of  mobile  rspondents  per  country,  ranging  from  0.237 
(23.7%) in Germany to 1 (all respondents) in Slovakia, Czech Republic and Hungary.  
While  cannot  investiage  the  effects  of  telephone-type  in  the  latter  three  member 
states, in which all repsondents were mobile users, we can do so in the other countries 
where we have variation.   

28 

 
 
 
 
 
 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

Table 8 : Proportion of mobile respondents by country 

country  
Austria 
Belgium 
Bulgaria 
Croatia 
Czech Republic 
Denmark 
Finland 
France 
Germany 
Greece 
Hungary 
Ireland 
Italy 
Netherlands 
Poland 
Portugal 
Romania 
Slovakia 
Spain 
Sweden 
UK 

proportion 
mobil 
respondents 
0.521 
0.453 
0.796 
0.482 
1.000 
0.957 
0.982 
0.647 
0.237 
0.519 
1.000 
0.382 
0.643 
0.552 
0.900 
0.745 
0.611 
1.000 
0.641 
0.905 
0.244 

In Figure 6, we show the summary results of multiple regression analyses, in which we 
test  whether  the  binary  variable  ‘mobile’  has  a  systematic  effect  on  individual’s 
repsonses to the EQI questions.  For the sake of parsimony, we combine the questions 
to  the  pillar  level  –  quality,  impartilaity  and  corruption  -  using  equal  weighting,  such 
that the scores are continuous range between 0.1 to 1, with higher scores equating to 
higher QoG assessments in all cases.   

To  avoid  potentially  misleading  results,  we  include  a  number  of  potentially 
confounding  covariates  at  the  individual  level  and  include  regional  dummy  variable 
and  survey  design  weights.    Further,  for  more  precision,  we  analysis  the  data  by 
country.  The model we test is the following: 

𝑄𝑜𝐺𝑖 = 𝛼 + 𝛽1(𝑚𝑜𝑏𝑖𝑙𝑒) + 𝛽2..𝑛(𝑐𝑜𝑛𝑡𝑟𝑜𝑙𝑠) + 𝜑𝑟 + 𝜀𝑖 

Whereby an individual’s assessment of the three QoG pillars - quality, impartiality and 
corruption  –  are  a  function  of  their  telephone  type,  a  set  of  individual  level  standard 
controls  (gender,  age,  education,  income,  population  of  residence  and  employment 
status) along with regional fixed effects at the NUTS level sampled in each country  𝜑𝑟 
and model error 𝜀𝑖.  𝛽1 is the main parameter of interest.  

29 

 
 
 
 
 
 
Figure  6 :  Summary  of  Telephone-type  on  QoG  assessments  by  pillar  and 
country 

Note :  dots  are  estimated  marginal  effects  of  mobile  contact  (landline=reference 
category).    Lines  are  95%  confidence  intervals  around  the  estimate  from  robust 
standard  errors.    Larger  confidence  intervals  are  due  to  sample  size  and  percentage 
mobile use.  Lines that cross the red horizontal lines (at ‘0’) imply that the responses 
are statistically indistinguishable between landline and mobile respondents.  

The Figure shows the results of 54 OLS models, whereby the dots show the marginal 
effect of mobile telephone-type on the  combined  set of  EQI pillar questions by pillar.  
Estimates that are positive (negative) imply that mobile uses assessed higher (lower) 
QoG  on  average  for  that  respective  pillar.    The  estimates  have  a  95%  confidence 
interval,  which  if  it  crosses  the  red  horizontal  line  at  ‘0’,  implies  that  the  effect  is 
negliable.   

We  find  that  in  the  vast  majority  of  cases,  telephone  type  plays  no  role  on  QoG 
assessments.  Of the 54 models estimated, we find that in 85% of the cases (46 of 54, 
or in 14 of the 18 countries tested), the effect is insignificant (e.g. p>0.05).  In eight 
cases,  the  effect  of  mobile  use  is  shown  to  have  a  significant  association  with  QoG 
assessments.  The effect is not uniform however.  The estimates colored in red show a 
significant  negative  effect,  while  those  in  blue  show  a  positive  one.    We  observe  the 
case of the quality pillar, only mobile users in Poland rate the quality signifncatly lower 
(𝛽1 = -0.025).  French and Portuguese mobile respondents tend to score their services 
as  less  impartial  and  more  corrupt  on  average,  while  Polish  mobile  users  also  rate 
their  services  as  more  corrupt  than  landline  users.    On  the  oher  hand,  mobile 
repsondents in Romania tend to rate their services as more impartila and less corrupt 
than those who repsonded via landline.   

30 

 
 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

7. Final Index: Regional variation of EQI and External Valididty Checks 

For the sake of space, all EQI, pillar and margin of error estimates  are listed in Table 
A2  in  the  appendix  of  this  document.    Figure  6  summarizes  the  final  index;  showing 
the countries in rank order from top to bottom on the y-axis and regional variation on 
the x-axis.  As with the 2013 EQI index, the Swedish speaking Finish island region of 
Åland is an outlier on the top of the index ranking.    

Figure 7: Countries in Rank Order and Regional Variation of 2017 EQI 

Note: highest regional score in each country with regional data labeled via NUTS code.   

The  following  three  figures  show  the  results  of  the  EQI  index  for  the  three  years  of 
data.   The previous two years have been  re-calculated with the current methodology 
and sample of the 2017 EQI for more direct comparison over time. 

31 

 
 
 
 
 
Figure 8: EQI 2017 

Figure 9: EQI 2013 

32 

 
 
 
 
 
 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

Figure 10: EQI 2010 

In  Table  8  we  report  results  from  a  simple  pairwise  correlation  test  with  the  EQI,  its 
pillars  and  several  alternative  measures  with  which  we  would  expect  the  EQI  to 
correlate.    The  first  group  of  measures  are  that  which  also  measure  some  aspect  of 
institutional  quality  at  the  regional  level.    One,  we  take  the  OECD’s’  Regions  at  a 
Glance’  report  (2016)  which  tracks  the  percentage  of  citizens  who  believe  their 
government  is  corrupt  from  surveys  since  2006-2014.    The  CRI  and  ‘%  single  bids’ 
come  from  Fazekas  and  Koscis  (2017)  which  track  the  risk  of  grand  corruption  in 
public procurement and have aggregated their  data to the  exact regional level in the 
EQI  sample.  The  final  measure  is  one  that  tracks  the  extent  to  which  civil  servants 
rate  their  place  of  employment  as  meritocratic  (as  opposed  to  clientalist)  at  the 
regional level in EU countries, from 2013 (Charron et al 2013). 

Next, we check the correlation with likely covariates from the literature – namely, the 
proportion  of  people  in  a  region  that  ‘trust  others’  (social  trust,  from  Charron  and 
Rothstein  2018),  gender  equality  in  the  form  of  the  percentage  of  women  in  local 
parliaments by region (Sundström 2014), the percentage of residents who are at risk 
of poverty and the level of economic development (PPP per capita, logged from 2012), 
the latter two measures are taken from Eurostat.  

We  find  strong  evidence  of  external  validity  for  the  measure.  In  particular,  we  see 
strong  correlation  with  the  alternative  measure  of  corruption  perceptions  from  the 
OECD,  both  the  EQI  measure  on  whole  and  especially  in  the  EQI’s  corruption  pillar 
(0.85).  The  measures  of  grand  corruption  risk  also  are  highly  correlated  and 
significant  with  the  EQI  and  all  pillars  (p<0.001).    The  measure  of  public  sector 
meritocracy correlates with the EQI at 0.69.   

In addition, we find that the outside covariates of other socio-economic indicators are 
all  significantly  correlated  with  the  EQI  and  in  the  expected  direction.  Regions  with 
higher  social  trust,  gender  equality  and  economic  development  tend  to  have  higher 

33 

 
 
 
quality government institutions, while the EQI and its pillars are negatively correlated 
with risk of poverty.   

Table 9: Pairwise correlations of EQI and Pillar with Additional Measures 

variable 

EQI 2017  Quality  Impartiality  Corruption 

2017 EQI and Pillar data 

EQI  

Quality 

Impartiality 

Corruption 

alternative measures of regional QoG 

1.00 

0.95 

0.97 

0.96 

1.00 

0.89 

0.87 

corruption perceptions (OECD) 

0.81 

0.72 

corruption risk indicator (CRI) 

-0.55 

-0.52 

% single bids 

-0.65 

-0.59 

meritocracy in public sector 

0.69 

0.65 

socio-economic covariates 

1.00 

0.92 

0.76 

-0.51 

-0.63 

0.67 

social trust (2013) 

% women in local parl. 

0.50 

0.44 

0.44 

0.47 

0.50 

0.40 

% poverty risk 

-0.42 

-0.44 

-0.43 

PPP per capita (logged, 2012) 

0.60 

0.61 

0.58 

Note : pairwise Pearson correlation coeffiecnets reported.  

1.00 

0.85 

-0.55 

-0.66 

0.66 

0.50 

0.41 

-0.35 

0.53 

Figure  11  highlights  the  relationship  between  the  OECD’s  measure  of  corruption 
perceptions (citizen based) and our EQI corruption pillar via a scatterplot.  The OECD’s 
measure,  which  is  temporally  prior  and  focus  only  on  one  aspect  of  corruption  – 
whether  the  government  is  perceived  as  corruption  –  explains  roughly  72%  of  the 
variation in the EQI corruption pillar. 

34 

 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

Figure  11 :  the  EQI’s  corruption  pillar  and  OECD  measure  of  corruption 
perceptions 

8. General Observable patters over time 

QoG tends to be stable over time.  EQI index correlations coefficients across years are 
all  over  0.9,  as  shown  in  Table  9.    Moreover,  the  correlations  among  the  pillars  are 
also above 0.80.  Further analysis in the subsequent section show that just 11.5% and 
16.9%  of  units  in  the  sample  have  had  a  significant  change  (p<0.05  or  p<0.10 
respectively) in score (positive or negative) during the time span. This finding is quite 
consistent  with  country  level  data  that  shows  that  time  trends  in  governance  are 
remarkably  ‘sticky’  (Andersson  and  Heywood  2009).    However,  we  observe  that  the 
EQI of 2017 and 2010 are least correlated of the pairs, showing some signs of change 
over time. 

35 

 
 
 
 
 
 
 
 
Table 10: Pairwise Correlations of EQI and Pillars across time 

variable 

quality 
2017 

impartity 
2017 

corruption 
2017 

EQI 
2017 

quality 
2017 

impartity 
2017 

corruption 
2017 

EQI 
2013 

quality 
2013 

impartity 
2013 

corruption 
2013 

EQI 
2010 

quality 
2010 

impartity 
2010 

0.955 

0.973 

0.893 

0.965 

0.870 

0.920 

EQI 2013 

0.938 

0.894 

0.914 

0.907 

quality 
2013 

impartity 
2013 

corruption 
2013 

0.906 

0.887 

0.875 

0.859 

0.971 

0.919 

0.868 

0.907 

0.883 

0.978 

0.927 

0.918 

0.858 

0.889 

0.909 

0.973 

0.913 

0.934 

EQI 2010 

0.913 

0.862 

0.903 

0.875 

0.953 

0.922 

0.936 

0.927 

quality 
2010 

impartity 
2010 

corruption 
2010 

0.873 

0.843 

0.858 

0.824 

0.928 

0.922 

0.902 

0.887 

0.967 

0.885 

0.827 

0.887 

0.844 

0.907 

0.863 

0.914 

0.873 

0.973 

0.910 

0.900 

0.839 

0.884 

0.880 

0.939 

0.898 

0.909 

0.939 

0.971 

0.905 

0.922 

Note: Pearson’s pairwise correlation coefficient reported.  All correlations sig p<0.001 

Geography still matters: yet a bit less so in 2017 than in 2010.  In the earliest round 
of the  EQI, we  observed a clear  East-West pattern whereby all regions and countries 
of the former socialist bloc were below the EU28 mean in the EQI, with southern EU15 
states and regions (albeit more spread out in some cases) in the next group, followed 
by a group of strong performing northern countries and regions.  While this pattern to 
a  large  degree  persists,  there  are  several  cases  where  we  observe  that  areas  from 
newer member state (NMS13) have risen above the mean score: the Czech regions of 
Jihovychod  (CZ06),  Stredni  Morava  (CZ07)  and  the  country  of  Estonia  (EE).  
Moreover,  several  other  regions  have  made  considerable  progress  in  governance 
convergence,  namely  Prague  region  (CZ01);  several  regions  in  Poland  and  Lithuania 
have  all  progressed  near  the  EU28  average.    On  the  other  hand,  while  the  Northern 
parts of the EU28 have remained strong, the south of Europe has slid, led by decline 
in  Italian,  Greek  and  some  Spanish  regions,  yet  Portugal  has  made  some  slight 
increases since 2010.   

Countries  with  lower  QoG  tend  to  have  wider  divergence  of  QoG  at  the  sub-national 
level.  This is an observed trend that has remained since 2010.  Top performers, such 
as  Finland,  Denmark,  Sweden  and  the  Netherlands  show  no  significant  regional 
variation  (Åland  (FI20)  is  an  exception  as  it  is  an  outlier  in  scoring  so  high).    And 
despite  being  federal  countries,  places  like  Germany  and  Austria  have  much  less 
regional variation in their QoG than countries like Bulgaria or Czech Republic which are 
more politically and fiscally centralized (Hooghe, Marks and Schakel 2010).  Countries 
at  or  below  the  EU  mean  for  the  EQI  tend  to  be  the  ones  with  the  largest  regional 

36 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

variation  –  Italy,  Spain,  Bulgaria,  Czech  Republic,  Portugal  (in  some  cases),  with 
Belgium  and  France  being  somewhat  exceptional  in  this  case.    A  counter  example  is 
also Poland, which has 16 provinces with political significance, clear  East/West/South 
historical  differences  in  culture  and  development,  yet  has  consistently  shown 
moderate  levels  of  regional  variation.    However,  the  northern  region  of  Pomorskie 
(PL63)  has  made  improvements  and  is  significantly  stronger  than  most  other  Polish 
provinces in the data. 

Countries with persistent and large gaps in QoG: Italy, Belgium and France.  In these 
cases,  there  is  a  clear  geographic  divide  -  north-south  In  Italy  and  Belgium  in 
particular - that persists in each of the three rounds and is highly significant according 
to  our  margin  of  error  calculations.    In  Belgium  in  all  cases,  the  Flemish  speaking 
region of Vlaams Gewest (BE2) outperforms the Wallonie (BE3) region as well as the 
capital region of Brussels (BE1).  BE2 stands out in particular with citizen satisfaction 
of the quality of public services and how they are delivered impartiality, as this region 
is an EU28 leader on several indicators, while the other two regions are near or below 
the  EU  mean  on  such  indicators.    In  the  case  of  Italy,  the  south  is  a  consistent  low 
performer  both  within  Italy  and  throughout  the  EU28,  while  the  Northern  regions,  in 
particular  the  smaller  Alpine  regions  of  Trento,  Bolzano,  Friuli  and  Valle  d’Aosta,  are 
consistently higher performers on all underlying items and are above or just below the 
EU28  mean  score.    Although  in  the  2017  round  we  do  observe  a  small  trend  of 
convergence, as the northern regions show a modest decline. In France we observe a 
stand-out region in each of the three rounds:  Bretagne, with the western part of the 
country  in  the  next  group  of  region,  followed  by  the  northeast  and  southern  regions 
with the overseas regions lagging significantly behind.   

Countries with growing divergence in QoG: Spain and Czech Republic.  The 2017 data 
show a widening gap in the regions of the Czech Republic, and even more so in Spain.  
For the latter, Spain is now the country with the second most regional variation in the 
data  (behind  Italy).    In  fact,  while  the  country  average  in  the  WGI  declined  by  0.27 
(resulting  in  a  drop  of  6  places  in  the  national  rankings)  and  many  regions  showed 
declining  score  in  particular  in  the  south  (Andalucía,  Valencia),  several  northern 
regions  showed  improvement  in  their  EQI  scores  (Cantabria,  Navarra,  Pais  Vasco).  
Czech Republic showed several regions with significant improvement – Prague (CZ01), 
Jihovychod  (CZ06),  Stredni  Morava  (CZ07),  while  the  border  region  of  Severozapd 
(CZ04) has consistently lagged behind and  

9. Systematic Tests of Time Trends 

In this section, we seek to identify if units have shown a significant trend in a positive 
or  negative  direction  in  the  data  over  time.      This  is  useful  to  identify  potentially 
interesting case studies and to investigate policy ideas from recent success cases.  The 
data have been organized into a panel dataset for all regions covered with three years 
each.    While  three  years  in  a  panel  data  set  per  observation  is  of  course  difficult  to 
identify  a  clear  trend,  even  based  on  limited  observations,  we  can  do  a  simple  test 
that can help us reveal and possible time trends in the data.  We begin with a simple 
analysis  of  variance  (ANOVA)  test  in  Table  10  to  determine  whether  significant 
changes have occurred within groups over time.  For this, we run an ANOVA test  with 
an  interaction  term  between  the  year  and  a  dummy  variable  for  each  region  (along 
with  the  two  constituent  terms)  to  determine  if  any  trends  within  regions  are 
significant.  

37 

 
 
 
 
Table 11: Analysis of Variance (ANOVA) Test 

Observations 

573 

R2 

0.987 

Root MSE 

0.203 

Adj. R2 

0.960 

Partial  Sum 
of  squares 
(SS) 

Degrees 
of 
freedom 
(df) 

MS 

F-stat. 

Prob>F 

571.589 

382 

1.496 

36.48 

0.000 

Source 

Model 

Year 

0.024 

2 

0.012 

0.30 

Regional dummy 

15.685 

190 

0.083 

2.01 

Reg. dummy#year  15.673 

190 

0.082 

2.01 

0.744 

0.000 

0.000 

Residual 

   7.793 

190 

0.041 

Total 

579.381 

572 

1.013 

The results in the above table show that there is significant variance within the regions 
over  time,  as  the  interaction  term  between  regional  dummy  variables  and  time  is 
significant  (p=0.000).    We  now  proceed  to  time  series  data  regression  in  order  to 
elucidate  which  regions  in  fact  have  made  such  significant  changes  and  in  which 
direction the change was made.   

The regression model is specified as the following: 

𝐸𝑄𝐼𝑟𝑡 = 𝜑𝑟𝑁𝑟 + 𝜃𝑟𝑡 + 𝛽𝑟(𝑁𝑟 ∗ 𝑡) + 𝜀𝑟𝑡                                                      (1) 

Where EQI is the index used to capture institutional quality in region (or country) r in 
the year t (r = 1, 2,…. r, and t = 0, 1, and 2, which equate to 2010, 2013 and 2017), 
and where 𝑁𝑟 = 1 for region r and 0 if otherwise, and 𝜀𝑟𝑡 the error term. The constant 
term  is  omitted.    This  model  can  be  estimated  with  simple  ordinary  least  squares 
(OLS) regression, where 𝜑𝑟elucidates fixed spatial differences in levels of the EQI and 
𝜃𝑟  captures  the  fixed  time  effects  (e.g.  a  year  count).    The  interaction  term  (𝑁𝑟 ∗ 𝑡) 
thus  captures  temporal  trends  in  the  EQI  for  each  region.    As  regards  time  trends 
within regions over time, the null hypothesis states that there are no significant time 
trends  (e.g.  𝛽𝑟is  insignificant).  Where  we  observe  significant  trends  (positive  or 
negative)  from  the  baseline  year,  such  regions  can  be  considered  to  have  made  a 
significant  change  in  governance.    The  interpretation  of  𝛽𝑟  is  thus  the  average 
marginal change in the EQI for each region over the two years since the baseline year 
of 2010.    

Table 12 reports the results for those regions where we observe a positive significant 
result (p <0.10).  Regions in darker blue shade have made a positive trend at p<0.05, 
while  regions  in  lighter  blue  shade  have  made  a  change  at  the  90%  level  of 
confidence.    We  observe  that  14  regions  made  a  significant  positive  change  at  the 
95%  level  of  confidence  (or  greater)  and  eight  regions  made  a  change  at  the  90% 

38 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
 
 
 
 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

level  of  confidence.    Several  Bulgarian  and  Romanian  regions  have  made  positive 
advances  in  the  data  over  time,  mainly  due  to  their  very  low  rank  in  the  first  year.  
For example, Bucharest had the second lowest score (-2.84) in 2010, and increase to 
-2.46 and -1.58 in 2013 and 2017 respectively.  While this constitutes an increase by 
about 1.25 standard deviations, the region still remains in the bottom 10 percentile of 
regions  in  the  sample.    Similar  patterns  also  describe  the  other  Romanian  and 
Bulgarian  regions  on  this  list.    Other  regions,  such  as  Prague,  Pomorskie,  Lithuania 
and Jihovychod, have made more substantial climbs in the data.  Prague, for example 
has moved from -1.02 to -0.55 to -0.14 in the three respective years, going from the 
bottom  15th%ile  to  near  the  median.    Lithuania  is  the  only  country  to  make  a 
significant  advance  in  this  time  period  according  to  the  data,  an  improvement  that 
certainly  warrants  further  investigation.    Several  regions  from  the  EU15  are  also  on 
this top list, including Bayern, London, West Midlands and Hessen.   

Near the 90% level of significance is also the region of Navarra (not shown), which is 
one  of  the  most  interesting.    Despite  the  negative  trend  in  country  average  of  Spain 
over time, as well as the negative decline in many of the (Southern) Spanish regions, 
Navarra (as well as Cantabria and Pais Vasco), has in fact shown steady improvement 
over time from 0.07 to 0.26 to 0.51 in the three years of the data.    

Table 12: list of regions with significant positive changes in EQI 

Source 
model 
residual 
total 

sum of squares 
589.17 
8.49 
597.66 

d.f. 
399 
200 

MS 
1.48 
0.042 

obs 
F (399, 200) 
pr > F 
R2 
Adj R2 
Root MSE 

600 
34.76 
0.0000 
0.9858 
0.9574 
0.2061 

39 

 
 
 
 
 
 
 
 
   
 
 
 
   
 
 
 
 
 
 
number 

Nuts code  Region name 

Beta 

t-score 

p value 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

21 

22 

RO32 

Bucharesti 

0.671 

3.95 

0.000 

BG32 

Severen tsentralen 

0.642 

3.78 

0.000 

CZ01 

Prague 

0.468 

2.75 

0.003 

RO42 

Vest 

0.467 

2.75 

0.003 

PL63 

Pomorskie 

0.420 

2.47 

0.010 

LT 

Lithuania 

0.404 

2.38 

0.010 

DE2 

Bayern 

0.385 

2.27 

0.016 

RO31 

Sud-Muntenia 

0.373 

2.19 

0.016 

CZ07 

Střední Morava 

0.369 

2.17 

0.021 

PL22 

Slaskie 

0.361 

2.13 

0.024 

PL61 

Kujawsko-Pomorskie 

0.360 

2.12 

0.024 

PL51 

Dolnoslaskie 

0.359 

2.11 

0.033 

UKI 

London 

0.357 

2.10 

0.034 

CZ06 

Jihovychod 

0.348 

2.05 

0.037 

PL41 

Wielkopolskie 

0.317 

1.86 

0.059 

PL42 

Zachodniopomorskie 

0.313 

1.84 

0.067 

PL43 

Lubuskie 

0.309 

1.82 

0.070 

PL34 

Podlaskie 

0.301 

1.77 

0.073 

PL21 

Małopolskie 

0.296 

1.74 

0.079 

DE7 

Hessen 

0.291 

1.71 

0.082 

PL12 

Mazowieckie 

0.286 

1.68 

0.085 

UKG 

W. Midlands 

0.284 

1.67 

0.089 

On the other side of the coin, nine regions made a negative change at the 90% level 
of  confidence  or  greater,  shown  in  Table  13.    We  see  a  significant  decline  in 
governance  assessments  in  several  Italian  regions,  mostly  in  the  north  and  central 
part  of  the  country.    In  addition,  Hungary,  Spain  and  France  (overseas  regions)  and 
the Greek capital region of Athens have regions that have seen a significant decline in 
their  EQI  scores  over  time,  with  the  overseas  French  region  of  Guyane  showing  the 
largest decline in the data.    

40 

 
 
 
 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

Table 13: list of regions with significant negative changes in EQI 

Source 
model 
residual 
total 

sum of squares 
589.17 
8.49 
597.66 

d.f. 
399 
200 

MS 
1.48 
0.042 

obs 
F (399, 200) 
pr > F 
R2 
Adj R2 
Root MSE 

600 
34.76 
0.0000 
0.9858 
0.9574 
0.2061 

Number 

Nuts code  Region name 

Beta 

t-score 

p value 

1 

2 

3 

4 

5 

6 

7 

8 

9 

FR93 

Guyane 

-0.512 

-3.02 

0.000 

ITC2 

Valle d'Aosta 

-0.499 

-2.93 

0.000 

ITF1 

Abruzzo 

-0.431 

-2.54 

0.001 

ITC1 

Piemonte 

-0.394 

-2.32 

0.003 

ES70 

Canarias 

-0.396 

-2.33 

0.004 

EL3 

Athens 

-0.394 

-2.32 

0.005 

ES11 

Galicia 

-0.391 

-2.3 

0.005 

HU32 

Észak-Alföld 

-0.333 

-1.96 

0.009 

RO11 

Nord Vest 

-0.287 

-1.69 

0.033 

Figure  12  highlights  the  results  of  the  previous  two  tables  in  the  map  below.    In  the 
darker (lighter) blue shades, are the regions with a p-value for positive significance of 
𝛽𝑟  at  0.05  (0.10)  or  less.    In  the  darker  (lighter)  red  shaded  regions  are  the  regions 
with a p-value negative significance of  𝛽𝑟 at 0.05 (0.10) or less.    

41 

 
 
 
 
 
 
 
   
 
 
 
   
 
 
 
 
 
 
 
Figure 12: map of regions with a significant change in EQI 

In  the  next  two  tables,  we  highlight  all  significant  changes  –  positive  and  negative 
respectively  –  in  the  three  pillars.    Similar  color  shades  are  used  to  distinguish 
significance levels for the regions in each pillar. 

42 

 
 
 
 
 
 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

Table 14: Positive Changes in individual EQI pillars 

Nuts code 

Region name 

Beta 

t-score 

p value 

QUALITY 

BG32 

Severen tsentralen 

DE2 

ITC4 

ES30 

ES23 

Bayern 

Lombardia 

Madrid 

La Rioja 

BG34 

Yugoiztochen 

UKI 

LT 

London 

Lithuania 

CZ01 

Prague 

PL34 

ITF4 

Podlaskie 

Puglia 

RO31 

Sud-Muntenia 

IMPARTIALITY 

RO42 

Vest 

RO32 

Bucharesti 

PL63 

PL61 

Pomorskie 

Kujawsko-Pomorskie 

CZ07 

Střední Morava 

BG32 

Severen tsentralen 

CZ06 

Jihovychod 

ITF2 

Molise 

CZ01 

Prague 

DE7  

PL51 

PL22 

PL43 

Hessen 

Dolnoslaskie 

Slaskie 

Lubuskie 

CZ05 

Severovýchod 

UK22 

Wales 

43 

0.816 

0.594 

0.514 

0.499 

0.471 

0.452 

0.122 

0.388 

0.388 

0.381 

0.381 

0.379 

0.793 

0.762 

0.684 

0.631 

0.603 

0.566 

0.527 

0.489 

0.479 

0.478 

0.476 

0.462 

0.459 

0.450 

0.437 

3.69 

2.69 

2.32 

2.26 

2.13 

2.04 

2.32 

1.76 

1.75 

1.72 

1.72 

1.71 

3.34 

3.21 

2.88 

2.66 

2.54 

2.39 

2.22 

2.06 

2.02 

2.02 

2.01 

1.95 

1.93 

1.90 

1.84 

0.000 

0.008 

0.021 

0.025 

0.034 

0.042 

0.022 

0.080 

0.081 

0.086 

0.087 

0.088 

0.001 

0.002 

0.004 

0.008 

0.012 

0.018 

0.027 

0.040 

0.045 

0.045 

0.046 

0.053 

0.055 

0.059 

0.067 

 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
 
 
1.82 

1.82 

1.80 

1.80 

1.69 

1.66 

4.79 

2.40 

2.39 

2.17 

2.08 

2.03 

2.01 

1.98 

1.87 

1.86 

1.77 

1.75 

1.74 

1.69 

1.67 

0.071 

0.071 

0.073 

0.073 

0.093 

0.098 

0.000 

0.017 

0.018 

0.031 

0.038 

0.043 

0.046 

0.049 

0.063 

0.064 

0.078 

0.081 

0.084 

0.092 

0.097 

ES13 

Cantabria 

PL22 

PL62 

Slaskie 

Warmińsko-mazurskie 

0.428 

0.431 

0.431 

BG41 

Yugozapaden 

DE2 

BE1 

Bayern 

Brussels 

CORRUPTION 

RO32 

Bucharesti 

BG32 

Severen tsentralen 

CZ01 

Prague 

PL42 

Zachodniopomorskie 

LT 

PL41 

PL22 

ITF6 

Lithuania 

Wielkopolskie 

Slaskie 

Calabria 

RO42 

Vest 

BE3 

Wallonie 

RO31 

Sud-Muntenia 

UKI 

BE1 

London 

Brussels 

CZ06 

Jihovychod 

PL43 

Lubuskie 

0.427 

0.400 

0.394 

0.989 

0.496 

0.494 

0.448 

0.430 

0.420 

0.415 

0.410 

0.387 

0.385 

0.365 

0.362 

0.358 

0.350 

0.344 

44 

 
 
 
 
 
 
 
 
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

Table 15: Negative Changes in individual EQI pillars 

Nuts code  Region name 

Beta 

t-score 

p value 

QUALITY 

FR93 

Guyane 

-1.210 

-5.47 

0.000 

ITC2 

ITF1 

Valle d'Aosta 

-1.139 

-5.15 

0.000 

Abruzzo 

-0.803 

-3.63 

0.000 

HU31 

Észak-Magyarország 

-0.542 

-2.45 

0.015 

AT11 

Burgenland 

-0.533 

-2.41 

0.017 

UKN 

DE3 

N. Ireland 

-0.506 

-2.29 

0.023 

Berlin 

-0.531 

-2.40 

0.017 

FR91 

Martinique 

-0.486 

-2.20 

0.029 

FR92 

Guyane 

-0.479 

-2.17 

0.031 

HU21 

Közép-Dunántúl 

-0.406 

-1.84 

0.068 

ES70 

Canarias 

-0.432 

-1.96 

0.052 

BG33 

Severoiztochen 

-0.387 

-1.75 

0.081 

FR22 

Picardie 

-0.379 

-1.71 

0.088 

FR43 

Franche-Comté 

-0.376 

-1.70 

0.091 

FR93 

Guyane 

-0.639 

-2.70 

0.008 

HU32 

Észak-Alföld 

-0.468 

-1.97 

0.050 

ITC1 

Piemonte 

-0.452 

-1.91 

0.058 

ES11 

Galacia 

-0.410 

-1.73 

0.085 

ITF1 

Abruzzo 

-0.756 

-3.66 

0.000 

ES11 

Galacia 

-0.580 

-2.81 

0.005 

ITC3 

Liguria 

-0.493 

-2.39 

0.018 

ES30 

Madrid 

-0.484 

-2.34 

0.020 

ITF5 

Basilicata 

-0.460 

-2.23 

0.027 

ES70 

Canarias 

-0.437 

-2.11 

0.036 

BG34 

Yugoiztochen 

-0.425 

-2.06 

0.041 

ITG2 

Sardegna 

-0.417 

-2.02 

0.045 

45 

IMPARTIALITY 

CORRUPTION 

 
 
  
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
 
 
 
 
  
  
  
  
 
 
 
 
 
 
 
 
ITC2 

Valle d'Aosta 

-0.416 

-2.02 

0.045 

RO11 

Nord Vest 

-0.408 

-1.97 

0.050 

AT21 

Kärnten 

-0.354 

-1.72 

0.088 

ES53 

Illes Balears 

-0.351 

-1.70 

0.091 

ITF2 

Molise 

-0.344 

-1.67 

0.097 

Figure 13 shows the trend lines of the EQI for the country with the largest increase in 
within-country disparities from 2010 to 2017, Spain.  As the country average of Spain 
on the WGI has dropped six total places (from 13 to 19) from 2010 to 2017 within the 
EU28,  this  clearly  has  implication  for  the  EQI  scores  of  the  Spanish  regions  in  an  EU 
comparative  perspective,  as  regional  scores  are  centered  on  national ones.  While  we 
observe significant and negative time trends in several regions as shown previously – 
Canarias, Galicia, Andalucía and Illes Balears for example – we see opposite trends in 
the regions in the north, such as Navarre, La Rioja, Pais Vasco and Cantabria.  While 
the  individual  regions  at  the  top  of  Spain’s  rank  order  did  not  show  significant  and 
positive changes in the full analysis, this divergence is clearly interesting and possibly 
worth deeper investigation.   

Figure 13: Regional Trends in the EQI in Spanish Regions 

46 

 
 
 
 
 
 
 
 
 
 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

10. Exploring potential case study regions 

Based  on  the  analysis  above,  we  provide  a  list  of  potentially  interesting  case  study 
regions  for  further  investigation.    For  the  purposes  of  possibly  drawing  out  policy 
recommendations  and  ‘best  practices’,  we  focus  mainly  on  regions  that  have  made 
notable positive changes.  We then perform a basic analysis showing changes in socio-
economic indicators over the time period for which we have EQI data to compare the 
QoG trends with trends in other data. 

In all, the data highlights many potentially interesting case studies, from many of the 
EU  areas  and  in  both  older  member  states  and  newer  ones.  In  the  table  below,  15 
possible regions are highlighted.  Most come from the new member states, such as the 
Czech Republic, Poland, Bulgaria and Romania, as these areas have made substantial 
increases in their overall score over time.  It is worth noting that several other Polish 
regions  have  also  made  significant  positive  increases,  yet  the  two  on  the  list  below 
were the most apparent candidates. Moreover, the country of Lithuania is present, as 
it  has  made  the  most  significant  increase  in  governance  scores  of  all  member  states 
during  this  time.    Yet  two  German  regions  –  Bayern  and  Hessen  –show  significant 
improvements  as  well13.    In  addition  to  the  results  of  the  significance  tests,  three 
Spanish regions are also of interest as they have trended upward away from the rest 
of their country’s regions in the data.  The French region of Bretagne is also included 
for consideration due to its consistent top ranking within the French regions as well as 
being in the top 15-20% of EU regions in each of the years. 

13   The region of London also made significant improvements over time in several areas as well as the EQI 
on whole.  It was left of this list of possible case studies due to the unique status of London as a wealthy 
financial global city region which is also likely to have left the EU by the end of this project. 

47 

 
 
 
 
 
                                                 
Table 16: List of potential case study regions 

country  NUTS 

region 
name 

Bulgaria 

total 
change in 
EQI since 
2010 

area of sig. 
change 

politically 
relevant? 

reason 

BG32 

Severen 
tsentralen 

1.20 

All 

Czech 
Rep. 

5 

- 

no 
provinces 
within region 

CZ06 

Jihovychod  0.62 

EQI, 
Corr 

Imp, 

no  -  2  Kraj 
within region 

CZ01  Prague 

0.85 

EQI,  Qual, 
Corr 

yes 

Second  largest  change 
in  EQI 
time. 
Highest  ranked  RO  or 
BG region 

over 

highest ranked region in 
NMS  after  EE  and  over 
EU 
steady 
improvement 

average, 

highest  ranked  capital 
in  NMS, 
in 
almost  1  s.d.  in  EQI 
data over time 

increase 

CZ07 

Střední 
Morava 

0.66 

EQI, Imp 

no  -  2  Kraj 
within region 

strong 
over EU mean in Imp 

improvements, 

Germany  

France 

DE2 

Bayern 

0.69 

EQI,  Qual, 
Imp 

yes 

DE7 

Hessen 

0.50 

EQI 

yes 

FRHO  Bretegne 

-0.19 

none 

yes 

Lithuania    

LT 

Lithuania 

0.73 

EQI,  Qual, 
Corr 

yes 

ranked  DE 
Highest 
strong 
region, 
each 
improvements 
year.  2nd  highest  EU 
region in service quality 

steady 
year in EQI 

increase  each 

overall 

strong 
EQI 
performer,  consistently 
highest  in  FR.  drop  in 
2017  due  mainly  to  FR 
country drop 

country  with  most 
significant 
positive 
change over time 

48 

 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
  
 
  
 
 
 
 
 
 
  
 
  
  
  
  
 
  
 
  
  
  
 
  
 
 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

Poland 

Spain 

PL63 

Pomorskie 

0.76 

PL22  Slaskie 

0.64 

EQI,  Qual, 
Imp 

yes 

EQI,  Corr, 
Imp 

yes 

highest ranked region in 
PL, steady improvement 
each round 

ES22  Navarra 

0.43 

none 

yes 

ES23 

La Rioja 

0.10 

Qual 

yes 

ES13  Cantabria 

0.38 

Imp 

yes 

Romania 

of 

change  just  under  90% 
level 
confidence, 
steady  increase  in  EQI 
despite 
ES 
average decline 

strong 

increase 

sig 
remains 
despite 
average decline 

high 
strong 

in  Qual, 
EQI 
ES 

steady  increase  in  EQI 
despite 
ES 
average decline 

strong 

RO32  Bucharest 

1.26 

EQI, 
Cor 

Imp, 

2 

- 
no 
counties 
within region 

largest  pos.  change  in 
timespan 
EQI 
(although 
in 
started 
second lowest spot) 

over 

RO31 

Sud-
Muntenia 

0.66 

EQI,  Qual, 
Corr 

7 

- 
no 
counties 
within region 

ranked 

RO 
highest 
region,  increase  of  0.75 
s.d. since 2013 

We then examine these regions’ trend lines on two variables taken from Eurostat for a 
relevant  time  period  around  EQI  measurement  –  GDP  per  capita  (PPP  in  in  EU28 
average)  and  the  unemployment  rate.  In  addition,  we  show  national  trends  in  both 
variables so as to provide some context for the regional development.   

49 

 
  
  
  
  
 
  
 
  
  
 
 
 
 
 
 
  
 
  
 
 
 
 
 
 
  
 
 
 
 
Table 17: Changes in GDP per capita and unemployment over time  

Country level 

Regional level 

year 

GDPpc  Unemployment 

GDPpc 

Unemployment 

GDPpc 

Unemployment 

 CZ 

CZ06 

CZ07 

2007 

84.3 

5.2 

2008 

81.8 

4.3 

2009 

83.3 

6.5 

2010 

81.4 

7.2 

2011 

83.1 

6.6 

2012 

82.2 

6.8 

2013 

83.3 

6.9 

2014 

84.7 

6.1 

2015 

5.0 

74 

72 

74 

73 

75 

76 

79 

79 

 PL 

 PL22 

2007 

49.8 

9.8 

2008 

51.2 

7.2 

2009 

55.3 

8.5 

2010 

57.8 

9.8 

2011 

60.0 

9.8 

2012 

62.0 

10.2 

2013 

62.3 

10.5 

2014 

63.0 

9.1 

2015 

7.6 

ES 

2007 

100.4 

8.3 

2008 

99.3 

10.7 

2009 

98.7 

16.6 

2010 

95.1 

18.6 

56 

58 

63 

66 

69 

70 

70 

70 

ES22 

127 

126 

125 

120 

5.1 

4 

6.4 

7.5 

7 

7.4 

6.7 

5.8 

4.8 

8 

6.4 

6.6 

9.1 

9 

9.3 

9.6 

8.5 

7.1 

4.5 

6.3 

10.1 

11.5 

50 

64 

64 

66 

64 

67 

66 

67 

70 

PL63 

52 

52 

58 

59 

62 

65 

64 

64 

5.9 

4.8 

7.3 

8.7 

7.4 

7.4 

7.9 

6.7 

5.2 

9.4 

5.4 

6.4 

9.1 

8.3 

9.4 

9.9 

8.5 

6.5 

ES23 

110 

5.3 

109 

7.3 

108 

11.8 

105 

13.4 

 
 
 
 
  
  
  
 
 
 
  
 
  
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
  
  
  
  
  
 
  
 
  
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
  
  
  
  
  
 
  
 
  
 
  
 
 
 
 
 
 
 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

101 

16.4 

98 

98 

20.1 

19.7 

100 

17.7 

14.9 

DE7 

144 

7.2 

143 

6.4 

140 

6.3 

144 

5.7 

145 

4.6 

143 

4.7 

143 

4.3 

145 

4.3 

4 

2011 

91.8 

20.2 

2012 

89.4 

23.9 

2013 

88.4 

25.3 

2014 

88.7 

23.6 

2015 

21.4 

DE 

2007 

113.5 

10.3 

2008 

114.2 

9.0 

2009 

112.6 

9.0 

2010 

117.2 

7.9 

2011 

119.6 

6.8 

2012 

119.8 

6.4 

2013 

119.9 

6.1 

2014 

121.2 

5.8 

2015 

5.3 

117 

113 

112 

113 

DE2 

133 

131 

132 

138 

142 

143 

143 

145 

BG 

BG32 

2007 

38.6 

7.2 

2008 

41.3 

5.9 

2009 

41.7 

7.0 

2010 

40.9 

10.5 

2011 

41.4 

11.6 

2012 

42.7 

12.6 

2013 

42.4 

13.3 

2014 

43.2 

11.8 

2015 

9.5 

29 

30 

30 

29 

30 

32 

32 

34 

. 

12.6 

15.9 

17.6 

15.1 

13.5 

5.2 

4.2 

5 

4.3 

3.2 

3 

3 

2.8 

2.9 

10.2 

8.4 

7.9 

11.4 

12.5 

14 

15.2 

13.1 

10.5 

FR 

FR52/FRH0    

2007 

91.6 

9.4 

2008 

89.8 

9.0 

94 

89 

6.5 

4.8 

51 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
  
  
  
  
  
 
  
 
  
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
  
  
  
  
  
 
  
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
  
  
  
  
  
 
  
 
 
 
 
 
 
 
 
 
 
 
 
2009 

91.6 

10.5 

2010 

91.2 

10.6 

2011 

91.5 

10.7 

2012 

90.5 

11.3 

2013 

91.8 

11.9 

2014 

90.5 

11.8 

2015 

11.8 

90 

89 

90 

89 

90 

88 

RO 

RO31 

2007 

42.8 

6.2 

2008 

49.8 

5.6 

2009 

50.3 

6.7 

2010 

51.4 

7.0 

2011 

52.8 

7.2 

2012 

54.8 

6.7 

2013 

55.1 

7.1 

2014 

56.2 

6.7 

33 

39 

41 

41 

42 

41 

43 

43 

5.3 

6.5 

6.8 

7.7 

7.6 

7.1 

7.4 

8.2 

6.6 

7.9 

7.7 

10 

9.5 

9.5 

8.8 

RO32 

97 

3.8 

122 

3.2 

116 

3.6 

121 

4.5 

131 

5.2 

126 

6.4 

127 

8 

129 

6.9 

2015 

6.8 

10.1 

5.2 

LT 

2007 

60 

4.2 

2008 

63 

5.7 

2009 

56 

13.7 

2010 

60 

17.8 

2011 

65 

15.4 

2012 

70 

13.5 

2013 

73 

11.9 

2014 

75 

10.8 

2015 

9.2 

52 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
  
  
  
  
  
 
  
 
  
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
  
  
  
  
  
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

Below, we show visual trends of the EQI for the regions discussed (see previous figure 
for  Spanish  regions).    Regions  are  group  based  on  the  similar  starting  points  of  the 
EQI scores in 2010 for purposes of comparison.  

Figure 14: Trends in EQI among potential case study regions: Polish regions 

Here in Figure 14 we see a distinction between PL61 (Kujawsko-Pomorskie) which had 
one  spike  in  the  EQI  from  2010  to  2013,  yet  a  slight decrease  from  2013-2017,  and 
the  other  three  regions,  which  show  more  steady  increases.    The  most  consistent  is 
PL63 (Pomorskie), which now ranks as Poland’s highest region in the EQI 2017. 

53 

-1-.50-1-.500120.511.52PL22PL51PL61PL63EQI0=2010,  1=2013,  2=2017 
 
 
 
 
 
 
Figure 15 : Trends in EQI among potential case study regions: Czech regions 
and Lithuania 

As per Czech Republic and Lithuania most linear progression over time is that of CZ06 
(Jihovýchod),  yet  there  is  also  consistent  progress  as  well  in  the  other  two  Czech 
regions.    Lithuania  saw  more  of  a  mild  increase  between  the  first  two  years  (from  -
0.99 in 2010 to -0.81 in 2013), only to make a much larger one (-0.81 to -0.26) from 
2013 to 2017. 

54 

-1-.50-1-.50012012CZ01CZ06CZ07LTEQI0=2010,  1=2013,  2=2017 
 
 
 
 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

Figure 16: Trends in EQI: Romanian and Bulgarian Regions 

The  starting  point  of  the  Bulgarian  and  Romanian  regions  is  considerably  lower  than 
the other regions in this discussion, so room for marginal gains in the EQI over time is 
much  greater.    Given  this,  it  is  clear  the  BG32  (Severen  Tsentralen)  ,  which  made  a 
total  gain  of  roughly  1.2  standard  deviations  in  the  data  over  time  has  been  most 
consistent  –  moving  from  -2.20  in  2010  to  -1.67  in  2013,  then  to  -0.99  in  2017.  
Bucarest  (RO32)  which  saw  the  largest  leap  –  about  1.3  standard  deviations,  mostly 
made gains between 2013 and 2017 (an increase of +0.9 standard deviations).  

55 

-3-2.5-2-1.5-1-3-2.5-2-1.5-1012012BG32RO31RO32RO42EQI0=2010,  1=2013,  2=2017 
 
 
 
 
Figure 17: Trends in EQI: Bayern and London 

Between  the  two  regions  that  started  above  the  EQI  mean  in  2010  –  Bayern  and 
London,  we  see  clearly  from  the  figure  above  that  Bayern  in  Germany  has  made  the 
steadiest gains in the data over time – from 0.64 in 2010, to 0.95 in 2013 to 1.38 in 
2017.    Whereas  London,  which  has  increased  about  0.7  standard  deviations,  mostly 
gained from 2010 to 2013.   

56 

.511.5012012DE2UKIEQI0=2010,  1=2013,  2=2017 
 
 
 
 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

PART II: Qualitative Report on Quality of 
Government in EU regions 

A study of Lubelskie and Pomorskie regions in 
Poland, and Catalonia and Navarra in Spain 

Coordinators: Nicholas Charron and Victor Lapuente 

Authors:  

Paweł Chmieliński, Institute of Agricultural and Food Economics - National 

Research Institute, Poland. 

Barbara Wieliczko, Institute of Agricultural and Food Economics - National 

Research Institute, Poland. 

Angustias  Hombrado,  Universidad  Nacional  de  Educación  a  Distancia, 

Spain.  

11. Introduction: country and case selection 

In  this  qualitative  report,  we  conduct  a  research  in  two  medium-large  EU  member 
states: Spain and Poland. One is an older member, the other a newer one. One is from 
the West, the other from the East. Within each country we have selected two regions: 
one that has experienced a notable improvement in (perceived) quality of government 
since the first EQI (2010) to the latest (2017), and one that acts as “control” because 
it  does  not  seem  to  have  experienced  the  same  degree  of  upgrading.  Pomorskie 
(PL63) and Navarra (ES22) fall within the former category, and Lubelskie (PL31) and 
Catalonia (ES51) within the latter. 

Figure 18 Regions selected for the study. 

57 

 
 
 
 
 
 
As  we  can  see  in  Figure  18,  both  Pomorskie  (PL63)  and  Navarra  (ES22)  started  in 
2010 with a slight (in the Polish case) or remarkable (in the Spanish one) advantage 
over  Lubelskie  (PL31)  and  Catalonia  (ES51)  in  terms  of  (again,  perceived)  levels  of 
quality of government. Theoretically, and as we will see in the subsequent chapters on 
the Polish and Spanish regions, it could expected that Pomorskie could have a lead  – 
in  terms  of  good  government  –  over  Lubelskie,  due  to  their  different  historical 
trajectories.  Yet,  as  we  can  see  in  Figure  1.2,  in  the  first  place,  that  historical 
difference  does  not  translate  into  a  significantly  better  position  for  Pomorskie  in  the 
EQI  2010.  The  scores  of  Pomorskie  and  Lubelskie  were  virtually  identical.  What  the 
evolution of the EQI indicator reveals is that, during the latest years, there  seems to 
have  been  a  divergent  pattern  in  terms  of  levels  of  quality  of  government  in  the 
regions.  At  least,  that  is  what  the  citizens  in  those  regions  perceive.  And,  as  we  will 
see  in  this  report,  this  popular  view  by  the  citizens  seems  to  fit  quite  well  with  the 
expert view by the key actors interviewed in these regions.  

Figure 19. Evolution of the EQI in the selected regions. 

In  the  second  place,  if  historical  differences  mattered  hugely,  we  would  probably 
observe an advantage, in the Spanish case, of Catalonia, one of the, and definitely the 
iconic,  industrial  powerhouse  of  Spain  since  the  19th  century.  Yet,  on  the  contrary, 
Navarra  exhibits  a  significantly  higher  quality  of  government  already  in  2010  than 
Catalonia. And, if any, the evolution of the EQI indicates a growing gap between these 
two Spanish regions.  

As  we  have  noted  several  times,  the  measurement  of  the  EQI  in  a  given  region  is 
based  on  the  subjective  views  of  their  citizens  in  a  given  moment  in  time.  And 
particular events may deteriorate those opinions. For instance, one argument used to 
criticize  the  poor  performance  of  Catalonia  in  the  2010  EQI  –  when  Catalonia  was 
Spain’s  worst  performing  region,  and  shocked  numerous  media  and  influential 
observers  –  was  that,  when  the  survey  was  conducted  in  December  2009,  several 
high-profile  corruption  scandals  in  the  region  were  unveiled.  Consequently,  these 

58 

 
 
 
 
 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

notorious events could have led many Catalan respondents to underrate the quality of 
government  in  their  region.  Yet,  almost  a  decade  –  and  many  corruption  scandals 
uncovered  all  over  Spain  –  afterwards,  Catalonia  still  performs  relatively  poorly.  In 
particular,  the  distance  with  high-performing  Spanish  regions,  such  as  the  Basque 
Country  or  Navarra,  has  not  diminished,  but,  on  the  contrary,  increased  in  recent 
years.  The  question  is  thus  what  explains  those  divergent  paths  within  EU  member 
states? 

An encompassing answer to that question is out of the scope of this report, but, in the 
following two chapters, we explore the differences between these higher- and poorer-
performing  regions  based  on  the  views  of  experts  active  in  these  four  regions.  The 
methodology  employed  is  interviews  with  public  officials,  and  representatives  of 
business, media and civil society in the regions. We asked about, first, their evaluation 
of the quality of government in the region. And, as we will see, they largely agree with 
the position their region gets in the EQI. Secondly, we questioned them on which are, 
according to their views, the reasons behind the high (or low) quality of government in 
the  administrations  operating  in  their  region.  As  in  the  rest  of  the  EQI  study,  it  is 
important  to  remark  here  that  we  are  not  assessing  the  quality  of  the  regional 
government,  but  the  quality  of  the  governments  operating in  the  region, irrespective 
of  whether  they  depend  on  national,  regional  or  local  authorities.  We  are  concerned 
about what happens in the regions, not whose responsibility it is.  

12. Study of Lubelskie and Pomorskie regions in Poland 

By Paweł Chmieliński and Barbara Wieliczko 

1. 

Introduction  

Selection of regions and methodology  

The  European  Quality  of  Government  Index  (EQI),  2017  edition,  developed  by  the 
Quality  of  Government  Institute  of  Gothenburg  University,  shows  that  the  Polish 
regions  still  lag  behind  most  of  the  EU-15  counterparts.  Yet,  within  Poland  there  are 
certain differences in the value of the EQI and its changes over time.  

Pomorskie  region  (PL63)  was  chosen  for  the  case  study  as  it  was  among  the  most 
dynamically and positively changing regions in Poland and Central and Eastern Europe. 
Total  current  rating  of  this  region  is  46.5  points,  which  translates  into  109  places 
among  202  European  regions.  It    is  one  of  the  best  result  among  the  regions  in 
Poland,  especially  compared  to  2013,  the  position  of  the  region  clearly  increased. 
Pomorskie has the highest EQI in Poland (tab. 18). 

Lubelskie region (PL31) was chosen for the case study due to the fact that its level of 
EQI  did  not  change  in  the  period  2013-2017.  Its  score  is  35.7  and  it  ranks  147th 
among 202 EU regions . Lubelskie has to lowest EQI in Poland.  

59 

 
 
 
 
 
Table 18. EQI2017 in Lubelskie and Pomorskie 

Specification 

Lubelskie 

Pomorskie 

Score 

Rank 

Score 

Rank 

Quality pillar 

50.7 

131 

56.3 

110 

Impartiality pillar 

38.1 

159 

58.0 

101 

Corruption pillar 

38.0 

140 

41.7 

126 

EQI2017 

35.7 

147 

46.5 

109 

Source: Own elaboration based on 
http://ec.europa.eu/regional_policy/en/information/maps/quality_of_governance#2  

The  next  two  chapters  of  the  report  concern  the  two  case  study  regions  and  they 
present  the  key  characteristics  of  them.  Chapter  2  is  devoted  to  Lubelskie  region, 
while chapter 3 to Pomorskie region. 

The  finding  of  the  survey  conducted  in  Lubelskie  and  Pomorskie  are  analysed  in 
chapter  4.  The  analysis  of  the  factors  contributing  to  the  level  of  regional  quality  of 
governance  presented  in  this  chapter  was  conducted  by  looking  at  following 
dimensions of regional status quo:   

Institutions: Politics & political parties. 
Institutions: Public Administration. 
Institutions: judiciary.  

 
 
 
  Media. 
  Civil society.  
 

Impact of the EU. 

The  report  finishes  with  conclusions  on  the  factors  contributing  to  the  observed 
differences in quality of governance in the two analysed Polish regions. 

60 

 
 
 
 
 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

Figure 20. Poland and case study regions 

Source: 
https://www.google.pl/search?q=mapa+polski+wojew%C3%B3dztwa&tbm=isch&source=iu&ictx=1&fir=W
1DFo0Fk2oEEFM%253A%252C13VGe9nA31uWlM%252C_&usg=__48Jahv07m89s43Ib3LxVludOqlk%3
D&sa=X&ved=0ahUKEwj8xva-2PDbAhVCOJoKHSNWAtkQ9QEIKjAA#imgrc=W1DFo0Fk2oEEFM: 

2. 

Description of Lubelskie region   

Polish  regions  in  their  current  boarders  and  powers  granted  to  regions  were 
established  in  1999  after  the  administrative  reform  which  significantly  changes  the 
responsibilities  and  powers  of  non-central  authorities.  In  the  period  1975-1998  there 
were 49 regions (voivodships) in Poland. Since 1999 there have been only 16 of them. 

1.  Historical background 

The history of the region as part of Poland started already in middle ages. Lublin, the 
region’s  capital,  was  founded  in  1317.  It  remained  part  of  Poland  until  the  3rd  
partition of Poland in 1795 when it became part of Austria. 

The high degree of regional inequalities in Poland has its historical roots dating back to 
the  19th  century.  Poland  was  divided  between  Prussia,  Austro-Hungary  and  Russia. 
Separate  development  conditions  of  these  three  territories  have  left  deep  traces  that 
have manifested itself in the level of economic growth, infrastructure networks (roads, 
railways),  the  level  of  urbanization,  the  legal  system,  the  level  of  education,  the 
behaviour  of  the  population  and  the  cultural  landscape.  These  differences  were  not 
offset  by  the  Second  Polish  Republic,  especially  as  the  modernization  efforts 
undertaken in the east were interrupted by the Second World War. 

With  regard  to  agriculture  and  rural  areas,  the  nature  of  regional  diversity  is  also 
rooted  in  the  agrarian  changes  taking  place  over  almost  200  years  of  the  turbulent 
history of  Poland. Reforms giving the peasants ownership of land were the beginning 

61 

 
 
 
 
of  the  process  of  transformation  of  agriculture  from  the  feudal  era  and  the  capitalist 
economy changing the agrarian structure. These reforms took place during more than 
100-year period of partitions, where individual parts of the country were incorporated 
into  various  state  organisms.  The  effect  was  that  these  reforms  were  carried  out  not 
only in three different periods of the nineteenth century (the appropriation took place 
over  50  years),  but  also  took  into  account  the  differences  in  the  law  of  the  three 
invader  countries.  The  conditions  for  running  a  farm  in  various  parts  of  Poland  were 
under  the  agricultural  law  of  Prussia,  Austria  and  Russia.  Also,  the  development  and 
character of agriculture in the areas of particular annexations was conditioned by the 
culture and the model of the agriculture prevailing in a given country. In the Prussian 
area, the appropriation was associated with concentration in agriculture, and thus with 
the creation of a smaller number of farms operating on large areas of land (over 67% 
of  agricultural  land  was  concentrated  in  farms  with  an  area  of  over  20  ha).  In  the 
other  regions  the  appropriation  of  land  to  peasants  did  not  bring  any  changes  in  the 
agrarian  structure,  which  was  characterized  by  agrarian 
fragmentation  and 
overpopulation. In the  Austrian part of Poland the land use status characterized by a 
mosaic  of  small  plots  of  land;  while  in  the  Russian  part  a  polar  structure  began  to 
form: on the one hand, small farms of appropriated peasants, and on the other, arable 
farms owned by the gentry. The policy of the invaders in the Polish lands, the manner 
and different dates of the enfranchisement reform carried out also affected agricultural 
culture  and  production  efficiency  in  particular  areas.  In  the  Prussian  partition,  where 
the  reform  took  place  at  the  earliest,  patterns  of  capitalism  and  agricultural  culture 
characteristic  of  Western  Europe  were  disseminated,  while  in  the  Russian  partition, 
where the reform was carried out at the latest, the authorities sought to minimize land 
ownership by the privileged gentry14. 

Differences  in  the  way  the  land  reform  was  carried  out  in  individual  parts  of  Poland 
permanently  set  the  limits  for  post-partition  diversification  of  agriculture  in  Poland. 
The effects of it are visible not only in the modern agrarian structure, but also in the 
regional differences of the socio-economic level of development15. 

A  significant  impact  on  the  scale  of  diversification  in  the  socio-economic  structure  of 
rural  areas  in  Poland  was  caused  by  changes  in  borders  as  a  result  of  the  Potsdam 
Conference  of 1945. From the point of view of agricultural development, it should be 
emphasized  that  the  amount  of  land  used  for  agriculture  in  Poland  decreased  by 
15.5%  (  up  to  21,656  thousand  ha),  with  up  to  a  third  of  all  agricultural  land  in  the 
newly connected  western and northern territories to Poland. With these  changes, the 
post-war  map  of  Europe  involved  a  massive  resettlement  action  of  about  3  million 
Germans and about 1.5 million Poles from the territories of the then USSR. 

14   B. M. Wawrzyniak: Przemiany struktury agrarnej w rolnictwie polskim (Transformation of the agrarian 

structure in Polish agriculture), WTN, Włocławek 2004, 25-27. 

15   See:  E.  Gorzelak,  Sytuacja  ekonomiczna  gospodarstw  rolnych  w  ujęciu  przestrzennym  przed  i  po 
zjednoczeniu Polski z Unią Europejską [The economic situation of farms before and after the unification 
of Poland with the European Union], In: Dostosowywanie polskiego rynku rolnego do wymogów Unii 
Europejskiej [Adjusting the Polish agricultural  market to the  requirements of the European Union],  A. 
Kowalski (ed.), IAFE, Warsaw 2003, pp. 77-79. 

62 

 
 
 
 
                                                 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

Figure 22. Historical regions in Poland 

Source: Chmieliński (2006). 

After the Second World War, the state policy aimed at creating a centrally controlled 
economy  resulted  in  the  creation  and  promotion  of  the  development  of  state-owned 
farms and agricultural production cooperatives. This is a subject that goes beyond the 
scope of this study, nevertheless to show the sources of spatial diversity of agriculture 
in the country, undoubtedly affecting the development of rural areas. 

Lubelskie  was  also  active  in  the  process  leading  to  the  system  transformation  in 
Poland. In July 1980 this were the  factories in this region where the strike broke  out 
and in August 1980 were followed up by workers in other regions. 

Due to historic developments and its peripheral position the region remains one of the 
poorest Polish regions. Today, it is classified by the Commission as a ‘lagging region’16 

16 

https://ec.europa.eu/regional_policy/en/information/publications/reports/2017/economic-challenges-of-

lagging-regions  

63 

 
 
 
 
 
                                                 
2.  Structural conditions 

Lubelskie  is  located  in  eastern  part  of  Poland.  Its  eastern  border  is  Poland’s  state 
boarder. It boarders with Ukraine and Belarus – both non-EU countries.  

The  current  area  of  the  region  consists  of  the  former  Lublin,  Chełm,  Zamość,  Biała 
Podlaska and (partially) Tarnobrzeg and Siedlce Voivodeships. 

The  area  of  Lubelskie  region  is  25,122  km2  which  amounts  to  8%  of  Poland’s  area, 
which  gives  it  3rd  place  in  the  ranking  of  regions  based  on  their  area.  In  2016  the 
number of its inhabitants was 1.017 million. The density is much lower than the Polish 
average – 85 and 123 people per 1 km2, respectively. 

Lubelskie region’s share in the Polish GDP amounts to 3.8%. Service sector constitutes 
the  most  impoartant  contributer  to  the  region’s  GDP.  Agriculture’s  share  in  the  GDP 
amounts to 4.4%, while industries to 22.2% (fig. 23). 

Figure 23. Structure of the GDP in Lubelskie region  

28,4

9,5

4,4

28,2

22,2

7,2

Agriculture

Industry

Construction

Trade

Financial and insurance
activities; real estate
activities
Other services

Source: Own elaboration based on Statistical Yearbook of the Lubelskie Voivodship 2017. 

The  fact  that  the  region  lags  behind  most  of  other  Polish  regions  is  clearly  visible  in 
the  GDP per  capita. Despite the gradual increase in the  GDP per capita in the period 
2010-2015,  the  region  did  not  reduced  the  gap  between  its  GDP  per  capita  and  the 
national average (tab. 19). 

Table 19. GDP per capita in current prices in Lubelskie region 

in EUR 

Poland = 100 

Region 

2010  2014 

2015 

2010 

2014 

2015 

Lubelskie  6,469  7,797 

8,019 

69.0 

69.8 

68.5 

Poland 

9,381  11,172  11,698  100.0 

100.0 

100.0 

Source: Own elaboration based on Statistical Yearbook of the Regions – Poland, 2017. 

64 

 
 
 
 
 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

The age structure of the region’s population is similar to the national average. During 
the  recent  decade  it  kept  a  slightly  higher  share  of  the  population  of  less  than  25 
years, which is related to a higher than the national average share of rural population 
characterised  in  the  whole  country  by  a  higher  share  of  young  people.  Yet,  at  the 
same  time  the  region  has  a  bit  higher  share  of  people  over  65  years  which  can  be 
attributed to outmigration of the people in the search for employment (fig. 24). 

Figure 24. Structure of the population in Lubelskie region, by age 

Source: Own elaboration based on Statistical Yearbook of the Regions – Poland, 2017. 

The  structure  of  the  employment  in  Lubelskie  region  is  not  similar  to  the  Polish 
average  structure.  The  importance  of  the  agricultural  sector  is  the  most  vivid  and 
distinctive  characteristic  of  this  region  making  it  the  most  agricultural  region.  The 
share of agriculture amounts to over 37%, which is over twice the Polish average (tab. 
20).  At  the  same  time  the  share  of  industry  in  the  employment  is  significantly  lower 
than  the  country  average  amounting  to  only  17.5%  while  the  national  average  is 
26.5%. 

Table  20.  Structure  of  the  employment  in  Lubelskie  region  and  Poland  (in 
2016) 
Sector 

Lubelskie 

Poland 

agriculture, forestry and fishing 

industry and construction 

trade 

financial & insurance and real estate services 

other services 

16.0 

26.5 

25.0 

3.8 

28.7 

37.2 

17.5 

18.4 

2.5 

24.4 

Trade also includes: repair of motor  vehicles; transportation and storage;  accommodation  and 
catering; information and communication 
Source: Own elaboration based on Statistical Yearbook of the Regions – Poland, 2017. 

65 

0%20%40%60%80%100%Lubelskie 2005Poland 2005Lubelskie 2010Poland 2010Lubelskie 2015Poland 2015Lubelskie 2016Poland 2016>6525-65<25 
 
 
 
In the last decade the unemployment rate in the region showed a different trend than 
in  Poland  as  a  whole.  In  2005,  the  unemployment  rate  in  the  region  was  lower  than 
the  national  average  which  can  be  attributed  to  the  importance  of  agriculture  in 
employment and the related to it phenomenon of the hidden unemployment. Yet, the 
growth of the region’s economy did not keep pace with the national average and this 
is  reflected  in  the  higher  unemployment  rate  as  compared  with  the  Polish  average 
(tab. 21). 

Table 21. Unemployment rate in Lubelskie and Poland (in %) 

 Region  2005 

2010 

2015 

2016 

Lubelskie  17.0 

13.1 

11.7 

10.3 

Poland 

19.2 

12.3 

8.9 

7.1 

Source: Own elaboration based on Statistical Yearbook of the Regions – Poland, 2017. 

Lubelskie  region  is  characterised  by  a  higher  share  of  people  under  extreme  poverty 
line which is set on the basis of the subsistence minimum estimated by the household 
expenditures.  In  the  period  2005-2016  it  fell  by  almost  a  half,  but  the  drop  in  the 
share  of  people  experiencing  extreme  poverty  in  Poland  was  even  bigger  thus 
increasing the gap between the Polish average and Lubelskie region (fig. 25). 

Figure 25. Share of population under extreme poverty line in Lubelskie 

16

14

12

10

8

6

4

2

0

Lubelskie

Poland

2005

2010

2015

2016

Source: Own elaboration based on Statistical Yearbook of the Regions – Poland, 2017. 

The development of the region can be expressed also by the changes in environmental 
protection.  As  an  important  indicator  the  share  of  people  connected  to  wastewater 
treatment can be named. In the period 2005-2016 the share of population connected 
to  wastewater  treatment  in  Lubelskie  region  grew  but  the  growth  was  much  lower 
than  in  the  Polish  average.  Moreover,  it  started  from  a  significantly  lower  base.  This 
means that Lubelskie still lags behind the rest of Poland when it comes to wastewater 
treatment.  Yet,  when  we  take  into  account  only  the  urban  population  the  share  of 
people  connected  to  wastewater  treatment  is  almost  the  same  as  the  national 
average.  The  problem  is  the  share  of  connected  rural  population  which  is  not  much 
more than a half of the Polish average (fig. 26). 

66 

 
 
 
 
 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

Figure 26. Share of population connected to wastewater treatment (per cent 
of total population; urban & rural in 2016) 

100

90

80

70

60

50

40

30

20

10

0

Lubelskie

Poland

2005

2010

2015

2016

Urban

Rural

Source: Own elaboration based on Statistical Yearbook of the Regions – Poland, 2017. 

3.  Regional autonomy and funding  

The  responsibilities  of  different  administrative  levels  in  Poland  vary.  Therefore,  the 
scale  of  their  revenue  and  expenditure,  as  well  as  their  structure  vary.  Regions 
(voivodships)  are  divided  into  powiats  and  cities  with  powiat  status,  while  there  are 
divided  into  gminas.  For  an  analysis  of  the  scale  of  funds  in  the  region  it  is  worth 
comparing  the  per  capita  revenues  and  expenditure  between  the  region  and  national 
average. 

Gminas  in  lubelskie  have  lower  revenue  per  capita  than  the  Polish  average  and  the 
same  applies  to  expenditure.  This  is  a  result  of  the  economic  situation  of  the  region 
which  is  even  more  visible  when  we  compare  the  share  of  own  revenue  in  the  total 
revenue. It is app. a quarter lower than the Polish average showing that the economic 
potential of the region lags behind the Polish average. 

The same applies to the next level of administrative system  – powiats and cities with 
powiat status. Both revenue and expenditure at this level is lower in Lubelskie region 
than the Polish average. 

Only  in  the  case  of  voivodship  –  the  region  the  situation  is  different.  The  per  capita 
revenue is by almost 9 p.p. higher than the Polish average. This is a result of the EU 
funds  directed  at  the  region.  Lubelskie  not  only  has  its  EU  co-financed  regional 
programme,  but  it  also  benefits  from  the  Eastern  Poland  EU  co-financed  programme 
that  is  targeted  at  five  Polish  eastern  regions  that  are  characterised  by  much  lower 
socio-economic development compared to the Polish average (tab. 22). 

67 

 
 
 
 
 
 
Table  22.  Revenue  and  expenditure  per  capita  at  different  administrative 
levels in Lubelskie region 
Administration 

Lubelski

Polan

level 

 Indicator 

Revenue per capita in EUR 

Gminas 

Poland = 100 

total own revenue per capita in EUR 

of  the  total  -  own  revenue  in  %  of  total 

revenue 

Expenditure in EUR 

Poland = 100 

Revenue in EUR 

Cities 

with powiat status 

Poland = 100 

Powiats 

Expenditure in EUR 

Poland = 100 

Revenue in EUR 

Poland = 100 

Expenditure in EUR 

Poland = 100 

Voivodship 

Revenue in EUR 

Poland = 100 

Expenditure in EUR 

Poland = 100 

e 

917 

93.0 

312 

34.0 

886 

93.2 

d 

986 

100.0 

437 

44.3 

951 

100.0 

1,315 

1,475 

89.2 

100.0 

1,311 

1,426 

91.9 

225 

97.1 

221 

97.8 

96 

100.0 

232 

100.0 

226 

100.0 

88 

108.7 

100 

86 

82 

104.8 

100.0 

Expenditure in EUR: exchange rate: 1 EUR = 4 PLN 
Source: Own elaboration based on Statistical Yearbook of the Regions – Poland, 2017. 

It  is  also  worth  to  analyse  the  structure  of  funds  at  the  gminas  level.  There  are  two 
key  categories  of  these  funds  –  own  revenues  and  transfers  from  the  state  budget. 
Generally  in  Poland  over  59%  of  funds  in  the  gminas  bugets  are  the  money 
transferred from the state budget. In Lubelskie the own revenue amount to app. 31% 
of  the  gminas  budgets  which  means  that  the  share  of  own  revenue  is  by  almost  a 
quarter lower than the Polish average. This is due to a much lower role played by the 
funds stemming from gminas’ share in the taxes collected in the region (tab. 23). 

68 

 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

Table 23. Structure of gminas budgets in Lubelskie region and in Poland (%) 
 Source of funds 

Lubelskie 

Poland 

Own revenue 

CIT 

PIT 

tax on real estate 

agricultural tax 

tax on means of transport 

0.47  0.84 

13.60  18.00 

9.16  13.68 

2.81  1.62 

0.64  0.79 

tax on civil law transactions 

0.83  1.05 

stamp duty 

revenue from property 

revenue from services 

0.16  0.18 

1.55  3.10 

1.54  1.64 

for government administration tasks  29.83  25.88 

Targeted grants from the state budget 

for own tasks 

General subvention from the state budget 

remaining part 

educational part 

5.17  4.67 

22.40  21.24 

11.83  7.32 

Source: Own elaboration based on Statistical Yearbook of the Regions – Poland, 2017. 

3. 

Description of Pomorskie region 

1.  Historical background 

The region became part of Poland already in 960 during the reign of the Poland’s first 
historic  ruler  –  Mieszko  I.  The  beginning  of  the  14th  century,  the  Brandenburg  army 
took  over  the  region.  Polish  king  asked  for  help  the  Teutonic  Knights  for  help. 
However, they took the land. The region was in the hands of the Teutonic Knights for 
many years. Strengthening their position and rule in the lands they gained, they built 
castles. The most magnificent was in Malbork. Until the mid-15th century, it served as 
the capital of the monastic state. 

After the Thirteen Years' War, which ended in 1466, the region returned to Poland. In 
the  16th  and  17th  centuries,  Gdańsk  became  the  most  wealthy  land  of  the  Polish-
Lithuanian  Commonwealth.  A  center  of  culture,  art  and  above  all  crafts.  Gdańsk 
prevailed, endowed with many economic and self-government privileges. The city set 
trends in Polish trade and step by step it became a monopoly in this field. 

In  1772  there  was  the  First  Partition  of  Poland.  Most  of  the  region  was  taken  by 
Prussia.  In    the  Second  Partition  of  Poland  (1793),  among  others  Gdańsk  and  Toruń 
became part of Prussia. 

After  the  World  War  I  Gdańsk  became  a  Free  City  under  the  protectorate  of  the 
League of Nations. The Treaty of Versailles restored Poland to a part of the region with 
a  small  72-kilometer  access  to  the  Baltic  Sea.  Poland,  having  no  real  access  to  the 
port in Gdańsk, built a new port in the neighboring Gdynia. 

After  the  World  War  II  many  of  the  Germans  left  the  region,  while  numerous  Poles 
from  the  regions  which  became  part  of  the  Soviet  Union  came  to  life  in  Pomorskie 
Region. 

During the socialist era in Poland (1945-1989) the region was an important industrial 
hub  specialized  in  such  branches  of  the  industry  as:  electromachinery,  energy, 
chemicals, furniture and food. The concentration of workers was partly responsible for 
the fact that Gdańsk and Gdynia were among the cities where the Polish 1970 protests 

69 

 
  
  
  
  
  
  
  
  
 
took place. In December 1970 the government suddenly announced massive increases 
in the prices of basic foodstuffs. The public reacted with a protest. People gathered at 
rallies,  demanding  the  authorities  to  withdraw  the  increase  in  prices,  to  regulate  the 
pay system (in particular, the rules for calculating bonuses), and finally to remove the 
authorities  responsible  for  the  increase.  The  government  used  the  army  and  other 
military  forces  to  pacify  the  protesters.  As  a  result  there  were  many  people  injured 
and over 40 people killed – in Pomorskie region 24. 

In August 1980 the shipyard in Gdańsk played a key role in the strikes taking place in 
numerous  Polish  cities.  The  strikes,  as  in  December  1970,  were  a  response  to  the 
government’s  authorization  of  the  increase  in  food  prices.  This  time  the  government 
did  not  use  military  force  to  pacify  the  protesters  and  The  Gdańsk  Agreement  (or 
Gdańsk  Social  Accord(s)  or  August  Agreement(s)  was  signed  in  the  Gdańsk  shipyard 
by the leader of the Solidarity movement, Lech Wałęsa. It was an accord reached as a 
direct result of the strikes.  

The Gdańsk shipyard is considered to be the cradle of the Solidarity movement. 

2.  Structural conditions 

Pomorskie Region is located in north-western Poland at the Baltic sea. The provincial 
capital is Gdańsk. It includes former voivodeships of Gdańsk, Elbląg (partially), Słupsk 
(partially) and Bydgoszcz (partially). 

A  province  of  rich  cultural  heritage.  The  Tricity  urban  area,  consisting  of  Gdańsk, 
Gdynia and Sopot, is one of the main cultural, commercial and educational centres of 
Poland. Gdańsk and Gdynia are two of the major Polish seaports. 

Depending on the estimates, app. 500,000 people living in Pomorskie Region are the 
representatives of the ethnic group Kashubians. They speak the Kashubian language, 
which is classified either as a separate language closely related to Polish, or as a Polish 
dialect.  Among  larger  cities,  Gdynia  has  the  largest  proportion  of  people  declaring 
Kashubian  origin.  Over  80%  of  the  people  in  towns  such  as  Linia,  Sierakowice, 
Szemud, Kartuzy, Chmielno and Żukowo are of Kashubian descent. 

Pomorskie  Region  has  an  area  of  18,310  km2,  that  is  5.9%  of  the  whole  area  of 
Poland.  This  gives  Pomorskie  the  8th  place  in  the  ranking  of  regions.  At  the  end  of 
2016  it  was  inhabited  by  2,315,600  people  (6%  of  the  Polish  total  population).  Over 
64%  of  the  region’s  population  lives  in  urban  areas.  Population  density  is  slightly 
higher than for Poland – 126 persons/km2 (123 in Poland).  

The  regions  share  in  the  Polish  GDP  is  5.8%.  The  region’s  GDP  structure  shows  that 
services  constitute  the  most  important  part  of  the  region’s  economy  (fig.  27). 
Agriculture is responsible for only 2% of the region’s GDP, while industry for 27%. 

70 

 
 
 
 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

Figure 27. Structure of the GDP in Pomorskie region and Poland 

Trade also includes: repair of motor  vehicles; transportation and storage; accommodation and 
catering; information and communication 
Source: Own elaboration based on Statistical Yearbook of the Pomorskie Voivodship 2017. 

GDP  per  capita  in  Pomorskie  region  in  the  period  2010-2015  was  gradually  growing, 
but it remained lower than the national average (tab. 24). 

Table 24. GDP per capita in current prices in Pomorskie region 

in EUR 

Poland = 100 

Region 

2010  2014 

2015 

2010 

2014 

2015 

Pomorskie  9,004  10,640  11,239  96.0 

95.2 

96.1 

Poland 

9,381  11,172  11,698  100.0 

100.0 

100.0 

Source: Own elaboration based on Statistical Yearbook of the Regions – Poland, 2017. 

The age structure of the population in the region is similar to the Polish average. Yet, 
the  region  has  a  slightly  better  structure  of  the  population  than  the  country  as  a 
whole, because the share of people  over 65 is lower.  However, in recent decade this 
group’s  share  increased  in  Pomorskie  region  more  significantly  than  in  Poland 
(fig. 28). 

71 

2%27%9%28%9%25%AgricultureIndustryConstructionTradeFinancial and insurance activities; realestate activitiesOther services 
 
 
 
 
 
 
Figure 28. Structure of the population in Pomorskie region, by age 

Source: Own elaboration based on Statistical Yearbook of the Regions – Poland, 2017. 

The  structure of  employment in the  region differs from the Polish average. The most 
important  difference  is  the  much  lower  share  of  agriculture  in  total  employment.  It 
amounts  to  8.1%  which  means  that  it  is  app.  a  half  of  the  figure  for  Poland.  At  the 
same  time  the  share  of  all  the  other  sectors  is  in  Pomorskie  a  bit  higher  than  the 
national average (tab. 25). 

Table  25.  Structure  of  the  employment  in  Pomorskie  region  and  Poland  (in 
2016) 
Sector 

Pomorskie 

Poland 

agriculture, forestry and fishing 

industry and construction 

trade 

financial & insurance and real estate services 

other services 

16.0 

26.5 

25.0 

3.8 

28.7 

8.1 

29.0 

27.8 

4.4 

30.7 

Source: Own elaboration based on Statistical Yearbook of the Regions – Poland, 2017. 

The unemployment rate in region used to be higher than the Polish average due to the 
closure  of  numerous  factories.  Yet,  gradually,  the  region  made  bigger  use  of  its 
development potential and currently, the unemployment rate is lower than the Polish 
average – 7.1% (8.2% in Poland) (tab. 26). 

Table 26. Unemployment rate in Pomorskie and Poland (in %) 
2015 
 Year 

2010 

2005 

2016 

Pomorskie  19.2 

12.3 

Poland 

17.6 

12.4 

8.9 

9.7 

7.1 

8.2 

Source: Own elaboration based on Statistical Yearbook of the Regions – Poland, 2017. 

72 

0%20%40%60%80%100%Pomorskie 2005Poland 2005Pomorskie 2010Poland 2010Pomorskie 2015Poland 2015Pomorskie 2016Poland 2016>6525-65<25 
 
 
 
 
 
 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

In  2005  the  share  of  people  experiencing  extreme  poverty  in  Pomorskie  region  was 
higher than the average for Poland which is in line with the higher unemployment rate 
observed  in  the  region  at  that  time  as  compared  with  Poland  as  a  whole.  Yet,  the 
reduction in unemployment rate was not fully followed by Pomorskie in the reduction 
of the share of people classified to the extreme poverty group. Yet, in 2016 the region 
finally managed to reduce its share of people under poverty line to the one lower than 
the Polish average (fig. 29). 

Figure  29.  Share  of  population  under  extreme  poverty  line  in  Pomorskie 
region 

16

14

12

10

8

6

4

2

0

Pomorskie

Poland

2005

2010

2015

2016

Source: Own elaboration based on Statistical Yearbook of the Regions – Poland, 2017. 

The development of the region can be expressed also by the changes in environmental 
protection.  As  an  important  indicator  the  share  of  people  connected  to  wastewater 
treatment can be named. The share of population connected to wastewater treatment 
in Pomorskie region was significantly higher than the national average all through the 
period  2005-2016.  Yet,  the  difference  between  Pomorskie  and  the  Polish  average 
decreased  showing  that  other  regions  made  bigger  progress  than  Pomorskie.  Yet,  it 
must be emphasized that the region is characterized by much higher than the national 
average  share  of  rural  population  connected  to  wastewater  treatment.  This  is 
important  also  due  to  the  fact  that  costal  part  of  the  region  is  an important  summer 
destination for tourists (fig. 30). 

73 

 
 
 
 
 
Figure 30. Share of population connected to wastewater treatment (per cent 
of total population; urban & rural in 2016) 

100

90

80

70

60

50

40

30

20

10

0

Pomorskie

Poland

2005

2010

2015

2016

Urban

Rural

Source: Own elaboration based on Statistical Yearbook of the Regions – Poland, 2017. 

3.  Regional autonomy and funding 

All Polish regions have  the same status. Poland is a  unitary state and the  regions do 
not  have  much  room  for  undertaking  their  own  political  agenda.  Yet,  the 
administrative reform implemented in 1999 gave them more power on managing their 
affairs and development. 

Administrative  power  in  the  Polish  regions  is  exercised  by  both  the  regional  self-
government  and  government  and  bodies  of  central  government  administration.  The 
self-government  operates  on  the  basis  of  the  Act  of  5  June  1998  on  the  voivodship 
self-government.  There  is  a  regional  assembly  (Sejmik)  elected  in  general  and  direct 
election  for  a  period  of  time  4  years  .  The  Marshal's  Office,  headed  by  a  marshal 
elected by the regional assembly, serves as the regional executive branch of authority. 

The  central  authorities  are  represented  by  the  regional  office,  headed  by  a  voivode 
appointed  by  the  prime  minister  who  exercises  supervision  over  the  legality  of 
operation voivodship self-government. 

The  basic  task  and  goal  of  the  self-government  is  to  define  the  voivodship 
development  strategy  and  conduct  the  regional  development  policy.  The  local 
government  cooperates  in  this  area,  among  others  with  local  self-government  units 
(municipal  offices,  cities,  starosts),  voivode  as  a  representative  of  state  power,  non-
governmental  and  voluntary  organizations,  universities  and  scientific  and  research 
units, other voivodships, as well as with organizations and regions of other countries. 

The  assembly  of  Pomorskie  region  consists  of  33  councilors.  In  Lubelskie  there  are 
also 33 councilors. The number of councilors depends on the number of inhabitants of 
the  voivodship.  In  regions  with  a  population  of  less  than  2  million,  there  are  30 
councilors for each additional 500 thousand there are three more councilors. 

The key competences of the regional assembly include: 

  drafting  local  law,  in  particular:  statute  of  the  voivodship,  principles  of 
managing  region’s  property,  rules  and  mode  of  using  regional  facilities  and 
public utilities; 

74 

 
 
 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

  adopting  the  region’s  development  strategy  and  multi-annual  regional 

programmes; 

  adopting a spatial development plan; 
  adopting  a  resolution  regarding  the  mode  of  work  on  the  draft  budget 

resolution; 

  adopting  a  resolution  regarding  the  detailed  implementation  of  the  region’s 

budget; 

  adopting the region’s budget; 
  defining the rules for granting subsidies from the region’s budget; 
  adopting regulations regarding taxes and local fees; 
  adopting  resolutions  on  entrusting  tasks  of  the  voivodship  self-government  to 

other local government units; 

  adopting priorities of foreign cooperation of the region; 
  undertaking decisions concerning issuing bonds and taking long-term loans and 
credits  as  well  as  determining  the  maximum  amount  of  short-term  loans  and 
credits taken by the voivodship board and the  maximum amount of loans and 
guarantees granted by the voivodship board. 

The  five-person  board,  headed  by  the  region’s  marshal,  is  the  executive  body.  The 
Board also  consists of  one or two deputy speakers and three  or two members of the 
board. All are elected by the regional councillors, but they do not have to belong to it - 
they may come from outside of the regional council. 

The  responsibilities  of  different  administrative  levels  in  Poland  vary.  Therefore,  the 
scale  of  their  revenue  and  expenditure,  as  well  as  their  structure  vary.  Regions 
(voivodships)  are  divided  into  powiats  and  cities  with  powiat  status,  while  there  are 
divided  into  gminas.  For  an  analysis  of  the  scale  of  funds  in  the  region  it  is  worth 
comparing  the  per  capita  revenues  and  expenditure  between  the  region  and  national 
average. 

In the case of Pomorskie region the per capita revenue and expenditure of gminas is 
significantly higher than the Polish average, yet the share of own revenue in the total 
revenue was slightly lower than the country average. 

In  the  case  of  cities  with  powiat  status  the  situation in  Pomorskie  region  was  almost 
the  same  as  the  Polish  average,  while  in  the  case  of  powiats  both  the  revenue  and 
expenditure  was  substantially  higher  than  the  Polish  average.  At  the  voivodeship  the 
level of revenue and  expenditure was lower than the Polish average.  This indicates a 
mixed  financial  situation  of  the  region  as  a  whole  in  comparison  with  the  Polish 
average (tab. 27). 

75 

 
 
 
 
 
 
Table  27.  Revenue  and  expenditure  at  different  administrative  levels  in 
region 
Administration 

Pomorski

Polan

level 

 Indicator 

per capita in EUR 

Gminas 

Poland = 100 

e 

d 

1,092 

986 

110.8 

100.0 

total own revenue per capita in EUR 

456 

437 

of  the  total  -  own  revenue  in  %  of  total 

revenue 

Expenditure in EUR 

Poland = 100 

Revenue in EUR 

Cities 

with powiat status 

Poland = 100 

Powiats 

Expenditure in EUR 

Poland = 100 

Revenue in EUR 

Poland = 100 

Expenditure in EUR 

Poland = 100 

Voivodships 

Revenue in EUR 

Poland = 100 

Expenditure in EUR 

Poland = 100 

41.8 

44.3 

1,052 

951 

110.6 

100.0 

1,476 

1,475 

100.1 

100.0 

1,409 

1,426 

98.8 

248 

100.0 

232 

106.7 

100.0 

244 

226 

108.1 

100.0 

79 

89.8 

76 

88 

100 

82 

93.0 

100.0 

Expenditure in EUR: exchange rate: 1 EUR = 4 PLN 
Source: Own elaboration based on Statistical Yearbook of the Regions – Poland, 2017. 

It  is  also  worth  to  analyse  the  structure  of  funds  at  the  gminas  level.  There  are  two 
key  categories  of  these  funds  –  own  revenues  and  transfers  from  the  state  budget. 
Generally  in  Poland  over  59%  of  funds  in  the  gminas  bugets  are  the  money 
transferred  from  the  state  budget.  In  Pomorskie  the  share  of  own  revenue  in  the 
gminas  budgets  is  34%,  which  means  it  is  by  almost  7  p.p.  lower  than  the  Polish 
average. This is due to a much lower role played by the funds stemming from gminas’ 
share in the taxes collected in the region (tab. 28).  

76 

 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

Table  28.  Structure  of  gminas  budgets  in  Lubelskie  Pomorskie  region  and  in 
Poland (%) 
 Source of funds 

Lubelskie  Pomorskie  Poland 

Own revenue 

CIT 

PIT 

0.47 

0.60 

0.84 

13.60 

14.04 

18.00 

tax on real estate 

agricultural tax 

tax on means of transport 

9.16 

2.81 

0.64 

tax on civil law transactions 

0.83 

stamp duty 

revenue from property 

revenue from services 

0.16 

1.55 

1.54 

11.49 

13.68 

1.22 

0.69 

1.12 

0.16 

3.32 

1.35 

1.62 

0.79 

1.05 

0.18 

3.10 

1.64 

Targeted grants 

for 

government 

from 

the  state 

administration tasks 

29.83 

25.24 

25.88 

for own tasks 

5.17 

3.82 

4.67 

educational part 

22.40 

20.45 

21.24 

budget 

General 

subvention 

from 

the  state 

budget 

remaining part 

11.83 

16.49 

7.32 

Source: Own elaboration based on Statistical Yearbook of the Regions – Poland, 2017. 

4. 

Comparative analysis  

The  study  involved  people  involved  in  the  socio-economic  life  of  the  regions: 
Pomorskie  and  Lubelskie.  In  individual  regions,  interviews  covered  respectively:  17 
people  in  Lubelskie  and  18  in  Pomorskie.  The  respondents  were  representatives  of 
many  professions:  economist,  brewer,  marketing  manager,  communication  officer, 
pensioner;  founder  of  the  NGO  and  chairman  of  the  board,  HR  manager,  culture 
animator, manager, entrepreneur, pensioner, consultant, beautician, lawyer, assistant 
to  the  director,  marketer,  engineer,  sailor,  public  officer  and  others.  The  study  was 
conducted  in  the  period  May-June  2018  using  the  direct  interview  method, 
supplemented with a questionnaire survey. 

This part presents a comparative analysis of the results of the questionnaire addressed 
to the participants of the socio-economic life of both selected regions. 

In  Lubelskie,  10  out  of  17  respondents  strongly  agreed  with  the  relatively  negative 
result of the assessment of their region in the QoG ranking in relation to the position 
of Poland and other regions, one person indicated that it partially agrees, and up to 5 
people could not formulate an opinion in this respect. 

In Pomorskie, as many as 15 out of 18 people agreed with the positive assessment of 
their  region,  one  respondent  pointed  to  the  lack  of  sufficiently  extensive  analysis  of 
factors  affecting  the  situation  in  other  regions  of  the  country,  and  another  2 
respondents  agreed  with  the  need  for  further  improvements  and  only  one  person 
could not formulate the opinion. Generally all respondents in Pomorskie confirmed the 
assessment  of  their  region  in  QoG  study.  The  approach  to  the  state  (government) 
resulting  from  the  historical  conditions  of  the  region's  development,  which  has  its 

77 

 
  
  
  
  
  
  
  
  
 
roots  in  different  influences  of  influence  during  (XVIII  and  XIX  century)  Poland’s 
partitions  (the  influence  of  the  Russians  in  Lubelskie  and  German  in  Pomorskie)  and 
the  varying  degree  of  influence  of  the  Catholic  church  in  particular,  is  of  great 
importance in the perception of local and regional authorities (clearly greater influence 
of religion on social life in the Lubelskie region). 

1.  Institutions 

It  should  be  emphasised  that  in  the  Pomeranian  region  respondents  relatively  better 
perceive public institutions in the region both in terms of level of their impartiality, as 
well  as  effectiveness  in  providing  public  services.  Also  in  the  Pomorskie  region,  the 
situation regarding the level of corruption in public administration was definitely better 
assessed. 

Amongst  the  main  reasons  for  the  situation  in  Pomorskie  region,  good  public 
management  and  close  cooperation  with  stakeholders  were  highlighted.  According  to 
respondents HR issues were of great importance in good management. 

One of the interviewee expressed the opinion that:  

“Pomorskie is known for its very high assessment of its local governments - especially 
the so  called Tricity (Gdansk, Sopot and Gdynia – agglomeration cities in the region) 
has  been  breaking  records  of  support  for  its  three  presidents  of  cities    in  local 
government  elections.  Presidents  have  been  in  office  for  several  terms.  Therefore, 
high region's ratings may result from high self-government work assessments.”  

One  also  emphasized  well-functioning  local  government,  economic  and  social 
traditions,  relatively  high  economic  resources,  as  well  as  transparency  and  level  of 
social  capital,  as  well  as  great  involvement  in  civil  affairs,  which  translates  into  a 
strong position of NGOs in the region. 

In turn, in the Lubelskie the position of the region in the ranking was motivated by a 
low  level  of  social  capital  and  economic  conditions  (low  level  of  physical  capital  and 
underdeveloped infrastructure). As one of the respondents emphasized:  

“The  weak  influence  of  civil  society,  certain  social  behaviours  that  have  been 
perpetuated over the years are not conducive to the development of civil society. The 
weak  economic  development  of  the  region  is  also  significant,  which  is  related  to  the 
fact that the region's potential is not used, which causes the outflow of population to 
other, more developed regions, especially young people.” 

The low level of economic development is combined with a low level of human capital, 
especially outside larger cities, which intensifies a large outflow of inhabitants to other 
regions,  especially  young  and  well-educated  people.  In  the  Lubelskie  region 
concentration  of  human  capital  is  interrupted  in  the  capital  city  of  the  region.  This 
translates  into  the  quality  of  power  and  legislation  characterized  by  politicization  and 
lack of transparency. 

As the respondent pointed out:  

“The entire bureaucracy process hinders the implementation of basic access to public 
goods. Maybe not well-trained team of people who have direct contact with customers 
and  clients.  Legislation  also  laps  and  often  tries  to  explain  procedures  for  the 
implementation of specific tasks in an intricate and complicated way. Work culture also 
often deviates from high quality customer service standards.  If not to explain such a 
situation  for  a  long  time,  it  is  still  economic  issues  that  dominate  the  situation  the 
most.” 

78 

 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

The  above-mentioned  characteristics  of  the  relationship  between  the  community  and 
local  centers  of  administration  power  translate  into  the  level  of  trust  in  individual 
institutions  of  public  life.  Therefore,  respondents  in  the  Pomeranian  region  are  more 
inclined  to  trust  the  police,  local  media  or  health  service  than  in  the  case  of  the 
inhabitants of the Lublin region. 

In  both  cases,  trust  in  political  parties  is  very  low,  which  has  its  reasons  in  the 
generally negative assessment of the level of political debate in Poland. 

It should also be emphasized that the general low level of public trust in the Lubelskie 
region translates into a more distrustful attitude to economic activity (business) in the 
region  than  in  the  case  of  respondents  from  the  Pomeranian  region.  This  has  its 
impact  on  the  way  the  administration  cooperates  with  local  entrepreneurs  and 
translates into general conditions for running a business. 

2.  Media 

Due  to  the  growing  general  perception  of  the  dependence  and  connections  between 
the  media  and  public  administration  in  Poland,  the  respondents  of  both  surveyed 
regions  showed  a  general  lack  of  trust  in  the  media  as  an  institution  that  stands  for 
the  proper  implementation  of  the  policy  and  respect  to  the  law.  While  the  level  of 
confidence  in  public  television  was  very  low  in  both  regions,  the  level  of  trust  in  the 
case  of  local  public  TV  stations  was  slightly  higher,  as  it  was  in  the  case  of  national 
private media. In general, the opinion was that the media had too large economic ties 
with  representatives  of  the  authorities,  primarily  through  advertising  and  public 
administration, which are a significant position in the broadcasters' budgets, especially 
at the local level. It should be pointed out that the Pomorskie region is of the opinion 
that  due  to  the  relatively  higher  level  of  civic  society,  regional  television  remains 
under  social  (public)  control  (that  is  mainly  reflected  in  the  audience)  to  a  higher 
degree than in the Lublin region, and "forces" a more objective approach to materials 
and topic coverage. 

In  the  case  of  radio  and  newspapers,  the  situation  is  similar,  but  the  radio  enjoys  a 
relatively greater social trust than television. Interestingly, in Lubelskie definitely more 
people  trusted  the  messages  of  national  public  radio  than  in  the  Pomeranian  region, 
where in turn local radio stations had a relatively greater confidence. 

Also,  internet  sources  had  a  low  level  of  trust,  which  is  connected  with  the  fact  that 
the sources of information for respondents were primarily online portals of nationwide 
and regional newspapers. 

The  attitude  to  the  media  as  a  source  of  opinion  and  observatory  medium  of  the 
actions  of  government  is  well  characterized  by  a  commentary  on  the  results  of  the 
latest  edition  of  the  World  Press  Freedom  2018,  where  Poland  was  ranked  at  58th 
position (among 180 countries) in terms of press freedom. 

Respondents  from  the  Lublin  region  commenting  on  this  result  emphasized,  among 
others,  that  there  are  no  real  restrictions  on  the  press,  however,  the  quality  and 
ethical standards and much lower than one-two decades ago.  While public media are 
largely  dependent  on  politics  (according  to  interviewee  due  to  “targeting  of 
advertisements  and  announcements  from  state  and  local  government  offices  to 
selected media”, thus generating additional revenues to government-friendly entities), 
private  media  plan  their  information  policies  based  on  economic  calculations, 
regardless of political sympathies, which is why they do not always have an interest in 
an  objective  approach  to  information,  other  respondents  emphasize  dependence  of 
several media on foreign capital. 

One  of  the  respondents  assessed  the  situation  of  the  media  in  the  Lubelski  region in 
this way: 

79 

 
“It is difficult for me to answer this question because I have no evidence to  say that 
the  regional  press  is  biased.  I  do  not  try  to  read  political  information,  I  do  not  like 
exaggerated  advertising,  my  luggage  is  not  realized  by  politicians.  For  me,  the 
information  I  can  use  directly  in  my  favour  is  important.  For  example:  what  are  the 
projects in the region, in which event I can participate, what investments are currently 
implemented or planned in the region, or interiors fragrances appreciating our region. 
(I  appreciate)  reportage  with  a  local  bakery  or  an  ornithologist  presenting  birds  of 
existence only in my region. As for residents in the press there is too much policy and 
so-called butter in butter (tautological statement). That's why it does not subscribe to 
logs.” 

Another  person  stressed  that  “The  freedom  of  expression  is  already  blocked  all  over 
the  country.  (This  is  a)  preparation  for  censorship.  Words  are  taken  away  from  their 
original  meaning.  For  example,  substantive  discussion  means  now  nodding  public 
authority. Our own opinion is a denial of today's understanding of the subject matter. 
It is common to take meaning away from words.” 

Also  in  the  Pomorskie  region  political  connections  of  media  were  called,  as: 
politicization  of  journalists.  In  recent  years  in  Poland,  some  representatives  of  the 
national  media  openly  admit  their  political  sympathies,  which  was  reflected  in  the 
respondents'  opinion  sharing  their  motivation:  “I  think  that  there  is  no  such  idea  as 
freedom of the press.”  

Respondent’s opinion: “The freedom of the press is influenced by large corporations or 
media  entities  that  have  appropriated  the  entire  media  space  with  their  narrative. 
Thus,  in  the  "market"  way,  they  limit  the  possibility  of  breaking  through  to  other 
heterogeneous and smaller media.” 

Respondent’s  opinion:  “Obstacles  to  freedom  of  the  press,  of  course,  exist,  Some 
press  titles  are  financed  (and  even  issued)  by  affiliates  of  a  political  party  / 
cooperating with a political party. Such a press is nothing but a propaganda tube.” 

An  interesting  picture  of  current  situation  is  described  in  relation  to  the  situation 
twenty years ago. In the Lublin region, the participants of the face-to-face interviews 
were  divided  in  opinions,  if  now  is  better  situation  or  worse,  pointing  to  the 
development  of  technologies  and  related  opportunities,  while  underlining  the 
significantly  lower  level  of  journalism  and  the  quality  of  information  as  compared  to 
the situation two decades before. 

In another case, the relatively higher media's dependence on politics was stressed: 

Twenty  years  ago,  the  free  press  was  just  beginning  to  shape.  It  has  come  a  long 
way.  Total  freedom,  of  course,  never  is  achieved.  Now  there  are  restrictions  and, 
above all, fear of the authorities' reaction. The media belong to the sphere of broadly 
understood culture, while this is the most delicate element and the most susceptible to 
the brutality of language and the primitivism of views. We already see the systematic 
replacement  of experienced people with political statements. And there is no  remedy 
for this. 

In  the  Pomorskie  region  it  was  emphasized  that  there  is  a  greater  choice  of 
newspapers and magazines, but this does not necessarily translate into the freedom of 
their message. 

Similarly  to  the  Lubelskie  region,  respondents  pointed  to  the  low  morale  of  people 
working  in  the  media,  due  to  the  excessive  economic  dependence  of  the  press  on 
public administration: 

“There  is  less  freedom  of  the  press  in  the  sense  that  currently  there  are  entire 
publishing  houses  that  are  on  the  services  of  the  ruling  party.  Some  of  these 

80 

 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

publications come from the Tricity - we know  people who work there. So it's not like 
"bad private media make us water from the brain and  slander  state  media", only we 
know  people  who  work  in  the  propaganda  press  and  we  know  from  the  first  mouth 
what's  going  on.  Most  people  professionally  practicing  party  propaganda  do  so  only 
because they have to work somewhere.” 

To sum up, it should be emphasized that the study shows a clear relationship between 
the quality of social capital in a given region, as well as civil society on the quality of 
media  functioning.  The  media  control  the  actions  of  the  authorities,  while  they  count 
with  the  public  opinion,  which  essentially  affects  the  popularity  and  revenues  from 
advertisements. 

5. 

Civil society  

When applying for positions in public administration and related units, respondents in 
both regions indicated the importance of political and personal connections in getting 
the job. In Lubelskie region, where 14 out of 17 interviewed person shared opinion on 
importance of personal connections, one of interviewees highlighted: 

“Unfortunately,  mainly  political  and  personal  connections  are  decisive,  it  almost  does 
not  happen  that  these  positions  include  people  because  of  their  skills  and 
competences.” 

Another  one  stressed  that:  “They  (key  public  officers)  are  only  representatives  of 
ruling  parties  or  struggling  for  influence,  even  if  they  are  not,  to  maintain  their 
position  they  must  be  loyal  to  their  superiors  from  the  provincial  or  governmental 
administration.” 

This  problem  also  appears  in  the  Pomeranian  region,  although  to  a  much  lesser 
extent,  where  only  5  people  out  of  18  indicated  that  personal  contacts  and  political 
connections are of great importance in obtaining work in the public sector, additionally 
there were definitely more indications that only competences or a mix of competences 
and connections: 

”This  is  not  a  problem,  the  level  of  qualifications  is  a  problem  -  nowadays  the 
management  staff  is  more  competent  than  it  used  to  be  in  the  past,  although 
nepotism often occurs.” 

This situation changes with the change of approach at the national level - respondents 
indicate that in the face of the growing devaluation of the public administration at the 
national  level,  also  at  the  regional  level,  this  process  is  growing,  as  stated  by  the 
Pomeranian citizen: 

”It  seems  to  me  that  in  the  public  sector  it  is  currently  the  case  that  a  person  with 
political connections gets a position, but probably so far the majority of positions are 
still with the people recruited years before, based on the competences.” 

Similarly,  the  answers  regarding  employment  at  lower  levels  of  public  administration 
are  distributed  -  if  this  process  in  the  Lubelskie  region  is  largely  related  to  the 
candidate's  personal  connections  and  current  policy,  in  the  Pomeranian  region  the 
recruitment  process  is  based  more  on  the  assessment  of  competences  then  personal 
connections. Interestingly, in both cases, the majority of respondents indicate that the 
situation in their region does not differ significantly from the situation in other regions 
of the country. 

In  turn,  when  asked  whether  officials  would  inform  superiors  about  irregularities 
detected  in  their  unit,  in  both  cases  the  respondents  were  not  able  to  express  their 
opinion  on  this  topic.  Also  in  both  cases,  the  majority  of  respondents  indicated  that 
employee could be afraid of repercussions if he/she decides to disclose the case. This 

81 

 
goes hand in hand with the opinion that at the regional level there is no clear strategy 
in order to enforce compliance with the rules and employees of public institutions. In 
both  regions,  respondents  basically  were  unable  to  point  to  anti-corruption 
regulations,  in  some  cases  they  pointed  to  central  regulations,  work  ethics  or  the 
institution of the Central Anticorruption Bureau (CAB, investigating body) operating at 
the national level.  

In  the  case  of  Lubelskie  region,  public  opinion  (citizens)  are  less  often  expressly 
related to media activity.  

In  the  Pomorski  region,  public  opinion  is  much  more  likely  to  react  to  the  case  of 
corruption,  pointing  to  numerous  cases  of  social  dissatisfaction  with  public-private 
scandals. There is a clear difference between the regions here, and the public opinion 
in the Pomeranian region responds very strongly to irregularities, although this is not 
always effective, in Lubelskie to a much lesser extent.  “Citizens - if they are active  - 
can  enforce  a  lot  of  changes.  The  inhabitants  of  the  Tricity  are  very  active  on  the 
Internet.” or: “Public opinion often reacts but its reaction and outrage is not taken into 
account” (Pomorski region). 

In  both  cases,  attention  is  paid  to  the  importance  of  the  media  in  tracking  and 
publishing cases of irregularities. 

6. 

Impact of the EU 

In  Pomorskie  region  relatively  greater  share  of  respondents  expressed  their  opinion 
that they observe a significant change in the quality of public services since joining the 
EU, than in Lubelskie region.  

In  both  cases,  there  are  reservations,  although  there  is  an  opinion  that  the 
introduction  of  polic-making  principles  based  on  EU  regulations  has  helped  introduce 
higher standards in this area. In the Lubelskie region, the majority of respondents still 
think  that  there  is  a  lot  to  do  in  this  area,  while  in  Pomerania  there  is  a  fear  that 
changes  at  the  central  level  will  adversely  affect  the  quality  of  public  life  in  their 
region.  In  the  Lubelskie  region  the  EU  membership,  apart  from  the  positive  aspects, 
was strongly perceived as the reason of the increase of bureaucracy in administration. 

In  the  case  of  both  regions,  still  almost  half  of  interviewees  maintained  that  wealthy 
people have a broader range of access to healthcare services (eg schools for children, 
preferences in access to healthcare,  running a business). Social inequalities remain a 
major  challenge  at  the  local  level,  but  with  the  development  of  civil  society,  these 
differences  are  decreasing.  Only  few  respondents  could  recall  loud  issues  related  to 
irregularities in public administration. In the Pomeranian region, the respondent gave 
the  following  example:  “The  control  of  the  Central  Anticorruption  Bureau  at  the 
Marshal's  Office  in  Gdańsk  (completed  in  March  2017)  showed  that  the  Pomeranian 
Voivodeship  Board  violated  the  Public  Procurement  Law  in  the  submission  of  false 
declarations  regarding  impartiality  by  members  of  the  Board  when  selecting  the 
contractor.  It  is  a  Pomeranian  Development  Agency  SA,  a  public  institution  whose 
100-percent  shareholder  is  Pomorskie  Voivodeship.  ARP  SA  was  selected  in  a 
competitive  mode,  in  accordance  with  the  PPL  act  as  a  service  provider  within  the 
Pomeranian  Smart-Up  project  evaluated  the  offers,  only  formally  approved  the 
outcome  of  the  work  of  the  tender  committee,  and  it  should  be  noted  that  the 
members  of  the  Pomorskie  Voivodship  Board  act  within  a  collegial  body,  rather  than 
individually,  it  is  difficult  to  recognize  that  members  of  the  Voivodship  Board  could 
obtain any benefit by selecting a public institution. On the one hand, members of the 
Management  Board  who  signed  a  declaration  of  impartiality  in  connection  with  the 
approval of the tender were accused of doing so, and on the other hand a member of 
the Management Board absent at the meeting because of the delegation was accused 
of not having signed the statement. Office, comments on public procurement have no 
legal and factual basis - they result from the erroneous interpretation of the law by the 

82 

 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

CAB and in the incorrect analysis and synthesis of the collected evidence. I think the 
likelihood of this situation reoccurring is small.” 

W Lubelskie region responded gave an following example: 

“The  court  in  Suwałki  impartially  handed  down  the  verdict  acquitting  people  who 
opposed  the  campaign  of  Mr.  Anders  in  the  museum.  Now  the  authorities  muttered 
and probably some judge would abolish this verdict. This is just a loud example.”  

Summing  up,  in  Poland  accession  to  the  EU  introduced  the  framework  of  policy 
making,  and  the  regulations  regarding  spending  public  funds,  supported  from  the  EU 
budget, introduced new rules for qualification and distribution of public support, which 
generally  had  a  positive  effect  on  the  quality  of  the  administration,  however  in 
different  regions  it  took  place  to  varying  degrees.  In  the  Pomeranian  region  to  a 
greater extent the principles of transparency were enforced by the media and society, 
in Lubelskie, due to the lower civic engagement, this situation was relatively worse. 

Conclusions 

Polish  regions  are  still  characterised  by  a  low  level  of  EQI  as  compared  with  most  of 
the  EU-15  regions,  excluding  the  many  regions  of  Spain,  Italy  and  Greece.  The  low 
level of EQI is in Poland accompanies by much lower level of GDP per capita. 

The  level  of  EQI  in  both  of  the  Polish  regions  analysed  seems  to  reflect  both  the 
current  economic  situation  of  these  regions  as  well  as  their  historical  and  cultural 
backgrounds. Different development pathways these regions went through during the 
Polish  partition  period  left  a  still  visible  mark  on  the  current  quality  of  governance. 
These differences still have an influence on the way the culture, including functioning 
of regional administration and relations between different institutions and citizens. 

This is reflected in the conducted study, which showed a relatively lower effectiveness 
and  quality  of  public  institutions'  functioning,  which  is  reflected  in  the  social 
characteristics  and  civic  participation  of  residents.  In  the  Pomorskie  region,  where 
historically  social  values  were  based  on  Western  culture  (associated  with  Protestant 
roots and ties with Western countries), the culture of public administration is greater - 
to a large extent it is dictated by a higher level of social capital and civil society, which 
affects the level of media objectivity and greater social control over local authority. 

In  the  subject  literature  in  Poland,  differences  in  mentality,  socio-economic 
development in particular regions of the country are widely commented, which has its 
causes due to different historical conditions (especially those related to the impact of 
eastern  and  western  culture).  These  divisions  are  evident,  for  example,  during  the 
parliamentary elections, also in this study. With outlined differences in the perception 
of  various  aspects  of  regional  socio-political  life,  membership  in  the  EU  had  an 
unambiguously  positive  effect  on  the  level  of  law-making,  and  currently  the  level  of 
governance  is  an  increasingly  important  determinant  of  the  development  of  a  given 
area. 

83 

 
 
 
 
13. Study of Navarre and Catalonia regions in Spain 

1.  Introduction 

1.  Regional QoG performance in the country 

Spain is a paradigmatic example of the complexity and very diverse levels of quality of 
government  (QoG)  that  can  be  found  within  the  same  country,  to  the  point  that  the 
actual  performance  of  the  Spanish  state  cannot  be  properly  captured  by  exclusively 
looking  at  the  central  government.  On  the  contrary,  the  existence  of  significant 
interregional  differences  recommends  gathering  information  at  sub-state  level,  so  to 
provide a more accurate picture of QoG within the country. 

All throughout the period covered by the investigation on regional QoG (2009-2017), 
Spain has obtained nation-wide EQI scores close to the EU average. But this apparent 
stability  conceals  a  decline  in  Spanish  QoG,  especially  when  compared  to  other 
countries.  In the first round of the analysis, Spain was ranked 15th out of the 27 EU 
member states and clustered together with France, Belgium, Malta, Portugal, Cyprus, 
Estonia  and  Slovenia.  When  the  study  was  replicated  four  years  later,  Spain  climbed 
up to the 14th spot, thus experiencing a slight improvement (See  Charron, Lapuente 
and Rothstein 2018: table 1); but it then had a great fall, down to the 19th position in 
the  last  round  (See  Charron  and  Lapuente  2018:  table  2A).  As  a  result,  Spain  now 
looks less like some Western -or even Southern- European countries with which it was 
initially  grouped,  and  it  is  currently  much  closer  to  new  EU  member  states  from 
Eastern  and  Central  Europe,  such  as  Lithuania,  Slovenia,  the  Czech  Republic,  Poland 
and Latvia.  

Most  importantly  for  the  purposes  of  this  qualitative  analysis,  great  spatial  and 
temporal  differences  can  be  found  between  Spanish  regions17,  while  the  gap  across 
them has broadened over time, too. Let’s see these differences in further details:  

  First  of  all,  Spain  is  the  only  country  that  includes  regions  both,  above  the 
mean  EQI  score  at  EU  level  -such  as  Cantabria,  the  Basque  Country  and 
Navarre-  and  below  it  -such  as  Madrid,  Galicia,  Andalusia  and  the  Canary 
Islands; while the number of regions with EQI scores below the European mean 
has  risen  from  four  in  2010,  to  six  in  2013  and  to  ten  in  2017.  ACs  with  EQI 
scores under the EU average are rather spread throughout the territory of the 
country,  whereas  most  regions  performing  over  the  EU  average  are  in  the 
North  of  the  country18.  This  suggests  some  connection  between  territorial 
location and quality of government19. 

17   In  the  Spanish  context,  the  terms  ”region”  and  ”autonomous  community  (AC)”  are  often  used 
interchangeably. Both of them refer to the intermediate territorial level between the centra state and the 
local  entities;  yet,  the  first  one  constitutes  mainly  a  geographical  concept,  whereas  the  second  one 
highlights  the  political-institutional  dimension  and  is  the  legal  form  the  Spanish  regions  adopted  as  a 
result of assuming the autonomy granted by the 1978 Spanish constitution. 

18   Extremadura seemed to be the only exception in 2017.  

19   As a matter of fact, some sort of territorial pattern has become apparent over time, because the number of 
Northern regions with poor EQI results and Southern regions with good ones is smaller in 2017 than it 
was in 2013 and 2010.  

84 

 
 
 
 
 
                                                 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

  Second  of  all,  Spain  is,  after  Italy,  the  country  with  the  widest  divergence  of 
QoG at sub-national level; and this divergence  has grown over time. In 2010, 
more  than  twenty-four  points  set  the  Basque  Country  (the  best  performing 
region  at  the  time,  with  an  EQI100  score  of  76.63)  apart  from  Catalonia  (the 
worst performing region, with an EQI100 score of 52.07). The gap between the 
regions  respectively  at  the  top  and  at  the  bottom  of  the  ranking  narrowed 
down  to  eighteen  points  in  2013;  only  to  broaden  once  again  in  2017.  As  a 
result, there is nowadays a thirty points-gap between the AC with the highest 
score -the Basque Country (63.3)- and the AC with the lowest one  -Andalusia 
(33.2).  

  Third  of  all,  Spanish  regions  vary  greatly  in  their  evolution  over  time.  In  line 
with  a  common  trend  within  Southern  Europe,  most  ACs  have  experienced  a 
deterioration  in  their  quality  of  government  in  recent  years:  Twelve  of  them 
showed  worse  EQI  scores  in  2017  than  they  did  in  2010;  besides,  in  some 
particular  cases  -such  as  Galicia,  Castile  La  Mancha,  Valencia  or  the  Balearic 
Island-  this  decline  have  entailed  a  jump  from  a  position  above  the  European 
average  to  a  position  below  it.  On  the  other  hand,  the  Basque  Country, 
Cantabria,  La  Rioja  and  Navarre,  all  regions  in  Northern  Spain,  have 
experienced  the  opposite  development:  they  had  scores  over  the  EU  average 
already in 2010 and have improved since then.  

2.  Selection of regions and methodology 

As it has just been pointed out, Spain features one the greatest levels of inter-regional 
variation  in  quality  of  government.  Taking  this  into  consideration,  the  main  selection 
criterion  for  this  qualitative  report  was  choosing  one  ‘high  QoG’  region  -Navarre 
(ES22)- and another ‘low QoG’ region -Catalonia (ES51).  

Since the first Quality of Government Index (QoG Index) was published, Navarre has 
consistently  registered  values  above  the  Spanish  and  European  averages;  and  it  has 
also  experienced  a  steady  progress,  relative  to  the  rest  of  Spanish  ACs.  More 
specifically,  Navarre  has  gone  from  the  10th  position  in  2010,  to  the  6th  and  2nd 
positions  respectively  in  2013  and  2017.    And  it  is  currently  among  the  top  35% 
European  regions,  with  similar  scores  to,  for  example,  Saxony-Anhalt  (Germany)  or 
Ile-de-France (France).  

Catalonia’s  performance  in  governance  (at  least  as  citizens  perceive  it)  is  quite 
different  to  Navarre’s.    In  2010,  Catalonia  received  the  worst  result  of  all  Spanish 
regions and was also below the European average. After some improvement in 2013, 
Catalonia’s  position  deteriorated  once  again  in  2017,  when  it  fell  down  to  the  12th 
position  within  Spain.  Consequently,  it  remains  in  the  middle-low  zone  of  European 
regions,  with  similar  scores  to  Moravskoslezsko  or  Jihozapad  (Czech  Republic)  and 
most Polish regions.  

85 

 
 
 
 
 
Table 29. EQI2017 in Catalonia and Navarre 

Specification 

Catalonia 

Navarre 

Score 

Rank  Score 

Rank 

Quality pillar 

53.5 

120 

72.5 

56 

Impartiality pillar 

47.1 

129 

65.9 

75 

Corruption pillar 

40.2 

133 

55.1 

89 

EQI2017 

40.8 

127 

60.3 

73 

Source: Own elaboration based on  
http://ec.europa.eu/regional_policy/en/information/maps/quality_of_governance#2 

Ultimately, it can be said that citizens in Navarre are largely satisfied with their public 
sector  and  they  believe  that  public  administration  in  their  territory  works  in  a  more 
impartial, more effective and less corrupt way than most ACs. Conversely, citizens in 
Catalonia  consider  that  Catalan  institutions  have  performed  in  all  three  dimensions 
assessed in the QoG Index far less satisfactorily20.   

These  differences  between  Catalonia  and  Navarre  become  particularly  striking  if  we 
take  into  consideration  that  both  are  Northern  and  economically  better-off  regions 
within Spain.  

As  for  methodology,  this  work  is  based  on  information  collected  through  a  series  of 
personal  interviews  with  experts  in  different  academic  and  professional  fields 
conducted  during  the  month  of  April  2018;  this  was  complemented  with  official  data 
and  other  secondary  sources.  The  remaining  of  the  chapter  is  structured  as  follows: 
sections  two  and  three  deal  with  one  region  each  and  pay  attention  to  their  key 
historical,  institutional  and  socio-structural  features.  Section  four  focuses  on  aspects 
such  as  political  parties,  civil  service  or  the  media,  in  an  attempt  to  provide  a 
provisional  explanation  of  Catalonia’s  and  Navarre’s  divergent  results  in  quality  of 
government. Section five offers some concluding remarks.  

2.  Catalonia 

1.  Structural conditions (geographical & economic features) 

Catalonia is Spain’s most North-eastern region. It is bordered by France and Andorra 
to the north, the Mediterranean Sea to the east, the AC of Valencia to the south, and 
the  AC  of  Aragon  to  the  west.  Its  surface  is  just  over  thirty-two  thousand  square 
metres,  thus  amounting  to  about  six  percent  of  the  Spanish  territory.    The  Catalan 
population reached seven and a half million inhabitants in 2017, which represents over 
sixteen per cent of the Spanish population. This makes of Catalonia the second biggest 
AC in population size, after Andalusia.  

20   The  only  similarity  seems  to  be  that  both  regions  perform  better  in  the  quality  pillar  followed,  in  this 
order, by the dimensions of impartiality in the provision of public services and control of corruption (See 
Table 1).   

86 

 
 
 
 
 
                                                 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

As for the economy, Catalonia slightly reduced its weight within the national economy 
during the crisis; but it has more  recently reached its historical peaks. As a  result, it 
currently constitutes the largest regional economy in the country, with a share around 
nineteen percent of the Spanish economy (see table 30).  

Table 30. Size of regional economy, Catalonia 

Source: Own elaboration based on data from INE: 
https://www.ine.es/dyngs/INEbase/es/operacion.htm?c=Estadistica_C&cid=1254736167628&m
enu=resultados&idp=1254735576581  

Catalonia is also one of the most highly industrialised parts of the country, where the 
industrial  sector  contributes  around  four  percentage  points  more  than  it  does  in  the 
country  as  a  whole.  It  has  traditionally  featured  a  strong  textile  industry.  Since  the 
process  of  diversification  set  off  in  the  1950s,  however,  metalworking,  food-
processing,  pharmaceutical,  and  chemical  industries  have  gained  more  prominence; 
furthermore,  services  (particularly  those  related  to  tourism  and  transportation)  are 
highly  developed,  too.  Additional  data  illustrate  Catalonia’s  weight  in  the  Spanish 
economy:  according  to  the  National  Institute  of  Statistics  (Instituto  Nacional  de 
Estadística-INE),  in  2016  Catalan  exports  accounted  for  25.6  percent  of  all  Spanish 
exports; its industries represented 22.7 percent of the Spanish industrial sector; and 
the region received over 24 percent of foreign tourists who visited Spain.  

Figure 31: GDP sectoral structure in Catalonia, 2015 

Source: Own elaboration with data from INE.  

87 

 GDP per capita GDP (% of country)  2010 2013 2016* 2010 2013 2016*  Euros % Euros % Euros % Catalonia 27.192 117,1 25.945 117,9 28.845 119,8 18,8 18,8 19,1 Spain 23.215 100,0 22.014 100,0 24.085 100,0 100,0 100,0 100,0   
 
 
 
 
 
 
 
Catalonia  is  also  a  wealthy  region,  with  a  GDP  per  capita  well  above  the  Spanish 
average (see table 2);  in fact, it has consistently  ranked among the four best ACs in 
the  country.  Catalonia  has  also  performed  well  in  employment,  with  unemployment 
rates  between  three  and  four  percentage  points  lower  than  the  Spanish  average, 
which  have  also  experienced  a  positive  evolution  from  19  percent  in  2015  to  about 
12.6  percent  in  late  2017.  Since  employment  and  GDP  per  capita  stand  as  good 
indicators  of  quality  of  life,  then  Catalan  inhabitants  are  among  the  Spaniards  who 
enjoy the best living standards. 

3.  Navarre 

1.  Structural conditions (geographical & economic features) 

Navarre  is  located  in  the  North  of  Spain,  in  the  western  end  of  the  Pyrenees.  It  is 
bordered by France to the North, the Basque Country to the West, the ACs of La Rioja 
to the Southwest and the ACs  of Aragon to the South  and East.  With an area of just 
under  ten  and  a  half  thousand  square  metres,  it  is  the  fourth  smallest  region  in  the 
country.  The  Navarrese  population  was  about  six  hundred  forty  thousand  inhabitants 
in  2017,  which  represents  under  one  and  a  half  per  cent  of  the  Spanish  population. 
This makes of Navarre the fourth smallest AC in population size.    

As  for  the  size  of  the  Navarrese  economy,  it  contributes  1,7  percent  of  the  Spanish 
GDP;  this  percentage  has  remained  rather  constant  over  time  and  it  constitutes  a 
slightly larger contribution by the region to the Spanish economy than its share in the 
Spanish population (see table 31). 

Table 31. Regional economy, Navarre 

Source: Own elaboration based on data from INE: 
https://www.ine.es/dyngs/INEbase/es/operacion.htm?c=Estadistica_C&cid=1254736167628&menu=resulta
dos&idp=1254735576581  

Navarre’s  agricultural  output  has  been  kept  relatively;  yet,  above  the  Spanish 
average.  Apart  from  that,  the  regional  economy  is  clearly  oriented  towards  industry 
and exports. In fact, the industrial sector represents about a third of the regional GDP, 
well  above  the  national  average,  and  with  a  specific  weight  of  the  car  industry  and 
auxiliary  sectors,  as  well  as  machinery,  house  appliances,  processed  foods  and 
beverages,  and  paper.  Moreover,  exports  constitute  over  40  percent  of  the  regional 
GDP,  which  almost  doubles  the  weight  of  imports  into  the  region.    Services  are  also 
important and concentrated in Pamplona, which is the commercial centre of Navarra.  

88 

 GDP per capita GDP (% of country)  2010 2013 2016* 2010 2013 2016*  Euros % Euros % Euros % Navarre 28.752 123,9 27.442 124,7 29.859 124,0 1,7 1,7 1,7 Spain 23.215 100,0 22.014 100,0 24.085 100,0 100,0 100,0 100,0   
 
 
 
 
 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

Figure 32: GDP sectoral structure in Navarre, 2015 

Source: Own elaboration with data from INE. 

According  to  INE,  Navarre  had  the  lowest  regional  unemployment  rate  in  2017,  with 
9.6  percent;  that  is,  6  percentage  points  lower  than  the  national  average  of  16.6 
percent. As for GDP per capita, it occupies the 3rd place in the ranking of ACs. It also 
enjoys one of the lowest regional public debts. All these figures show that Navarrese 
are among the Spaniards with the best living standards within the country. 

4.  Comparative analysis 

This  section  offers  an  analysis  of  nineteen  in-depth  personal  interviews  conducted 
either  face  to  face  or  over  the  phone,  during  the  month  of  April  2018.  Twelve 
interviewees  came  from  Catalonia  and  the  remaining  seven  were  from  Navarre21.  As 
many as fourteen interviewees had public responsibilities as politicians,  civil servants 
or  employees  either  at  the  regional  level  or  in  various  Catalan  and  Navarrese 
municipalities; three interviews were addressed to scholars in government and public 
administration or in the legal field; and two more interviewees came from the private 
sector (see Annex for the professional profile of interviewees).  

As  it  will  be  shown  next,  the  results  suggest  that  the  variation  between  Catalonia’s 
and  Navarre’s  performance  in  quality  of  government  may  have  to  do  both  with 
structural conditions, and political-institutional and/or administrative choices.  

1.  Expert  assessment  on  the  regions’  ranking  and  their  evolution  over 

time 

Experts were split in their evaluations of Catalonia. In fact, the  only clear agreement 
among them was about the distorting effects that the territorial conflict and the social 
division  over  the  issue  of  independence  was  likely  to  have  both  on  the  citizens’ 
perceptions and on their own views.  

21   The difference in the number of interviews carried out in each region was largely junctural, since it was 

easier the access to potential interviewees in Catalonia than in Navarre.  

89 

 
 
 
 
                                                 
Some  of  them  expressed  a  very  critical  position,  thus  in  line  with  the  results  of  the 
citizens’  survey.  According  to  interview  10,  for  example,  Catalonia  has  for  long 
benefited from its “apparent modernity” and, for a while, it even served as an example 
to  other  administrations.  However,  “issues  related  to  institutional  quality  and 
institutional  organization  have  never  been  high  in  the  agenda  of  the  regional 
government”; so that, after the surface is scrubbed out, internal contradictions and a 
rather mediocre situation are revealed. 

On the other side, most interviewees shared the view that Catalonia might be lagging 
behind the smaller regions in the North of Spain (particularly the Basque Country and 
Navarre),  but  it  was  still  performing  better  than  most  of  the  remaining  ACs.  They, 
thus, found it difficult to understand Catalonia’s EQI scores -that set their region apart 
from  a  Northern  pattern-  and  the  reasons  why  Catalan  citizens  have  consistently 
ranked  it  so  low  over  time.  For  some  experts  within  this  group,  the  way  citizens 
perceive  the  functioning  of  public  administration  and  the  quality  of  services  in 
Catalonia  might  have  experienced  a  reversal  especially  due  to  major  cases  of 
corruption,  that  are  somehow  associated  to  problems  of  impartiality,  too.  But,  it  was 
further  argued  that  these  corruption  cases  recently  known  and/or  judicialized  are  in 
fact old ones, go many years back and do not necessarily reflect the current situation; 
whereas  a  deeper  inquiry  suggests  the  existence  of  a  great  difference  between 
it  (Interview  4)22.  Moreover,  the 
“perceiving”  corruption  and  “experiencing” 
Generalitat  of  Catalonia  is  deploying  efforts  in  implementing  and  developing 
transparency  measures  at  the  present  time,  that  seem  to  have  less  influence  on 
citizens’ perceptions (Interviews 4 and 7).  

In contrast to Catalonia, the experts’ assessment was more homogeneous concerning 
Navarre.  Interviewees  broadly  agreed  with  the  results  of  the  citizens’  survey,  that 
places  their  region  at  the  top  of  the  ranking  of  ACs  and  well  above  the  European 
average  scores  in  all  three  dimensions  under  examination.  Most  of  them  were  also 
proud to highlighting that Navarre’s good governance has remained quite stable over 
time,  to  the  point  that  the  economic  crisis  seemed  to  have  affected  the  region  only 
very slightly (see specially Interviews 9 and 17).  

As  a  matter  of  fact,  the  people  interviewed  shared  the  view  that  public  services  are 
provided with high efficiency in Navarre; they considered their regional government to 
be  more  efficient  than  the  central  one;  and  that  corruption  was  mainly  a  problem 
associated to the central government or to other regions23. If anything, some of them 
warned that the change in government brought about by the regional elections held in 
2015  might  affect  impartiality  in  the  process  of  decision-making,  although  the  brief 
period elapsed since then could explain that it was not reflected in the citizens’ survey 
yet.  

22   According  to  the  barometer  conducted  by  the  Catalan  Antifraud  Office  in  2018,  over  70  percent  of 
Catalans perceive that there is much corruption and it is a very serious problem, but less than 30 percent 
of them affirm to have  witnessed actual cases of corruption, pity corruption or inappropriate conducts. 
See  “La  corrupción  en  Cataluña:  percepciones  y  actitudes  Ciudadanas” 
(available  at: 
https://www.antifrau.cat/es/barometro-2018.html).   

23   As  one  expert  pointed  out,  the  only  single  conviction  for  corruption  dates  back  to  the  late  1980s 
(Interview 5). This idea has been reinforced in the media where many pieces of news can be lately found 
stressing  that  Navarra  is  the  only  AC  under  no  investigation  for  suspected  political  or  administrative 
corruption (See, for example, Elia, N. “La burbuja de la corrupción pincha en Navarra”, eldiario.es, June 
17th, 
(https://www.eldiario.es/norte/navarra/burbuja-corrupcion-pincha-
Navarra_0_655484787.html)).  

2017 

90 

 
 
 
 
                                                 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

2.  Contributing factors of the regional QoG 

The  main  factors  identified  that  seem  to  be  contributing  to  the  variation  in  regional 
QoG  can  be  organised  in  four  main  categories:  (1)  socio-structural  factors,  including 
aspects  of  the  civil  society,  public  opinion  and  political  culture;  (2)  factors  related  to 
the  institutional  setting,  including  the  employment  and  hiring  practices  in  the  public 
sector; (3) factors related to the political parties’ ideologies and dynamics; and (4) the 
role played by the media.   

a.  Socio-structural factors 

Interviewees from Navarre were quick and unanimous to suggest that the small size of 
the  region  and  its  municipalities24  has  a  positive  impact  upon  the  quality  of 
government, through a series of direct and undirect mechanisms, that one interviewee 
referred to as  “informal  anti-corruption controls” (Interview 16): First of all, the very 
size of the region leads to a public administration of equally small size that leaves little 
room for discretional appointments. As one interviewee illustrated it, whereas in many 
ACs there used to be five or six discretional appointees between the regional ministry 
and the general director, in most departments in Navarre there were none (Interview 
11).    Second  of  all,  the  closeness  and  proximity  between  citizens  and  administrators 
enables the former to more easily watch over the later what, in turn, “makes it hard, if 
not  impossible,  to  take  decisions  according  to  ideological,  political  or  personal 
interests” (Interview 17).  

Finally,  its  small  size  has  also  facilitated  the  knowledge  and  spreading  of  good 
practices,  a  healthy  competition  and  processes  of  imitation  not  only  among  public 
institutions  but  also  between  the  public  and  private  sectors.  In  two  key  policy  fields 
such  as  health  and  education,  for  example,  there  are  renowned  private  institutions 
that constitute a “close reference” and even a “challenge” for the public institutions to 
become  more  self-demanding  and  to  aspire  to  similar  performance  levels  (Interview 
11).  

Importantly, however, this provisional relationship between size and QoG can only be 
established in the case of small regions, whereas no disadvantage can be necessarily 
derived  from  being  a  large  region;  and  no  interviewee  from  Catalonia  has  suggested 
that any consequence could be derived from this very feature.  

As for the role of the citizenship, there was a broad agreement both in Catalonia and 
Navarre  about  the  (theoretical)  importance  of  having  an  active  and  assertive  civil 
society  that  may  serve  as  an  appropriate  context  and  fertile  ground  for  the 
development  of  a  more  transparent  public  action  and  a  higher  quality  public 
administration. In fact, Navarrese society was described as being very demanding, to 
the  point  that  “citizens  were  used  to  watch  over  even  minor  issues  like  the  way  of 
tiling  the  pavement,  so  that  public  institutions  couldn’t  afford  to  do  things  wrongly” 
(Interview 11). Ideally, then, “the society should always act as the first sword and the 
politician as a pawn” (Interview 18). However, doubts were also raised on the type of 
civil society that  exists  in Catalonia and Navarre and/or their actual impact  on public 
management. 

On the one hand, both ACs feature a powerful and dynamic “social fabric”, with many 
associations that are very involved in social and cultural issues. And these associations 
have  a  decisive  influence  over  public  policies  in  their  respective  fields,  particularly  at 

24   According  to  the  official  data  for  2017,  Navarre  has  just  under  six  hundred  and  fifty  thousand 
inhabitants,  which represents less than 1,5 percent of the  whole Spanish population. Most importantly, 
almost  half the Navarrese population lives in  municipalities smaller than 2000 inhabitants  whereas, on 
the  other  side,  only  twelve  percent  of  the  people  live  in  municipalities  larger  than  20000  inhabitants 
(See: National Institute of Statistics, https://www.ine.es/jaxiT3/Tabla.htm?t=2917&L=0). 

91 

 
                                                 
local level (Interviews 1, 2, 6, 12 and 18). Moreover, organisations of the civil society 
defending transparency and open data have proliferated in Spain over the last years, 
such  as  ‘Civio’,  ‘Access  Info  Europe’,  ‘Qué  hacen  los  diputados’  or  ‘Openkratio’  that, 
autonomously or in coordination, have demanded the adoption of transparency laws in 
line with international standards (Magallón et al., 2017: 62-63).  This recent culture of 
open  data  has  pushed  for  the  creation  of  open  data  projects  at  various  territorial 
levels, as it has happened in Catalonia and Navarre (Interviews 5 and 15).  

Beyond  this,  most  associations  are  not  primarily  oriented  towards  good  public 
management  or  they  are  highly  dependent  on  public  funding;  and both  factors  seem 
to  have  ultimately  limited  the  capacity  of  organisations  of  the  civil  society  to  watch 
over  political  parties, 
functioning  of  public 
administrations.  In  the  case  of  Catalonia,  for  instance,  having  a  small  public 
administration has led to an increase of concerted policies. And these have opened an 
arena of confluence between the private and public spheres, where counterproductive 
exchanges or even corruption are more likely to occur. According to one interviewee, 
Catalan  civil  society  “has  not  contributed  to  cut  corruption  down;  on  the  contrary,  it 
has got used to coexist with institutionalized corruption” (Interview 13).  

individual  politicians  and 

the 

In  both  ACs,  moreover,  citizens  and  civil  organizations  are  spilt  over  the  issues  of 
nationalism  and  territorial identity  and,  somehow,  they  are  ideologically-biased  when 
performing their overseeing role. Specifically, they are more willing to voice criticisms 
against opposition parties and more willing to tolerate kindred ones (Interviews 12, 13 
and 18).  

b.  Institutional setting 

To  start  with,  many  interviews  mentioned  one  element  of  the  general  institutional 
framework  of  the  State  of  Autonomies.  The  financing  system  established  for  the 
Spanish ACs results in a clear asymmetry between Catalonia and Navarre, that some 
Catalan  interviewees  and  all  Navarrese  ones  has  argued  to  wield  a  non-negligible 
influence  on  their  performance  in  terms  of  quality  of  government.  The  common 
financial  system  that  applies  to  most  ACs,  in  practice  leaves  Catalonia  with  fewer 
resources  that  it  would  enjoy  under  a  model  with  greater  fiscal  autonomy.  This 
imposes  budgetary  constraints  upon  the  Catalan  Generalitat  that  are  likely  to  affect 
public services there (Interviews 6 and 7).  

By contrast, its own “Foral system” allows Navarre to levy all the taxes in its territory 
and  to  pay  a  compensation  to  the  central  government  later,  in  order  to  finance  the 
expenses  this  had  incurred  in  the  Navarrese  region.  Navarre’s  financial  regime, 
coupled  with  the  fact  of  being  one  of  the  wealthiest  ACs,  translates  into  more 
economic  resources  what,  in  turn,  contributes  to  the  provision  of  high-quality  public 
services.  As  one  expert  from  Navarre  pointed  out,  “we  tend  to  consider  ourselves  to 
be better; but, of course, it is difficult to do with 3000 euros per student the same as 
one does having 6000” (Interview 11; see also interview 9). 

More importantly, although at first glance the abundance of resources might create no 
pressure for the regional administration to work creatively and in an efficient manner, 
the  greater  fiscal  responsibility  derived  from  the  Navarrese  fiscal  autonomy  does.  In 
fact,  fiscal  responsibility  seems  to  matter  more  than  wealth  because  it  develops  and 
strengthens  a  larger  sense  of  ownership  both  among  citizens  and  within  the  public 
administration:  On the one hand, Navarrese people are more willing to contribute to 
the  regional  treasury  -which  they  consider  to  be  their  own  and  to  which  they  feel 
somehow  emotionally  linked-  than  they  would  if  taxes  were  levied  by  the  central 
government (Interview  16). On  the other hand, since the  Navarrese government has 
almost  no  economic  reliance  on  the  central  government,  it  becomes  more  conscious 
and stricter about its taxation system and how its revenues are spent (Interview 16).  

92 

 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

Apart  from  this,  the  experts  intervieweed  largely  shared  the  opinion  that  good 
governance  is  inevitably  linked  to  the  principles  of  transparency,  accountability, 
participation  or  integrity.  Yet,  they  immediately  conceded  that  Spain  lacks  a  long 
public  culture  of  transparency  and  integrity.  In  fact,  the  first  Spanish  Law  on 
Transparency only goes back to 201325, and it was preceded -and in some cases also 
improved and surpassed in its contents- by the regulations at regional level. These are 
particularly  the  cases  of  Catalonia  and  Navarre,  that  passed  their  own  laws  on  this 
matter respectively in 201426 and 201127.  

In  both  ACs,  the  monitoring  role  over  the  working  of  public  institutions  have 
traditionally  been  carried  out  by  general  bodies  such  as  comptrollers,  public 
prosecutors, regional Courts of Audit and regional Ombudsmen, as it was pointed out 
by  many  interviewees  (Interviews  3,  15  and  16).  Furthermore,  based  on  their 
respective regulatory frameworks, each region has created (or is currently preparing) 
specific  bodies  and  instruments  for  the  management  and  control  of  transparency, 
participation  and  good  governance;  among  them,  there  are  ethical  codes  for  civil 
servants and senior officials28, regional and local registries of activities and interests29 
and transparency portals at regional and local levels30. Finally, Catalonia and Navarre 
have also established an institution specifically devoted to the prevention and control 
of  corruption  and  any  other  fraudulent  practices,  namely,  the  regional  anticorruption 
office31. As it will be shown next, the assessment of all these instruments appears to 
be far from unanimous.  

Experts  from  Navarre  demonstrated  little  knowledge  of  the  existence  of  the  various 
mechanisms dedicated to controlling corruption and some of them even doubted about 
their  necessity.  According  to  interviewee  11,  “Navarre  works  very  well,  regardless  of 

25   See  the  Spanish  Act  19/2013,  of  December  9th,  on  Transparency,  Access  to  Public  Information,  and 
Good  Governance  (available  at:  http://transparencia.gob.es/transparencia/dam/jcr:2fcd5d26-cf51-4775-
8d44-6683586f1ee9/ley-de-transparencia-ingles.pdf). 

26   See  the  Catalan  Act  19/2014,  of  December  29th,  on  Transparency,  Access  to  Public  Information  and 
Good Governance (available at: https://www.boe.es/boe/dias/2015/01/21/pdfs/BOE-A-2015-470.pdf). 

a  Code 

of  Good  Government 

27   Navarre  was  one  of  the  first  ACs  to  legally  address  this  issue,  and  it  approved  its  Foral  Act  2/2011 
establishing 
at: 
http://www.lexnavarra.navarra.es/detalle.asp?r=12243). A year later, the Foral Act 11/2012, of June 21st 
on Transparency and Open Government was approved and subsequently modified in 2016 (available at: 
https://www.boe.es/buscar/act.php?id=BOE-A-2012-9370).  Finally,  on  May  17th  2018,  the  regional 
Parliament  passed  a  new  Foral  Act  5/2018  on  Transparency,  Access  to  Public  Information  and  Good 
Governance,  that  is  far  more  comprehensive  and  largely  abolishes  the  previous  one  (available  at: 
https://www.boe.es/diario_boe/txt.php?id=BOE-A-2018-7642). 

on  March 

(available 

2011 

17th 

28   Although  most  ACs  have  not  approved  these  codes  yet,  exceptions  can  be  found  for  example  in 
Barcelona town council (https://ajuntament.barcelona.cat/transparencia/es/codigo-conducta), whereas the 
Ethical Code of the Public Service of Catalonia is also in the making.   

29   See, for example, the registry of Barcelona Provincial Council: 

(https://www.diba.cat/es/web/secretaria/registre-interessos)  and  the  registry  of  the  Catalan  Generalitat 
(https://web.gencat.cat/es/tramits/tramits-temes/registre-grups-interes).   

30   To  mention  just  a  few:  “Gobierno  Abierto  de  Navarra”  (http://gobiernoabierto.navarra.es/es),  the 
Generalitat of Catalonia (http://governobert.gencat.cat/ca/transparencia/), and most large municipalities, 
(http://www.pamplona.es/VerPagina.asp?IdPag=1936&Idioma=1),  Tudela 
such 
(http://governobert.gencat.cat/ca/transparencia/), 
(https://tudela.transparencialocal.gob.es/),  Barcelona 
Girona 
Lleida 
(http://www.paeria.cat/transparencia/es/).   

(http://www.girona.cat/transparencia/cat/index.php#&panel1-1) 

Pamplona 

as 

or 

31   See  the  Oficina  Antifrau  de  Catalunya    (https://www.antifrau.cat/es/),  in  operation  from  2009  and  the 
most  recent  Navarrese  Good  Practices  and  Anticorruption  Office,  created  in  April  2018  but  not 
implemented at the time interviews were conducted.   

93 

 
                                                 
 
the inexistence of this type of bodies. Here, an anti-corruption office would have very 
little work and I wish that by not having one -simply because we do not need it here- 
Navarre  is  not  ranked  lower  than  other  regions  where  anti-corruption  offices  exist 
because they are needed” (see also Interview 16).  

In  the  case  of  Catalonia,  the  regional  law  on  transparency  is  one  of  the  most 
demanding, but some experts have denounced that few effective resources have been 
invested  in  this  matter,  either  because  it  was  not  high  in  the  government’s  list  of 
priorities or due to budgetary constraints (Interview 5). As for the  Oficina Antifrau de 
Catalunya, three main features have been praised: (1) its institutional dependency on 
the regional parliament (rather than the regional government); (2) its scope of action 
that  encompasses  all  Catalan  public  sector,  including  the  regional  and  local 
governments, public universities and public enterprises; and (3) its two-headed nature 
with functions of prevention as well as investigation, in some sort of “carrot and stick 
approach” (Interview 3).  

From  the  point  of  view  of  prevention,  the  Catalan  Office  is  a  service-provider 
institution  that  acts  as  a  public  consultancy  and  can  make  recommendations  and 
allegations  to  reform  procedures  and  regulations.  It  has  also  developed  awareness 
campaigns  about  acceptable  and  non-acceptable  behaviours  addressed  to  public 
employees.  It  writes  reports  and  studies;  publishes  a  barometer  on  corruption  every 
other year; responds to queries; and offers advice and training on its own initiative or 
under  request.  However,  some  interviewees  regretted  that  the  institution  does  not 
always  have  enough  resources  or  case-oriented  expertise  to  give  advice  on  key 
undertakings  like  designing  an  ethical  code  within  a  framework  of  institutional 
integrity, what entails more than talking about ethics and values in a broad sense. As 
they  put  it,  “at  local  level  we  often  deal  with  ‘ethical  dilemmas’  of  little  relevance  if 
compared  to  major  cases  of  political  corruption,  such  as  a  janitor  leaving  the  facility 
where he works half an hour earlier, or a member from the administrative staff taking 
office material home. Yet, when we sought the Oficina Antifrau’s help, we were simply 
given some photocopies from a book or a journal article” (Interviews 1 and 2).  

Regarding  the  second  dimension  of  investigation  and  control,  the  Antifraud  Office  is 
not  a  judicial  institution,  neither  it  has  any  sanctioning  powers;  therefore,  if  an 
investigation  reveals  solid  facts,  the  case  must  be  brought  before  the  competent 
administrative  authority,  the  prosecutor's  office  or  the  court.  Moreover,  the  starting 
point  for  most  of  its  investigations  are  alleged  misbehaviours  or  fraudulent  actions 
reported by citizens or public employees through an anonymous box of complaints. If 
only  because  of  the  negligible  results  of  the  Office  in  this  area,  most  experts  have 
focused their criticism here.  

For  example,  it  has  been  pointed  out  that  the  Office  does  not  include  a  proper 
investigation  unit,  and  it  has  only  uncovered  one  or  two  prosecutable  cases  since  it 
was created (Interview 5). Worries were also raised about the double-sword nature of 
anonymity, for it can lead to the unlawful use of complaints. In fact, “for the system to 
work  properly,  it  is  essential  to  protect  the  identity  of  the  complainant,  but  also  to 
provide enough guarantees for the defendant’s rights” (Interview 2). Moreover, there 
seems to be some blur of roles between institutions, that “sometimes makes it difficult 
to establish who does what” (Interview 1). Ultimately, since the Office’s main goal has 
always  been  “building  an  anti-corruption  culture”,  it  has  performed  an  important 
pedagogical task, but it has failed to achieve a good balance between the prevention 
and control functions (Interviews 10 and 12). 

The ethical codes have been valued as a positive instrument, but their usefulness and 
practical  effects  ultimately  rest  on  their  enactment  and  implementation.  According  to 
some interviewees, it is essential that they are built in a bottom-up fashion, because 
having  officials  and  public  employees  involved  in  the  elaboration  of  their  own 
regulations  facilitates  that  they  become  socialised  into  the  appropriate  values  and 
practices. “Based on a top-down approach, however, many of these codes have been 

94 

 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

made  by  a  commission  of  experts;  this  was  the  case  of  the  Catalan  regional  police 
forces, what left the mossos d’esquadra irritated and disengaged” (Interview 7). 

Regarding the issue of employment and hiring practices in the public sector, there is a 
broadly  accepted  view  that  public  employees  are  fundamental  for  the  development 
and  promotion  of  governance.  Therefore,  it  is  essential  a  good  model  of  human 
resources management, recruitment and selection that focuses on attracting the best 
candidates  as  well  as  retaining  and  enhancing  talented  workers  already  within  the 
public  administration  (García,  2018).  Moreover,  the  professionalization  of  public 
employees is considered an effective mechanism to fight corruption (Ibid).  

Taking  this  into  consideration,  most  experts  recognised  the  shortcoming  of  some 
widespread  hiring  practices  of  public  employees  that  can  be  found  at  all  territorial 
levels  in  Spain.  Arguably,  “although  street-level  bureaucrats  tend  to  enter  public 
administration  through  a  clean  selection  process,  the  system  was  designed  over  a 
century ago and it, thus, seems no longer appropriate for the public administration of 
the 21st century” (Interview 2). At higher-ranked positions, where most discretionary 
appointments take place, there is a second trend that prioritises personal and political 
connections over professional capabilities and merits; or as it has been put in a more 
refined  argument,  “merits  and  capabilities  do  not  necessarily  guarantee  reaching  the 
top positions at public administration” (Interview 1). This is a typical problem for the 
bulk of state enterprises and foundations, where discretionary appointments are very 
significant, too. Even if the  economic  crisis has allowed a  reduction in the number  of 
discretionary  positions, 
relatives, 
the  common  practice 
acquaintances, liked-minded people or those loyal to the political cause” (Ibid).  

remains  of  hiring 

As  many  interviewees  have  pointed  out,  Spanish  ACs  and  municipalities  -including 
Catalonia and Navarre- need meritocratic reforms to modify and modernize what can 
be considered an obsolete system of provision of public employment. Specifically, “it is 
necessary  to  develop  a  professional  career  that  allows  to  keep  public  employees 
motivated,  promotes  an  objective  assessment  of  their  merits  and  achievements  and 
takes  the  latter  into  account  for  their  promotion,  as  a  means  to  retain  talent  and  to 
contribute to the professionalization of public employees (Interview 6). 

14. Summary of Conclusions of the 2017 EQI Report 

This  report  has  presented  the  findings  of  the  third  round  of  the  European  Quality  of 
Government Index (EQI), which is based on a large citizen survey of  perceptions and 
experiences  with  their  local  and  regional  public  services  and  institutions.  In  addition, 
we  presented  the  improvements  vis-à-vis  previous  rounds  of  the  index  (2010  and 
2013)  and  demonstrated  how  the  index  was  retrospectively  changed  to  make  valid 
comparisons  of  regional  governance  across  and  within  countries  and  over  time. 
Adjustments  were  made  via  several  analyses  of  previous  rounds  of  the  data  that 
pointed to areas of possible improvement (see Annoni and Charron 2018).  The data 
was  presented  over  time  to  show  trends  associated  with  regional  quality  of 
government in Europe. Finally, we sought to corroborate and better understand some 
of the mechanisms for divergent changes in QoG within regions in the same country.  
Two in depth case study analyses were done following up the results of the third round 
of the QoG data – in Poland and Spain, where we observed positive trends over time 
in  two  regions  (Pomorskie  and  Navarre)  and  more  stagnant  patterns  in  two  other 
regions (Catalonia and Lubelske). 

As this report has shown through the European Quality of Government Index (EQI), – 
i.e.  QoG  defined  as  exercising  power  and  implementing  policies  in  an  impartial,  non-
corrupt, and  efficient way  – is still wide across both European  countries and  regions.  
And that the EQI is highly related with socio-economic development across space and 

95 

 
 
time.  On  the  one  hand,  the  findings  of  this  paper  seem  in  line  with  the  pessimistic 
literature noting that, instead of regional convergence, Europe is experiencing, if any, 
an  increase  in  regional  divergence  in  terms  of  economic  growth,  productivity,  and 
employment (Farole, Rodríguez-Pose and Storper 2011:1090).  

On  the  other  hand,  there  is  also  a  more  optimistic  interpretation  of  the  data.  A 
principal finding of this study  is the  relatively high stability in quality of government, 
as  measured  by  the  index.  Nevertheless,  it  is  worth  noting  that  the  old,  mainly 
geographic  dividing  lines  in  quality  of  government,  between  Northern  and  Southern 
Europe,  and  very  particularly,  between  Western  and  Eastern  Europe,  do  seem  to  be, 
even if  slowly, blurring. The divergences in  the EQI are not as  stark in 2017 as they 
were  in  2010.  For  instance,  we  have  high-performing  Eastern  regions  that  have 
surpassed  many  Western  regions  in  quality  of  government,  such  as  Jihovychod 
(CZ06),  or  Stredni  Morava  (CZ07)  in  the  Czech  Republic,  and  the  country  of  Estonia 
(EE). In addition, the list of regions with the most significant improvements in quality 
of  government  in  the  period  under  study  is  dominated  by  Eastern  regions,  such  as 
Bucharesti,  (RO32)  in  Romania,  Severen  Tsentralen  (BG32)  in  Bulgaria,  Prague 
(CZ01) in the Czech Republic, or Pomorskie (PL63) in Poland. 

In contrast it is regions in Western Europe  that are the ones demonstrating the most 
noticeable declines in quality of government, such as Guyane (FR93) in France, Valle 
d'Aosta (ITC2), Abruzzo (ITF1), or Piemonte (ITC1) in Italy, Canarias (ES70) in Spain, 
or Athens (EL3) in Greece. Yet regions in Western Europe are quite heterogeneous in 
terms  of QoG. While most regions in Northern Europe have  remained among the top 
performers,  recent  years  have  seen  a  fall  of  numerous  Southern  regions,  particularly 
in  Italy,  Greece  and  Spain.  At  the  same  time,  most  regions  in  Portugal,  as  well  as 
some  Spanish  ones  in  the  northern  part  of  the  country,  have  shown  improvements 
since 2010. In other words, geographic and historical legacies do matter, but they do 
not  fully  determine  the  quality  of  government  in  a  region.  In  line  with  the  literature, 
QoG  is  a  generally  stable  characteristic  of  the  region,  but  there  are  also  notable 
changes. 

These results were largely confirmed by the expert interviewees in the four case study 
regions.  Mechanisms that were proposed to explain the variation in the regions within 
the  same  country  were  –  differences  in  administrative  practices,  media  culture,  civil 
society, fiscal autonomy, and historical legacies.  These mechanisms can surely aid in 
crafting  policy  recomendations  for  improving  the  quality  of  government  in  more 
regions within Europe.   

96 

 
 
 
 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

References  
Annoni,  P.,  &  Charron,  N.  (2017).  Measurement  assessment  in  cross-country 
comparative  analysis:  Rasch  modelling  on  a  measure  of  institutional  quality.  Social 
Indicators Research, 1-30.  

Bauhr,  M.  (2012).  Need  or  Greed  Corruption.  Good  Government:  The  Relevance  of 
Political Science, 68-86. 

Charron,  Nicholas,  Victor  Lapuente  and  Lewis  Dijkstra.  2012.  “Regional  governance 
matters:  a  study  on  regional  variation  in  quality  of  government  within  the  EU”, 
European Commission WP 01/2010.  

Charron,  Nicholas,  Victor  Lapuente  and  Bo  Rothstein.  2018.  “Mapping  the  Quality  of 
Government  in  Europe:  An  analysis  at  the  national  and  regional  level  within  the  EU 
member states”.  

Charron, N., Dijkstra, L., & Lapuente, V. (2014). Regional governance matters: quality 
of government within European Union member states. Regional Studies, 48(1), 68-90. 

Charron,  N.,  Dijkstra’,  L.,  &  Lapuente,  V.  (2015).  Mapping  the  regional  divide  in 
Europe:  A  measure  for  assessing  quality  of  government  in  206  European  regions. 
Social Indicators Research, 122(2), 315-346. 

Charron, Nicholas and Victor Lapuente. 2013. ”Why do Some Regions in Europe Have 
Higher Quality of Government?” Journal of Politics. vol 75(3): 567-582. 

Charron,  Nicholas,  Victor  Lapuente  and  Bo  Rothstein.    2013  ’Quality  of  Government 
and  Corruption  from  a  European  Perspective:  A  Comparative  Study  of  Good 
Governance in EU Regions’’.  Edward Elgar Publishing. 

Charron, Nicholas, Victor Lapuente and Bo Rothstein. 2010. “Measuring the Quality of 
Government  in  the  EU  and  Sub-national  Variation”,  Report  for  the  European 
CommissionDirectorate-General  Regional  Policy  and  Directorate  Policy  Development, 
2010 

Charron, Nicholas, Carl Dahlström, and Victor Lapuente. "Measuring Meritocracy in the 
public sector in Europe: A new national and sub-National Indicator." European Journal 
on Criminal Policy and Research 22.3 (2016): 499-523. 

Charron,  Nicholas,  and  Bo  Rothstein.  "10.  Regions  of  trust  and  distrust:  how  good 
institutions  can  foster  social  cohesion."  Bridging  the  Prosperity  Gap  in  the  EU:  The 
Social Challenge Ahead (2018): 220. 

Farole, T., Rodríguez‐Pose, A., & Storper, M. (2011). Cohesion policy in the European 
Union:  growth,  geography,  institutions.  JCMS:  Journal  of  Common  Market  Studies, 
49(5), 1089-1111. 

Fazekas,  M.,  &  Kocsis,  G.  (2017).  Uncovering  high-level  corruption:  Cross-national 
objective  corruption  risk  indicators  using  public  procurement  data.  British  Journal  of 
Political Science, 1-10. 

García  Jiménez,  Antonio.  2018.  “El  empleado  público  como  epicentro  del  buen 
gobierno”,  
Revista General de Derecho Administrativo, 49.  

Herrero, Carmen, Antonio Villar and Ángel Soler. 2018. Las facetas de l bienestar. Una 
aproximación  multidimensional  a  la  calidad  de  vida  en  España  y  sus  Comunidades 
Autónomas (2006-2015). Bilbao: Fundación BBVA 

97 

 
 
 
 
 
Kaufmann,  Daniel,  Aart  Kraay  and  Massimo  Mastruzzi.    2009.    ‘The  Worldwide 
Governance  Indicators:  Methodology  and  Analytical  Issues.’    World  Bank  Policy 
Research Working Paper, No. 5430. 

Lapuente,  Víctor.  2014.  “Mapa  de  la  calidad  de  gobierno  en  las  autonomías”, 
eldiario.es, 
at: 
https://www.eldiario.es/piedrasdepapel/Mapa-Calidad-Gobierno-
Autonomias_6_308729137.html 

September 

Available 

30th. 

Magallón, Raúl, Victoria Bolanos and Victoria Anderica. 2017. “Changing civil society in 
Spain through transparency and civic technologies”, Observatorio (September), 61-77. 
DOI: 10.15847/obsOBS1132017969. 

Nardo,  Michela,  Michaela  Saisana,  Andrea  Saltelli  and  Stefano  Tarantola.    2008.  
‘Handbook on Constructing Composite Indicators: Methodology and User Guide’  

OECD, European Commission, Joint Research Centre. Published by : OECD Publishing  

OECD,  2016.    ‘Regions  at  a  Glance’.    https://www.keepeek.com//Digital-Asset-
Management/oecd/governance/oecd-regions-at-a-glance-2016_reg_glance-2016-
en#page43  

Tabellini,  Guido.  2005.  “Culture  and  Institutions:  Economic  Development  in  the 
Regions  of  Europe” 
  IGIER  Working  Paper  No.  292.  Available  at  SSRN: 
http://ssrn.com/abstract=754086 

98 

 
 
 
 
 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

Appendix 

Table A1 : Factor analysis: Principle component of regional items 

Factor 
analysis/correlation  Number 

of 

obs 

Method: 

principal-component 

factors 

Retained 

factors 

Rotation: 

(unrotated) 

Number 

of 

params 

= 

= 

= 

185 

3 

51 

-------------------------------------------------------------------------- 

Factor 

| 

Eigenvalue  Difference  Proportion  Cumulative 

-------------+------------------------------------------------------------ 

Factor1 

Factor2 

Factor3 

Factor4 

Factor5 

Factor6 

Factor7 

Factor8 

Factor9 

Factor10 

Factor11 

Factor12 

Factor13 

Factor14 

Factor15 

Factor16 

Factor17 

Factor18 

| 

| 

| 

| 

| 

| 

| 

| 

| 

| 

| 

| 

| 

| 

| 

| 

| 

| 

9.95408 

7.47533 

0.553 

0.553 

2.47874 

0.66645 

0.1377 

0.6907 

1.8123 

0.98815 

0.1007 

0.7914 

0.82415 

0.08842 

0.0458 

0.8372 

0.73572 

0.25103 

0.0409 

0.8781 

0.48469 

0.07526 

0.0269 

0.905 

0.40943 

0.10651 

0.0227 

0.9277 

0.30292 

0.00838 

0.0168 

0.9446 

0.29455 

0.06119 

0.0164 

0.9609 

0.23336 

0.05689 

0.013 

0.9739 

0.17647 

0.08582 

0.0098 

0.9837 

0.09065 

0.01669 

0.005 

0.9887 

0.07396 

0.02978 

0.0041 

0.9928 

0.04418 

0.015 

0.0025 

0.9953 

0.02918 

0.00836 

0.0016 

0.9969 

0.02082 

0.00188 

0.0012 

0.9981 

0.01894 

0.00309 

0.0011 

0.9991 

0.01585 

. 

0.0009 

1 

-------------------------------------------------------------------------- 

LR test: chi2(153)=4911.15, prob>chi2=0.0000 

99 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table A2 : Rotated factor loadings: Verimax 

-------------- 

----------- 

--------- 

----------- 

-------------- 

Variable | 

Factor1 

Factor2 

Factor3 | 

Uniqueness 

-------------+ 

----------- 

--------- 

----------+ 

-------------- 

EdQual | 

0.0528 

0.7574 

0.4037 

0.2606 

HelQual | 

0.3066 

0.868 

0.0477 

0.1503 

LawQual | 

0.1132 

0.875 

0.1042 

0.2107 

EdImpart1 | 

0.5677 

-0.0431 

0.6974 

0.1894 

HelImpart1 | 

0.6584 

0.2272 

0.438 

0.3229 

LawImpart1 | 

0.7937 

0.2326 

0.3713 

0.1781 

EdImpart2 | 

0.1679 

0.4976 

0.712 

0.2174 

HelImpart2 | 

0.3809 

0.7072 

0.2935 

0.2686 

LawImpart2 | 

0.429 

0.5469 

0.5404 

0.2247 

EdCorr | 

0.8807 

0.1168 

0.2202 

0.1621 

HelCorr | 

0.8747 

0.3448 

0.0602 

0.1124 

LawCorr | 

0.8935 

0.1975 

0.2641 

0.0929 

NeedCorr | 

0.8776 

0.2907 

0.245 

0.0852 

GreedCorr | 

0.8438 

0.2109 

0.1914 

0.2069 

ElecCorr | 

0.4602 

0.1315 

0.6798 

0.3088 

TaxImpart | 

0.3186 

0.0523 

0.8164 

0.2292 

noAskB_any | 

0.4693 

0.7085 

-0.1338 

0.2599 

noPayB_any | 

0.4774 

0.5517 

-0.4393 

0.2747 

100 

 
 
 
 
 
 
 
 
 
 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

Table A3 : Pairwise correlations 

StEd 
Qual 

StHel 
Qual 

stLaw 
Qual 

stEd 
Impart1 

stHel 
Impart1 

StLaw 
Impart1 

stEd 
Impart2 

StHel 
Impart2 

stEdQual 

stHelQual 

0.641 

stLawQual 

0.711 

0.818 

stEdImpart1 

0.310 

0.177 

0.139 

stHelImpart1  0.346 

0.430 

0.270 

0.729 

stLawImpart1  0.391 

0.466 

0.427 

0.755 

0.776 

stEdImpart2 

0.628 

0.469 

0.399 

0.566 

0.497 

stHelImpart2  0.592 

0.794 

0.573 

0.352 

0.744 

stLawImpart2  0.545 

0.575 

0.601 

0.559 

0.618 

stEdCorr 

0.322 

0.384 

0.251 

0.674 

0.608 

stHelCorr 

0.392 

0.604 

0.393 

0.507 

0.737 

stLawCorr 

0.355 

0.444 

0.373 

0.682 

0.680 

stNeedCorr 

0.305 

0.557 

0.383 

0.618 

0.675 

stGreedCorr 

0.261 

0.451 

0.243 

0.512 

0.637 

stElecCorr 

0.362 

0.250 

0.238 

0.606 

0.502 

stTaxImpart 

0.376 

0.213 

0.153 

0.686 

0.510 

stnoAskB_any  0.494 

0.673 

0.651 

0.175 

0.311 

stnoPayB_any  0.243 

0.520 

0.375 

-0.063 

0.250 

0.415 

0.528 

0.730 

0.778 

0.776 

0.907 

0.810 

0.718 

0.551 

0.523 

0.452 

0.283 

0.655 

0.750 

0.381 

0.334 

0.392 

0.494 

0.450 

0.626 

0.611 

0.355 

0.157 

0.675 

0.434 

0.684 

0.492 

0.581 

0.517 

0.392 

0.367 

0.535 

0.418 

stLaw 
Impart2 

StEd 
Corr 

stHel 
Corr 

StLaw 
Corr 

stNeed 
Corr 

StGreed 
Corr 

stElec 
Corr 

StTax 
Impart 

Stno 
AskB_any 

stHelImpart2 

stLawImpart2    

stEdCorr 

0.467 

stHelCorr 

0.510 

0.881 

stLawCorr 

0.666 

0.917 

0.874 

stNeedCorr 

0.702 

0.840 

0.842 

0.891 

stGreedCorr 

0.611 

0.727 

0.775 

0.792 

0.906 

stElecCorr 

0.646 

0.557 

0.432 

0.589 

0.671 

stTaxImpart 

0.540 

0.459 

0.348 

0.483 

0.509 

stnoAskB_any  0.515 

0.509 

0.585 

0.533 

0.597 

stnoPayB_any  0.304 

0.308 

0.497 

0.333 

0.477 

0.631 

0.468 

0.456 

0.537 

0.745 

0.309 

0.067 

0.136 

-0.111  0.695 

101 

 
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table  A4 :  2017  EQI  and  Pillar  Estimates  with  Past  Data  Retroactively 
Changed 

nuts 

name 

eqi2017 

quality_17 

impartiality_17 

corruption_17 

eqi2013 

eqi2010 

AT 

AT11 

AT12 

AT13 

AT21 

AT22 

AT31 

AT32 

AT33 

AT34 

BE 

BE1 

BE2 

BE3 

BG 

Austria 

Burgenland 

Niederöstrerreich 

Wien 

Kärnten 

Steiermark 

Oberösterreich 

Salzburg 

Tirol 

Voralberg 

Belgium 

Brussels 

Vlaams Gewest 

Wallonie 

Bulgaria 

BG31 

Severozapaden 

BG32 

Severen Tsentralen 

BG33 

Severoiztochen 

BG34 

Yugoiztochen 

BG41 

Yugozapaden 

BG42 

Yuzhen Tsentralen 

CY 

CZ 

Cyprus 

Czech Rep. 

CZ01 

Praha 

CZ02 

Stredni Cechy 

CZ03 

Jihozapad 

CZ04 

Severozapad 

CZ05 

Severovychod 

CZ06 

Jihovychod 

CZ07 

Stedni Morava 

CZ08 

Moravskoslezsko 

DE 

DE1 

DE2 

DE3 

DE4 

DE5 

DE6 

DE7 

DE8 

DE9 

DEA 

Germany 

Baden Wuttemberg 

Bavaria 

Berlin 

Brandenburg 

Bremen 

Hamburg 

Hessen 
Mecklenburg-
Vorpommen 

Lower Saxony 
North 
Westphalia 

Rhine 

0.805 

0.787 

0.724 

0.860 

0.653 

0.760 

0.705 

0.894 

1.032 

1.084 

0.616 

-0.105 

0.969 

0.221 

-1.731 

-2.264 

-0.997 

-1.364 

-2.189 

-1.882 

-1.541 

-0.106 

-0.296 

-0.163 

-0.647 

-0.291 

-0.992 

-0.171 

0.015 

-0.023 

-0.276 

1.013 

1.076 

1.343 

0.610 

0.738 

1.094 

1.247 

1.064 

1.167 

1.247 

0.757 

0.674 

0.385 

0.448 

0.709 

0.355 

0.611 

0.713 

0.833 

1.271 

0.844 

0.635 

-0.343 

1.125 

0.082 

-1.635 

-2.113 

-1.581 

-1.317 

-1.814 

-1.778 

-1.272 

-0.134 

-0.142 

0.153 

-0.075 

-0.250 

-0.740 

-0.247 

0.066 

-0.081 

-0.085 

0.980 

1.238 

1.924 

0.008 

0.372 

0.632 

1.316 

0.888 

0.975 

1.200 

0.483 

1.000 

1.315 

1.064 

1.050 

0.993 

0.952 

0.701 

1.031 

1.066 

1.373 

0.383 

-0.477 

0.875 

-0.215 

-1.854 

-2.781 

-0.955 

-1.524 

-1.957 

-1.676 

-2.264 

-0.215 

-0.051 

0.104 

-0.940 

0.115 

-0.869 

0.280 

0.382 

0.315 

-0.031 

0.904 

0.833 

0.809 

0.742 

0.874 

1.407 

1.096 

1.145 

1.333 

1.286 

0.704 

102 

0.654 

0.575 

0.582 

0.727 

0.542 

0.637 

0.625 

0.723 

0.647 

0.917 

0.762 

0.516 

0.802 

0.772 

-1.519 

-1.654 

-0.347 

-1.104 

-2.561 

-1.992 

-0.921 

0.044 

-0.663 

-0.728 

-0.856 

-0.707 

-1.262 

-0.527 

-0.406 

-0.299 

-0.681 

1.047 

1.041 

1.152 

1.015 

0.890 

1.127 

1.193 

1.046 

1.067 

1.119 

1.003 

0.820 

0.945 

0.998 

0.391 

0.778 

1.010 

0.886 

0.778 

1.247 

0.452 

0.629 

0.043 

1.088 

0.001 

1.063 

1.310 

1.062 

1.088 

1.213 

0.936 

0.984 

0.964 

1.192 

1.135 

0.345 

-0.416 

0.733 

-0.098 

-1.860 

-1.902 

-2.270 

-2.655 

-1.677 

-2.200 

-0.467 

-1.195 

-1.860 

-2.275 

-2.817 

-2.016 

-1.245 

-1.344 

0.011 

0.198 

-0.498 

-0.582 

-0.534 

-1.016 

-0.486 

-0.410 

-0.344 

-0.212 

-0.989 

-1.007 

-0.388 

-0.302 

-0.277 

-0.598 

-0.452 

-0.675 

-0.662 

-0.527 

0.770 

0.889 

0.946 

0.410 

0.511 

0.753 

0.688 

0.755 

0.750 

0.965 

0.773 

0.877 

0.648 

0.869 

0.864 

0.847 

0.853 

0.565 

0.840 

0.830 

0.636 

0.648 

 
 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

DEB 

DEC 

DED 

DEE 

DEF 

DEG 

DK 

Rhineland-Palatinate 

Saarland 

Saxony 

Saxony-Anhalt 

Schleswig-Holstein 

Thuringia 

Denmark 

DK01 

Hovedstaden 

DK02 

Sjaelland 

DK03 

Syddanmark 

DK04 

Midtylland 

DK05 

Nordjylland 

EE 

ES 

ES11 

ES12 

ES13 

ES21 

ES22 

ES23 

ES24 

ES30 

ES41 

ES42 

ES43 

ES51 

ES52 

ES53 

ES61 

ES62 

ES63 

ES64 

ES70 

FI 

FI13 

FI18 

FI19 

FI1A 

FI20 

FI1B 

FI1C 

FI1D 

FR 

FR10 

FR21 

FR22 

Estonia 

Spain 

Galicia 
Principado 
Asturias 

Cantabria 

Pais Vasco 

Navarra 

La Rioja 

Aragón 
Comunidad 
Madrid 

de 

de 

Castilla y León 

Castilla-La Mancha 

Extremadura 

Cataluña 
Comunidad 
Valenciana 

Illes Balears 

Andalucia 

Región de Murcia 

Ceuta (ES) 

Melilla (ES) 

Canarias (ES) 

Finland 

Itä-Suomi 

Etelä-Suomi 

Länsi-Suomi 

Pohjois-Suomi 

Åland 

Helsinki-Uusimaa 

Etelä-Suomi 
Pohjois- 
Suomi 

ja 

Itä-

France 

Ile-de-France 
Champagne-
Ardenne 

Picardie 

1.128 

1.036 

0.819 

0.588 

1.107 

0.949 

1.400 

1.341 

1.230 

1.362 

1.648 

1.350 

0.231 

-0.327 

-0.432 

0.218 

0.423 

0.652 

0.502 

0.244 

0.098 

-0.220 

-0.322 

-0.301 

0.023 

-0.392 

-0.445 

-0.545 

-0.739 

-0.136 

1.205 

0.984 

0.615 

0.288 

0.875 

0.871 

1.231 

1.097 

0.861 

1.239 

1.674 

1.164 

0.054 

-0.013 

-0.194 

0.529 

0.631 

0.948 

0.732 

0.885 

0.380 

0.494 

0.309 

-0.127 

0.353 

-0.168 

-0.046 

-0.429 

-0.606 

0.302 

1.025 

0.847 

0.787 

0.636 

1.249 

0.885 

1.280 

1.197 

1.145 

1.209 

1.528 

1.322 

0.248 

-0.381 

-0.309 

0.387 

0.726 

0.672 

0.486 

0.104 

0.097 

-0.452 

-0.689 

-0.223 

-0.023 

-0.445 

-0.564 

-0.411 

-0.805 

-0.143 

1.035 

1.166 

0.967 

0.776 

1.078 

0.991 

1.539 

1.584 

1.553 

1.491 

1.566 

1.420 

0.369 

-0.553 

-0.747 

-0.286 

-0.133 

0.268 

0.235 

-0.284 

-0.195 

-0.679 

-0.552 

-0.519 

-0.264 

-0.520 

-0.678 

-0.738 

-0.728 

-0.551 

-0.711 

1.428 

-0.814 

1.195 

-0.471 

1.328 

-0.773 

1.608 

1.336 

2.323 

1.495 

1.406 

1.442 

0.409 

0.498 

0.338 

0.407 

1.106 

1.218 

1.540 

2.033 

1.315 

1.203 

1.114 

0.424 

0.343 

0.464 

0.194 

2.176 

1.407 

1.269 

1.380 

0.361 

0.486 

0.175 

0.377 

103 

2.512 

1.602 

1.597 

1.676 

0.397 

0.612 

0.338 

0.605 

0.931 

0.927 

0.710 

0.322 

0.995 

0.425 

1.545 

1.518 

1.347 

1.576 

1.638 

1.636 

0.732 

0.932 

0.969 

0.771 

1.129 

1.181 

1.549 

1.431 

1.557 

1.542 

1.762 

1.441 

-0.052 

-0.103 

-0.047 

-0.054 

-0.511 

0.432 

0.423 

0.407 

0.283 

0.263 

0.368 

0.111 

0.262 

0.243 

-0.221 

0.144 

0.371 

0.048 

0.511 

0.073 

0.137 

0.204 

-0.153 

-0.130 

0.107 

0.289 

-0.181 

-0.480 

-0.275 

-0.026 

0.053 

0.019 

-0.119 

-0.251 

0.326 

0.164 

-0.574 

1.497 

1.419 

1.525 

1.496 

1.521 

2.639 

0.421 

0.401 

0.258 

0.258 

0.163 

1.398 

1.398 

1.398 

1.398 

1.398 

1.398 

1.398 

1.398 

1.398 

0.690 

0.536 

0.211 

0.463 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
FR23 

FR24 

FR25 

FR26 

FR30 

FR41 

FR42 

FR43 

FR51 

FR52 

FR53 

FR61 

FR62 

FR63 

FR71 

FR72 

FR81 

FR82 

FR83 

FR91 

FR92 

FR93 

FR94 

EL 

EL5 

EL6 

EL3 

EL4 

HR 

HR03 

HR04 

HU 

HU10 

Haute-Normandie 

Centre 

Basse-Normandie 

Bourgogne 

Nord - Pas-de-Calais 

Lorraine 

Alsace 

Franche-Comte 

Pays de la Loire 

Bretagne 

Poitou-Charentes 

Aquitaine 

Midi-Pyrenees 

Limousin 

Rhone-Alpes 

Auvergne 
Languedoc-
Roussillon 
Provence-Alpes-
Cote d'Azur 

Corse 

Guadeloupe 

Martinique 

Guyane 

Reunion 

Greece 

Voreia Ellada 

Kentriki Ellada 

Attica 

Nisia Aigaiou-Kriti 

Croatia 

Jadranska Hrvatska 
Kontinentalna 
Hrvatska 

Hungary 
Közép-
Magyarország 

HU21 

Közép-Dunántúl 

HU22 

Nyugat-Dunántúl 

HU23 

Dél-Dunántúl 

HU31 

Észak-Magyarország 

HU32 

Észak-Alföld 

HU33 

Dél-Alföld 

IE 

IE01 

IE02 

IT 

ITC1 

ITC2 

Ireland 
Border,  Midland  and 
Western 
Southern 
Eastern 

and 

Italy 

Piemonte 

Valle d'Aosta 

0.454 

0.420 

0.390 

0.283 

0.293 

0.238 

0.380 

0.185 

0.719 

0.769 

0.321 

0.695 

0.434 

0.619 

0.578 

0.441 

0.130 

0.213 

0.072 

-1.030 

-0.726 

-1.557 

-0.413 

-1.387 

-1.707 

-1.236 

-1.212 

-1.462 

-1.211 

-1.156 

-1.239 

-1.150 

-1.455 

-0.969 

-1.016 

-0.980 

-1.089 

-1.266 

-0.748 

0.839 

0.901 

0.817 

-1.130 

-1.190 

-0.677 

0.524 

0.328 

0.304 

0.162 

0.637 

0.479 

0.648 

0.164 

0.694 

0.795 

0.446 

0.850 

0.381 

0.587 

0.635 

0.490 

0.225 

0.259 

-0.091 

-1.294 

-0.835 

-2.700 

-0.221 

-1.397 

-1.756 

-1.181 

-1.302 

-1.262 

-1.224 

-1.225 

-1.224 

-1.227 

-1.469 

-1.334 

-1.237 

-0.961 

-1.481 

-1.019 

-0.769 

0.693 

0.280 

0.443 

0.495 

0.228 

0.074 

-0.109 

0.128 

0.209 

0.712 

0.744 

0.169 

0.513 

0.471 

0.642 

0.489 

0.370 

0.162 

0.377 

0.318 

-1.014 

-0.886 

-1.191 

-0.322 

-1.324 

-1.737 

-1.069 

-1.153 

-1.378 

-1.381 

-1.282 

-1.431 

-1.092 

-1.312 

-0.660 

-1.010 

-0.956 

-0.925 

-1.565 

-0.717 

0.914 

0.510 

0.443 

0.328 

0.430 

0.136 

0.316 

0.321 

0.162 

0.674 

0.683 

0.313 

0.648 

0.403 

0.561 

0.548 

0.416 

0.319 

0.774 

0.683 

0.289 

0.145 

0.359 

0.555 

0.511 

0.577 

0.961 

0.723 

0.767 

0.722 

0.546 

0.624 

0.693 

0.153 

0.595 

0.496 

0.474 

0.531 

0.262 

0.468 

0.483 

0.362 

0.973 

0.733 

0.779 

0.394 

0.692 

0.752 

0.550 

-0.012 

0.369 

0.522 

-0.020 

-0.020 

-0.672 

-0.379 

-0.613 

-0.651 

-1.293 

-1.445 

-1.325 

-1.053 

-1.590 

-0.899 

-0.839 

-0.929 

-1.008 

-1.427 

-0.807 

-0.693 

-0.917 

-0.745 

-1.080 

-0.677 

0.821 

0.058 

0.180 

0.232 

0.152 

-0.402 

-0.484 

-0.101 

-0.358 

-0.617 

-0.449 

-0.100 

-0.106 

-0.326 

-0.300 

-1.142 

-1.333 

-1.213 

-1.040 

-1.297 

-0.343 

-0.906 

-0.909 

-1.003 

-0.997 

-1.503 

-1.494 

-1.294 

-1.494 

-0.794 

-0.647 

-0.972 

-1.046 

-0.607 

-0.417 

-0.607 

-0.417 

-0.607 

-0.417 

-0.802 

-0.524 

-0.802 

-0.524 

-0.802 

-0.524 

0.738 

0.797 

0.844 

0.901 

0.862 

0.844 

0.797 

0.639 

-0.759 

-0.769 

-1.417 

0.918 

-1.315 

-1.509 

-0.098 

104 

0.806 

-1.195 

-1.163 

-0.444 

0.700 

0.797 

-1.162 

-1.079 

-0.878 

-0.324 

0.354 

0.407 

 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

ITC3 

ITC4 

ITH1 

ITH2 

ITH3 

ITH4 

ITH5 

ITI1 

ITI2 

ITI3 

ITI4 

ITF1 

ITF2 

ITF3 

ITF4 

ITF5 

ITF6 

ITG1 

ITG2 

LT 

LU 

LV 

MT 

NL 

NL11 

NL12 

NL13 

NL21 

NL22 

NL23 

NL31 

NL32 

NL33 

NL34 

NL41 

NL42 

PL 

PL11 

PL12 

PL21 

PL22 

PL31 

PL32 

PL33 

PL34 

PL41 

PL42 

Ligura 

Lombardia 

Bolzano 

Trento 

Veneto 

Friuli-Venezia Giulia 

Emilia-Romagna 

Toscana 

Umbria 

Marche 

Lazio 

Abruzzo 

Molise 

Campania 

Puglia 

Basilicata 

Calabria 

Sicilia 

Sardegna 

Lithuania 

Luxembourgh 

Latvia 

Malta 

Netherlands 

Groningen 

Friesland (NL) 

Drenthe 

Overijssel 

Gelderland 

Flevoland 

Utrecht 

Noord-Holland 

Zuid-Holland 

Zeeland 

Noord-Brabant 

Limburg (NL) 

Poland 

Lodzkie 

Mazowieckie 

Malopolskie 

Slaskie 

Lubelskie 

Podkarpackie 

Swietokrzyskie 

Podlaskie 

Wielkopolskie 

Zachodniopomorskie 

-1.251 

-0.481 

-0.364 

-0.364 

-0.459 

-0.488 

-0.457 

-0.849 

-1.510 

-1.383 

-1.530 

-1.978 

-1.183 

-1.877 

-1.545 

-1.668 

-2.183 

-1.544 

-1.234 

-0.263 

1.200 

-0.513 

-0.075 

1.205 

1.351 

1.351 

1.351 

1.331 

1.331 

1.331 

1.105 

1.105 

1.105 

1.105 

1.228 

1.228 

-0.461 

-0.660 

-0.522 

-0.401 

-0.480 

-0.632 

-0.626 

-0.511 

-0.456 

-0.464 

-0.367 

-0.589 

0.512 

-0.496 

-0.496 

0.399 

-0.353 

-0.028 

-0.942 

-1.418 

-1.398 

-1.353 

-2.404 

-0.787 

-1.642 

-1.096 

-1.766 

-2.574 

-1.381 

-1.186 

-0.043 

1.051 

-0.298 

-0.179 

1.224 

1.357 

1.357 

1.357 

1.273 

1.273 

1.273 

1.081 

1.081 

1.081 

1.081 

1.420 

1.420 

-0.373 

-0.474 

-0.477 

-0.267 

-0.390 

-0.300 

-0.332 

-0.407 

-0.075 

-0.364 

-0.178 

-1.433 

-1.098 

-0.151 

-0.151 

-0.728 

-0.540 

-0.766 

-1.049 

-1.481 

-0.890 

-1.691 

-0.779 

-0.868 

-2.004 

-1.587 

-0.889 

-2.293 

-1.875 

-0.813 

-0.253 

1.019 

-0.533 

-0.003 

1.125 

1.426 

1.426 

1.426 

1.362 

1.362 

1.362 

0.981 

0.981 

0.981 

0.981 

1.060 

1.060 

-0.523 

-0.757 

-0.648 

-0.472 

-0.594 

-0.891 

-0.725 

-0.680 

-0.727 

-0.754 

-0.565 

105 

-1.595 

-0.806 

-0.405 

-0.405 

-1.000 

-0.519 

-0.528 

-0.466 

-1.468 

-1.714 

-1.380 

-2.539 

-1.768 

-1.783 

-1.786 

-2.168 

-1.449 

-1.212 

-1.572 

-0.467 

1.401 

-0.654 

-0.034 

1.136 

1.127 

1.127 

1.127 

1.214 

1.214 

1.214 

1.132 

1.132 

1.132 

1.132 

1.071 

1.071 

-0.437 

-0.676 

-0.384 

-0.422 

-0.405 

-0.635 

-0.755 

-0.391 

-0.517 

-0.222 

-0.320 

-1.057 

-0.665 

-0.773 

-0.781 

0.686 

0.720 

0.526 

0.268 

-0.433 

-0.621 

0.092 

-0.037 

-0.464 

-0.518 

-0.760 

-0.704 

-0.725 

-0.383 

-0.760 

-0.624 

-1.679 

-1.340 

-1.291 

-1.023 

-1.822 

-1.308 

-2.370 

-2.284 

-1.771 

-1.756 

-1.602 

-1.333 

-1.845 

-2.167 

-1.749 

-1.843 

-1.488 

-0.999 

-0.809 

-0.992 

1.223 

1.031 

-0.885 

-0.937 

0.028 

1.236 

1.296 

1.333 

1.120 

1.530 

1.226 

1.191 

1.333 

1.114 

1.274 

1.173 

1.154 

1.210 

0.297 

1.096 

1.430 

1.430 

1.430 

1.030 

1.030 

1.030 

1.122 

1.122 

1.122 

1.122 

0.945 

0.945 

-0.679 

-0.964 

-0.782 

-0.878 

-0.826 

-1.014 

-0.567 

-0.913 

-0.933 

-1.123 

-0.687 

-0.931 

-0.801 

-0.886 

-0.731 

-0.842 

-0.399 

-0.979 

-0.666 

-1.019 

-0.543 

-0.907 

 
PL43 

PL51 

PL52 

PL61 

PL62 

PL63 

PT 

PT11 

PT15 

PT16 

PT17 

PT18 

PT20 

PT30 

RO 

Lubuskie 

Dolnoslaskie 

Opolskie 
Kujawsko-
Pomorskie 
Warminsko-
Mazurskie 

Pomorskie 

Portugal 

Norte 

Algarve 

Centro 

Lisboa 

Alentejo 

Açores 

Madeira 

Romania 

RO11 

Nord-Vest 

RO12 

Centru 

RO21 

Nord-Est 

RO22 

Sud-Est 

RO31 

Sud-Muntenia 

RO32 

Bucuresti-Ilfov 

RO41 

Sud-Vest Oltenia 

RO42 

Vest 

SE 

SE1 

SE2 

SE3 

SI 

SK 

Sweden 

Östra Sverige 

Södra Sverige 

Norra Sverige 

Slovenia 

Slovakia 

SK01 

Bratislavský kraj 

SK02 

Západné Slovensko 

SK03 

Stredné Slovensko 

SK04 

Východné Slovensko 

UK 

UKC 

UKD 

UKE 

UKF 

UKG 

UKH 

UKI 

UKJ 

UKK 

UKL 

UKM 

UKN 

United Kingdom 

Northeast England 

Northwest England 

Midland 

Yorkshire-Humber 
East 
England 
West 
England 

Midland 

East of England 

London 

South East England 

South West England 

Wales 

Scotland 

N. Ireland 

-0.411 

-0.482 

-0.298 

-0.338 

-0.342 

-0.133 

0.032 

-0.063 

-0.292 

0.070 

0.108 

0.247 

0.009 

0.169 

-1.555 

-1.849 

-1.434 

-1.580 

-1.973 

-1.104 

-1.578 

-1.615 

-1.341 

1.403 

1.417 

1.440 

1.282 

-0.293 

-0.811 

-0.954 

-1.013 

-0.626 

-0.681 

0.986 

1.129 

0.753 

0.926 

0.894 

1.133 

1.074 

1.004 

1.034 

1.124 

1.046 

0.977 

0.559 

-0.711 

-0.509 

-0.379 

-0.267 

-0.380 

-0.065 

-0.210 

-0.507 

-0.416 

-0.433 

-0.955 

-0.936 

-1.120 

-0.250 

-0.672 

-0.535 

-0.082 

-0.360 

-0.289 

-0.973 

-0.423 

-0.034 

0.148 

0.368 

-0.891 

-0.014 

0.123 

0.056 

0.200 

0.745 

-1.856 

-1.811 

-1.591 

-1.803 

-1.896 

-1.538 

-2.422 

-2.046 

-1.910 

1.220 

1.260 

1.238 

1.083 

-0.297 

-0.413 

-0.605 

-0.531 

-0.202 

-0.379 

0.814 

1.167 

0.829 

0.859 

0.706 

0.920 

0.836 

0.972 

0.895 

0.738 

0.541 

0.772 

-0.331 

-0.199 

0.096 

-0.022 

-0.359 

-0.040 

0.307 

0.091 

0.292 

-0.112 

-0.192 

-1.449 

-2.015 

-1.430 

-1.237 

-2.141 

-1.079 

-1.237 

-1.524 

-0.909 

1.280 

1.253 

1.366 

1.128 

-0.292 

-0.977 

-1.121 

-1.106 

-0.765 

-0.949 

0.933 

1.068 

0.545 

0.763 

0.713 

1.241 

0.994 

0.823 

0.930 

1.210 

1.320 

1.073 

0.787 

106 

-0.369 

-0.447 

-0.034 

-0.193 

0.085 

-0.092 

0.099 

0.365 

-0.062 

-0.063 

-1.194 

-1.523 

-1.127 

-1.531 

-1.669 

-0.577 

-0.908 

-1.100 

-1.059 

1.558 

1.584 

1.560 

1.497 

-0.260 

-0.958 

-1.034 

-1.293 

-0.845 

-0.644 

1.106 

1.033 

0.803 

1.056 

1.167 

1.116 

1.277 

1.109 

1.166 

1.303 

1.165 

0.981 

1.162 

-0.500 

-0.723 

-0.425 

-0.890 

-0.125 

-0.124 

-0.295 

-0.411 

0.148 

0.073 

-0.131 

-0.153 

-0.231 

0.777 

0.406 

-0.066 

0.005 

0.535 

0.337 

0.124 

-1.924 

-1.874 

-1.907 

-1.197 

-1.369 

-1.596 

-1.940 

-1.986 

-2.186 

-2.001 

-1.758 

-1.768 

-2.465 

-2.838 

-1.932 

-1.504 

-1.869 

-2.191 

1.432 

1.468 

1.443 

1.323 

1.302 

1.289 

1.361 

1.186 

-0.200 

-0.196 

-0.824 

-0.813 

-0.920 

-0.632 

-0.721 

-0.889 

-0.728 

-0.802 

-0.981 

-0.808 

0.720 

0.625 

0.766 

0.841 

0.748 

0.751 

0.858 

0.520 

0.609 

1.045 

0.577 

0.813 

0.903 

0.963 

0.451 

0.331 

0.543 

0.651 

0.647 

0.617 

0.368 

0.903 

0.908 

0.656 

1.071 

0.768 

 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

Note: country estimates for member states included in the EQI survey (e.g. those with regional 
data) are calculated via population weighted averages after computing the  final index so as to 
avoid ‘double counting’ 

107 

 
 
 
Table A5 : The structure of the case-study questionnaire (English version) 

Profession 

Position, grade, title 

Sector 

Sex 

Age 

1. Do  you agree with the assessment of  your  region in the above mentioned ranking 
against  the  background  of  your  country  and  the  EU?  Do  you  think  that  the  province 
XXXXX distinguishes the quality of public services, the level of corruption or equality in 
the treatment of citizens, if not, why? // 

2.  How  do  you  perceive  public  institutions  operating  in  your  region  (regardless  of 
whether they are dependent on local, regional or national authorities) in terms of: [a) 
Level of their impartiality] 

2.  How  do  you  perceive  public  institutions  operating  in  your  region  (regardless  of 
whether  they  depend  on  local,  regional  or  national  authorities)  in  terms  of:  [b)  The 
level of their effectiveness in providing public services] 

2.  How  do  you  perceive  public  institutions  operating  in  your  region  (regardless  of 
whether they are dependent on local, regional or national authorities) in terms of: [c) 
Level of their corruption] 

2a.  In  your  opinion,  how  should  public  institutions  in  your  region  be  evaluated  in 
comparison to other regions in Poland/Spain? Are you better or worse? 

3. In your opinion, what are the main reasons for your region's position compared to 
other regions of Poland/Spain? Is this a matter of economic resources? Is this related 
to  which  political  parties  rule  in  the  region?  Is  this  related  to  the  role  played  by  civil 
society (e.g. business associations, trade unions, non-governmental organizations)? 

4. What is the level of your trust in each of these institutions? [A) Police] 

4. What is the level of your trust in each of these institutions? [B) Media] 

4. What is the level of your trust in each of these institutions? [c) Marshal's Office] 

4. What is the level of your trust in each of these institutions? [d) Regional Office] 

4. What is the level of your trust in each of these institutions? [e) Political parties] 

4. What is the level of your trust in each of these institutions? [F) Business] 

4. What is the level of your trust in each of these institutions? [G) Hospitals] 

4. What is the level of your trust for each of these institutions? [H) Army] 

5.  What  is  the  level  of  your  confidence  in  the  news  that  can  be  found  on:  [Public 
Television] 

5.  What  is  the  level  of  your  confidence  in  the  news  that  can  be  found  on:  [Regional 
Public Television] 

5.  What  is  the  level  of  your  confidence  in  the  news  that  can  be  found  on:  [Private 
Television] 

5.  What  is  the  level  of  your  confidence  in  the  news  that  can  be  found  on:  [Regional 
Television] 

5.  What  is  the  level  of  your  trust  in  the  news  that  can  be  found  on:  [Public  radio 
(nationwide stations)] 

5.  What  is  the  level  of  your  trust  in  the  news  that  can  be  found  on:  [Private  radio 

108 

 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

(nationwide)] 

5.  What  is  the  level  of  your  trust  in  the  news  that  can  be  found  on:  [Local  (public) 
radio] 

5. What is the level of your trust in the news that can be found on: [Local radio (non-
public)] 

5. What is the level of your confidence in the news that can be found in: [nationwide 
newspapers] 

5.  What  is  the  level  of  your  confidence  in  the  news  that  can  be  found  in:  [Local 
newspapers] 

5.  What  is  the  level  of  your  confidence  in  the  news  that  can  be  found  in:  [Internet 
(news portals)] 

6.  In the latest  edition of  World Press Freedom 2018 Poland/Spain ranked in the Xth 
position (among 180 countries) in terms of press freedom. Do you think that there are 
obstacles  to  freedom  of  the  press  in  your  region?  Which  of  them  are  the  most 
important in your opinion? Is there any specific factor or entity that negatively affects 
the freedom of the press today? 

7. Do you think that news presented in local media really reflect what is happening in 
the region?   

7a. Are any events hidden or presented in an exaggerated way? 

8. How independent are the media in your region? 

9. Is the media entirely free to present what is happening on the following issues: [a) 
Corruption] 

9.  Are  the  media  entirely  free  to  present  what  is  happening  on  the  following  issues: 
[b) Security] 

9.  Are  the  media  entirely  free  to  present  what  is  happening  on  the  following  issues: 
[c) Economy] 

9.  Are  the  media  entirely  free  to  present  what  is  happening  on  the  following  issues: 
[d) Politics] 

9.  Are  the  media  entirely  free  to  present  what  is  happening  on  the  following  issues: 
[e) Public health] 

10a. Do you think that the media protect or attack the authorities (regional office)? 

10b. Do you think that the media protect or attack the authorities (Marshal's office)? 

11. Which media do you consider more credible: state or private ones? Why? 

12.  Do  you  think  that  today  there  is  more  press  freedom  than  twenty  years  ago? 
Why? 

13.  Focusing  primarily  on  people  at  the  head  of  public  sector  organizations  (hospital 
directors, school heads, directors of other public entities), what is the most important, 
in  your  opinion,  to  get  these  positions:  personal  /  political  connections?  Or  maybe 
skills and experience? 

13a.  Do  you  think  this  way  of  recruitment  for  the  highest  positions  in  the 
administration is similar to that in other regions of Poland/Spain? 

14. Looking at the rest of the employees of public institutions (e.g. policemen, school 
teachers,  doctors,  lower-level  officials),  what  is  the  most  important  to  get  the 
employment: personal / political connections? Or maybe experience and skills? 

14a. Do you think this way of recruiting for lower positions in administration is similar 
to the one functioning in other regions of Poland/Spain? 

109 

 
15. In general, what do you think is the most important for a successful career in the 
public  sector  in  the  region:  personal  /  political  connections  or  maybe  skills  and 
experience of candidates? 

Do  you  think  that  this  pattern  is  similar  to  that  existing  in  other  regions  of 
Poland/Spain? 

16. Imagine that an employee of a public institution discovers that something is wrong 
in their organization (eg that the superior or politician has been involved in suspicious 
business or plans to do so), what do you think they would do? Would they report it to 
the media, the relevant authority or the judiciary? 

17.  Will  this  employee  be  afraid  of  repercussions  if  he/she  decides  to  disclose  the 
case? 

18.  Do  you  think  that  there  is  adequate  protection  against  unjustified  sanctions 
against public employees who, in the public interest, report instances of corruption or 
other malfunctioning to relevant institutions or media? 

19.  What  strategy  is  applied  in  your  region  in  order  to  enforce  compliance  with  the 
rules and good conduct of employees of public institutions? 

20.  Can  you  give  examples  of  measures  implemented  to  enforce  good  behaviour  of 
public  authorities?  For  example,  do  you  know  if  there  is  an  ethical  code  for  officials 
and / or elected regional authorities? 

21. Do  you think public opinion (citizens) express their dissatisfaction when they see 
cases  of  corruption?  Can  this  public  opinion  act  as  a  restraint  for  the  authorities?  Is 
public pressure present and effective in enforcing good behaviour of public officials? Is 
the public opinion "tolerant" or "indifferent" to corruption scandals? 

22.  Who  discovers  corruption  scandals  in  your  region?  Are  these  media?  Judicial 
authorities? 

23. Is there any public body involved in prosecuting corruption? Has anything changed 
in the last 10-15 years (for better / worse)? Why? 

24.  Have  you  observed  a  significant  change  in  the  quality  of  public  services  since 
joining the EU? If so, please provide the scale of changes: 

25. Do you expect a change in government quality in the next five years (for better or 
for worse)? 

26.  Please  specify  the  level  of  improvement  of  services  provided  by  the  following 
public institutions after the EU accession/the financial crisis: [a. school] 

26.  Please  specify  the  level  of  improvement  of  services  provided  by  the  following 
public institutions after the EU accession/the financial crisis: [b. universities] 

26.  Please  specify  the  level  of  improvement  of  services  provided  by  the  following 
public institutions after the EU accession/the financial crisis: [c. hospitals] 

26.  Please  specify  the  level  of  improvement  of  services  provided  by  the  following 
public institutions after the EU accession/the financial crisis: [d. courts] 

26.  Please  specify  the  level  of  improvement  of  services  provided  by  the  following 
public institutions after the EU accession/the financial crisis: [e. Police] 

27.  What  are  the  positive  and  negative  aspects  of  the  functioning  of  public 
administration in the last 10 years in your region? 

28.  With  regard  to  various  public  services  (health  care  services,  education,  law 
enforcement, business license, etc.). To what extent, in your opinion: [... people with 
appropriate political connections seem to have the possibility of preferential access to 
services  (e.g.  schools  for  children,  preferences  in  access  to  healthcare,  running  a 
business)?] 

110 

 
 
Measuring the quality of Government at the subnational level and comparing results with previous studies 

28.  With  regard  to  various  public  services  (health  care  services,  education,  law 
enforcement, business license, etc.). To what extent, in your opinion: [... people with 
personal  or  family  connections  seem  to  have  preferential  access  to  services  (e.g. 
schools for children, preferences in access to healthcare, running a business)?] 

28.  With  regard  to  various  public  services  (health  care  services,  education,  law 
enforcement,  business  license,  etc.).  To  what  extent,  in  your  opinion:  [...  wealthy 
people  seem  to  have  preferential  access  to  services  (e.g.  schools  for  children, 
preferences in access to healthcare, running a business)?] 

28.  With  regard  to  various  public  services  (health  care  services,  education,  law 
enforcement, business license, etc.). To what extent, in your opinion: [... people who 
have  a  certain  ethnic  /  linguistic  origin  seem  to  have  preferential  access  to  services 
(e.g. schools for children, preferences in access to health care, running a business)?] 

29. Do you recall any matter (presented in the media or elsewhere) where the public 
institution  in  your  region  (whether  of  national,  regional  or  local  administration  level) 
has  been  accused  of  favoring  or  violating  impartiality?  What  happened?  In  your 
opinion, what is the probability that this situation will happen again? Smaller, bigger, 
the same? 

111 

 
 
 
 
Annex: Interviewees (Spain) 

Interview 1: 

High civil servant at local level. Catalonia. 

Interview 2:   

Local government employee. Catalonia. 

Interview 3:   

Member of a regional anti-corruption body.  

Interview 4: 

High civil servant at local level. Catalonia. 

Interview 5:  

Businessman. Navarra.  

Interview 6:  

High civil servant at local and regional level. Catalonia.  

Interview 7:  

High  civil  servant  at  regional  level;  department  responsible  for 

transparency and open government. Catalonia.  

Interview 8: 

Member of the regional government. Catalonia.  

Interview 9:  

Journalist. Navarra.  

Interview 10:  

Political scientist.  

Interview 11: 

Former member of the regional government. Navarra.  

Interview 12:  

Political scientist.  

Interview 13:  

Former member of the regional government. Catalonia.  

Interview 14: 

Civil servant at local level. Catalonia. 

Interview 15: 

High civil servant at local level. Catalonia. 

Interview 16: 

Former member of the regional government. Navarra. 

Interview 17:   Representative of a civil society organization. Navarra.   

Interview 18:  

Legal scholar. 

Interview 19:  

Local government employee. Navarra.   

112 

 
 
 
 
 
 
 
 
 
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). 

Priced subscriptions: 

•  via one of the sales agents of the Publications Office of the European Union 

(http://publications.europa.eu/others/agents/index_en.htm). 

 
 
 
 
 
 
K
N
-
0
4
-
1
9
-
2
9
2
-
E
N
-
N

   doi:10.2776/868107