COMMISSION STAFF WORKING DOCUMENT IMPACT ASSESSMENT REPORT Accompanying the document Proposal for a Regulation of the European Parliament and of the Council amending Regulation (EC) No 223/2009 on European statistics
Date: 2023-07-10
Institutions: European Commission, Eurostat
Subjects: EU statistics, European Statistical System, data collection, data protection, data sharing, data-processing law, personal data, source of information

EN 

  EN 

   EUROPEAN COMMISSION  Brussels, 10.7.2023  SWD(2023) 240 final  COMMISSION STAFF WORKING DOCUMENT  IMPACT ASSESSMENT REPORT  Accompanying the document Proposal for a Regulation of the European Parliament and of the Council amending Regulation (EC) No 223/2009 on European statistics {COM(2023) 402 final} - {SEC(2023) 269 final} - {SWD(2023) 241 final}  
 
 
1. 

INTRODUCTION: POLITICAL AND LEGAL CONTEXT ..................................... 1 

  European statistics ............................................................................................. 1 
  Changing societal, political and legal context ................................................... 2 

2.  PROBLEM DEFINITION .......................................................................................... 5 

  What is/are the problems? ................................................................................. 5 
  What are the problem drivers? .......................................................................... 7 
  How likely is the problem to persist? .............................................................. 10 

3.  WHY SHOULD THE EU ACT? .............................................................................. 11 

  Legal basis ....................................................................................................... 11 
  Subsidiarity: Necessity of EU action ............................................................... 11 
  Subsidiarity: Added value of EU action .......................................................... 12 

4.  OBJECTIVES: WHAT IS TO BE ACHIEVED? ..................................................... 13 

  General objective ............................................................................................. 13 
  Specific objectives ........................................................................................... 13 

5.  WHAT ARE THE AVAILABLE POLICY OPTIONS? .......................................... 15 

  What is the baseline from which options are assessed? .................................. 15 
  Description of the policy options .................................................................... 16 
  Options discarded at an early stage ................................................................. 23 

6.  WHAT ARE THE IMPACTS OF THE POLICY OPTIONS? ................................. 23 

Introduction ..................................................................................................... 23 
Impact of policy option 0: the baseline option ................................................ 25 
Impact of policy option 1: the first legislative option ..................................... 27 
Impact of policy option 2: the second legislative option ................................. 31 

7.  HOW DO THE OPTIONS COMPARE? .................................................................. 34 

  Effectiveness .................................................................................................... 34 
  Efficiency ........................................................................................................ 35 
  Coherence ........................................................................................................ 36 
  Feasibility ........................................................................................................ 37 

8.  PREFERRED OPTION ............................................................................................. 38 

  The preferred option and its implementation .................................................. 38 
  Estimated impact of the preferred option ........................................................ 41 
  REFIT (simplification and improved efficiency) ............................................ 42 
  Application of the ‘one in, one out’ approach ................................................. 43 
8.4.1.  Potential new burden on citizens ................................................................ 43 

8.4.2.  Potential new costs on businesses .............................................................. 43 

9.  HOW  WILL  ACTUAL 

IMPACTS  BE  MONITORED  AND 

EVALUATED? ......................................................................................................... 44 

ANNEX 1: PROCEDURAL INFORMATION ................................................................ 48 

1.  LEAD DG, DECIDE PLANNING/CWP REFERENCES ........................................ 48 

1 

 
 
 
 
 
2.  ORGANISATION AND TIMING ............................................................................ 48 

3.  CONSULTATION OF THE RSB ............................................................................. 48 

4.  EVIDENCE, SOURCES AND QUALITY ............................................................... 53 

ANNEX 2: STAKEHOLDER CONSULTATION (SYNOPSIS REPORT) .................... 56 

1. Introduction ........................................................................................................ 56 

2. Key stakeholders ................................................................................................. 56 

3. Summary of results............................................................................................. 58 

ANNEX 3: WHO IS AFFECTED AND HOW? ............................................................... 66 

1.  PRACTICAL IMPLICATIONS OF THE INITIATIVE .......................................... 66 

2.  SUMMARY OF COSTS AND BENEFITS ............................................................. 69 

3.  RELEVANT SUSTAINABLE DEVELOPMENT GOALS ..................................... 82 

ANNEX 4: ANALYTICAL METHODS .......................................................................... 83 

ANNEX 5: LEGAL CONTEXT ....................................................................................... 86 

ANNEX 6: ASSESSED USE CASES .............................................................................. 90 

ANNEX  7:  DATA  GAPS  IN  TERMS  OF  CONTENT,  FREQUENCY, 

TIMILINESS AND GRANULARITY OF EUROPEAN STATISTICS .................. 95 

ANNEX 8: SME TEST ................................................................................................... 100 

Table 1: Policy options and measures .............................................................................. 18 
Table 2: Comparison of quantitative impact of policy options ........................................ 36 
Table 3: Comparison of policy options ............................................................................ 38 
Table 4: Expected REFIT Cost Savings under the preferred option PO1 ........................ 42 
Table 5: Monitoring of impacts ........................................................................................ 44 
Table 6: Stakeholder participation.................................................................................... 57 
Table 7: Results of targeted consultations ........................................................................ 62 
Table 8: Stakeholders’ estimate of costs in various use cases .......................................... 63 
Table 9: Cost and benefit for PO0: Reuse of new data sources and crisis response ........ 73 
Table 10: PO0: Cost and benefit: Voluntary ESS data sharing ........................................ 74 
Table 11: PO1: Estimated differential cost and benefit for data reuse and crisis response
 .......................................................................................................................................... 75 
Table  12:  PO1:  Estimated  differential  benefits  and  costs  of  the  measure  on  mandatory 
ESS data sharing ............................................................................................................... 76 
Table 13: Overview of benefits – preferred option  ......................................................... 77 
Table 14: Overview of costs – preferred option ............................................................... 78 
PO1 – Distribution of costs and benefits between statistical offices and data 
Table 15: 
holders 
78 
Table  16:  PO2  –  estimated  differential  costs  and  benefits  for  data  reuse  and  crises 
response ............................................................................................................................ 80 

 
 
 
  
 
PO2 – Distribution of costs and benefits between statistical offices and data 
80 

Table 17: 
holders 
Table 18:  PO2 -  Estimated differential  benefits  of the measure on mandatory ESS data 
sharing .............................................................................................................................. 81 
Table 19: 
PO2 - Estimated costs of additionally providing access to shared raw data 
for research purposes ........................................................................................................ 81 
Table 20: Overview of relevant Sustainable Development Goals – Preferred Option ..... 82 

Figure 1: The problem definition ....................................................................................... 5 
Figure 2: The intervention logic ....................................................................................... 13 
Figure 3: PO0: Application of statistical products / use cases ......................................... 73 
Figure 4: PO1: Application of statistical products / use cases ......................................... 75 
Figure 5: PO2 - Application of statistical products / use cases ........................................ 79 
Figure  6:  Visual  representation  of  B2G  data  sharing  chains  for  official  statistical 
production, and of the benefits and the B2B data sharing chains they generate .............. 85 
Figure 7: Model of stakeholders related to the ESS [source: ICF]................................... 89 

 
 
 
 
 
 
 
Glossary 

Term or acronym 

Meaning or definition 

AWP 

B2B 

B2G 

B2G4S 

CEDS 

DGA 

ECA 

EDIB 

ESP 

Annual Work Programme for European statistics 

Business to business data sharing 

Business to government data sharing for the public interest 

Business  to  government  data  sharing  for  the  purpose  of 
generating official statistics 

Common European Data Spaces 

Data Governance Act 

European Court of Auditors 

European Data Innovation Board 

European Statistical Programme 

European  Statistical  System 
(ESS) 

the  Community 

statistical 
The  partnership  between 
authority, which is the Commission (Eurostat), and the NSIs 
and  other  national  authorities  responsible  in  each  Member 
State for the development, production and dissemination of 
European statistics 

GDPR 

General Data Protection Regulation  

Internet of Things (IoT) 

A  network  of  physical  devices,  vehicles,  home  appliances 
and  other  items  embedded  with  connectivity  software, 
which enables these objects to connect and exchange data 

LFS 

MNO 

EU Labour Force Survey 

Mobile Network Operator 

National  Statistical 
(NSI) 

Institute 

The  national  statistical  authority  designated  by  a  Member 
State as the body having the responsibility for coordinating 
all  activities  at  national 
the  development, 
production and dissemination of European statistics 

level  for 

PET 

REFIT 

SCM 

SILC 

SMEI 

Privacy-enhancing techniques 

Regulatory  Fitness  and  Performance  Programme  of  the 
European Commission 

Standard Cost Model 

EU Statistics on Income and Living Conditions 

Single Market Emergency Initiative 

 
 
 
1. 

INTRODUCTION: POLITICAL AND LEGAL CONTEXT  

  European statistics  

This impact assessment accompanies the proposal for a Regulation amending Regulation 
(EC) No 223/2009 of the European Parliament and of the Council of 11 March 2009 on 
European  statistics.  This  regulation  provides  the  legal  framework  at  EU  level  for  the 
development, production and dissemination of European statistics. 

Since  1953  along  with  the  evolution  of  the  European  Union,  European  statistics  have 
played an increasingly important role for underpinning the Union activities, policies and 
legislative acts from their design and implementation to their monitoring and evaluation. 
Today,  they  are  an  indispensable  element  of  the  European  democratic  society  and  a 
fundamental  tool  for  fostering  transparency  and  accountability  on  policymaking  at  all 
levels of public administration. European statistics are, therefore, by their very nature, an 
invaluable and consensual public good1, provided to everybody at the same time and for 
free. They are reliable and impartial and open to public scrutiny, and they must comply 
adequately with internationally agreed quality and other professional standards, such as 
privacy and confidentiality.    

European  statistics  are  provided  by  the  European  Statistical  System  (ESS)  that  is  a 
partnership between Eurostat and the national statistical institutes (NSIs), as well as other 
national  authorities  responsible  for  the  development,  production  and  dissemination  of 
European  statistics  in  each  Member  State.  Eurostat  is  the  statistical  authority  of  the 
Union  that  leads  the  development  of  European  statistics  in  response  to  evolving  policy 
needs  at  EU  level;  coordinates  statistical  production  based  on  common  EU  statistical 
standards,  methods,  procedures,  practices,  and  tools;  and  assesses  the  quality  of  data 
transmitted  by  the  Member  States.  It  also  coordinates  on  statistical  matters  with  other 
European Institutions and bodies and at global level with international organisations such 
as the United Nations (UN), the International Monetary Fund (IMF) and the Organisation 
for Economic Co-operation and Development (OECD). 

European  statistics  are  determined  in  the  European  Statistical  Programme  (ESP),  the 
duration of which coincides with the Multiannual Financial Framework. The current ESP 
is  an  integral  part  of  the  Single  Market  Programme  2021-27.  They  are  developed, 
produced  and  disseminated  in  conformity  with  the  statistical  principles  enshrined  in 
Article 338(2) of the Treaty on the Functioning of the European Union and specified in 
Regulation (EC) No 223/2009. The set of statistical principles is further elaborated in the 
European  statistics  Code  of  Practice  that  is  progressively  adapted  to  the  fast  evolving 
institutional, policy and professional environment within which development, production 
and dissemination of European statistics takes place.  

1 Cf European Court of Auditors’ (ECA)  press release of 29 November 2022, “European statistics should 
better meet user needs”, where the ECA member, Ildikó Gáll-Pelcz, is quoted for stating that “[Statistics] 
are a public good, and must be generated first and foremost with users in mind. In an age of disinformation 
and serial crises, it is paramount that European official statistics  must be high-quality,  meet users’ needs 
and explore innovative ways of production.” 

1 

 
                                                 
  Changing societal, political and legal context 

Digital transformation and changing society  

The  ESS  operates  in  a  fast-evolving  world  marked  by  digital  transformation.  Since  the 
adoption of the Regulation (EC) No 223/2009 the society has changed profoundly, and at 
an unprecedented speed. The amount of data generated yearly is counted in zettabytes2. 
Data, which are to a large extent created as a by-product of digital services and connected 
devices,  contain  a  huge  potential  as  an  essential  infrastructural  resource  for  economic 
growth,  innovation  and  the  overall  well-being  of  society3.  Data  are  becoming  a  critical 
resource  for  start-ups  and  SMEs’  growth  as  well  as  for  achieving  the  Green  Deal 
objectives.  

Together  with  the  ‘twin’  green  transition,  the  digital  transformation  has  led  to  the 
emergence of new business models (e.g., platform economy) and of new digital services, 
as well as to profound changes in labour markets and well-being. Such transformations 
are  fed  with,  and  are  the  source  of,  large  amounts  of  data  that  have  the  potential  to  be 
used  as  primary  input  for  compiling  official  European  statistics  through  adequate 
methods  and  modern  data  technologies.  In  some  cases,  data  that  are  the  by-product  of 
digital services are the only data sources that can be used to measure emerging economic 
phenomena brought by digital transformation such as the platform economy. 

Growing demands for timelier and more granular European statistics especially in 
times of crises 

Digitalization and the emergence of big data and the Internet of Things have brought a 
growing availability of real-time and close to real-time data for decision-making, that in 
turn  have  opened  opportunities  for  providing  real-time  views  of  the  economies,  labour 
markets  and  societies.  Additionally,  big  data  provide  opportunities  for  statistics  and 
statistical insights with more granularity to paint the picture of various social groups (e.g. 
youth,  women  and  migrants)  and  regional  entities  and  possibly  foretell  changes  for  the 
years to come.  

These trends have raised questions about the relevance of the economic and social data 
and  indicators  by  official  statistics,  provided  usually  on  a  quarterly  and  monthly  basis 
and with a certain lag after the end of the reference period. High-frequency and close to 
real-time  data  appeared  to  be  critical  for  example  when  it  came  to  track  the  rapid 
economic  disruption  brought  by  the  Covid-19  pandemic.  Furthermore,  European 
statistics  on  labour  markets  reported  on  a  quarterly  and  monthly  basis  had  struggled  to 
keep  pace  with  the  wave  of  unemployment  and  growth  in  the  labour  market  slack, 
accompanied by government furlough and other schemes.   

Overall,  the  recent  financial,  migration  and  Covid-19  crises,  followed  by  the  Russian 
military aggression against Ukraine, have amplified these demands and expectations for 
timelier and more detailed European statistics, which are needed to  ensure the best  EU 
response to crises and support informed decisions. 

2 1 zettabyte (ZB) = 1021. 
3 This is elaborated in, e.g., the  report of the High-Level Expert Group on Business-to-Government Data 
Sharing: Towards a European strategy on business-to-government data sharing for the public interest. 

2 

 
                                                 
The growing demands for timelier, more frequent,  and more granular data, has made it 
necessary for Eurostat and the national statistical authorities to establish data partnerships 
and  engage  continuously  in  collaboration  with  partners  from  both  public  and  private 
sector for the reuse of new and innovative data sources by removing barriers of reuse of 
these sources, while ensuring full respect of privacy and confidentiality.  

Over the recent past years, the ECOFIN Council has consistently addressed in its annual 
Conclusions on European statistics the need for the ESS to explore new data sources and 
technologies  and  develop  innovative  methods  for  producing  high-quality,  richer  and 
timelier  European  statistics.  Issues  related  to  a  sustainable  access  to  new  data  sources, 
enhanced data sharing and data linking, improved efficiency and burden reduction, and 
increased  agility  of  the  system,  especially  in  times  of  crises,  have  been  particularly 
highlighted. Most recently, in the ECOFIN Council conclusions on EU statistics of 8 
November 20224, the Council stated that it was looking forward to a possible proposal 
by  the  Commission  on  the  revision  of  Regulation  (EC)  No  223/2009  on  European 
statistics,  addressing  in  particular  the  issue  of  access  to  new  data  sources  from  the 
specific perspective of European statistics, as well as ways to ensure increased agility and 
responsiveness of the ESS to policy needs. 

These issues have been also systematically raised in the context of the annual statistical 
dialogue of Director General of Eurostat with the European Parliament Committee on 
Economic  and  Monetary  Affairs.  Furthermore,  they  have  been  consistently  raised  by 
the  European  Statistical  Governance  Advisory  Board  (ESGAB)  and  the  European 
Statistics Advisory Committee (ESAC), which are two independent statistical bodies at 
EU  level,  as  well  as  in  the  recent  European  Court  of  Auditors  special  report  on  the 
relevance  of  European  Statistics.  In  particular,  the  European  Statistical  Governance 
Advisory Board highlighted in its 2021 Annual Report5 the potential of new digital data 
sources  to  contribute  to  the  objectives  of  relevance,  accuracy,  and  timeliness,  and  to 
reduce  the  burden  on  respondents  and  increase  cost-effectiveness.  It  also  identified  the 
need for Eurostat to be able to react quickly to unexpected and urgent statistical demands 
for  policymaking.  In  the  same  vein,  the  European  Court  of  Auditors  noted,  when 
recently releasing its Special Report 26/20226, that the ESS was  not  flexible enough to 
respond quickly with new sets of data when new needs arise.  

Finally, as part of the overall policy context, it is important to note that the Presidents and 
Directors-General of the NSIs have also consistently called for the ESS to be equipped 
with the necessary regulatory instruments and innovative tools to enhance the access, use 
and integration of all data available and allow the production of multi-source European 
statistics.  For  instance,  as  an  outcome  of  the  high-level  meeting  organised  in  Lyon  in 
April  2022  under  the  French  Presidency  and  of  a  subsequent  dedicated  meeting  in 
Luxembourg  in  May  2022,  they  reached  a  large  consensus  on  a  number  of  topics 
considered valuable in the revision of the Regulation, not only on the need for sustainable 
access  to  new  data  sources,  but  also  on  the  recognition  of  the  possibility  for  NSIs  to 
assume new roles  and tasks  in  the emerging  data ecosystems,  and the  fostering  of data 
sharing in the ESS. 

4 Council adopted conclusions on statistics - Consilium (europa.eu) 
5 ESGAB Annual report 2021 (europa.eu) 
6 Special report 26/2022: European statistics – Potential to further improve quality (europa.eu) 

3 

 
                                                 
Recent EU policy and legal initiatives 

The overall political context is also marked by the recent legislative initiatives that have 
been  undertaken  as  part  of  the  European  Strategy  for  Data  put  forward  by  the 
Commission in February 20207 with the aim to strengthen Europe’s position globally by 
making better use of data driven innovation. These initiatives  – Data Governance  Act 
(DGA)8   and  the  proposal  for  a  Data  Act9  establish  provisions  that  increase  data 
availability  and  data  reuse,  establish  data  governance  and  open  room  for  new  actors  in 
the emerging European and national data ecosystems. These are developing rapidly and 
constitute an opportunity for European statistics producers to harness the power of digital 
data  for  high-quality  statistics  in  response  to  unmet  users’  demands.  The  evolution  of 
these  data  ecosystems  imply  that  large  amounts  of  data  can  be  available  as  input  for 
producing European statistics using adequate methods and modern data technologies. 

The INSPIRE Directive10, that has established an infrastructure for spatial  information 
in  Europe  to  support  environmental  policies  and  the  Interoperable  Europe  Act 
proposal11  that  is  aiming  to  strengthen  cross-border  interoperability  and  cooperation  in 
the  public  sector  across  the  EU,  are  also  part  of  the  legal  context  to  consider  for  this 
initiative  as  well  as  the  current  proposal  for  a  Single  Market  Emergency  Initiative 
(SMEI)12  that  foresees  to  allow  the  Commission  to  receive  targeted  information  for 
official statistics, but only from economic operators in the crisis-relevant supply chains.  

Finally,  special  mention  should  be  made  to  the  General  Data  Protection  Regulation 
(GDPR)13  and  the  ePrivacy  Directive14,  which  provide  a  solid  and  trusted  legal 
framework for the protection of personal data at EU level.  

A more detailed overview of the existing and very dynamic legal context is presented in 
Annex 5. 

7 See https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/europe-fit-digital-
age/european-data-strategy_en  
8 Regulation (EU) 2022/868 of the European Parliament and of the Council of 30 May 2022 on European 
data governance and amending Regulation (EU) 2018/1724.  
9 Proposal for a Regulation of the European Parliament and of the Council on harmonised rules on fair 
access to and use of data (Data Act). 
10 Cf. Directive 2007/2/EC of the European Parliament and of the Council of 14 March 2007 establishing 
an Infrastructure for Spatial Information in the European Community (INSPIRE), OJ L 108, 25.4.2007, p. 
1. 
11 Commission proposal for a Regulation of the European Parliament and of the Council laying down 
measures for a high level of public sector interoperability across the Union (Interoperable Europe Act), 
COM(2022) 720 final, adopted on 18 November 2022. 
12 See Proposal for a Regulation of the European Parliament and of the Council establishing a Single 
Market emergency instrument and repealing Council Regulation No (EC) 2679/98.  
13 Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the 
protection of natural persons with regard to the processing of personal data and on the free movement of 
such data, and repealing Directive 95/46/EC (General Data Protection Regulation) 
14 Directive 2002/58/EC of the European Parliament and of the Council of 12 July 2002 concerning the 
processing of personal data and the protection of privacy in the electronic communications  This directive 
is expected to be eventually repealed by the Regulation on Privacy and Electronic Communications based 
on a proposal by the Commission (COM/2017/010) currently under discussions between the European 
Parliament and the Council. 

4 

 
 
                                                 
2.  PROBLEM DEFINITION15 

Figure 1: The problem definition  

  What is/are the problems? 

The  problem  that  this  initiative  is  aiming  to  address  is  that  European  statistics  is  not 
enough  timely,  frequent,  detailed  and  cost-efficient;  it  is  not  sufficiently  responsive  to 
urgent information needs in times of crises. 

European statistics are developed, produced and disseminated according to the principles, 
mechanisms, tools and governance laid down in Regulation (EC) No 223/2009. The latter 
was developed in the early 2000s. Therefore, it reflects the way statistics were produced 
at  that  time,  almost  fully  based  on  sample  surveys,  population  and  other  censuses  and 
administrative records held by public authorities. The legal framework and the statistical 
practice based on it do not reflect the new realities brought by digital transformation such 
as  new  data  sources  and  technologies.  As  a  consequence,  the  ESS  is  facing  growing 
difficulties to fill the emerging data gaps and meet user demands in terms of timeliness 
and  frequency  of  providing  those  statistics  as  well  as  of  granularity  at  which  they  are 
needed  to  make  informed  policy  decisions  (e.g.  decomposed  by  social  groups  and 
regional and local dimensions).  

Recent crises, in particular Covid-19, have shown that while the ESS has demonstrated 
strong  resilience  and  continued  producing  and  disseminating  traditional  statistics 
according to the established deadlines, it has not responded quickly enough and has not 

15 The  problem definition has been  elaborated in more detail by ICF, in their Study to support an impact 
assessment  for  the  revision  of  Regulation  (EC)  No  223/2009  on  European  statistics,  final  report.  Later 
references to this study are denoted by [ICF]. That study also includes an assessment of stakeholders of the 
problem. See also Annex 1.4 for evidence used and Annex 2 on stakeholders consultation. 

5 

 
 
 
                                                 
met the new urgent information needs. It is essential that the ESS is agile and effective in 
its  response  to  urgent  information  needs  arising  in  times  of  crises  and  following 
emergency  mechanisms  established  by  EU  law  such  as  those  on  Public  Health16  or 
Migration17. 

Going  beyond  the  traditional  data  sources  used  for  compiling  European  statistics  and 
especially through reusing of data already collected for other purposes has a potential to 
reduce  costs  and  administrative  burden  on  businesses  and  people.  This  potential  is 
currently  not  sufficiently  utilised,  thus  leaving  room  for  improving  efficiency  of 
producing European statistics.  

The problem entails the following direct consequences:  

  European  statistics  do  not  meet 

increasing  demands  for  more  detailed 
information,  produced  faster,  at  a  higher  frequency  and  offering  more  in-depth 
insights in support of evidence-based EU policies. 

  There  is  non-availability  of  timely  (close  to  real  time)  European  statistics,  with 

the necessary details, in times of crises. 

  There  is  insufficient  efficiency  in  producing  European  statistics  regarding  the 

costs and burden on enterprises and persons. 

Over  recent  years,  a  solid  evidence  has  been  accumulated  about  the  existence  of  the 
problem  of  insufficient  timeliness,  frequency,  granularity  and  cost-efficiency  of 
European  statistics  as  well  as  of  its  inadequate  responsiveness  to  urgent  information 
needs in times of crises. 

European statistics are determined by the ESP, and each ESP is evaluated as stipulated in 
Regulation  (EC)  No  223/2009.  The  most  recent  evaluation  of  European  pertains  to  the 
ESP for the period 2013 – 202018. The evaluation report covers the entire period of the 
programme 2013-2020, and it is accompanied by a European Commission staff working 
document.  The  evaluation  study  was  conducted  by  an  external  contractor  to  give  an 
independent opinion. To get robust results, the contractor used different sources, starting 
with  the  review  of  existing  documents.  Then,  the  contractor  carried  out  an  extensive 
consultation  with  users  and  producers  of  statistics.  This  included  notably  i)  scoping 
interviews  with  representatives  of  Eurostat  and  other  Directorates-General  of  the 
Commission; ii) a public consultation; iii) targeted surveys of users and producers; and 
iv) 50 interviews with different types of stakeholders. The contractor also carried out four 
thematic case studies and five country case studies.  

The evaluation report concluded that “while the ESP implemented appropriate activities 
to meet its objectives, the analysis showed that these activities were not enough to deliver 
all the statistics that users had wished for.” Moreover, the report  pointed to “remaining 
weaknesses with the timeliness and the completeness of European statistics”  and in the 
remarks related to coherence, it highlighted that: “(…) the lack of flexibility of European 
statistics to respond to emerging needs (…) might cause a misalignment with other EU 

16 Cf. https://health.ec.europa.eu/system/files/2021-09/hera_2021_decision_en_0.pdf 
17 Cf. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex%3A32001L0055 
18 Report from the Commission to the European Parliament and the Council on the final evaluation of the 
implementation of the European statistical programme 2013-2020, COM(2021) 794 final. 

6 

 
                                                 
strategies should these needs not be covered.” A prominent recommendation is to focus 
on innovation, new methods and better use and integration of new data sources in order 
to satisfy the increasing demands for new and timelier statistics, while reducing costs and 
administrative burden on businesses and citizens in the production of European statistics.  

The fact that European statistics are not fit to the digital age and times of crises has also 
been  recognised  by  a  number  of  bodies  concerned  with  European  statistics,  as  already 
mentioned in the previous chapter: the ECOFIN Council, the ESGAB, the ESAC, and the 
European  Court  of  Auditors.  In  a  number  of  specific  areas  of  statistics,  impact 
assessments have been carried out that corroborate the existence of the problem19. During 
the on-line public consultation, to the question, what needs to be done to make European 
statistics fit for the future and more relevant to user needs 70% of respondents considered 
it most important to combine sources to provide more and better insights into economic 
and  societal  developments,  66  %  to  provide  more  granular  statistics  (e.g.  for  social 
groups and territorial units), and equally 66% to  provide more up-to-date statistics, e.g. 
through flash estimates and more frequent statistics. Only  11% of respondents consider 
that  European  statistics  are  sufficiently  responsive  to  emerging  user  demand,  including 
during public emergencies and crises, whereas 72% consider that European statistics are 
somewhat responsive, but not enough and 8% consider them not responsive at all. 

Insufficient timeliness and granularity of European statistics have appeared prominently 
in  the  last  two  user  satisfaction  surveys  conducted  by  Eurostat  in  2019  and  2022 
respectively. This report substantiates and specifies the problem of European statistics in 
Annex  7  where  an  illustration  is  provided  of  issues  related  to  important  statistical 
domains,  together  with  their  effects,  thereby  demonstrating  the  importance  of  the 
problem and the expected consequences and impacts of addressing it. Addressing these 
issues would be beneficial for different stakeholders. Policy makers will be supported in 
designing implementing and monitoring policies, businesses will have access to detailed 
information  about  potential  markets,  individuals  will  be  better  informed  and  can  better 
participate  in  the  democratic  process,  research  can  contribute  to  society  by  better 
informing on problems, and media can use statistics to provide timely information based 
on trustworthy sources. 

  What are the problem drivers?  

Three problem drivers have been identified: 

  Statistical authorities do not sustainably reuse new and innovative data sources 

emerging as by-products of digital services. 

  Current rules and tools, at the disposal of the ESS, are insufficiently adapted to 

crises. 

  The current allocation of tasks and roles of ESS partners does not reflect the 

digital context in which the ESS operates. 

19  Examples  are  the  impact  assessment  for  the  Regulation  on  European  statistics  on  population  and 
housing,  SWD(2023)  14  final,  the  impact  assessment  for  the  Regulation  on  European  business  statistics, 
SWD(2017) 98 final, and the impact assessment for the Regulation on a common framework for European 
statistics relating to persons and households, SWD(2016) 283 final. 

7 

 
 
                                                 
 
Problem  driver  1:  Statistical  authorities  do  not  sustainably  reuse  new  and 
innovative data sources emerging as by-products of digital services 

The new and innovative data sources that are by products of digital services or connected 
devices are mostly privately held. Examples of these sources include mobile phone data, 
banking services data, smart meters data, on-line job advertisements. Currently, they are 
used  on  an  ad  hoc  basis,  based  on  voluntary  agreements  that  are  limited  in  time,  i.e., 
partnerships  agreements  between  data  holders  and  the  national  statistical  authorities. 
These  agreements  most  often  cover  one-off  projects  focused  on  experimentation  and 
research. New digital data sources are hardly used in the regular statistical production. In 
many cases, the process for setting up agreements may take at least one year, if not more 
– for projects limited in time and purposes.    

The current legal framework does not cover the reuse of privately held data emerging as 
by-products  of  digital  services  and  the  Internet  of  Things  for  the  sake  of  compiling 
European  statistics.    Moreover,  the  legal  environments  in  Member  States  vary  with 
respect  to  business  to  government  data  sharing  for  the  compilation  of  official  statistics 
(B2G4S). Businesses are faced with legal uncertainties The absence of an encompassing 
legal framework also discourages investing in B2G4S. 

The  High-level  expert  group  on  facilitating  the  use  of  new  data  sources  for  official 
statistics  concludes  that  the  European  statistical  system  “has  struggled  to  keep  up  with 
the new data-rich world, although it has very seriously tried to access new data sources 
and  privately  held  data  in  partnership  with  businesses”  (p.16  of  the  Report20).  
Companies  may  have  little  or  no  economic  incentives  to  share  data  with  public  sector 
organisations and perceive various obstacles: 

  Setting  up  and  implementing  data-sharing  collaborations  could  be  costly  and 
bring  additional  administrative  burden.  For  example,  additional  specialized 
manpower  is  needed  for  negotiating  a  contractual  arrangement  or  setting  up  the 
operational elements to make the data reuse possible. 

  Companies  may  be  afraid  of  incurring  possible  revenue  losses  and  opportunity 
costs  or  lose  their  competitive  advantage  in  upstream  or  downstream  markets 
because  of  making  their  data  available  to  public  authorities  such  as  NSIs  or 
Eurostat. 

  Data  providers  may  fear  that  their  data  can  be  used  to  impose  new  regulations 
(hence  costs  and/or  additional  administrative  burdens)  on  their  operations  (for 
example in the case of the regulation of the gig economy). 

  Sharing data could involve risks of data leaks or hacks. 

The lack of an encompassing legal framework not only means that businesses as well as 
ESS partners are faced  with  legal  uncertainties,  but  also  that they have to comply  with 
the different rights, obligations and safeguards of the Member States. Most of the current 
B2G4S  initiatives  have  been  established  spontaneously  and  are  very  diverse  and 
dissimilar,  for  instance  in  their  ‘rules  of  engagement’.  There  is  also  a  general  lack  of 

20 Report of the High-Level Expert Group on facilitating the use of new data sources for official statistics: 
Empowering  society  by  reusing  privately-held  data  for  official  statistics  –  a  European  approach. 
References to this report are denoted by [EG B2G4S] in this impact assessment, with B2G4S standing for 
Business-to-Government for statistics. 

8 

 
 
                                                 
transparency about the B2G4S data sharing that takes place, which also contributes to the 
low awareness of the potential value of such activity. 

Problem  driver  2:  Current  rules  and  tools,  at  the  disposal  of  the  ESS,  are 
insufficiently adapted to crises 

Unanticipated events may cause urgent data needs that require quick action at EU level, 
underpinned by prompt, often close to real-time statistics that are comparable across the 
Member  States.  Responding  to  such  needs  by  the  ESS  is  hampered  by  the  lack  of  a 
mechanism  to  initiate  urgent  actions  at  the  EU  level,  since  this  cannot  be  addressed 
within  the  regular  planning  framework.  The  ESS  has  already  strong  collaboration 
mechanisms that take into account the differences among national statistical systems and 
respect  the  subsidiarity  principle.  However,  Regulation  (EC)  No  223/2009  does  not 
provide  mechanisms  and  tools  to  react  fast  and  in  collective  coordinated  manner  in 
response  to  urgent  information  needs  in  times  of  crises.  The  response  can  include 
temporary actions such as new data collections or utilising existing data to produce new 
statistics  and  provide  additional  statistical  insights.  It  can  also  include  the  creation  of 
temporary  task  forces  or  expert  groups  to  develop  methodological  guidelines  or 
harmonised  methodologies  for  producing  urgently  statistics  that  are  comparable  across 
the EU member States.   

There  are  several  on-going  initiatives  at  EU  level  to  lay  down  provisions  and 
mechanisms in certain sectors, as well as the proposal for the Single Market Emergency 
Initiative  (SMEI),  that  aim  at  establishing  a  crisis  response  mechanism.  A  mechanism 
that would allow the ESS to react in times of crises will be strictly complementary to and 
support the other crisis response mechanisms at EU level once these are activated. This 
means that the ESS will clearly not decide on its own on the existence of a crisis but will 
take  action  only  in  the  case  a  crisis  mechanism  has  been  formally  triggered  by  an 
Institution according to established procedures in Union law. 

Problem  driver  3:  The  current  allocation  of  tasks  and  roles  of  ESS  partners  does 
not reflect the digital context in which the ESS operates. 

The digital revolution has brought new opportunities to exchange and share data under a 
secure  infrastructure  as  well  as  allowed  for  linking  data  across  different  sources  and 
producing  new,  deeper  insights  into  society  and  economy.  As  regards  the  ESS,  data 
sharing among its partners is rather limited and fragmented. Multiple obstacles persist in 
the sharing of data, particularly in transferring micro-level data across borders. This can 
significantly  hinder  the  collaboration  and  cooperation  of  NSIs,  and  other  partners,  as 
demonstrated  in  the  Nordic  Mobility  project21.  Furthermore,  such  differences  can  be 
detrimental  to  fair  competition,  transparency,  and  equal  treatment  of  economic 
operators22.  With fragmentation  across the Member States regarding the modalities and 
scope of data sharing, the cost of European statistics is negatively affected [EG B2G4S 
p7].  Issues  such  as  data  localization  requirements,  a  lack  of  standardisation  of  data 

21 Brun, N., Ekmark, S., Munch Haagensen, K., Harðarson, Ó., Rustad Holseter, A.M., Nome Næsheim, H. 
and  Ruotsalainen,  K.,  2021.  Nordic  Cross-border  Statistics:  The  results  of  the  Nordic  Mobility  project 
2016-2020. Nordic Council of Ministers.  
22 ESS (2017), Position paper on access to privately-held data which are of public interest. 

9 

 
                                                 
production and storage all increase the administrative and economic burden on NSIs to 
both produce and access required data23.  

European statistics on cross-border flows and phenomena will have the highest quality, 
coverage  and  detail  if  the  available  data  can  be  combined  in  the  compilation  of  the 
statistics concerned  [EG B2G4S  p7]. This  requires data sharing, the lack of which  also 
contributes to inefficiencies, as seen in problem driver 2. Examples are migration, labour 
mobility and foreign investment, where data sharing is essential for getting consistent and 
correct  statistics.  The  lack  of  clear,  accurate,  and  available  statistical  data  related  to 
informed 
cross-border  flows  and  phenomena 
decisions24.  

inhibits  policymakers 

in  making 

The  task  distribution  among  the  ESS  partners  also  does  not  reflect  the  current  digital 
context. Historically, European statistics have been based on sample surveys, population 
and  other  censuses  and  reuse  of  administrative  records  held  by  public  authorities.  Data 
collection took place at national level. Increasing availability of data sources at European 
or global level raises questions on the efficiency of this model. It might be more efficient 
to make data from international sources (usually multinationals) available to Eurostat and 
then allow further reuse by NSIs under strict conditions rather than to have data requests 
from  up  to  27  countries,  which  may  not  be  harmonised.  In  the  latter  case,  the 
administrative burden may then also  be higher than needed. However,  Regulation (EC) 
No 223/2009 currently does not explicitly provide such a possibility. 

Interaction between the problem drivers  

The problem drivers are interconnected. The  first  one, that statistical  authorities do not 
sustainably reuse new data sources emerging as by-products of digital services, interacts 
with  the  second  one,  that  current  rules  and  tools,  at  the  disposal  of  the  ESS,  are 
insufficiently  adapted  to  crises.  If  new  data  sources  can  be  reused  by  statistical 
authorities, this facilitates the response of urgent information needs in times of crises. 

The  third  problem  driver,  that  the  current  allocation  of  tasks  and  roles  of  ESS  partners 
does not reflect the digital context in which the ESS operates, interacts with the other two 
problem  drivers.  Sustainable  reuse  of  new  data  sources  requires  an  update  of  the 
allocation of tasks and roles of ESS partners for situations where such data sources can 
best be accessed by one ESS partner on behalf of the whole ESS, in particular Eurostat. 
Similarly,  a  quick  response  to  crises  requires  an  initiating  and  coordinating  role  for 
Eurostat,  which  is  not  recognized  in  the  current  allocation  of  tasks  and  roles  of  ESS 
partners.  

  How likely is the problem to persist?  

First and foremost, continuing with the current production methods limited to traditional 
data  sources  (e.g.  surveys  and  administrative  records)  and  not  embracing  the 
opportunities,  brought  by  digital  transformation,  will  make  it  increasingly  difficult  and 

23 World Economic Forum, 2020,  A Roadmap for Cross-Border Data Flows: Future-Proofing Readiness 
and Cooperation in the New Data Economy.  
24 European Commission, 2017. Boosting growth and cohesion in EU border regions. Communication from 
the Commission to the Council and the European Parliament. 

10 

 
                                                 
close  to  impossible  for  the  ESS  to  meet  user  demands  for  timelier,  more  frequent  and 
more granular European statistics, even if additional resources are allocated to statistical 
authorities.  

Second, the lack of responsiveness and agility of the ESS to meet emerging user demands 
especially in times of crises will increase. This will be made even more pronounced by 
the  lack  of  a  crisis  response  mechanism  at  the  ESS  level  as  well  as  the  general 
deterioration  of  the  ESS  ability  to  make  relevant,  detailed  and  timely  statistics  in  a 
context marked by fast changing data.  

Third, the costs and burden on respondents will not be reduced, rendering production of 
European statistics more and more inefficient and ineffective in the context of the recent 
EU policy initiatives related to data. 

Fourth,  the  mismatch  between  the  current  and  desirable  roles  of  statistical  authorities 
within European and national data ecosystems will increase given that these ecosystems 
are likely to develop further.  

3.  WHY SHOULD THE EU ACT?  

  Legal basis  

Article 338 of the Treaty on the Functioning of the European Union provides the overall 
legal basis for European statistics. Based on Article 338(1), the European Parliament and 
the  Council,  acting  in  accordance  with  the  ordinary  legislative  procedure,  shall  adopt 
measures  for  the  production  of  statistics  when  necessary  for  the  performance  of  the 
activities of the Union. Furthermore, Article 338(2) sets out the requirements to produce 
European  statistics,  stating  that  they  must  conform  to  standards  of  impartiality, 
reliability,  objectivity,  scientific 
independence,  cost-effectiveness  and  statistical 
confidentiality.  

Article 338(1) TFEU is thus the legal basis for this initiative. 

Subsidiarity: Necessity of EU action  

In order to demonstrate that EU action is necessary to address the problem  that European 
statistics are not enough timely, frequent, detailed and cost-efficient, and that they are not 
sufficiently responsive to urgent information needs in times of crises, the problem drivers 
have to be considered.  

The first problem driver, i.e., that statistical authorities do not sustainably reuse new data 
sources emerging as by-products of digital services, is related to the fact that the EU legal 
framework  does  not  mandate  such  access  and  reuse.  This  is  also  true  for  the  legal 
frameworks of the large majority of Member States, as explained in section 2.2, and this 
is  not  expected  to  substantially  change  in  the  near  future.  Moreover,  to  the  extent  that 
reuse of privately held data for official statistics is possible in certain Member States, the 
conditions and safeguards differ or are ad hoc. As a consequence, reuse of privately held 
data for statistics tends to be on a voluntary and temporary basis, and thus not sustained.  

Thus,  addressing  the  first  problem  driver  necessitates  EU  action,  not  only  to  open  the 
door to reuse of privately held data for official statistics at a European scale, but also to 
make  this  reuse  sustainable  and  to  harmonise  such  reuse  within  the  EU.  Such 

11 

 
 
 
 
 
harmonisation would also ensure a level playing field for businesses as data holders. A 
harmonised approach at EU level would bring legal clarity and ensure a fair treatment of 
data holders that are active in multiple Member States. 

The second problem driver, i.e., that current rules and tools, at the disposal of the ESS, 
are insufficiently adapted to crises, necessitates EU action, since such situations require 
coordinated information at the European level. In such situations, action at the European 
level is needed to initiate and coordinate the production of the information needed. 

The  third  problem  driver,  i.e.,  that  the  current  allocation  of  tasks  and  roles  of  ESS 
partners does not reflect the digital context in which the ESS operates, is cross-border in 
nature.  An  optimal  allocation  would  improve  efficiency  as  well  as  effectiveness,  for 
instance by increased data sharing between ESS partners, or more effective measurement 
of  cross-border  phenomena.  Since  such  improvements  involve  the  interaction  between 
ESS partners in different Member States, action at EU level is necessary.  

Without action at EU level, the problems that have developed will continue. The existing 
legislative  framework  governing  European  statistics  may  become  less  relevant  and  less 
effective  in  achieving  its  objectives  in  the  years  to  come  as  new  data  sources  and 
technologies emerge. Over time, European statistics may also diverge further from users’ 
needs in terms of content, granularity, desired frequency or timeliness. Finally, Member 
States’ approaches regarding data sharing and use of new data sources will increasingly 
diverge  leading  to  less  comparable  statistics,  which  consequently  risks  compromising 
policymaking at EU level. 

Subsidiarity: Added value of EU action  

The  revision  of  Regulation  (EC)  No  223/2009  aims  to  make  the  ESS  fit for  the  digital 
age,  strengthening  the  capacity  of  statistical  offices  to  make  them  more  responsive  in 
times  of  crises.  By  its  very  nature,  the  objective  of  the  revision  can  only  be  better 
achieved because of the scale and effects of the proposed action at EU level and clearly 
cannot be sufficiently achieved by the Member States alone. This is all the truer since the 
underlying  phenomena,  digitalisation  and  the  creation  of  a  common  digital  market,  are 
already  within  the  scope  of  EU  action.  Moreover,  reuse  of  privately  held  data  as  by-
products of digital services could have a cross-border aspect, for instance if the relevant 
data are kept by multi-national enterprises.  

The  added  value  of  new,  timelier  or  more  granular  European  statistics  at  EU  level  lies 
primarily in their significance to various policy areas of the Union and their relevance to 
the  Union  political  priorities  (i.e.,  European  Green  Deal,  an  economy  that  works  for 
people  and  a  Europe  fit  for  the  digital  age).  An  analysis  carried  out  by  the  European 
Commission in the preparation of the Data Act proposal shows, for example, that a 20% 
increase in the supply of official statistics would generate an additional EUR 4-12 billion 
a year in the EU from direct and indirect effects alone25. 

25  European  Commission,  impact  assessment  Report  accompanying  the  Proposal  for  a  Regulation  on 
harmonised rules on fair access to and use of data (Data Act), SWD(2022) 34 final.  

12 

 
 
                                                 
 
4.  OBJECTIVES: WHAT IS TO BE ACHIEVED? 

Figure 2: The intervention logic 

  General objective 

The  general  objective  is  to  solve  the  problem  as  defined  in  chapter  2,  namely  to  make 
European  statistics  timelier,  more  frequent,  more  detailed  and  cost-efficient  as  well  as 
more responsive to urgent information demands in times of crises. 

Realisation of the general objective implies that the difficulties in sustainably accessing 
and  reusing  privately  held  data  as  by-products  of  digital  services  have  been  overcome, 
that clear rules and safeguards apply to such reuse throughout the EU, and that demand in 
times  of  crises  is  met.  Moreover,  the  ESS  will  have  improved  its  efficiency,  both  in 
respect  of  its  internal  data  sharing  and  task  distribution,  as  well  as  its  effectiveness  in 
providing  consistent  and  accurate  measurement  of  cross-border  phenomena.  It  will 
exploit the possibilities of the data ecosystems  of which the ESS partners  are  part. The 
ESS will produce statistics that are more relevant, faster, better, and more detailed. The 
costs  and  burdens  on  the  Member  States  and  respondents  will  have  decreased  and  the 
ESS will be fit for the digital age and times of crises26. 

Specific objectives 

The overall objective of the initiative will be achieved through three specific objectives.  

26  European  Commission  (2022),  European  Statistical  System  –  making  it  fit  for  the  future,  Call  for 
evidence for an impact assessment.  

13 

 
 
 
 
                                                 
 
Specific objective 1: To embrace fully new technologies and  sustainably reuse new 
data  sources  emerging  as  by-products  of  digital  services  to  meet  increasing  user 
demands for timelier, more frequent and more detailed European statistics27. 

This  specific  objective  is  linked  to  the  difficulties  in  accessing  and  reusing  sustainably 
privately  held  data  emerging  as  by-products  of  digital  services  for  the  compilation  of 
European  statistics,  i.e.,  the  first  problem  driver.  This  problem  driver  was  explained  in 
chapter 2 by noting  (1) that  the EU legal  framework, in  particular Regulation (EC) No 
223/2009, does not provide for a mandate regarding the reuse of such data, (2) that the 
legal  systems  of  the  Member  States,  with  a  few  exceptions,  do  not  include  such  a 
mandate either, and (3) that as a result of the disparities among Member States, no clear 
rules and safeguards apply to such reuse throughout the EU.  

Addressing  the  difficulties  that  currently  impair  the  access  and  reuse  of  new  digital 
sources  especially  those  that  are  privately  held  will  allow  for  embracing  fully  the 
opportunities  provided  by  those  sources  for  producing  relevant,  timely  and  enough 
detailed  European  statistics  in  both  times  of  crises  and  in  regular  times  free  of 
unexpected events.  

Specific objective 2: To provide a mechanism and tools for the ESS to react fast, in 
a collective and coordinated manner to urgent data demands in times of crises 

This  specific  objective,  which  is  linked  to  the  second  problem  driver,  involves 
establishing of a mechanism and toolbox that will ensure that the ESS can react fast and 
in  a  collective  and  coordinated  manner  during  the  crises  and  can  provide  timely  and 
relevant statistics that are comparable across Member States to support decisions. Within 
the usual planning cycle, the incubation time for a new statistic typically covers several 
years,  but  if  the  demand  is  urgent  and  important,  a  fast-track  solution  is  needed  with 
appropriate  safeguards  for  all  partners  and  for  the  quality  and  harmonisation  of  the 
resulting  statistical  information.  This  could  include  producing  statistics  based  on  new 
data  collections,  calculating  new  indicators  or  providing  additional  insights  based  on 
existing data.  

The realisation of specific objective 1 would, in particular, facilitate reaching the second 
specific  objective.  Only  by  tapping  the  new  digital  data  sources  can  the  timeliness  of 
official  statistics  be  substantially  improved,  in  some  cases  potentially  to  close  to  real-
time. 

Specific  objective  3:  To  update  the  tasks  and  roles  of  ESS  partners  to  leverage 
opportunities  offered  by  digital  transformation  for  more  cost-efficient  and  less 
burdensome statistical production   

This  specific  objective  is  linked  to  the  fact  that  the  ESS  is  not  sufficiently  efficient. 
Limited data sharing results in extra work for the ESS and a higher burden than necessary 
on businesses and citizens. Since data sharing takes place on the basis of Regulation (EC) 
No  223/2009,  the  specific  objective  implies  that  barriers  need  to  be  removed  resulting 

27 Although digital data may have quality issues in some cases, such as their veracity or representativeness, 
their  reuse  for  official  statistics  will  take  place  while  respecting  the  quality  norms  as  stipulated  in 
Regulation (EC) No 223/2009. 

14 

 
                                                 
from  diverging  EU  and  national  rules  and  practices.  In  particular,  the  aim  is  to  enable 
data  sharing  for  the  production  of  statistics  in  areas  with  a  high  degree  of  European 
integration,  such  as  business  trade  or  cross-border  phenomena  such  as  migration  and 
labour mobility. 

The current task distribution results in extra work for the ESS and suboptimal collection 
of  data  from  sources  with  data  from  multiple  countries.  An  optimal  task  distribution 
could, for instance, entail the possibility for Eurostat to access data from data holders that 
operate in multiple Member States, on their request.  

Measuring  cross-border  phenomena  may  require  that  some  statistical  actions  are 
performed at the EU level that cannot be done at national level only. This concerns, for 
example, the compilation of European aggregates or ensuring coherence and consistency 
of national estimates in domains where the European aggregates cannot be produced as a 
simple sum of national aggregates. Examples of such domains are globalisation (to avoid 
double  counting  of  some  multinational  enterprises  and  where  national  statistical 
authorities can only observe the relevant operations of the enterprises in their country) or 
global migration flows. 

This  specific  objective  will  also  include  outlining  possible  roles  and  specifying  new 
functions that statistical authorities could perform in the emerging European and national 
data ecosystems in full respect of the subsidiarity principle, while recognising that not all 
statistical  authorities  are  yet  in  a  position  to  perform  all  functions  related  to  these  new 
roles at national level. 

5.  WHAT ARE THE AVAILABLE POLICY OPTIONS? 

  What is the baseline from which options are assessed? 

In  the  baseline  option  (PO0)  no  revision  of  Regulation  (EC)  No  223/2009  is  foreseen. 
Non-binding measures  that  are  currently  foreseen or being  implemented  are part of the 
baseline  option.  For  instance,  Eurostat  is  currently  fostering  the  development  of 
methodological  and quality  frameworks  for integrating new data sources into European 
statistics as well as knowledge sharing and acquisition of new skills to enable the use of 
new technologies and methods in the ESS.  

Apart from current and foreseen measures, the changing data environment of the ESS is 
also  relevant  to  the  baseline.  The  European  Data  Strategy  is  profoundly  changing  the 
environment  of  the  ESS,  affecting,  for  instance,  the  availability  of  open  data, 
accessibility 
standardisation  and 
interoperability,  data  market  rules,  data  governance  rules,  and  possibly  also  public  and 
business  attitudes  towards  data  sharing  for  statistical  purposes.  Particularly  relevant  to 
the ESS in this context are the following acts and initiatives28: 

statistical  purposes  of  non-open  data, 

for 

  The  Open  Data  Directive  and  its  implementing  acts.  These  will  result  in  wider 

availability of open data, in particular in the form of ‘High Value Datasets’. 

28 For more information and references see Annex 5. 

15 

 
 
                                                 
  The  Data  Governance  Act.  The  ‘common  European  data  spaces’  that  are  being 
created in this context are potentially very relevant to the ESS, to the extent that 
data become available for reuse for European statistics. The work of the European 
Data  Innovation  Board,  created  through  the  DGA,  will  enhance,  among  other 
things, data and metadata standardisation, and interoperability, which is obviously 
important to European statistics. The DGA also regulates so-called data altruism, 
with potential benefits to European statistics. 

  The  Data  Act  proposal.  For  cases  of  emergencies  and  other  exceptional  needs, 
and  only  for  those  cases,  this  act  may  result  in  limited  and  strictly  conditional 
data access for official statistics.  

  The  Single  Market  Emergency  Initiative.  Data  collected  by  the  Commission  on 
the  basis  of  the  SMEI  can  be  used,  under  certain  conditions,  for  European 
statistics.  The  data  would  refer  to  targeted  information  from  the  economic 
operators in crisis-relevant supply chains.  

  The Interoperable Europe Act proposal. To the extent that this act results in better 
cross-border  and  public  sector  interoperability,  and  actual  data  sharing  through 
the ‘Interoperability Europe Portal’, this may benefit the ESS. 

It  is  worth  noting  that  these  acts  and  initiatives  make  the  baseline  dynamic,  which  is 
important  because  it  is  used  as  benchmark  for  assessing  the  impact  of  the  other  policy 
options. 

  Description of the policy options  

Apart from the baseline option, two policy options were designed to achieve the general 
objective  of  this  initiative,  namely  to  make  European  statistics  timelier,  more  detailed 
and  cost-efficient,  as  well  as  more  responsive  to  urgent  information  needs  in  times  of 
crises. Each policy option contains measures that are classified in three groups, each of 
which corresponds to a specific objective. In this way, it is ensured that all three specific 
objectives will be well covered by the planned measures. 

Policy  option  0,  the  baseline  option,  implies  no  revision  of  Regulation  (EC)  No 
223/2009. This option focuses on preserving the existing incentives and mechanisms to 
nudge the voluntary access and reuse of emerging digital sources that are privately held 
for  the  compilation  of  European  statistics,  voluntary  participation  in  collective  and 
coordinated statistical activities in times of crises and non-binding measures to promote 
more  efficiency  and  less  burden  on  businesses  and  Member  States’  national  statistical 
authorities.  The  measures  under  this  option  involve  minimal  intervention  and  are  non-
legislative by nature.  

Both  policy  options  1  and  2  include  a  targeted  revision  of  the  Regulation  (EC)  No 
223/2009  but  they  differ  in  the  intensity  and  stringency  of  the  legislative  measures 
(amendments)  on  the  businesses  and  Member  States’  national  statistical  authorities. 
Policy option 2 is more ambitious in terms of imposing obligations on the data holders 
and on the Member States’ national statistical authorities whereas policy option 1 is less 
stringent  with  lower  intensity.  The  measures  covered  by  this  option  aim  at  introducing 
legal certainty and empowering the key actors (the data holders and the ESS members) to 
harness  opportunities  offered  by  the  digital  age  and  to  have  adequate  instruments  for 
responding fast and in a coordinated way to urgent user demands in times of crises. Data 
reuse arrangements foreseen in policy option 1 should enable the reuse of digital data for 

16 

 
compiling European statistics in a framed way, while reducing the burden on businesses 
and Member States’ national statistical authorities.  

A summary of the main features of the three policy options is provided in Table 1. 

The three policy options have the following in common: 

  Each option builds on earlier analyses and discussions with stakeholders; 
  Each  option  assumes  compliance  with  applicable  rules  of  data  protection,  trade 

secrets and statistical confidentiality; 

  Each option foresees transparency measures.  

17 

 
Table 1: Policy options and measures  

Policy Option 0 

Policy Option 1 

Policy Option 2 

  The baseline option 
  No revision of Regulation 223/2009 
  Non-binding measures 
  Leverage the general provisions of other 

EU legal acts 

  Continuation of existing measures  

  Revision of Regulation 223/2009 
  Low legal intensity of amendments 
  Less stringent obligations on 

businesses especially on small and 
micro enterprises and on national 
statistical authorities 

  Focus on legal certainty and 

empowering data holders and national 
statistical authorities  

Specific objective 1 

  Revision of Regulation 223/2009 
  High legal intensity of amendments 
  More stringent obligations on businesses 

and national statistical authorities 

To embrace fully new technologies and sustainably reuse new data sources emerging as by-products of digital services to meet increasing user 
demands for timelier, more frequent and more detailed European statistics 

Policy Option 0 

Policy Option 1 

Policy Option 2 

1.1: Recommendations, exchange of best 
practices, grants aimed at enhanced re-use of 
new data sources: 
  Create financial and non-financial incentives 
and mechanisms (e.g., ensuring the public 
recognition of involved stakeholders, or 
creating incentives for NSIs to use innovative 
techniques) to increase the re-reuse of new 
and innovative data sources in the production 
of European statistics.  

18 

 
 
 
 
 
 
  Develop guidelines based on best practices 
for establishing partnerships and voluntary 
data agreement models. 

1.2: Develop common standards on data quality 
in the context of B2G4S data sharing for the 
purpose of European official statistics. 

2.1: Same as measure 1.2. 

3.1: Same as measure 1.2. 

2.2: Provide transparency obligations for both 
private and public actors engaging in data 
sharing collaboration. 
2.3: Introduce enforceable mechanisms for 
reusing emerging new digital data sources that 
are privately held, for the compilation of 
European statistics, under specific conditions 
and subject to a set of binding safeguards: 
  The right of having access and reuse data 
includes a consultation stage where the 
feasibility of various parameters of the data 
requests (e.g., level of aggregation, 
deadlines, mode of data provision, 
confidentiality protection) are discussed.  
  Compensation limited to costs related to 

data processing and data extraction needed 
to make the data usable for the compilation 
of European statistics.  

  Dispute resolution mechanisms are 

foreseen. 

  Exemption of micro and small enterprises 
from enforceable mechanisms for reusing 
emerging new digital data sources that are 
privately held, for the compilation of 

19 

3.2: Same as measure 2.2. 

3.3: Measure with higher legal intensity than 2.3: 
Introduce enforceable mechanisms for reusing 
emerging new digital data sources that are 
privately held, for the compilation of European 
statistics: 
  Obligation to appoint data stewards to 

facilitate collaboration with statistical offices 
in data re-using activities. 

  No consultation stage is included on the 
various parameters of data requests. 

  No compensations of any costs incurred by the 

data holders.  

  Dispute resolution mechanisms are not 

foreseen.  

  No exemption of micro and small enterprises 
from enforceable mechanisms for reusing 
emerging new digital data sources that are 
privately held. 

 
 
 
European statistics. 

2.4: Limit the access and reuse of privately 
held data to NSIs and Eurostat, only for the 
compilation of European statistics.  

3.4: Measure with higher legal intensity than 2.4: 
  Extend the right to access and re-use privately 
held data to the other national authorities that 
produce European Statistics. 

  Allow the data made available by private data 

holders to be shared with the research 
community. 

Specific objective 2 
To provide mechanism and tools for the ESS to react fast, in collective and coordinated manner to urgent data demands in times of crises 
Policy Option 0 
1.3: Recommendations, exchange of best 
practices, financial support to enable the ESS to 
react fast in times of crises: 

Policy Option 1 
2.5: Same as measure 1.3. 

Policy Option 2 

  Develop recommendations to NSIs for 

preparedness and resilience action plans. 
  Using financial support to stimulate the 
ESS partners to participate in voluntary 
statistical actions in times of crises. 

2.6: Provide a legal basis for Eurostat to initiate 
statistical actions conducted at EU level in 
response to urgent user demands in times of 
crises, with voluntary participation of NSIs, but 
ensuring that it will result in sufficiently timely, 
frequent and detailed representative data and 
information at EU level. Such statistical actions 
could only be initiated in the case a crisis 
mechanism has been formally triggered by an 
Institution according to established procedures 

3.5: Measure with higher legal intensity than 2.6: 
Provide a legal basis for Eurostat to initiate 
statistical actions conducted at EU level in 
response to urgent user demands in times of crises, 
with obligation of the Member States to take part 
in the actions, ensuring that it will result in 
sufficiently timely, frequent and detailed 
representative data and information at EU level. 

20 

 
 
 
 
 
in Union law; it would be not for the ESS to 
decide on its own on the existence of a crisis. 

3.6: Oblige the Member States (NSIs) to establish 
resilience mechanisms and crises preparedness 
action plans to ensure that national statistical 
systems will be able to function in times of crises 
and respond to urgent users demands. 
3.7: Provide a legal basis for Eurostat to initiate 
statistical actions conducted at EU level, with 
voluntary participation of NSIs, in response to 
urgent user demands, other than in times of crises. 

Specific objective 3 
To update the tasks and roles of ESS partners to leverage opportunities offered by digital transformation for more cost-efficient and less 
burdensome statistical production 
Policy Option 0 
1.4: Recommendations, exchange of best 
practices, communication activities, grants, in 
particular:   
  Develop common standards regarding 

Policy Option 2 

Policy Option 1 

technical interoperability of data across 
borders and sectors for the ESS. 

  Develop guidelines and recommendations for 

voluntary data sharing within the ESS.  
  Support projects aimed at enhancing data 
sharing within the ESS through existing 
collaboration networks defined in Regulation 
(EC) No 223/2009. 

21 

 
 
 
 
 
 
 
 
  Develop recommendations for more active 
participation of NSIs in the emerging data 
ecosystems at national level. 

2.7: Empower Eurostat to collect data on behalf 
of the ESS and act as a data hub to share data 
with NSIs for reasons of efficiency or 
effectiveness, in particular in domains related 
to cross-border flows and phenomena, and in 
cases where this would reduce the burden on 
businesses and citizens or the workload of the 
ESS. 
2.8: Establish that NSIs and Eurostat may 
assume data governance and data stewardship 
functions, for instance in respect of standards 
and data interoperability within their data 
ecosystems. 
2.9: Foresee possibility for Eurostat to initiate 
the development of experimental statistics in 
close cooperation with the ESSC. 
2.10: Make data sharing among NSIs and 
between NSIs and Eurostat mandatory for 
statistical purposes based on cost-benefit 
analysis, in particular in domains related to 
cross-border flows and phenomena and: 
  Empower NSIs and Eurostat to share these 
data for statistical research purposes. 
  Create support mechanisms and incentives 
to move towards data sharing based on 
privacy-enhancing technologies (PET). 

22 

3.8: Same as measure 2.7. 

3.9: Same as measure 2.8. 

3.10: Same as measure 2.9. 

3.11: Measure with higher legal intensity than 
2.10: 
Make data sharing among NSIs and between NSIs 
and Eurostat mandatory for all statistical domains 
and: 
  Empower NSIs and Eurostat to share these 
data for statistical research purposes. 
  Mandate applications based on privacy-

enhancing technologies (PET) to be used for 
the data sharing within the ESS. 

 
 
 
 
 
  Options discarded at an early stage  

No options were discarded at an early stage. The three options were fully assessed. 

6.  WHAT ARE THE IMPACTS OF THE POLICY OPTIONS? 

Introduction 

This  chapter  provides  an  assessment  of  the  policy  options  in  terms  of  their  impacts. 
These impacts are described qualitatively and, to the extent possible, quantitatively, and 
where  relevant  with  differentiation  between  groups  of  stakeholders.  This  assessment 
includes an impact  analysis of  PO0, the baseline  option, because this is  the benchmark 
for assessing the impact of PO1 and PO2. 

The impact of the three policy options have been assessed against criteria of effectiveness 
(in terms of achieving the specific objectives), efficiency (benefits and costs in reaching 
the specific objectives), coherence (with other policy and legal initiatives) and feasibility 
(technical and non-technical, taking into account the political context). Given the domain 
of  the  initiative,  i.e.,  European  statistics,  specific  attention  has  also  been  given  to  the 
potential of policy options to reduce the burden on businesses and respondents as well as 
to the distributional effects of the impact (e.g. the effect on small and micro enterprises 
will be different  from  the effect  on large businesses holding data). The impact  analysis 
takes  also  into  account  the  risks  that  benefits  that  cannot  be  realised  because  of,  for 
instance, issues with the quality of the new data sources.  

In  the  current  initiative,  the  stakeholder  groups  that  are  most  relevant  to  distinguish  in 
respect of impact include (see also Annex 2): 

  providers of primary data for the production of European statistics (including data 

holding businesses, individual respondents, and public administrations); 

  producers  of  European  statistics,  i.e.,  the  partners  of  the  ESS  (in  particular  the 

NSIs and Eurostat); 

  users of European statistics (including institutional and business users, the media 

and the general public); 

  other  stakeholders,  including  subjects  to  which  data  collected  for  European 

statistics pertain. 

It is noteworthy that businesses appear in this grouping in two roles: as data holders and 
as users of European statistics. For businesses, moreover, a distinction in respect of size 
is also very relevant, in particular distinguishing  small and micro enterprises. Similarly, 
citizens  may  be  subjects  of  data  collection,  but  are  also  part  of  society  at  large,  whose 
functioning  depends  on  the  public  availability  of  impartial  and  high  quality  official 
statistics.  Hence,  the  social  impact  of  the  initiative  is  also  considered  in  the  impact 
analysis. 

For  the  analysis  of  the  impact  of  the  policy  options,  several  sources  of  evidence  have 
been  used  (see  also  Annexes  1  and  4).  Where  possible,  a  distinction  has  been  made 
between  direct,  indirect,  one-off  and  recurrent  costs  and  benefits.  The  quantification  of 
impacts,  where  possible,  was  based  actual  experiences  with  B2G4S  sharing  of  specific 
types  of  digital  data  held  by  the  private  sector  and  with  internal  ESS  data  sharing  (i.e. 
Intrastat data sharing), to which several measures of PO1 and PO2 refer. Historical data, 
existing  studies  and  experts’  judgements  were  also  heavily  used  to  derive  quantitative 

23 

 
 
estimates of benefits and costs of policy options. In addition, several use cases of B2G4S 
data sharing (described in Annex 6) have been also used as input for the quantification of 
costs  and  benefits  related  to  the  various  policy  options.  For  the  calculation  of  the 
quantitative impact of the policy options, assumptions had to be made as to the number 
and  type  of  use  cases  that  will  be  realised  following  the  adoption  of  the  revision  of 
Regulation  (EC)  No  223/2009.  When  describing  the  costs  and  benefits  of  the  policy 
options in this chapter, the uncertainty of the assumptions is taken into account.  

It  is  important  to  note  that  the  use  cases  presented  in  this  report  are  not  meant  to 
determine the scope of access in the revised legal framework. Even if limited in number, 
they do represent the most typical cases of new data sources with the highest potential to 
address  the  problem  identified  under  this  initiative  and  as  such  provide  valuable 
information in terms of expected impact. These use cases represent therefore a fair basis 
for the measurement of the direct and indirect benefits as well as of the costs.  

The overall direct  benefits  and costs were  estimated using a standard cost-based model 
that  considers  direct  costs  and  benefits  for  modifying  existing  or  setting  up  new  data 
collections  for  producing  enhanced  statistical  output.  The  model  distinguishes  between 
costs and benefits incurred by the producers of official statistics and by the respondents 
(here  the  data  holders).  The  costs  are  further  distinguished  according  to  their  type: 
upfront  (preparation),  organisational,  infrastructure  and  operational  costs,  which  are 
shared  between  statistical  offices  and  data  holders.  The  considered  benefits  are  quality 
improvements and increased statistical output from which statistical offices are profiting. 
Cost savings due to smaller number of respondents are incurred by statistical offices and 
are mirrored as burden reductions at the side of the businesses. It has to be emphasized 
that the costs are incurred by the data holders while the savings due to burden reduction 
are incurred by the entire business sector in general. 

An extensive stakeholders’ consultation has also fed into the analysis of the impact of the 
policy  options,  but  these  inputs  are  mostly  qualitative  (see  also  Annexes  2  and  4).  The 
dimensions looked at included: 

technological development and the digital economy 
innovation and research 

  employment and economic growth 
 
 
  EU evidence-based policymaking 
  conduct  of  business,  administrative  costs  on  businesses  and  sectoral 

competitiveness 
  position of SMEs 
  public authorities and their budgets 

When  the  impact  of  policy  options  is  considered,  it  should  be  kept  in  mind  that  the 
Regulation (EC) No 223/2009 is a framework regulation with an enabling character. The 
foreseen revision of the Regulation will have its full effect through subsequent decisions, 
at  both  the  EU  and  national  level.  For  example,  the  concrete  demands  on  a  specific 
digital  data  source  and  its  concrete  use  for  compilation  of  European  statistics  will  be 
carried out through the Annual Work Programme for European statistics (AWP) adopted 
by the Commission and based on conditions specified in the basic act. In accordance with 
Regulation (EC) No 223/2009, the Commission must ensure, when preparing the AWP, 
effective  priority  setting,  including  reviewing,  reporting  on  statistical  priorities  and 
allocation of financial resources; these are important elements to take into account when 

24 

 
adjusting  the  statistics  referred  to  in  the  AWP  to  the  evolving  needs  of  the  users  and 
eventually  adding  or  removing  statistics.  The  draft  AWP  lists  the  eligible  actions  and 
refers  to  the  main  activities  and  outputs  as  well  as  to  the  main  statistics  produced  and 
disseminated  by  Eurostat.  The  AWP  is  submitted  to  the  ESS  Committee  and  the 
Commission  must  take  the  utmost  account  of  the  comments  of  the  ESS  Committee. 
Every  time  a  statistic  is  added  to  the  AWP  that could  make  use  of  privately  held  data, 
either  as  its  main  source  or  as  a  supplementary  source,  this  will  require  a  convincing 
justification,  including  a  test  of  the  proportionality  of  the  costs  and  benefits  to  society 
and to the concerned data holders. When executing the AWP, specific data holders will 
be identified and requested to  enable the reuse of the data held by them. Such requests 
must also be explicitly justified. The actual impacts will thus be evaluated on a case-by-
case  basis  before  relevant  decisions  are  taken  on  the  basis  of  the  future  revised 
framework regulation. This is further explained in chapter 8. 

The actual impact of the policy options will thus take some years to become fully visible. 
For the impact analysis of the remainder of this chapter, a timeframe of 10 years is used 
as reference. 

Impact of policy option 0: the baseline option 

The  baseline  option  assumes  that  the  current  regulation  on  European  statistics  is  not 
changed.  Ongoing  efforts  of  the  ESS  to  meet  the  increasing  demand  for  European 
statistics  would  be  continued  as  described  in  chapter  5.  The  opportunities  provided  by 
new legislative measures including the Open  Data Directive, the Data Governance Act, 
the  Data  Act  proposal,  the  Single  Market  Emergency  Initiative  and  the  Interoperable 
Europe Act proposal would be utilized. 

Measures  under  this  policy  option  are  of  non-binding  nature.  They  rely  on  voluntary 
participation  supported  by  limited  financial  contributions  at  European  level.  Therefore, 
their  impact  will  depend  on  their  take  up  by  the  stakeholders.  Based  on  accumulated 
experience  in  the  last  decade,  the  measures  related  to  the  first  specific  objective 
(concerning  B2G4S  data  reuse)  are  expected  to  result  in  a  very  slow  uptake  of  B2G4S 
data sharing. These measures will lead to participation of only  few data holders in data 
sharing  activities  at  EU  level  and  in  some  Member  States,  mainly  those  who  have 
additional legislation in place to enable data sharing activities. The voluntary character of 
the actions will most likely not reach a level to replace or substantially decrease the scope 
of  current  data  collections.  The  resulting  statistics  will  most  likely  not  meet  the  high-
quality  requirements,  notably  comparability  and  cost-efficiency,  as  expressed  in  the 
regulation on European statistics due to the risk  of non-take up of new data sources by 
each  Member  State.  This  would  lead  to  a  more  heterogeneous  situation  and  a  growing 
gap  among  the  Member  States.  It  would  very  likely  render  impossible  to  compile 
comparable  European  statistics  from  such  a  disperse  situation.  This  assessment  is 
confirmed  by  the  results  of  the  stakeholder  consultation  and  by  own  experiences  from 
projects  conducted  by  EU  statistical  offices  for  the  use  cases  described  in  Annex  6 
(mobile network operators data, smart meter, financial transactions, scanner data). 

The  measures  related  to  the  second  specific  objective  (concerning  crisis  response)  and 
the third one (concerning efficiency) are also expected to have a very limited effect due 
to their entirely voluntary nature, reflecting current experience. Crisis response would be 
improvised and not be harmonised, comparable to the insufficiently fast and coordinated 

25 

 
 
response  to  the  urgent  information  demands  under  COVID-19  crisis,  and  internal  ESS 
data sharing would remain clearly suboptimal. 

The  baseline  option  entails  the  impact  of  the  other  existing  or  under  negotiations 
legislative  acts,  outside  the  statistical  legislation.  The  implementation  of  Common 
European Data Spaces (CEDS) and activities related to achieving interoperability within 
and  between  data  spaces  could  contribute  to  improving  availability  of  data  for  the 
production of European statistics. Considering the principle of voluntary participation in 
the  CEDS29  and  the  right  of  each  individual  data  supplier  to  determine  the  type  and 
conditions  of  use  for  each  case  of  data  reuse,  the  required  quality  conditions  for  the 
production  of  European  statistics  are  difficult  to  meet.  Reasons  are  the  insufficient 
number  of  participating  data  holders,  the  lack  of  application  of  common  statistical 
methods  and  standards,  and  insufficient  transparency  in  pre-processing  of  the  source 
data. However, CEDS can be used to further experiment, develop methodologies, quality 
frameworks or standards for integrating data into European statistics.  

The SMEI and the Data Act limit the use of data for statistical purposes to very narrowly 
defined  measures  of  crisis  management  and  recovery.  Hence,  it  is  unlikely  that  the 
narrow  definition  of  the  SMEI  would  constitute  conditions  for  producing  official 
statistics  that  would  comply  with  the  quality  criteria  of  European  statistics.  The 
conditions in the Data Act are limited to cases of exceptional need. 

Despite the uncertainties accompanying the implementation of the baseline scenario, the 
expected costs and benefits have been estimated, to serve as a benchmark for comparison 
with the other two policy options.  More detailed calculations are presented in Annex 3. 
The quantification of the impact has been made under the assumption that, in 10 years, 
PO0 will result in one case of B2G4S reuse of data at the national level (for 18 out of 27 
NSIs),  one  case  of  effective  ESS  response  to  a  crisis  (new  data  collection  or  new 
statistical  insight,  e.g.,  people  mobility  flows  under  lockdowns)  and  one  case  of  data 
sharing  within  the  ESS  (e.g.,  on  data  on  multinationals).  This  assumption  can  be 
considered realistic, since it is derived from the extensive experience of implementation 
of  measures  foreseen  to  be  continued  under  PO0  and  associated  risk  analysis  of  non-
taking up the measures by the data holders and the ESS partners. 

The estimates show that the balance of all direct costs and benefits would be positive and 
amount  to  EUR  87.2  million  a  year  (Table  2,  C12  and  C13),  but,  importantly,  the 
baseline option would not  realise much higher benefits  through intensified  B2G4S  data 
sharing,  mechanisms  for  responding  to  urgent  user  demands,  and  intensified  ESS  data 
sharing.  Moreover,  already  declining  response  rates  would  take  their  toll,  and  further 
investments would still be needed. These conclusions would remain valid and would not 
be affected as such with a slight change in the assumptions concerning the three cases of 
B2G4S data reuse, crisis response and data sharing. The results of the impact assessment 
would continue to hold.  

Concerning  the  different  categories  of  stakeholders,  additional  burden  on  data  holders 
could  be  generated  through  the  implementation  of  the  above-mentioned  legislation, 
mainly the Data Governance Act. However, the benefits created by these legislative acts 

29 Commission staff working document on Common European Data Spaces, SWD(2022) 45 final. 

26 

 
                                                 
would outweigh additional burden on private enterprises30. Additional legislation related 
induce  additional  burden  on  public 
to  sectoral  European  data  spaces  could 
administrations through data sharing obligations. 

The  burden  on  producers  of  European  statistics  would  slightly  increase  due  to  some 
voluntary agreements of data reuse, activities contributing to achieving interoperability, 
methodological developments, a voluntary participation in statistical actions under crises 
and data sharing within the ESS.  

Users of European statistics  would profit  only  in  a limited way, proportionally to  the 
increase  in  European  statistics  output,  mainly  based  on  more  intense  use  of 
administrative data. The increase will very unlikely close the gap between the supply and 
the  demand  for  more  granular  and  timelier  statistics.  The  growing  gap  will  have  a 
negative  impact  on  the  quality  of  public  debate  and  the  use  of  European  statistics  for 
evidence-based policy making. The lack of European statistics is more severe in domains 
heavily affected by digitalisation, such as the digital economy, as traditional methods of 
statistical data collections are not adequate to provide high-quality information. 

The  baseline  option  does  not  directly  change  the  burden  on  SMEs.  However, 
opportunities  to  lower  the  burden  on  SMEs  through  alternative  ways  of  data  collection 
are missed in this scenario. In addition, an increase in quality of current statistics through 
collection  of  data  via  larger  data  holders  is  only  possible  to  a  very  limited  extent.  An 
example of this effect is the collection of statistical data on accommodation services from 
internet  platforms,  which  cover  the  offers  from  enterprises  exempted  from  traditional 
data collections due to their size. 

The  baseline  option  is  unlikely  to  significantly  impact  employment  and  growth, 
technological development, innovation and research, or the situation of SMEs and public 
authorities. On the contrary, the baseline scenario would rather have negative effects of 
EU evidence-based policy making, because of the dynamic nature of the baseline with a 
increasing gap between demand and supply of statistics. 

Impact of policy option 1: the first legislative option 

For  PO1,  the  impact  is  analysed  for  the  measures  associated  with  each  of  the  specific 
objectives, respectively. Where an impact is realised by the combination of measures of 
more than one specific objective, this is indicated.  

Overall impact by specific objective 

Due  to  the  obligation  of  making  their  data  available  for  compilation  of  European 
statistics,  PO1  will impact  data  holders  more  intensively.  Concerning  the  first  specific 
objective, the burden on data holders will increase as compared to  the baseline option. 
This  will  affect  those  enterprises  which  are  in  the  possession  of  large  digital  datasets 
suitable  for  producing  European  statistics.  Examples  are  enterprises  holding  data  from 

30  European  Commission,  impact  assessment  Report  Accompanying  the  document  Proposal  for  a 
Regulation  on  European  data  governance  (Data  Governance  Act),  SWD(2020)  295  final,  and  European 
Commission, impact assessment Report accompanying the Proposal for a Regulation on harmonised rules 
on fair access to and use of data (Data Act), SWD(2022) 34 final. 

27 

 
 
                                                 
smart meters, financial transactions, metadata from mobile phone communication records 
and  from  internet  platforms.  Usually,  these  enterprises  can  be  categorized  as  big 
enterprises. Due to market concentration, typically few enterprises cover a large part of 
the  statistical  target  population.  In  case  of  mobile  communication  providers,  these  are 
typically  3-4  per  Member  State;  in  case  of  internet  platforms,  these  are  between  4  and 
600  across  Europe  depending  on  the  domain  (see  Annex  3  and  the  example  of 
accommodation  platforms  and  online  job  portals  of  Annex  6).  In  addition,  the  largest 
platforms operate across Europe.  

The  impact  analysis  assumes  that  in  the  first  10  years  after  the  revision  of  Regulation 
(EC)  No  223/2009  comes  into  force  there  will  be  reuse  of  privately  held  data  for 
European statistics  in  15 statistical  domains, which are deemed more mature for use of 
digital data sources based on recent pilots undertaken by the ESS in areas such as labour 
market,  price  and  social  statistics  as  well  as  energy  and  business  domains.  In  some  of 
these cases, data collection will be centralised at EU level, with Eurostat providing a data 
hub  service  to  the  ESS.  This  role  builds  on  the  corresponding  measures  of  specific 
objective 3.  Annex  3  provides  a  detailed  calculation  of  costs  and  benefits,  taking  into 
account  whether  such  reuse  is  carried  out  at  central  or  national  level,  and  whether  it  is 
aimed at replacing existing surveys or at compiling new statistics.  

European  statistics  based  on  B2G4S  data  sharing  require  considerable  investments  in 
infrastructures  and  the  development  of  methodologies  and  processes,  with  increasing 
operational costs. On the other hand, savings are incurred due to decreasing samples, and 
increasing  numbers  and  quality  of  statistics  output.  Considering  only  direct  costs  and 
benefits,  relevant  for  the  first  specific  objective  within  the  time  span  of  10  years  it  is 
estimated to  achieve  net  benefits  for businesses  and the ESS together of approximately 
EUR  653.5  million  a  year,  of  which  more  than  one  third  would  pertain  to  businesses 
(Table  2,  E3  and  E4).  Although  for  businesses  the  costs  would  be  more  than 
compensated  by  savings  due  to  burden  reduction  for  current  surveys,  the  distribution 
among businesses of costs and benefits would be uneven. Data holders would incur costs, 
whereas SMEs are expected to particularly benefit, since they make up the large majority 
of respondents in business surveys. This aspect of the impact is given special attention in 
chapter 8.  

In  addition  to  the  direct  net  benefits,  there  would  be  considerable  efficiency  gains 
through  indirect  benefits,  in  particular  better  policy  decisions  and,  for  businesses, 
efficiency  gains  due  to  better  informed  economic  decisions.  Society  at  large  would 
benefit  from  effects  induced  by  better  informed  policies  and  evidence-based  public 
debate. This, however, cannot be quantified.  

It  should  be  noted  that  the  costs  and  benefits  of  the  measures  of  the  first  specific 
objective depend on the assumptions made regarding the number of use cases that will be 
realised within the time span of 10 years, for the benchmark (PO0) as well as for PO1. 
Making such assumptions cannot be avoided, given the framework nature of Regulation 
(EC) No 223/2009. For instance, the estimation of net benefits of EUR 653.5 million a 
year, based on 15 statistical domains, would be higher if the number of use cases realised 
was underestimated, or lower if overestimated: the net benefits are roughly proportionate 
to the number of use cases (see Annex 3). The assumption of 15 cases is based on user 
demands for statistical data related to different policy areas of the European Union, the 
maturity  of  available  data  sources,  the  preparatory  work  of  the  ESS,  which  led  to  a 
number  of  pilot  applications  and  development  of  standardized  procedures  and  the 

28 

 
capacity  of  the  ESS  in  conducting  new  statistics  production.  Thus  this  assumption  is 
realistic and the conclusions are not changed by a slight variation of use cases. Therefore, 
the conclusions on the nature of the impact can thus be considered robust.  

The  measures  of  the  second  specific  objective  for  PO1  concern  mainly  the  ESS 
capability to respond adequately to urgent user demands in times of crises. Use would be 
made of new data sources that emerge as by-products of digital services, thus building on 
the  measures  of  objective 1.  The  measures  would  affect  businesses,  since  they  would 
need to share data. 

The  impact  analysis  assumes  that  there  will  be  five  cases  of  response  to  urgent  user 
demands  in  times  of  crises  based  on  historical  experience  about  urgent  statistical 
demands originating from crises and an assessment of the overall resource capacity of the 
ESS  to  deliver  new  statistical  information  products.  These  five  cases  would  all  lead  to 
new statistical products. In some of these cases, data collection will be centralised at EU 
level,  with  Eurostat  providing  a  data  hub  service  to  the  ESS.  Therefore,  this  role  also 
builds  on  the  corresponding  measures  of  specific  objective 3.  Annex 3  provides  a 
detailed  calculation  of  costs  and  benefits,  taking  into  account  whether  such  reuse  is 
carried out at central or national level. 

European statistics based on the capability to respond adequately to urgent user demands 
in times of crises would also require considerable investments in infrastructures and the 
development  of  methodologies  and  processes,  with  increasing  operational  costs.  There 
would also be benefits in the form of increasing numbers and quality of statistics output, 
but there would not be savings due to lowering response burden. Considering only direct 
costs and benefits, relevant for the second specific objective, within the time span of 10 
years it is expected to achieve benefits for the ESS of approximately EUR 10.7 million 
(mainly due to additional outputs) and a net loss for businesses of approximately EUR 9 
million  a  year  (Table  2,  E6  and  E7).  This  is  the  price  to  be  paid  for  the  huge  indirect 
benefits,  consisting  of  being  well-informed  and  take  prompt  and  efficient  decisions  in 
times  of  crises.  These  benefits  cannot  be  quantified  but  would  pertain  to  society  as  a 
whole.  

As was the case for the first  specific objective, the quantitative impact  of the measures 
depend  on  the  assumptions  regarding  the  number  of  use  cases.  In  the  case  of  crisis 
response, the prediction is based on past experience with the pandemic and energy crisis. 
Nevertheless,  as  was  the  case  for  the  first  specific  objective,  the  conclusions  on  the 
nature of the impact are fairly robust, since the impact is roughly linear to the number of 
cases. 

The measures of the third specific objective for PO1 concern not only the data hub role 
as mentioned above, but notably also mandatory data sharing among NSIs and between 
NSIs and Eurostat. In addition, there are measures such as on the possibility for NSIs and 
Eurostat  to  assume  data  governance  and  data  stewardship  functions.  These  additional 
measures are hard to quantify and are expected to have an impact that is much less than 
the impact of mandatory ESS data sharing.  

The  impact  analysis  assumes  that  within  10  years  there  will  be  four  new  cases  of  ESS 
data  sharing.  Annex  3  provides  a  calculation  of  costs  and  benefits,  building  on  the 
experiences  with  Intrastat  statistics.  The  application  of  data  sharing  for  the  Intrastat 
statistics  resulted  in  a  reduction  by  EUR  155  million  a  year,  largely  eliminating  the 

29 

 
collection of mirror statistics.  Intrastat had the biggest  potential for savings and burden 
reduction due to the size of the data collection, so this will not be repeated for the four 
new  cases  of  data  sharing.  However,  a  considerable  reduction  of  burden  can  still  be 
expected  when  applying  this  measure  to  statistical  domains  such  as  migration  or  data 
collections  in  business  statistics.  The  impact  analysis  shows  estimated  net  benefits  of 
EUR 138.3 million a year (Table 2, E9 and E10) for the first specific objective, in a time 
span of 10 years. These benefits are the result of the burden reduction on respondents that 
can be realised, together with lower net costs for the statistical authorities. It is primarily 
the respondents who will benefit.  

Expected impact on categories of stakeholders 

In addition to the information on the expected impact of the measures on specific groups 
of stakeholders provided above, some further observations can be made. In total, it can be 
estimated  that  around  one  thousand  relatively  large  enterprises  across  Europe  will  be 
affected  by  mandatory  data  sharing  requests  (Annex  3).  Considerable  savings  can  be 
envisaged  related  to  decreasing  the  size  of  surveys  and  thus  reduction  of  burden  on 
businesses  and  citizens.  The  reduction  of  burden  will  affect  all  sizes  of  businesses, 
including SMEs.  The estimations show that the savings due to  decrease of samples are 
more  than  10  time  higher  than  the  additional  burden  on  enterprises  due  to  new  data 
demands (Annex 3).   

Users  of  European  statistics  will  profit  considerably  from  increased  availability  of 
statistical data and of higher quality of European statistics through the application of the 
measures  in  PO1.  The  most  effective  measure  will  be  reuse  of  privately  held  data 
followed  by  actions  in  case  of  urgent  user  demands  related  to  crises.  Intensified  data 
sharing  and  Eurostat  acting  as  a  data  hub  will  mainly  result  in  savings  and  burden 
reduction by increasing the quality and quantity of European statistics. Increased ability 
of the ESS to provide timelier, more frequent and more detailed statistics in response to 
urgent information needs in times of crises will contribute to higher impact of initiatives 
at European level addressing those crises. 

Public administrations will profit from increased availability of statistical data and from 
additional activities of statistical offices related to data governance and stewardship. The 
ESS  and 
its  partners  have  well-advanced  governance  mechanisms,  processes, 
methodologies  and  quality  frameworks,  and  taxonomies,  which  have  the  potential  to 
increase interoperability within and between different domains. Although implementation 
will vary among Member States, all NSIs will profit from concepts and guidelines being 
elaborated by the ESS and by international statistical organisations, such as the statistical 
divisions of the UN. However, PO1  requires  considerable investment  from  the NSIs in 
methodology,  processes,  infrastructure  and  training  of  staff  due  to  the  very  different 
nature  of  data  collection  and  data  treatment  in  the  context  of  B2G4S  data  sharing  for 
European  statistics  as  compared  to  traditional  processes.  Nevertheless,  necessary 
investments to realize the potential of the measures of PO1 may be offset by savings. The 
investments and savings will occur at different points in time, with upfront investment at 
the beginning of the period required to realize savings at a later stage. Due to the fact that 
the digital data sources are not optimized for use in official statistics, some extra effort 
will be necessary to ensure high quality of official statistics outputs.   

The  academic  sector  will  profit  from  availability  of  additional  microdata  for  research 
purposes based on new statistical information products derived from the policy measures. 

30 

 
The  current  regulation  on  European  statistics  already  includes  the  legal  framework  and 
limitations for access to microdata for research purposes, which remain fully in place. 

Impact of policy option 2: the second legislative option 

For PO2, the impact is also analysed for the measures associated with each of the specific 
objectives, respectively. Where an impact is realised by the combination of measures of 
more than one specific objective, this is indicated.   

The estimation of direct benefits and costs of the measures related to B2G4S data reuse, 
crisis  response  and  ESS  data  sharing  follows  the  same  methodological  approach  as  for 
PO1. For the reasons given in section 6.3, the conclusions on the nature of the impact can 
be  considered  robust.  The  estimation  of  the  number  of  use  cases  takes  into  account 
different  factors  related  to  frequency  of  crisis  based  on  recent  experiences,  demand  by 
users for new or improved statistics, capacity of the ESS, readiness of the ESS and the 
envisaged legal conditions of the policy option.  

Overall impact by specific objective  

Measures  of  the  first  specific  objective  for  PO2  also  concern  the  reuse  of  new  data 
sources that emerge as by-products of digital services, and the measures will impact such 
data  holders  more  intensively  than  PO0  and  PO1.  The  burden  on  data  holders  will 
increase as compared to the baseline option. This will affect those enterprises which are 
in  the  possession  of  large  digital  datasets  suitable  for  producing  European  statistics. 
However,  the  framed  reuse  conditions,  foreseen  under  PO1  in  respect  of  B2G4S  data 
reuse  do  not  apply  to  PO2.  In  particular,  there  will  be  no  consultation  stage  to  discuss 
feasibility  issues  and  operational  modalities,  no  dispute  resolution  mechanisms  and  no 
cost  compensation  including  marginal  costs  related  to  the  preparation  of  those  data  for 
reuse  by  NSIs  and  Eurostat.  As  a  result,  disputes  can  only  be  settled  in  courts,  and 
sustainable data reuse in the production of official statistics will take much more time to 
be realised.  

As a consequence, to quantify the costs and benefits of PO2, the impact analysis assumes 
an  effective  sustainable  reuse  of  privately  held  data,  implemented  in  5  European 
statistics’ domains in the first 10 years after the revision of Regulation (EC) No 223/2009 
comes into force. In some of these cases, data collection will be centralised at EU level, 
with  Eurostat  providing  a  data  hub  service  to  the  ESS.  This  role  builds  on  the 
corresponding measures of specific objective 3. Annex 3 provides a detailed calculation 
of costs and benefits, taking into account whether such reuse is carried out at central or 
national level, and whether it is aimed at replacing existing surveys or at compiling new 
statistics.   

Even  though  B2G4S  data  reuse  will  take  more  time,  European  statistics  based  on  such 
data sharing still require considerable investments in infrastructures and the development 
of  methodologies  and  processes,  with  increasing  operational  costs.  On  the  other  hand, 
savings will be incurred due to decreasing samples and increasing numbers and quality of 
statistics  output.  Considering  only  direct  costs  and  benefits,  within  the  time  span  of  10 
years  it  is  expected  to  reach  net  benefits  for  businesses  and  the  ESS  together  of  EUR 
207.8 million a year (Table 2, G3 and G4), of which more than a third would pertain to 
businesses  (Annex  3).  Although  for  businesses  the  costs  would  be  more  than 
compensated  by  savings  due  to  burden  reduction  for  current  surveys,  the  distribution 
among businesses of costs and benefits would be uneven, as was the case for PO1. Data 

31 

 
 
holders would incur costs, whereas SMEs would particularly benefit, since they make up 
the majority of respondents in business surveys.   

In addition  to  the direct  net  benefits,  to  the  extent that B2G4S  data reuse is  realised in 
PO2, there would be considerable efficiency gains through indirect benefits, in particular 
better  policy  decisions  and,  for  businesses,  efficiency  gains  due  to  better  informed 
economic  decisions.  Society  at  large  would  benefit  from  effects  induced  by  better 
informed policies and evidence-based public debate. This, however, cannot be quantified.   

Finally, PO2 also comprises a measure aimed at  providing access to the data shared by 
data  holders  for  research  purposes.  This  will  lead  to  costs  for  the  ESS  to  provide  this 
access. These costs are estimated at approximately EUR 2 million a year (Table 2, G11 
and Annex 3).  It  is  expected that the benefits  to  society of the research done are much 
higher, but this cannot be quantified. 

The  measures  of  the  second  specific  objective  for  PO2  concern  not  only  the  ESS 
capability to respond adequately to urgent user demands in times of crises. For meeting 
urgent user demands, use would be made of new data sources that emerge as by-products 
of  digital  services,  thus  building  on  the  measures  of  objective  1.  The  measures  would 
affect businesses, since they would need to share data.  

The  impact  analysis  assumes  that  there  will  be  ten  cases  of  response  to  urgent  user 
demands  including  in  times  of  crises.  In  some  of  these  cases,  data  collection  will  be 
centralised at EU level, with Eurostat providing a data hub service to the ESS. Therefore, 
this  role  also  builds  on  the  corresponding  measures  of  specific  objective  3.  Annex  3 
provides  a  detailed  calculation  of  costs  and  benefits,  taking  into  account  whether  the 
response  is  carried  out  directly  at  EU  level  or  through  coordinated  actions  at  national 
level.  

European statistics based on the capability to respond adequately to urgent user demands 
in times of crises would also require considerable investments in infrastructures and the 
development  of  methodologies  and  processes,  with  increasing  operational  costs.  There 
would also be benefits in the form of increasing numbers and quality of statistics output. 
Considering only direct costs and benefits, within the time span of 10 years it is expected 
to  reach  net  benefits  of  approximately  EUR  42.9  million  annually  for  the  ESS,  mainly 
due to additional outputs, and a net cost of EUR 2.3 million a year for businesses (Table 
2, G6, G7 and Annex 3). The costs for businesses are lower than for PO1 mainly because 
the  scenario  includes  improvements  of  statistical  products  as  opposed  to  producing 
completely  new  statistical  information,  which  is  the  basic  assumption  for  PO1.  The 
indirect benefits, consisting of being well informed and meeting urgent user demands in 
times of crises, cannot be quantified but would pertain to society as a whole. 

As was the case for PO1, the measures of the third specific objective for PO2 concern 
not only the data hub role as mentioned above, but notably also mandatory data sharing 
among NSIs and between NSIs and Eurostat. In addition, there are measures such as on 
the  possibility  for  NSIs  and  Eurostat  to  assume  data  governance  and  data  stewardship 
functions.  These  additional  measures  are  hard  to  quantify  and  are  expected  to  have  an 
impact that is much less than the impact of mandatory ESS data sharing.   

The impact analysis assumes that within 10 years there will be six new cases of ESS data 
sharing  rather  than  the  four  of  PO1,  because  there  will  be  less  conditions  attached  and 
based  on  the  analyses  of  cases  of  cross-border  phenomena  where  the  ESS  data  sharing 

32 

 
could be realised. Annex 3 provides a calculation of costs and benefits, again building on 
the experiences with Intrastat statistics. For ESS data sharing, the impact analysis shows 
estimated  net  benefits  of  EUR  176.7  million  a  year  (Table  2,  G9  and  G10).  These 
benefits  are  the  result  of  the  burden  reduction  on  respondents  that  can  be  realised, 
together with lower net costs for the statistical authorities. It is primarily the respondents 
who will benefit.  

Expected impact on categories of stakeholders  

In addition to the information on the expected impact of the measures on specific groups 
of  stakeholders  provided  above,  some  further  observations  can  be  made.  It  can  be 
estimated that less enterprises than for PO1 will be affected by mandatory data sharing 
requests due to the lower number of statistical domains. The number of enterprises will 
be  at  most  a  few  thousand.  Still,  considerable  savings  can  be  envisaged  related  to 
decreasing the size of surveys and thus  reduction of burden on businesses and citizens. 
The  reduction  of  burden  will  affect  all  sizes  of  businesses,  including  SMEs.  However, 
more  enterprises  will  be  affected  than  for  PO1  by  requests  to  provide  data  in  cases  of 
urgent user demands.  

Users  of  European  statistics  will  profit  considerably,  although  less  than  for  PO1,  from 
increased  availability  of  statistical  data  to  higher  quality  of  European  statistics  through 
the  application  of  the  measures  in  PO2.  The  most  impactful  measures  will  be  reuse  of 
privately  held  data  and  actions  in  case  of  urgent  user  demands  in  times  of  crises. 
Intensified  data  sharing  and  Eurostat  acting  as  a  data  hub  will  mainly  result  in  savings 
and burden reduction by increasing the quality and quantity of European statistics.  The 
overall  societal  and  economic  impact  resulting  from  initiatives  at  European  level  in 
response to urgent user demands will however be much higher.  

Public administrations will profit from increased availability of statistical data and from 
additional  activities  of  statistical  offices  related  to  data  governance  and  stewardship. 
Again, all NSIs will profit from concepts and guidelines being elaborated by the ESS and 
by  international  statistical  organisations,  such  as  the  statistical  divisions  of  the  UN.  As 
was  the  case  for  PO1,  PO2  requires  considerable  investments  from  the  NSIs  in 
methodology,  processes,  infrastructure  and  training  of  staff  due  to  the  very  different 
nature  of  data  collection  and  data  treatment  in  the  context  of  B2G4S  data  sharing  for 
European statistics as compared to traditional processes. However, necessary investments 
to  realize the potential of the measures  of PO2  may be partially offset  by  savings.  The 
investments and savings will occur at different points in time, with upfront investment at 
the beginning of the period required to realize savings at a later stage. Due to the fact that 
the digital data sources are not optimized for use in official statistics, some extra effort 
will be necessary to ensure high quality of official statistics outputs.    

The  academic  sector  will  profit  considerably  from  availability  of  additional  microdata 
for  research  purposes.  The  current  regulation  on  European  statistics  already  includes 
certain obligations, but data access will be substantially increased in PO2. 

33 

 
 
 
 
7.  HOW DO THE OPTIONS COMPARE?  

  Effectiveness 

The collected evidence suggests that PO0 would not be effective. The voluntary nature of 
measures  included  in  the  option  makes  it  impossible  to  address  the  obstacles  of 
legislative  nature  to  the  sustainable  use  of  new  and  innovative  data  sources  for  the 
production of European statistics. As a result, the existing fragmentation of national legal 
frameworks, if present, will continue. In such environment, as revealed by the use cases 
(Annex 6) it will remain difficult for the private data holders to find commonalities in the 
operational implementation of data sharing for the statistical production.  

Regarding the second strategic objective, to equip the ESS with a mechanism and tools to 
respond  fast  and  in  a  coordinated  manner  in  times  of  crises,  PO0  will  also  not  be 
effective.  Based  on  experience  with  Covid-19  and  lessons  learnt,  the  purely  voluntary 
nature of participation of the statistical authorities in activities risks not achieving enough 
coverage  or  representativeness  of  the  statistics  and  statistical  insights  that  would  be 
developed  to  meet  urgent  information  demands  (e.g.,  if  an  insufficient  number  of 
Member  States  participate).  Even  more,  those  Member  States  who  would  be  voluntary 
contributing and investing in statistical actions will be discouraged to do it when the next 
urgent response would be needed. Achieving a satisfactory level of the preparedness of 
the  ESS  for  the  potential  crises  will  also  be  strongly  dependent  on  their  take  up  of  the 
recommendations  and  good  practices  that  will  be  promoted  by  the  Commission 
(Eurostat).  

Overall,  during  the  consultation  process  stakeholders  made  it  clear  that  the  general 
objective of making European statistics more relevant, timelier, more frequent and more 
granular  as  well  as  more  responsive  in  times  of  crises,  could  be  achieved  only  if  legal 
action is taken, because all other non-legal possibilities have already been exhausted. To 
see better statistics, one would need a “quantum leap” that would be implemented only 
with the legislative measures foreseen in PO1 and PO2. Being not effective in achieving 
the  general  goal  of  the  initiative,  PO0  would  not  be  effective  for  closing  multiple 
information  gaps  (described  for  example  in  Annex  7).  Taking  decision  on  partial 
information  might  lead  to  suboptimal  policy  interventions,  which  can  potentially  be 
extremely costly. 

While  both  PO1  and  PO2  are  more  effective  than  PO0  for  all  specific  objectives,  a 
comparison of PO1 and PO2 shows differences for the three specific objectives. PO1 is 
by  far  more  effective  than  PO2  in  achieving  the  benefits  of  sustainable  B2G4S  data 
reuse. This is caused by the conditions for enterprises in PO2. There are less safeguards, 
no  consultation  stage,  no  dispute  resolution  mechanisms  and  no  cost  compensation, 
which is expected to result in a lower degree of success in B2G4S data reuse, especially 
given  the  fact  that  the  ESS  will  rely  on  the  cooperation  of  enterprises.  Concerning  the 
second specific objective, on the response in crisis situations, the stricter obligations for 
data  holders  in  PO2  will  make  that  option  somewhat  more  effective,  although  this 
depends  on  the  occurrence  and  the  nature  of  crises.  In  contrast  to  PO1,  we  expect  that 
data  production  in  PO2  would  also  imply  major  improvements  in  existing  statistics, 
which would lower the response burden on enterprises. However, this effect would only 
be of temporary nature as the crisis measures would be limited in time as well. Similarly, 
for specific objective 3,  on the tasks and roles of ESS partners, PO2 will be somewhat 
more effective than PO1, due to the stronger internal data sharing obligations. The clear 

34 

 
advantage of PO1 regarding specific objective 1 outweighs the much smaller advantages 
of PO2 regarding the other two specific objectives. This is true in a qualitative as well as 
a quantitative sense. It is worth noting that, concerning effectiveness, on the whole PO1 
compares favourably to PO2 for the ESS as well as for businesses (see also table 2 of the 
next  section).  The  increased  effectiveness  of  PO2  would  affect  the  principle  of 
subsidiarity,  which  has  to  be  taken  into  account  in  the  overall  assessment  of  the  three 
policy options.  

  Efficiency 

PO0  would  be  of  limited  efficiency  because  the  stakeholders  that  would  be  affected 
would  react  on  their  own  initiative,  based  on  their  assessment  of  the  optimal  ratio 
between results to be achieved and the resources to be spent on that.    

The  measures  under  PO1  will  entail  higher  costs  than  PO0,  related  mainly  to  the 
consultation  stage  when  the  parameters  of  data  requests  by  NSIs  and  Eurostat  (e.g., 
frequency  of  data  extraction,  granularity  of  data),  operational  modalities  of  making  the 
data available, costs implications, privacy and statistical confidentiality protection will be 
discussed. The costs incurred during the consultation are expected to lead to lower costs 
during the implementation phase of the contract arrangements between the data holders 
and the statistical authorities, which means that PO1 is expected to be more cost-effective 
than  P02.  In  addition,  the  small  and  micro  enterprises  will  be  excluded  from  the 
enforceable  mechanism  to  make  the  new  digital  data  sources  available  for  official 
statistics purposes, which can only increase the cost-efficiency of PO1 compared to PO2.    

During the consultation, in the online workshop, stakeholders shared the view that PO1 
would  be  the  most  efficient.  According  to  them,  PO2  would  be  more  efficient  in 
answering urgent user needs in times of crises, by reducing the autonomy of the Member 
States,  while  PO1  would  be  more  efficient  in  ESS  data  sharing  making  actual 
applications  conditional  to  cost-benefit  analysis.  Regarding  the  sustainable  use  of  new 
and  innovative  data  sources  held  by  the  private  sector  available  for  compilation  of 
European  statistics,  PO2  bears  the  risk  of  putting  disproportionate  costs  and  burden  on 
data holders. 

Stakeholders  acknowledged  that  PO1  and  especially  PO2  would  be  more  burdensome, 
but  pointed  out  that  the  benefits  of  automated  data  sharing  could  reduce  the  overall 
burden  of  current  methods  of  data  collections  in  the  medium  term.  Finally,  they 
underlined that dedicated funding programmes could alleviate concerns for both private 
data holders and NSIs (in addition to potentially encouraging investments in security and 
privacy  technology).  Financial  compensation  for  private  data  holders  could  also  be  a 
solution. In this regard, PO1 provides more flexibility than PO2. 

Table 2  compares the most  quantitatively  effective measures  of  PO1 and PO2 with  the 
baseline option in terms of balance of benefits (benefits minus costs of policy measures). 
While  the  B2G4S  measure  goes  hand  in  hand  with  a  reduction  of  burden,  the  crisis 
response  focusses  on  production  of  new  data  and  statistical  insights.  The  measure  of 
enforced  sharing  of  data  within  the  ESS  results  in  a  high  burden  reduction  due  to 
avoiding  redundant  data  collection.  Overall,  PO1  turns  out  as  the  optimal  option  with 
considerable  reduction  of  burden  that  can  overcompensate  additional  burden  on  data 
holders. The quantitative analysis therefore suggests that PO1 would be the best option 
with  considerable  increase  of  benefits  and  burden  reduction  as  compared  to  PO0.  PO2 

35 

 
would result in less benefits for both the statistical and business sector. For the business 
sector,  the  relative  drop  in  benefits  from  PO1  to  PO2  is  higher  than  for  the  statistical 
sector.  

A 

B  

C 

Baseline 
(Million 
EUR) 

Sector 

Balance of benefits   ESS 

Balance of benefits  ESS 

1  Policy measure 
2  B2G4S 
3 
4 
5  Crisis response 
6 
7 
8  EU data sharing 
9 
10 

Savings   ESS 

Burden reduction  Businesses 

Businesses 

Businesses 

24.2 
21.7 

0.3 
-0.2 

2.3 
39.0 

D 
PO1 
(value)  
(Million 
EUR) 

E 

∆ PO1 
(Million 
EUR) 

F 
PO2 
(value)  
(Million 
EUR) 

G 

∆ PO2 
(Million 
EUR) 

403.3 
296.2 

379.0 
274.5 

150.1 
103.6 

125.9 
81.9 

11.0 
-9.3 

10.7 
-9.0 

43.2 
-2.5 

24.6 
155.0 

22.3 
116.0 

28.0 
190.0 

Microdata for 
research 

11 
12  Total 
13 

ESS 
ESS 
Businesses 

26.8 
60.5 

438.8 
441.9 

412.0 
381.4 

221.3 
291.1 

Table 2: Comparison of quantitative impact of policy options  

42.9 
-2.3 

25.7 
151.0 

-2.2 
192.3 
230.6 

 Coherence  

While the non-binding nature of the measures under PO0 makes it very unlikely that this 
option will be incoherent with the legal context, there is a risk that this option will not be 
fully coherent with the rapidly changing European context, and not well aligned with the 
overall goals of EU policies on data and crises response mechanisms.  

PO1 is far more coherent and in line with recent EU legislative and policy developments 
such as the European Data Strategy, the DGA and the Data Act. This was confirmed by 
stakeholders  in  the  online  workshop,  who  also  specified  that  PO1  would  be  the  most 
coherent.  

The  European  Data  Strategy  is  based  on  pillars  such  as  a  cross-sectoral  governance 
framework  for  data  access  and  use,  investments  in  data,  strengthening  Europe’s 
capabilities  and  infrastructures  for  hosting,  processing  and  using  data,  including 
interoperability.  The  foreseen  targeted  revision  of  Regulation  (EC)  No  223/2009  under 
PO1 will be coherent with the cross-sectoral elements of these legal acts and will specify 
complementary  the  conditions,  procedures  and  modalities  of  data  sharing  for  the 
purposes  of  compiling  European  statistics  (B2G4S).  The  measures  under  PO2,  in 
particular  the  lack  of  a  consultation  stage,  no  possibility  for  compensating  any  costs 
including  the  marginal  ones,  make  this  option  less  coherent  with  the  overall  political 
approach to data sharing taken in initiatives under the European Data Strategy. 

36 

 
 
  
  
  
  
  
  
  
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
Both PO1 and PO2 are coherent with the existing crises response mechanisms and with 
the  proposal  for  the  Single  Market  Emergency  Initiative  because  the  measures  will  be 
complementary  to  those  foreseen  in  these  acts  and  focused  only  on  ways  for  the  ESS 
partners  to  be  prepared  and  to  provide  data  in  response  to  urgent  demands  under  the 
public emergencies defined in these acts. 

Finally,  the  GDPR  specifies  that  further  processing  of  personal  data  for  scientific  or 
historical research purposes or statistical purposes should not be considered incompatible 
with the initial purposes, in accordance with Article 89(1) of Regulation (EU) 2016/679. 
As B2G4S would not exclude personal data, these data must be collected, processed and 
anonymised  in  full  compliance  with  the  GDPR,  which  is  what  is  currently  foreseen  in 
PO1 through several policy measures. 

Feasibility  

Non-binding measures foreseen in PO0 are fully feasible. They are not only technically 
feasible, but they will be supported by all stakeholders. PO2 will create more feasibility 
issues  and  is  expected  to  face  resistance  from  both  the  data  holders  (on  measures  to 
achieve  the  specific  goal  1)  and  the  national  statistical  authorities  (on  the  measures  to 
achieve the specific objectives 2 and 3). PO1 appears to be easier to be accepted on all 
three specific objectives. During the consultation phase, stakeholders concluded that PO1 
and  especially  PO2  are  likely  to  have  a  stronger  impact  on  the  conduct  of  business, 
administrative costs, as well as public authorities and their budgets, compared to PO0. At 
the same time, they also highlighted the missed relevant benefit for them (if mandatory 
data sharing was systematically implemented), which would be the (partial) replacement 
of direct data collection mechanisms (i.e., surveys), which would alleviate the burden on 
businesses  and  households.  PO2  puts  more  burden  on  businesses  and  members  of  the 
ESS than PO1. Therefore, the stakeholders especially from the private sector expressed 
clear preference for measures foreseen under PO1. 

The  support  of  stakeholders  affects  the  technical  feasibility  of  the  measures.  This  is 
expected to be especially the case for the first specific objective if PO2 is implemented. 
B2G4S  data  reuse  depends  on  the  technical  cooperation  of  enterprises,  which  are 
expected  to  be  less  than  enthusiastic  under  the  obligations  of  PO2,  since  that  option 
provides less safeguards, no consultation stage, no dispute resolution mechanisms and no 
cost compensation. The technical feasibility of the second and third specific objective is 
also not enhanced by measures in PO2 that reduce the autonomy of the NSIs.  

Having  safeguards  against  excessive  burden  and  costs  are  especially  important  to 
businesses, but privacy and confidentiality concerns of the wider public also play a role 
[ICF, section 9.2]. In line with the ‘do no significant harm’ principle, Eurostat has given 
the  effect  of  the  initiative  on  businesses  serious  consideration.  For  achieving  specific 
objective  1,  there  is  no  real  alternative  to  creating  the  possibility  of  mandatory  data 
sharing for European statistics, given the fact that an exclusively voluntary approach to 
data sharing has already been seriously tested without resulting in, nor expected to result 
in, data reuse for statistical purposes at a scale that would respond to the needs of users. 
The  best  way  to  treat  potential  negative  implications  on  data  holders  is  to  give  them 
adequate  safeguards  (as  described  for  PO1)  and  to  make  sure  that  these  are  respected 
during  the  implementation  of  PO1,  in  particular  in  the  context  of  the  Annual  Work 
Programme.  The  feasibility  of  this  approach  is  supported  by  the  conclusions  of  the 
Expert  Group  on  facilitating  the  use  of  new  data  sources  for  official  statistics  [EG 

37 

 
 
B2G4S],  which  included  independent  experts  from  the  business  sector.  Moreover,  it  is 
fully  in  line  with  opinions  issued  by  the  G2031,  the  Conference  of  European 
Statisticians32, the ESS Committee33, the European Statistical Advisory Committee34 and 
the European Statistical Governance Advisory Board35. 

A  summary  of  the  assessment  of  the  three  policy  options  against  criteria  of  their 
effectiveness, efficiency, coherence and feasibility, is presented in Table 3. 

PO  Effectiveness  Efficiency 
0 
1 
2 
Table 3: Comparison of policy options36 

O 
++ 
+ 

-- 
++ 
+ 

Coherence 
O 
++ 
+ 

Feasibility 
++ 
+ 
- 

8.  PREFERRED OPTION 

  The preferred option and its implementation 

The choice of the preferred option 

The comparative assessment of the three policy options presented in the previous chapter 
showed  that  the  baseline  option  (PO0)  has  the  least  desirable  outcome  in  terms  of 
effectiveness,  efficiency,  and  coherence,  and  the  first  policy  option  (PO1)  the  most 
desirable,  with  the  second  policy  option  in  between.  PO0  is  the  most  feasible  of  the 
options,  but  the  baseline  option  is  evidently  ineffective  in  achieving  the  general  and 
specific objectives of the initiative. This clearly points to choosing PO1 as the preferred 
option.  

Also at the level of the individual measures of the policy options, PO1 generally scored 
better than PO2. However, there are two measures of PO2 that scored somewhat better 
on the dimension of effectiveness in comparison to PO1 (see section 7.1). These are the 
measures  3.5  and  3.11  of  Table  1  of  chapter  5,  concerning  the  obligation  for  Member 
States to take part in actions at the time of crises, and the obligations of NSIs concerning 
data sharing, respectively. These two measures are not part of PO1, not only because they 
score worse in respect of coherence and feasibility, but above all because they reduce the 
autonomy of the Member States. Since these measures are not indispensable to achieve 
the general and specific objectives of the revision of Regulation (EC) No 223/2009, they 
have not been included in PO1. 

31 The Data Gaps Initiative. 
32 CES declaration on new data sources. 
33 The ESS position paper on access to privately held data which are of public interest and the ESS position 
paper on the future Data Act proposal. 
34 ESAC Doc. 2022/1, The Data Act proposal and the use of private data for official statistics. 
35 ESGAB annual report 2021. 
36 The scores reflect the expected magnitude of impact : (++) strong positive, (+) positive,  (O) no impact, 
(-) negative impact, (-) strong negative impact 

38 

 
 
                                                 
Implementation aspects are discussed next, followed by a closer look at the distributional 
aspects of PO1, in particular on SMEs.  

The implementation of the preferred option 

Regulation (EC) No 223/2009 is a framework regulation with enabling clauses and with 
main  effects  arising  through  subsequent  decisions  at  both  the  EU  and  national  level. 
Concerning mandatory reuse of privately held data, the list of safeguards and conditions 
will similarly only take  effect  through  implementing regulations  that may, for instance, 
provide details on the way to ensure respect for business interests, on modalities of reuse, 
or on time-bound dispute resolution. 

Once the revised Regulation (EC) No 223/2009 takes effect, actual mandatory data reuse 
requires taking two steps. As  the first step, for  any statistic to  be  partly or completely 
based on new data sources, a decision has to be taken to add the statistic to the AWP, the 
process of which was described in section 6.1. As a consequence of the conditions listed 
under the measures for the first policy option, the proportionality of the addition has to be 
demonstrated. That is, it will be necessary to demonstrate and document that the benefits 
for society as a whole can be expected to significantly surpass the costs to society as a 
whole. For this to take place, an adequate process and criteria have to be worked out.  

The second step consists of the selection and contacting of the specific data holders that 
will be requested to enable the sharing of data they hold. When a specific data holder is 
requested to enable data reuse for such a statistic, this request must be explicitly justified. 
It is the second step that must incorporate the safeguards and conditions of PO1 for the 
businesses concerned, including procedural guarantees. This shall include procedures to 
decide  on  the  modalities  of  reuse,  and  mechanisms  to  resolve  disputes  between  data 
holders  and  statistics  producers,  in  particular  for  cases  where  no  agreement  can  be 
reached  on  the  modalities  of  reuse  or  the  respect  of  business  interests.  Throughout  the 
process,  transparency,  confidentiality  and  other  already  existing  professional  statistical 
standards and requirements will apply. 

Distributional aspects will play a prominent role in the second step. NSIs are not required 
to  pay  for  the  data  from  the  data  holders,  in  line  with  the  rules  in  place  today  for 
accessing  and  using  traditional  data  sources  in  European  statistics.  However,  sizeable 
initial  investments  or  marginal  costs  may  be  incurred  by  the  data  holders  when 
processing the data, especially for aggregating or running algorithms on the primary data 
to  make  them  ready  for  use  for  European  statistics.  Possible  incentives  could  also  take 
the  form  of  aggregate  customised  information  derived  from  the  data  by  the  NSIs  and 
provided in return to the data holders. 

At the implementation phase, care has to be taken that data reuse is effective. There are 
risks concerning the quality and usability of the data considered for reuse, and there may 
be a lack of available skills to deal with new data sources. It is the ESS that has to make 
sure  that  data  reuse  only  takes  place  if  professional  quality  requirements  can  be  met. 
Likewise, the ESS has to ensure adequate training for its staff and the availability of the 
skills  required on new and innovative data sources for the production of timelier, more 
frequent and more detailed statistics.  

The  ESS  also  needs  to  be  able  to  develop  effective  coordinated  statistical  actions  that 
provide fast  and relevant ESS responses to information  needs arising in  times of crisis. 
However,  the  aim  is  clearly  for  the  ESS  to  be  responsive  to  information  needs  arising 

39 

 
from a crisis situation that has been officially declared by an Institution but not to decide 
on its own on the existence of a crisis. It is only in cases where an emergency mechanism 
has been formally triggered in accordance with procedures established by Union law that 
Eurostat should have the capacity to organise in parallel a response at ESS level to meet 
urgent information needs arising from that crisis and when these needs have not already 
been addressed through  the relevant  emergency  mechanism. For instance, this could  be 
the case when the Single Market emergency mode has been activated by the Council and 
that  the  objective  is  to  meet  information  needs  that  cannot  be  covered  by  information 
requests  addressed  to  representative  organisations  or  economic  operators  in  crisis-
relevant  supply  chains.  Another  example  is  the  need  to  respond  to  information  needs 
arising from an energy crisis, for example in the context of a Union alert declared by the 
Commission  when  there  is  a  substantial  risk  of  a  severe  gas  supply  shortage  or  an 
exceptionally  high  demand  of  gas  occurs.  Responding  to  urgent  information  needs 
stemming from a crisis in the field of migration is also an example where a coordinated 
action by the ESS would prove necessary, for instance in the context of a mass influx of 
displaced persons from third countries as decided by the Council. 

Furthermore,  intensified  ESS  data  sharing  should  take  place  for  the  development  and 
production of European  statistics and for improving its quality,  notably  with  regards to 
cross-border  phenomena  while  limiting  or  reducing  the  burden  on  respondents,  in  full 
respect of confidentiality. 

Although the implementation would have a timeline of several years, the revision would 
have  a  tangible  effect  already  after  a  few  years.  The  timeline  assumed  for  the  impact 
analysis was 10 years for having a substantial number of statistics added to the AWP and 
integrated into statistical production, and for having a substantial increase in reuse of new 
data  sources.  There  are  no  circumstances  known  that  this  stage  that  could  prevent 
Member  States  to  apply  Regulation  (EC)  No  223/2009  directly  after  its  revision.  The 
mechanism for collective and coordinated reaction in times of crises will not require any 
transitional implementation time. As for the update of the tasks and roles of ESS partners 
regarding  data  ecosystems,  the  optimal  use  of  the  opportunities  of  the  evolving  data 
ecosystems is an ongoing process for which only targets can be specified37. 

Small and microenterprises 

Regarding data reuse, the safeguards and processes that will be put in place, such as on 
proportionality, are expected generally not to result in requests for the reuse of data from 
SMEs. The impact analysis of chapter 6 and the calculations of Annex 3.2 not only show 
that the population of interest consists of large enterprises, but also that it is only a small 
part  of  them  that  are  expected  to  be  subject  to  data  reuse  requests.  Nevertheless,  there 
may be cases where a medium-sized enterprise plays an important or even dominant data 
holding  role  in  a  specific  statistical  domain,  meriting  its  inclusion  in  respect  of  data 
sharing  for  statistical  purposes.  It  is  conceivable  that  there  are  small  or  even  micro 
enterprises whose data held are of interest for European statistics, since a small enterprise 
may hold the data on behalf of a large enterprise, but such cases would be exceptional. 

Whereas  the  costs  of  enabling  data  sharing  would  be  incurred  by  large  and,  to  a  much 
lesser extent, medium-sized enterprises, the benefits  to  enterprises in the form  of lower 

37  Cf, Table 5 on monitoring of impacts in chapter 9. 

40 

 
                                                 
response burden if surveys can be replaced by new data sources would predominantly be 
enjoyed  by  SMEs.  Therefore,  the  overall  picture  of  this  initiative  is  very  favourable  to 
SMEs, especially small and micro enterprises.  

Nevertheless,  micro  and  small  enterprises  will  not  incur  costs  due  to  mandatory  data 
sharing. Therefore, a threshold in terms of the size of businesses will apply to mandatory 
data sharing.  For surveys the decision on thresholds  for businesses  as data providers  is 
taken in the context of the AWP on the basis of its effect on the contents and quality of 
the  statistics  concerned  and  the  associated  public  benefits.  However,  for  holders  of 
private data as by-products of digital services, a blanket exemption for micro and small 
enterprises  is  included  in  the  preferred  option,  irrespective  of  merits.  This  is  justified, 
since it provides the strongest safeguard conceivable.  

  Estimated impact of the preferred option 

The estimations show that the preferred option could bring additional total direct annual 
benefits amount to approximately EUR 793.5 million a year (Table 2, E12 and E13) as 
compared  to  the  baseline  option.  It  is  assumed  that  the  number  of  applications  using 
B2G4S  data  sharing  and  crisis  response  will  be  introduced  gradually  and  will  reach  an 
estimated total of 20 statistical domains (use cases) within a time period of 10 years after 
entry into force of the amended regulation. 

The additional total costs for the statistical system under the assumption of implementing 
20  data  collections  are  EUR 560.3  million  a  year  (Table  11,  J10),  broken  down  to 
EUR 10.9 million at European level (Table 11, D10 and H10) and EUR 549.4 million at 
national level (Table 11, B10 and F10). The additional total costs for the data holders are 
estimated  at  EUR 195.5  million  annually  (Table  11,  K10).  The  costs  have  to  be 
contrasted with  the benefits,  which are for the business  sector mainly  related to  burden 
reduction  and  to  some  compensations.  The  additional  total  benefits  for  the  business 
sector  sum  up  to  EUR  460.9  million  a  year  (Table  11,  K15),  not  including  savings 
through burden reduction due to intensified ESS data sharing of EUR 116 million (Table 
12). The total direct benefits for the statistical system are EUR 950 million a year (Table 
11, J15). 

The costs for European actions are considerably lower as compared to implementation of 
data collections in all EU Member States. The burden on data holders will also be lower 
as most of the activities assume to produce new statistics and the data holders will share 
data only with one party in a harmonised format and do not have to adjust their systems 
to  27  different  requirements.  The  measure  for  direct  data  collection  at  European  level 
with sharing between all ESS members could therefore be a preferred option in case of 
European wide acting data holders. 

Through  increase  in  data  sharing  of  statistical  domains  collecting  data  on  cross-border 
phenomena,  experience  shows  that  the  response  burden  on  businesses  can  be  at  least 
halved for a specific data collection. In the specific example, the response burden could 
be additionally lowered by EUR 116 million annually (Table 12, B3). Likely, the savings 
for  the  statistical  system  was  14%  totalling  EUR  6.5  million  for  the  specific  data 
collection.  It  is  expected  to  realize  a  similar  approach  for  other  statistical  domains. 
Assuming four similar cases with however lower impact, as the Intratrade represents the 
largest  European  statistical  data  collection,  this  would  result  in  an  additional  burden 
reduction of EUR 116 million and additional annual net savings for the statistical system 

41 

 
of  EUR 23  million  (Table  12,  B4).  The  additional  central  annual  operating  cost  are 
estimated at EUR 0.7 million (Table 12, B5), which would reduce the annual net savings 
to EUR 22.3 million. 

  REFIT (simplification and improved efficiency) 

The preferred option is expected to generate scope for burden reduction. The wider and 
sustainable  reuse  of  emerging  new  digital  data  source  that  are  privately  held,  the 
possibility  to  allow  Eurostat  to  act  as  a  data  hub,  and  the  mandatory  data  sharing  for 
statistical purposes and on certain conditions will open room for decreasing sample sizes 
in  some  surveys  or  completely  replace  them.  Data  sharing  within  the  ESS  will  also 
contribute  to  the  reduction  of  administrative  burden  for  businesses,  citizens  and  the 
producers  of  European  statistics.  It  should  be  also  stressed  that  micro  and  small 
enterprises would be exempted from enforceable mechanisms for reusing privately held 
data. 

The  nature  of  the  measures  proposed  (i.e.,  the  fact  that  most  measures  would  place 
additional requirements and therefore burden on certain selected private data holders and 
entail initial investments for both data holders and the producers of European statistics), 
made it challenging to quantify the net effects. Therefore provided estimates have to be 
taken with certain element of caution.  

For  costs  savings,  the  Table  4  below  outlines  the  expectations  regarding  the  different 
reuse  and  data  sharing  scenarios.  By  intensifying  and  facilitating  reuse  of  new  digital 
sources  and  data  sharing,  the  initiative  should  reduce  burden  mainly  as  a  result  of  a 
global reduction in the number of surveys and automated and simplified processes. This 
will be to the benefit of both the public and the private sector, even if selected big data 
holders would experience an increased burden in terms of providing data. However, this 
increased burden may also be outweighed by the reduction in response cost in terms of 
fewer surveys. In addition, the increased effectiveness, brought about by the central data 
collection  relating  to  cross-border  flows  and  phenomena  would  reduce  the  burden  on 
NSIs  and  internationally  operating  businesses,  and  mandatory  data  sharing  on  certain 
conditions  would  increase  efficiency  by  means  of  a  collect-only-once  approach,  that 
would entail a net burden reduction on businesses, citizens and NSIs. 

Table 4: Expected REFIT Cost Savings under the preferred option PO1 

Description – specific objectives 

Expected REFIT Cost saving 

To embrace fully new technologies and sustainably reuse 
new  data  sources  emerging  as  by-products  of  digital 
services  to  meet  increasing  user  demands  for  timelier, 
more frequent and more detailed European statistics 

EUR 826 Mill38 

38 Cf. Annex 3, Table 11: PO1: Cost and benefit for data reuse and crisis response (B12-E12). 

42 

 
                                                 
To  provide  mechanisms  and  tools  for  the  ESS  to  react 
fast  in  a  collective  and  coordinated  manner  to  urgent 
data demands in times of crises 

EUR 0 Mill39 

To update the tasks and roles of ESS partners to leverage 
opportunities offered by digital transformations for more 
cost-efficient and less burdensome statistical production 

EUR 139 Mill40 

  Application of the ‘one in, one out’ approach 

8.4.1.  Potential new burden on citizens 

Regarding  new  burden  on  citizens,  it  should  be  stressed  that  Regulation  (EC)  No 
223/2009  is  a  framework  regulation  for  which  reason  no  immediate  direct  burden  on 
citizens  will  flow  from  the  revision.  Nevertheless,  PO1  entails  a  potential  burden 
reduction for citizens since the extended use of new data sources is expected to result in 
fewer surveys. Therefore, the burden reduction for citizens will be mainly measured by 
time saved on responding to surveys. In conclusion the preferred option will not generate 
any net burden increase relevant for OI-OO. 

8.4.2.  Potential new costs on businesses 

Regarding  new costs on businesses, and as pointed out  in  Annex 3, businesses have  a 
double role  as both  respondents  and users.  In their role as users, they  will  only benefit 
from PO1. As respondents, they will also benefit from the decrease of response burden as 
result  of  the  automated  data  requests  and  the  one-stop  shop  reporting,  that  the  data 
sharing of PO1 will bring about.  The estimated benefits  for businesses amount to  EUR 
460.9 million, as indicated in Table 11 (K15) [Table 11: PO1: Estimated differential cost 
and benefit for data reuse and crisis response]. 

Nevertheless,  the  use  of  new  data  sources  will  result  in  costs  on  a  small  portion  of 
businesses  disposing  of  large  data  assets,  such  as  Mobile  Network  Operator  (MNOs), 
banks,  energy  providers,  or  internet  platforms.  As  also  indicated  in  Annex  3,  such 
businesses  might  incur  additional  costs  or  might  have  certain  additional  obligations  in 
order to enable statistical authorities accessing their data. The costs for those businesses 
are estimated at EUR 265.4 million (Table 11, K16) but will be compensated over time 
by  the  recurrent  offsetting  excess  cost  saving  mentioned  above.  In  addition,  the 
compensation  for  the  marginal  costs  related  to  making  the  data  available  for  statistical 
purposes could be envisaged.   

9.  HOW WILL ACTUAL IMPACTS BE MONITORED AND EVALUATED?  

In  general,  the  existing  monitoring  and  evaluation  tools,  in  place  and  valid  for  the 
statistical production and dissemination of European statistics, will be used. These should 

39 Cf. Annex 3, Table 11: PO1: Cost and benefit for data reuse and crisis response (G12, I12). 
40 Cf. Annex 3, Table 12: PO1: Estimated differential benefits and costs of the measure on mandatory ESS 
data sharing (B3,B4). 

43 

 
 
                                                 
enable an analysis of the effectiveness and efficiency of the suggested statistical initiative 
and of the quality of the data produced. These tools are: 

  The  European  Statistical  Programmes  (currently  based  on  Regulation  (EU) 
interim  and  final  evaluation  reports41. 
2021/690)  foresee  systematically 
Regulation  (EC)  No  223/2009,  as  the  framework  regulation  for  European 
statistics, is indirectly part of this reporting mechanism and will be covered. 

  The  Eurostat  Strategic  Plans  foresees  the  follow  up  of  key  performance 

indicators, which also apply to Regulation (EC) No 223/200942. 

  User satisfaction surveys are carried out on a regular basis43 

The  application  of  the  revised  Regulation  (EC)  No  223/2009  will  be  monitored  and 
evaluated  against  the  general  and  specific  objectives  described  in  section  4.2.  An 
assessment  of  their  impact  will  be  included  in  Commission  progress  report  and  final 
evaluation report on the European statistical programme to the European Parliament and 
the Council in accordance with Article 13(5) of Regulation (EC) No 223/2009.  

Eurostat and the NSIs will  further improve the standard metadata and quality reporting 
system  for  European  statistics.  This  will  allow  a  more  sophisticated  monitoring  and 
evaluation  of  the  statistical  processes  used  in  Member  states  and  of  the  output 
disseminated.  For  example,  more  detailed  information  will  be  available  on  the  use  of 
private and administrative data sources (leading to burden saving) by Member States or 
on the use of shared services or IT tools (leading to cost savings).  

Measuring  the  progress  towards  achieving  the  objectives  of  the  initiative  the  following 
list of monitoring indicators based on SMART principles has been defined. The progress 
on  these  indicators  will  be  measured  against  the  benchmark  targets  indicated  in  the 
second last  column of  Table 5. The table also  indicates  the sources  of information  that 
will be used measure the indicators 

Table 5: Monitoring of impacts 

Operational 
objectives 

Indicators 

Targets 

Sources of information 

Specific Objective: 
To embrace fully new technologies and sustainably reuse new data sources emerging as by-
products of digital services to meet increasing user demands for timelier, more frequent and 
more detailed European statistics 

Significant 

Number of statistics 

In 10 years, the 

The AWP itself and 

41 Regulation (EU) 2021/690 of the European Parliament and of the Council of 28 April 2021 establishing 
a  programme  for  the  internal  market,  competitiveness  of  enterprises,  including  small  and  medium-sized 
enterprises, the area of plants, animals, food and feed, and European statistics (Single Market Programme) 
and  repealing  Regulations  (EU)  No  99/2013,  (EU)  No  1287/2013,  (EU)  No  254/2014  and  (EU)  No 
652/2014, OJ L 153, 3.5.2021, p. 1. 
42 The result indicators are the following: User trust in European statistics, Share of users not satisfied with 
the  quality  of  data  and  services  provided  by  Eurostat,  Statistical  coverage,  Timeliness  of  statistics:  news 
releases, Number of new experimental statistics datasets published.  
43 Cf. Evaluation - Eurostat (europa.eu) 

44 

 
                                                 
that are based, 
partially or 
completely, on 
innovative data 
sources. 

decision has been 
taken to add at least 
15 such statistics to 
the AWP. 

increase in 
statistics of the 
AWP that are 
based, partially or 
completely, on 
data  innovative 
data sources 
emerging as by-
products of digital 
services. 

In 10 years, the  
downloads of 
statistical products 
that are based,  
partially or 
completely, on  
innovative data 
sources has risen to 
at least 30%. 

100% compliance 

Substantial 
increase in the use 
of statistics that 
are based, 
partially or 
completely, on 
innovative data 
sources emerging 
as by-products of 
digital services. 

Percentage of 
downloads  of 
statistical products 
from the Eurostat 
database of statistics 
that are based,  
partially or 
completely, on  
innovative data 
sources. 

Full application of 
all required 
safeguards. 

For each statistic of 
the AWP that makes 
use of privately held 
data, documentation is 
available on prescribed 
due process, including 
evidence on 
proportionality and 
expected effects on the 
data holders.  

documents produced in the 
process establishing the 
AWP. These documents 
also allow a qualitative 
evaluation in terms of 
statistical content (better, 
more timely, more detailed 
statistics, statistics on new 
phenomena, statistics 
linked to new policy 
needs). 

The ESS has already a 
system in place that 
systematically follows what 
and how many statistical 
products are downloaded. 

The ESS has a quality 
monitoring system in place, 
based on the Code of 
Practice, including peer 
reviews and oversight. The 
monitoring system includes 
the application of all 
required safeguards. The 
Code of Practice will be 
updated to reflect all needs 
entailed by the revision of 
Regulation (EC) No 
223/2009.  

Specific Objective: 
Mechanisms and tools for the ESS to react fast, in collective and coordinated manner to 
urgent data demands in times of crises 

Prompt action by 
the ESS in 
response to urgent 
user demands. 

Number of actions 
undertaken with 
specific reference to 
the relevant provisions 
of the revised 
Regulation (EC) No 
223/2009. 

In 10 years, the 
decision has been 
taken to invoke the 
relevant provisions 
at least 5 times. 

The ESS Committee will 
always be informed and 
consulted on such actions. 
The ESS Committee has 
official and extensive 
records on the execution of 
all its responsibilities. The 
minutes of the ESS 
Committee will also be one 
of the sources to 
qualitatively evaluate the 
adequacy of the actions 
undertaken.  

45 

 
Specific Objective: 
Update of the tasks and roles of ESS partners to leverage opportunities offered by digital 
transformation for more cost-efficient and less burdensome statistical production 

Increase data 
sharing within the 
ESS with burden 
reducing effects. 

Increase of data 
sharing agreements 
with burden reducing 
objectives. 

In 10 years, at least 
4 such additional 
data sharing 
agreements. 

Update the tasks 
of ESS partners 

Uptake of centralised 
data collection from 
aggregate data sources 
(e.g., web scraping 
agencies) as well as 
individual data holders 
(such as EU-wide 
operating businesses). 

Realisation of 
gains in 
effectiveness 
regarding cross-
border 
phenomena 
through data 
sharing. 

Increase of data 
sharing agreements 
with effectiveness 
objectives regarding 
cross-border 
phenomena. 

In 10 years, at least 
5 of the additions 
of statistics based, 
partially or 
completely, on data 
held by the private 
sector, are at least 
partially based on 
centralised data 
collection. 

In 10 years, at least 
4 such additional 
data sharing 
agreements. 

Outline possible 
new roles of 
statistical 
authorities in the 
data ecosystem 

Uptake of new roles in 
the data ecosystem by 
ESS partners. 

Uptake of new 
roles in at least two 
thirds of the 
Member States, in 
10 years, according 
to peer review 
reports. 

All data sharing between 
partners of the ESS is based 
on agreements that include 
the justification of data 
sharing. Where these 
agreements are currently 
not systematically 
monitored, this will be done 
in the future, starting with 
the revision of Regulation 
(EC) No 223/2009 coming 
into force.  

For each instance of 
centralised data collection 
on behalf of the ESS, the 
ESS Committee will be 
consulted. Hence such data 
collection will be 
documented and also be 
available for evaluation.  

All data sharing between 
partners of the ESS is based 
on agreements that include 
the justification of data 
sharing. Where these 
agreements are currently 
not systematically 
monitored, this will be done 
in the future, starting with 
the revision of Regulation 
(EC) No 223/2009 coming 
into force.  

Given the fact that the 
operational objective is 
linked to subsidiarity and 
the indicator is qualitative 
in nature, the monitoring 
will take place by the 
individual partners of the 
ESS, with reporting in the 
context of the Code of 
Practice (e.g., peer 
reviews). 

46 

 
  
 
ANNEX 1: PROCEDURAL INFORMATION 

1.  LEAD DG, DECIDE PLANNING/CWP REFERENCES 

The  proposal  for  a  Regulation  amending  Regulation  (EC)  No  223/2009  on  European 
statistics  was  prepared  under  the  lead  of  Eurostat.  In  the  DECIDE  Planning  of  the 
European Commission, the process is referred to under item PLAN/2021/11938.  

This initiative is not included in the Commission Work Programme. 

2.  ORGANISATION AND TIMING 

An  Inter-Service  Steering  Group  (ISSG)  assisted  Eurostat  in  the  preparation  of  the 
impact assessment and legal proposal. It included representatives of Commission services 
from 25 Directorate-Generals, including the Commission’s Legal Service and Secretariat 
General. 

The ISSG contributed to the initiative’s preparation in February 2022 (discussion on the 
consultation  strategy,  the  Call  for  Evidence  and  the  terms  of  reference  for  the  support 
study)  and  a  written  consultation  in  June  2022  on  the  public  online  consultation 
questionnaire.  Following  a  written  consultation,  one  ISSG  meeting  (6  December  2022) 
reviewed  the  draft  impact  assessment  before  submission  to  the  Regulatory  Scrutiny 
Board (RSB). 

The  Call  for  Evidence  was  published  on  21  February  2022  and  was  open  to  feedback 
from all stakeholders on the Better Regulation Portal for a period of 4 weeks. The public 
online consultation was launched on 19 July and closed on 25 October 2022. 

The draft impact assessment report and all supporting documents were submitted to the 
RSB on 14 December 2022, in view of a hearing on 18 January 2023.  

3.  CONSULTATION OF THE RSB  

The  Regulatory  Scrutiny  Board  reviewed  the  impact  assessment  report  and  gave  a 
negative opinion on 20 January 2023. The impact assessment was revised as follows. 

Comments of the RSB 

(B) Summary of findings 

How  and  where  comments  have  been 
addressed 

(1)  The  problem  definition  appears  too 
narrow and is not supported by the earlier 
evaluation.  It  does  not  present  evidence 
other than limited stakeholder views. 

The problem definition  has  been revised.  It 
is supported, inter alia, by the evaluation of 
the European Statistical Programme (ESP).  

intervention 
The 

is  not 
(2)  The 
established. 
are 
inconsistent  with  the  identified  problems 
and  the  range  and  scope  of  the  proposed 

logic 
objectives 

The intervention logic has been revised. The 
general  objective  is  now  closely  linked  to 
the  problem  definition.  Specific  objectives 
link  the  problem  drivers  to  the  policy 
options.  The  measures  of  the  proposed 

47 

 
options are insufficient to address them. 

(3)  The  analysis  of  impacts  is  incomplete 
for  all  considered  options  and  does  not 
allow for their comparison in  the  absence 
of a well-defined baseline scenario. 

options  are  directly  linked  to  the  specific 
objectives.  

to  allow  more 

The baseline scenario has been elaborated in 
more  details 
rigorous 
assessment  of  the  impacts  of  the  other  two 
policy options. The impact of all options has 
been  presented  in  tabular  form  for  ease  in 
comparison,  with  the  baseline  scenario  as 
benchmark. 

(C) What to improve 

(1) The report should make clear how the 
‘evaluate first principle’ has been adhered 
to. It should clarify the problem definition 
given  that  the  stakeholder  consultation 
synopsis report suggests that the problems 
related  to  the  objectives  identified  in  the 
report  are  wider  than  described  in  the 
problem  section.  The  evidence,  from  a 
limited  number  of  stakeholders, 
that 
supports  the  existence  and  scale  of  the 
problems  should  be  corroborated  with 
other  types  of  evidence  coming  from  the 
evaluation or other sources. In addition, it 
should be specified whether the problems 
can  only  be  attributed  to  the  statistical 
institutions  or  to  businesses  that  hold 
electronic data as well. 

(2)  The  report  should  clarify  the  logic  of 
intervention.  Either  the  general  objective 
should  be  narrowed,  or  the  problems 
should  be  identified  differently.  Once  the 
objectives 
the 
identified  problems,  the  report  should 
review the range of options and measures 
they  consist  of  that  could  remedy  the 
identified  problems  and  achieve 
the 
desired objectives. 

consistent  with 

are 

(3)  The  report  should  explain  what 
constitutes  a  ‘crisis  situation’  as  invoked 
in the problem definition and whether it is 
a  necessary  element  to  trigger  an  ‘agile’ 
response from the ESS. 

Based on the revised problem definition, the 
evaluation  of  the  ESP  now  plays  a  central 
role  as  shown  by  its  use  in  chapter  2.  In 
addition,  the  problem  definition  builds  not 
only on a wide range of stakeholders views, 
but also on a report of the Expert Group on 
facilitating  the  use  of  new  data  sources  for 
official  statistics,  composed  of  experts  (of 
various backgrounds, including the business 
sector)  and  impact  assessments  of  other 
statistical  regulations.  The  scope  of  the 
problem  definition  has  been  adapted 
accordingly. 
and 
evaluation  is  covered  in  chapter  9.  The 
the  problem 
current 
attribution (chapter 2).  

Future  monitoring 

text  also  clarifies 

The  intervention  logic  has  been  clarified, 
starting  with  the  problem  definition,  and  is 
now  easier  to  understand  (see  Summary  of 
findings (2)). For each of the policy options, 
measures  have  been  specified  (chapter  5). 
The  impact  analysis  addresses  all  options 
they  each 
to  what  extent 
and  shows 
contribute to the specific objectives (chapter 
6).  Together  with  an  assessment  of 
stakeholders’  preferences,  the  comparative 
analysis (chapter 7) led to the choice of the 
preferred option (chapter 8). 

It is explained (chapter 2) that a mechanism 
that  would  allow  the  ESS  to  react  in  times 
of  crises  will  be  complementary  to  and 
support 
response 
mechanisms  at  EU  level  once  these  are 

crisis 

other 

the 

48 

 
 
activated. 

(4)  The  report  should  include  a  well-
defined dynamic baseline scenario, which 
cannot  be  dismissed  from  the  analysis. 
The  dynamic  baseline  scenario  needs  to 
consider the likely developments affecting 
the  ESS  such  as  the  impacts  of  the 
recently  adopted  Data  Act  and  the  Single 
Instrument.  The 
Market  Emergency 
baseline  scenario  should  be  quantified  to 
the  extent  this  is  feasible  and  used  as  a 
reference  to  assess  the  impacts  of  all 
considered options. 

The  baseline  scenario  has  been  further 
developed,  including  its  dynamic  aspects. 
Apart  from  what 
is  mentioned  under 
comment (4), the dynamic aspects discussed 
include  the  growing  availability  of  open 
data  and  the  development  of  ‘common 
European data spaces’ in the context  of the 
Data  Governance  Act.  The  impact  of  all 
options  has  been  tabulated  and  compared, 
with  the  baseline  scenario  –  quantified  to 
the extent feasible – as benchmark. 

(5) As the range of identified problems is 
potentially  wider  than  currently  presented 
in  the  report,  the  corresponding  range  of 
options to address the problems should be 
expanded  and  should  go  beyond  only 
addressing the use of privately held data. 

See point (2) under (B) and point (2) under 
(C). All options have been developed at the 
level  of  measures,  and  linked  to  the  three 
specific  objectives,  only  one  of  which 
concerns  the  use  of  privately  held  data 
(chapter 5). 

(6) The report should clarify what type of 
assessment  will  be  undertaken  to  justify 
the  inclusion  of  certain  data  collections 
from private actors in the annual statistical 
work programmes. 

A  two-step  approach  is  foreseen,  in  which 
the decision of inclusion in the annual work 
programme is the first step. The second step 
is  the  decision  on  individual  data  requests. 
Both  steps  require  proper  justification.  The 
type  of  assessment  to  be  undertaken  is 
explained (chapter 8).  

(7)  For  each  option,  the  report  should 
identify  and  quantify  the  corresponding 
costs and benefits, considering their direct, 
indirect,  one-off  and  recurrent  elements. 
The  estimates  should  be  transparently 
presented  to  avoid  a  risk  of  double 
counting. The report should  be clearer on 
the  distributional  impacts,  in  particular  as 
regards  the  data  owners.  To  that  end,  a 
proper SME test should be conducted. The 
report  should  be  more  specific  about  the 
burden 
the 
initiative,  linked  to  possible  replacement 
of  traditional  surveys  with  collections  of 
digital  data.  Once  the  impact  analysis  is 
improved, the report should use its results 
in 
the  comparison  of  options  and 
justification of the preferred option(s). 

reduction 

potential 

of 

In  the  previous  version  of  the  report  the 
costs  and  benefits  of  the  preferred  option 
were  quantified.  The  presentation  of  the 
estimates  has  been  improved  now,  also  to 
make clear that there is no double counting. 
In addition to the preferred option, the costs 
and benefits  of the other options have been 
estimated as well. The distributional impact 
of the measures is explicitly included in the 
impact  analysis;  their  effect  on  SMEs  is 
analysed  in  section  8.1,  and  Annex  8 
regarding  the  SME  test  is  added.  The 
burden  reduction  potential,  for  statisticians, 
respondents  as  well  as  data-holders,  was 
already an element of the costs and benefits 
overview for the preferred option but it has 
been  developed  further  for  all  options. 
These 
impact  analysis 
the 
(documented  in  chapter  6  and  Annex  3) 
the 
have,  of  course,  been  used 
comparison  of  options  and  justification  of 

results  of 

in 

49 

 
(8) The risks associated with the quality of 
privately  owned  digital  data  and  skill 
shortages  as  well  as  the  measures  to 
mitigate those risks should be discussed in 
more detail. 

the preferred option. 

In principle, the risks related to all costs and 
benefits are relevant to the impact  analysis. 
For risks related to the quality of the reused 
the  quality 
data  and  skills 
framework  for  European  statistics  applies, 
and the way to deal with these risks and the 
mitigating  measures  is  discussed  in  chapter 
8.  

required, 

The technical comments directly received from the Regulatory Scrutiny Board have been 
addressed as well.  

The  Regulatory  Scrutiny  Board  reviewed  the  revised  impact  assessment  and  gave  a 
positive opinion with reservations on 27 March 2023. The impact assessment was revised 
as follows.  

Comments of the RSB 

(B) Summary of findings 

How  and  where  comments  have  been 
addressed 

(1) The report does not present the options 
in  a  way  that  brings  out  clearly  the  key 
policy choices. 

The  key  policy  choices  have  been  clarified 
by an improved presentation and assessment 
of the related measures of the options.  

(2) The report is not clear on what type of 
assessment  will  be  undertaken  to  justify 
the  inclusion  of  certain  data  collections 
from private actors. 

A  description  is  added  of  the  process  and 
assessment 
for  adding 
to  be  applied 
statistics that are partly or completely based 
on new data sources.  

(3)  The  mechanism  to  trigger  the  crisis-
is  not  sufficiently 
response  measures 
explained. 

The explanation of the mechanism to trigger 
the  crisis  response  measures  has  been 
elaborated. 

(4) Some key assumptions used in the cost 
benefit analysis are not explained. 

All  key  assumptions  in  the  cost  benefit 
analysis have been identified and discussed. 

sufficiently 

(5)  The  choice  of  the  preferred  option  is 
to  address 
not 
effectively  and  efficiently  each  specific 
objective. 

justified 

This point has been addressed together with 
(1)  by  a  more  elaborated  assessment  of  the 
related measures of the options, in terms of 
each specific objective. 

(C) What to improve 

(1) The presentation of measures proposed 
under  each  option  should  be  improved  to 
the  measures  which 
increase  clarity; 
appear  to  be  common  for  all  or  two 

Measures  that  figured  in  more  than  one 
option have been disentangled. Measures of 
different  options  that  are  related  are  now 
presented  in  a  way  that  clearly  shows  their 

50 

 
 
options should be presented in  a coherent 
way.  In  addition,  the  rationale  for  having 
common measures should be reconsidered 
in  several  cases  where  there  seems  to  be 
inconsistency with the proposed approach. 
The  report  should  consider  alternative 
combinations  of  measures  to  bring  out 
clearly  the  available  policy  choices  or 
explain  why  these  are  not  relevant  or 
clearly  less  performing  than  the  two 
options  presented.  For  instance,  it  should 
consider combining some policy measures 
of policy options 1 and 2, including for the 
specific objective 2 to react faster in time 
of crisis. 

relationship  (chapter  5).  In  some  cases  this 
had  led  to  a  reassessment  of  specific 
measures.  For  all  related  measures,  the 
available  policy  choices  are  discussed  and 
evaluated  (chapters  6  and  7), 
thereby 
the  key  policy  options  and 
clarifying 
improving their consistency. The discussion 
now explicitly links the performance of the 
options  and  their  measures  to  the  specific 
that  alternative 
shows 
objectives  and 
combinations  of  measures  would  clearly 
result in inferior performance. This includes 
the  policy  measures  of  option  2  related  to 
the  responsiveness  in  times  of  crisis  (see 
also (5) below). 

(2)  The  report  should  better  explain  the 
process  of  including  new  data  collections 
in  the  Annual  Work  Programme  of  the 
ESS, what type of assessment would have 
to be undertaken and whether this process 
would  be  different  for  the  specific  digital 
data collections from private data owners. 

A  description  of  the  process  of  adding 
statistics  to  the  AWP  has  been  added, 
together  with  a  specification  of  what  needs 
to  be  done  in  addition  in  the  case  of 
statistics that are partly or completely based 
on new data sources (chapters 6 and 8). 

be 

the 

that 

clear 

should 

(3)  The  report  should  explain  how  the 
crisis  mode  measures  would  be  triggered. 
It 
under  what 
circumstances,  based  on  which  criteria 
and under which decision making process 
is  reached.  Despite 
the  crisis  mode 
assuring 
initiative  will  be 
complementary  to  other  crisis  response 
legislation 
the  Single  Market 
Emergency  Instrument  (SMEI)),  it  is  not 
clear  under  what  circumstances  the  ESS 
would  respond  to  urgent  data  demands  in 
times of crises. The report should explain 
which  of  the  modes  envisaged  in  the 
SMEI  Regulation  (if  any)  would  trigger 
the  application  of  the  crisis  response 
measures within the ESS. 

(e.g. 

is  only 

It  has  been  clarified  that  the  aim  is  for  the 
ESS  to  be  responsive  to  information  needs 
arising  from a  crisis  situation that has been 
officially  declared  by  an  Institution  but  not 
to  decide  on  its  own  on  the  existence  of  a 
crisis.  It 
in  cases  where  an 
emergency  mechanism  has  been  formally 
triggered  in  accordance  with  procedures 
established  by  Union  law  that  Eurostat 
should  have  the  capacity  to  organise  in 
parallel  a  response  at  ESS  level  to  meet 
urgent  information  needs  arising  from  that 
crisis  and  when  these  needs  have  not 
already been addressed through the relevant 
emergency mechanism.  

It  has  also  been  clarified  that  in  the  case 
where  the  single  market  has  been  impacted 
by  specific  disruptions  and  shortages  or 
possible  intra-EU  restrictions  to  the  free 
movement  of  goods,  services  and  persons,  
the  ESS  statistical  response  will  only  be 
initiated when the Single Market emergency 
mode has been activated by the Council and 
with  the  objective  to  meet  information 
needs that cannot be covered by information 
representative 
requests 

addressed 

to 

51 

 
organisations  or  economic  operators 
crisis-relevant supply chains (chapter 8). 

in 

It  should  explain  how 

(4)  The  report  should  explain  and  justify 
the  assumptions  used  in  the  cost  benefit 
the 
analysis. 
numbers  of  expected  crisis  and  ESS  data 
sharing  cases  as  well  as  new  statistical 
domains were estimated. It should provide 
the  justification  for  the  different  numbers 
of  cases  expected  under  each  option.  As 
those assumptions significantly impact the 
cost and benefit analysis, the report should 
undertake  a  sensitivity  analysis  and  be 
clear about  the level  of  uncertainty in the 
analysis. 

The  costs  and  benefits  critically  depend  on 
assumptions  on  the  numbers  for  each  type 
of use cases. These numbers have now been 
explained  and  justified,  taking  into  account 
that  the  revision  concerns  a  framework 
regulation.  The  consequences  of 
the 
intrinsic  uncertainty  of  the  assumptions 
have  been  assessed,  which  shows  that  the 
conclusions  of 
impact  assessment 
remain  valid  as  long  as  the  number  of  use 
cases are within a very  reasonable range of 
values  around 
the  assumed  numbers 
(chapters 6 and 7).  

the 

in 

the 

is  not 

included 

(5) The results of the cost benefit analysis 
should  be  more  transparently  reflected  in 
the  justification  of  the  preferred  option. 
The  report  should  explain  why  the  policy 
measure  3.7 
the 
preferred  option  package  instead  of  the 
report 
policy  measure  2.7,  as 
concludes that the policy option 2 is more 
effective and efficient then policy option 1 
regarding  the  achievement  of  the  specific 
objective  2  to  provide  mechanism  and 
tools to react faster in times of crisis. The 
report  should  be  more  explicit  that  the 
preferred  option  is  the  most  costly  for 
businesses  as  regards  measures  related  to 
crisis 
the 
corresponding  impacts  on  competiveness. 
It  should  differentiate  more  clearly  the 
technical  feasibility  of  options  from  the 
support these received by stakeholders. 

response 

assess 

and 

The  disentanglement  of  the  measures  as 
described under (1) led to some adjustments 
of  the  measures.  (chapter  5).  Their  impact 
has  been  assessed  for  each  option  (chapter 
6)  in  a  way  that  allows  for  the  transparent 
comparison  of  the  options  (chapter  7).  The 
criteria used  for assessing the realisation of 
the  specific  objectives  have  been  further 
clarified,  in  particular  by  distinguishing 
between 
the 
estimated effect of political aspects (such as 
differences  between  measures  in  respect  of 
subsidiarity)  on  feasibility.  This  made  it 
possible  to  better  explain  why  measure  2.7 
rather  than  3.7  is  included  in  the  preferred 
option,.  

feasibility  and 

technical 

4.  EVIDENCE, SOURCES AND QUALITY 

Parts of the evidence and sources are described in other annexes. Annex 2 describes the 
consultations  that  have  taken  place,  in  particular  the  public  consultation,  the  in-depth 
stakeholder  interviews,  the  online  survey,  the  online  stakeholder  validation  workshop, 
and  other  targeted  consultations.  Annex  3  includes  a  summary  of  costs  and  benefits, 
which is partly based on evidence already gathered during the preparation of the impact 
assessment  of  the  Data  Act,  or  related  to  other  statistical  initiatives  such  as  the 
intensification of data sharing in the context of making statistics on intra EU trade. The 
evidence,  sources  and  quality  of  the  summary  of  costs  and  benefits  are  described  in 
Annex 3 as well.  

52 

 
 
More evidence has been gathered, within the ESS and the  wider statistical  community, 
and by using external expertise. This evidence, and its sources and quality, is described 
below.  

Evidence gathered within the ESS and the wider statistical community 

The  decision  by  the  Commission  to  start  an  initiative  to  revise  Regulation  (EC)  No 
223/2009  was  taken  after  a  long  period  in  which  the  ESS  gathered  evidence  of  its 
necessity, especially in respect of its first specific objective, to exploit the full potential 
of digital data sources for official statistics. First of all, quite some research was done to 
explore  the  potential  for  official  statistics  of  making  use  of  data  held  by  the  private 
sector. A number of potential digital data sources were investigated and research carried 
out resulting in experimental statistics. One of the biggest such efforts was the so-called 
ESSnet  Big  Data,  which  was  carried  out  from  2016  to  2021  on  the  basis  of  a  multi-
beneficiary  grant  agreement  involving  28  partners,  including  the  NSIs  of  23  countries. 
The  results44  showed  that  the  potential  in  terms  of  faster,  more  detailed  and  better 
statistics  was  very  high  indeed,  but  that  the  basis  of  voluntary  partnerships  between 
businesses  and  NSIs  was  by  far  insufficient  to  systematically  sustain  official  statistics. 
Moreover,  the  potential  benefits  would  not  only  comprise  the  statistical  information 
produced, but also efficiency and a reduction of the need for statistical surveys. Lack of 
data access  appeared to  be one of the main bottlenecks to  exploit  relevant  data sources 
and  special  attention  was  paid  to  the  issue  of  data  access45.  The  ESSnet  Big  Data  was 
overseen by an external review board, to ensure the quality of the results.  

From the research outcomes a number of use cases of potential use of new data sources 
for official statistics were derived; some of them were used for the impact assessment for 
the  Data  Act  (see  Annex  3).  For  the  impact  assessment  for  the  revision  of  Regulation 
(EC)  No  223/2009  these  and  other  use  cases  were  updated  and  used  in  the  in-depth 
interviews with stakeholders (see Annex 2), to get a more concrete view on the obstacles, 
the potential costs to  data holders and producers  of statistics, and the potential benefits 
for, among other things, policymaking.  

Within  the  ESS,  a  group  of  Directors-General  of  NSIs  also  investigated  in  2020  the 
situation at national level in respect of legal access to privately held data. A questionnaire 
was designed with questions on a large number of aspects, from actual use of new data 
sources to obstacles to data reuse, from existing legal provisions to intentions to initiate 
legal  changes,  from  data  access  during  the  pandemic  to  policy  needs.  Answers  were 
obtained from 26 ESS Member States, on the condition of confidentiality. The results not 
only  showed  the  limits  of  an  approach  based  on  voluntary  partnerships,  but  also 
demonstrated  the  wide  divergence  at  national  level  in  data  access  practices  and 
associated legal conditions. At this stage the need for a legislative initiative at EU level 
was  evident  to  all  NSIs,  and  in  June  2021  the  ESS  Committee  (the  highest  authority 
within the ESS) unanimously adopted a position paper on the future Data Act proposal 
about the need for access to privately held data for official statistics46. 

44 https://ec.europa.eu/eurostat/cros/content/essnet-big-data-1_en 
45 For instance by organising a seminar with data holders on this subject, see 
 https://ec.europa.eu/eurostat/cros/content/WP5_Meeting_2016_09_22-23_Luxembourg_Workshop_en 
46 ESS Committee position paper on the future Data Act proposal, June 2021. 

53 

 
                                                 
Evidence was also needed concerning the choice of safeguards and conditions that should 
accompany  the  reuse  of  privately  held  data  for  official  statistics.  Since  data  access  is 
crucial to statistical systems worldwide, data access principles received attention by the 
international statistical community at an early stage. The UN Global Working Group on 
the  Use  of  Big  Data  for  Official  Statistics  drafted  a  set  of  eight  data  access  principles 
already in 2015, which were well received when presented at a conference with business 
sector participation47. Data access principles were also articulated by other organisations 
to  which  data  sharing  is  relevant,  such  as  the  OECD48,  and  the  Commission  has  also 
provided guidance on such data sharing49.  

Use of external expertise 

In April 2021, the Commission created the High-Level Expert Group on facilitating the 
use of new data sources for official statistics50. Its task was to provide recommendations 
aimed  at  enhancing  data  sharing  between  businesses  and  government  (B2G)  for  the 
purpose  of  producing  statistics  (B2G4S),  after  a  similar  Expert  Group  provided 
recommendations on enhancing data sharing between the private and the public sector in 
general51.  The  B2G4S  Expert  Group  consisted  of  20  independent  experts  with  various 
backgrounds  that  were  particularly  relevant  to  B2G4S,  including  from  the  business 
sector, research and academia, public administration and the civil sector. In June 2022 it 
produced its final report52, entitled Empowering society by reusing privately held data for 
official  statistics,  a  European  approach.  The  report  comprised  an  articulated  set  of 
recommendations aimed at enabling the sustainable reuse of data collected by the private 
sector for the development, production and dissemination of official statistics, including 
recommendations on safeguards and conditions [EG B2G4S p48-56]. This report is one 
of the main sources of evidence of this impact assessment.  

For this impact assessment, the Commission contracted ICF SA, Belgium, to carry out a 
support  study53.  Apart  from  desktop  research,  ICF  carried  out  the  in-depth  stakeholder 
consultation,  the  online  survey  and  the  online  stakeholder  validation  workshop  (see 
Annex 2). Thus, much of the effort was aimed at collecting evidence from stakeholders, 
not only on their views and positions, but also on the problems and their drivers, on the 
merits  of  the  policy  options  and  measures,  and  on  experienced  and  expected  costs  and 
benefits.  The  wide  variety  and  number  of  stakeholders  consulted  make  the  results 
reasonably robust, but the data that could be obtained on costs was limited, and should be 
considered  anecdotal  rather  than  representative.  Therefore,  the  costs  and  benefits 
mentioned in Annex 3 are largely based on other sources (specified in that annex). Many 

https://ec.europa.eu/eurostat/cros/content/expert-group-facilitating-use-new-data-sources-official-

47 Global Conference on Big Data for Official Statistics, Abu Dhabi, 20-22 October 2015.  
48  OECD  (2021),  Recommendation  of  the  Council  on  Enhancing  Access  to  and  Sharing  of  Data, 
OECD/LEGAL/0643, October 2021.  
49 European Commission (2018), Staff Working Document, Guidance on Sharing Private Sector Data in the 
European Data Economy, 25 April 2018.  
50 
statistics_en 
51 
1.pdf 
52 
9ce4-c063-7e806c045dac?t=1656335798606  
53  ICF,  Study  to  support  an  impact  assessment  for  the  revision  of  Regulation  (EC)  No  223/2009  on 
European statistics, Final Report, 18 November 2022. 

https://ec.europa.eu/eurostat/documents/7870049/14803739/KS-FT-22-004-EN-N.pdf/052b4357-bf8e-

https://www.euractiv.com/wp-content/uploads/sites/2/2020/02/B2GDataSharingExpertGroupReport-

54 

 
                                                 
parts  of  this  impact  assessment  are  partly  based  on  evidence  provided  by  the  support 
study of ICF, in particular the scoring and comparison of the policy options. Eurostat has 
monitored  the  work  of  ICF  on  a  weekly  basis  and  considers  the  results  to  be  of 
professional quality. 

ANNEX 2: STAKEHOLDER CONSULTATION (SYNOPSIS REPORT) 

1. Introduction 

In the context of the impact assessment on the revision of Regulation (EC) No 223/2009 
on European statistics, various consultation activities were conducted between February 
2022 and November 2022. The purpose of the consultation was to collect evidence and 
views from a broad range of stakeholders, giving them an opportunity to provide relevant 
data and information on the problems and potential solutions concerning the challenges 
that European statistics are facing. While attempting to reach the widest possible range of 
stakeholders,  the  results  of  the  consultation  activities  are  not  designed  to  be 
representative. This annex presents the results of the consultation activities carried out.  

The consultation activities included:  

  a  call  for  evidence  published  on  the  “Have  your  say”  portal  and  open  from  21 

February to 21 March 2022,  

  an online public consultation conducted via a questionnaire published on the same 

portal and open from 19 July to 25 October 2022, 

  stakeholder interviews carried out between October and November 2022,   
  an online survey launched on 5 October and closed on 7 November 2022, 
  an online stakeholder workshop carried out on 8 November 2022. 
  High-level meeting of DGs of NSIs. 

Concerning  the  call  for  evidence  and  the  public  consultation,  those  were  open  to  the 
public.  

2. Key stakeholders  

For all stakeholder activities, the main stakeholder groups identified were:  

1.  Producers of European statistics  
2.  Users of European statistics, including 

Institutional users of European statistics 

o 
o  Business users of European statistics 
o  The media 
o  The general public 

3.  Providers of primary data for production of European statistics, including 

o  Public administrations 
o 
o  Businesses and other organisations 

Individual data providers (respondents) 

4.  Other stakeholders, including 

55 

 
 
o  Subjects to which data collected for the production of European statistics 

pertain  

For the call for evidence, 21 answers were received coming mainly from EU citizens (8 
respondents), followed by public authorities (4) and NGOs (4). Respondents to the call 
for evidence were mainly located in Netherlands (7), Germany (4) and Belgium (4).  

(83 

respondents), 

For  the  public  consultation,  204  answers  were  received  coming  mainly  from  EU 
citizens 
(15), 
academic/research  institutions  (14)  and  business  associations  (10).  Respondents  to  the 
public  consultation  were  mainly  located  in  Spain  (31),  Germany  (26)  and  Greece  (25). 
The  respondents  were  from  33  countries  (26  EU  Member  States,  Bangladesh,  China, 
Iceland, Norway, Russia, Switzerland and United Kingdom).  

followed  by  public  authorities 

(71),  others 

For  the  stakeholder  interviews,  30  in-depth  interviews  were  conducted,  16  with 
institutional  statistics  users,  10  with  data  producers  (NSIs),  3  with  private  data  holders 
and  1  with  an  individual  expert.  The  participants  were  located  in  Belgium,  Estonia, 
France,  Germany,  Greece,  Ireland,  Italy,  Netherlands,  Norway,  Poland,  Spain,  United 
Kingdom. 

For the online survey, it received a total of 27 replies, 18 from NSIs and 9 from private 
data holders. The respondents were located in Austria, Belgium, Bulgaria, Cyprus, Czech 
Republic,  Denmark,  El  Salvador,  Finland,  France,  Greece,  Italy,  Hungary,  Latvia, 
Lithuania, Netherlands, Norway, Poland, Slovenia, Spain, United States. 

For  the  online  stakeholder  validation  workshop,  it  gathered  26  participants 
representing  21  organisations  –  5  NSIs,  8  statistical  users,  4  business  associations,  2 
Private data owners, 2 NGOs. The respondents were located Belgium, Estonia, Finland, 
France,  Germany,  Italy,  Netherlands,  Norway,  Poland,  Portugal,  United  Kingdom,  the 
United States. 

Table 6: Stakeholder participation 

Stakeholder type  

Public 
consultatio
n and Call 
for 
Evidence 
on “Have 
your say” 

Targeted 
consultation(
in-depth 
performed 
by ICF) 

Targeted 
consultation 
(online survey 
performed by 
ICF) 

Final 
workshop  

Producers of European 
statistics  

Institutional users 

Business users 

General public 

 x 

 x 

x  

 x 

 x 

56 

x 

x 

x 

 x 

 x 

x  

 
 
 
 
  
 
  
 x 

 x 

x 

x 

 x 

x 

x 

x 

x 

 x 

x 

x 

x 

x 

Public administrations  

Individual data providers 
(respondents) 

Businesses and other 
organisations (private 
data holders) 

Businesses and other 
organisations (other) 

Subjects of data 
collection 

3. Summary of results  

3.1 Call for evidence  

Public  authorities  provided  detailed  feedback  on  the  initiative.  They  mainly  agree  with 
the  problems  that  need  to  be  tackled,  as  identified  in  the  Call  for  Evidence,  and  most 
dominantly refer to the need for access to new data sources. In that respect they mostly 
point  to  the  need  of  ensuring  that  such  access  to  privately  held  data  should  be  free  of 
charge for official statistics and that the inclusion of new data sources must not entail that 
the  reliability  and  credibility  of  official  statistics  are  compromised.  Several  public 
authorities also refer to the need of a strengthened coordination and possibility of sharing 
data.  

A few EU citizens express concerns about citizens privacy, but one also points to it being 
essential for the compilation of statistics that there is access to relevant data sources, and 
that  data  holders  should  not  be  able  to  maximise  their  profit  from  this  need.  A  few 
business  associations  point  to  the  value  of  European  statistics  but  point  to  the  need  of 
treating data with confidentiality when analysing it.  

3.2 Online public consultation  

The  questionnaire  of  the  online  public  consultation  gathered  feedback  on  the  different 
measures  considered  in  preparing  the  revision  of  the  legal  framework  for  European 
statistics. It notably touched on the following questions: 

  What  needs  to  be  done  to  make  European  statistics  fit  for  the  future  and  more 

relevant to user needs? 

  What  are  the  most  important  factors  in  ensuring  that  European  statistics  better 

meet user needs? 

  How important is it to  make digital data held by the private sector available for 

the production of European statistics? 

  What  are  the  thematic  areas  of  European  statistics  that  might  benefit  the  most 

from making data held by the private sector available? 

57 

 
  
 
  
 
  
 
 
  How important are a number of conditions for ensuring responsible use of digital 
data  in  European  statistics  (including  conditions  relating  to  only  collecting  for 
statistical purposes and to the minimisation principle)? 

  Are  European  statistics  sufficiently  responsive  to  emerging  user  demand, 

including during public emergencies and crises? 

  How can European statistics be made more responsive?  
  For  what  purposes  would  easier  and  more  systematic  data  sharing  between 

statistics authorities be helpful within the ESS? 

  What  kind  of  conditions  and  safeguards  should  apply  when  sharing  data  within 

the ESS? 

Of all respondents 3% (or 654 out of 204 respondents) replied that they considered their 
company/business a data holder, i.e., a company/business holding personal data or non-
personal  data  that  could  be  used  or  is  known  to  be  used  for  the  production  of  official 
statistics. To the question, what needs to be done to make European statistics fit for the 
future  and  more  relevant  to  user  needs,  respondents  could  select  one  or  more  options. 
70%  of  respondents  (or  143  out  of  204  respondents)  considered  it  most  important  to 
combine  sources  to  provide  more  and  better  insights  into  economic  and  societal 
developments,  66  %  (or  135  out  of  204  respondents)  considered  it  most  important  to 
provide more granular statistics (e.g. for social groups and territorial units), and equally 
66%  (or  135  out  of  204  respondents)  of  respondents  considered  it  most  important  to 
provide  more  up-to-date  statistics,  e.g.  through  flash  estimates  and  more  frequent 
statistics. According to 57% of respondents (or 117 out of 204 respondents) it was most 
important to respond faster to emerging demand for data and statistics, especially during 
public  emergencies  and  crises,  to  56%  (or  114  out  of  204  respondents)  it  was  most 
important to improve the way statistics are disseminated and communicated, to 51% (or 
105 out of 204 respondents) it was most important to provide more statistics about new 
phenomena,  equally  to  51%  (or  104  out  of  204  respondents)  it  was  most  important  to 
provide more metadata, explaining the statistics, keep a change log and notify the most 
recent update, and finally to 32% (or 66 out of 204 respondents) it was most important to 
publish  more  statistics  under  development,  to  engage  more  intensively  with  users  of 
European statistics. 

To the question, what are the most important factors in ensuring that European statistics 
better  meet  user  needs,  respondents  could  select  one  or  more  options,  where  the  most 
important options were considered to be as follows: 

  Sustainable access to relevant data sources to be used for production of European 
statistics  (75%)  (or  154  out  of  204  respondents);  broken  down  by  selected 
stakeholders, this was considered important by 56 out of 83 EU citizens, 61 out of 
71 public authorities, and 2 out of 5 businesses 

  Modern  IT  infrastructure  to  support  the  production  and  dissemination  of 

European statistics (65%) (or 133 out of 204 respondents) 

  Sufficient resources for the ESS (60%) (or 123 out of 204 respondents) 
  Reskilling  and  upskilling  of  staff  in  national  statistical  authorities  and  Eurostat 

(50%) (102 out of 204 respondents) 

54  However,  only  15  respondents  out  of  204  identified  themselves  as  either  “company/business 
organization” or “business association” 

58 

 
                                                 
  Better statistical processes (34%) (or 69 out of 204 respondents) 

On  the  question  of  the  importance  of  making  digital  data  held  by  the  private  sector 
available for the production of European statistics, 83% (or 169 out of 204 respondents) 
of the respondents considered this being of very high or high importance. Broken down 
by selected stakeholders, this view was supported by 69 out of 83 EU citizens, 64 out of 
71 public authorities, and 3 out of 5 businesses. 

According to respondents the thematic areas of European statistics that might benefit the 
most from making data held by the private sector available, are the area of Economy and 
Finance (70%) (or 142 out of 204 respondents) followed by the area of Environment and 
Energy (60%) (or 123 out of 204 respondents). 

To the question of the importance of different conditions for ensuring the responsible use 
of digital data in European statistics, respondents identified the following factors: 

  The statistics authority and the data holder should ensure, where relevant, 
data privacy and confidentiality (99% of very high or high importance) (or 
201 out of 204 respondents)  

  The statistics authority and the private data holder should practise full 

transparency towards the public and the people the data relate to (87% of 
very high or high importance) (or 177 out of 204 respondents)  

  The statistics authority and the data holder should be mutually obliged to 
collaborate in good faith (86% of very high or high importance) (or 175 
out of 204 respondents)  

  The request for access to data should explain why the data are necessary 
or useful for compilation of official statistics (79% of very high or high 
importance) (or 162 out of 204 respondents) 

  Mechanisms should be in place to address potential disagreements over 

data requests between the data holder and the statistics authority (77% of 
very high or high importance) (or 157 out of 204 respondents)  
  The reputation and business interests of the data holder should be 

respected and safeguarded (77% of very high or high importance) (or 156 
out of 204 respondents)  

  Data made available for the production of official statistics should be used 
only for that purpose (68% of very high or high importance) (or 139 out of 
204 respondents) 

  The statistics authorities should request only the minimum data they need 
(minimisation principle) (58% of very high or high importance) (or 119 
out of 204 respondents) 

11% of respondents (or 23 out of 204 respondents) consider that European statistics are 
sufficiently  responsive  to  emerging  user  demand,  including  during  public  emergencies 
and crises, whereas 72% (or 146 out of 204 respondents) consider that European statistics 
are  somewhat  responsive,  but  not  enough,  and  8%  (or  16  out  of  204  respondents) 
consider  those  statistics  not  responsive.  When  considering  the  share  of  selected 
stakeholders  supporting  the  dominant  view,  that  European  statistics  are  somewhat 
responsive, but not enough, this was supported by 53 out of 83 EU citizens, 58 out of 71 
public authorities, and 3 out of 5 businesses. 

59 

 
According  to  56%  of  respondents  (or  115  out  of  204  respondents)  European  statistics 
could  be  made  more  responsive  by  means  of  a  more  intensive  use  of  digital  data  to 
follow fast societal, environmental and economic changes, whereas 50% of respondents 
(or  103  out  of  204  respondents)  considered  it  could  be  achieved  by  making  more  and 
better use of already existing statistical data to respond to demand. 49% of respondents 
(or  99  out  of  204  respondents)  considered  that  it  could  be  achieved  through  more 
collaboration with research and the academic sector, 47% (or 96 out of 204 respondents) 
considered  that  it  could  be  done  through  more  coordination  at  European  level  to  better 
react  to  crises  and  emergencies,  38%  (or  77  out  of  204  respondents)  considered  that  it 
could be achieved by more experimentation and use of statistics under development for 
greater  engagement  with  users,  31%  (or  63  out  of  204  respondents)  considered  that  it 
could be achieved through more dialogue with users, and 30% of respondents (or 62 out 
of 204 respondents)  considered it could be achieved through more resources invested in 
dialogue, collaboration and experimentation.  

As regards purposes for which easier and more systematic data sharing between statistics 
authorities  would  be  helpful  within  the  ESS,  72%  of  respondents  (or  147  out  of  204 
respondents) consider it helpful to reduce response burden and allow for reuse of already 
collected  data,  69%  (or  140  out  of  204  respondents)  consider  it  helpful  to  increase  the 
quality of official  statistics, 65%  (or 132 out of  204 respondents) consider it helpful to 
garner synergies and cost efficiency in production of official statistics at EU and national 
levels, 63% (or 128 out  of 204 respondents) consider it helpful to enable production of 
cross-border  official  statistics  that  cannot  be  compiled  correctly  as  a  sum  of  national 
estimates, 61% (or 125 out of 204 respondents) consider it helpful to increase potential 
for  research  in  official  statistics  (i.e.,  developing  new  methods  for  compiling  official 
statistics), and 56% of respondents (or 115 out of 204 respondents) consider it helpful to 
help develop new statistics (including for cross-border regions). 

To the question, what kind of conditions and safeguards should apply when sharing data 
within  the  ESS,  75%  of  respondents  (or  154  out  of  204  respondents)  consider  that 
purpose  limitation  for  sharing  personal  data  (sharing  of  data  only  for  agreed  purposes) 
should apply, 72% (or out of 204 respondents) consider that effective protection of data 
using  state-of-the-art  technologies,  should  apply,  45%  (or  92  out  of  204  respondents) 
consider that technologies to minimise the need to transmit data should be used, 31% (or 
63  out  of  204  respondents)  considers  that  purpose  limitation  for  sharing  non-personal 
data (sharing of data only for agreed purposes) should apply, and 8% (or 16 out of 204 
respondents) consider that no conditions should apply. 

Finally, on the extent to which respondents agreed to a number of statements, the results 
were as follows:  

  Statistics authorities should set standards for interoperability (86% of 
respondents agree or strongly agree) (or 176 out of 204 respondents) 
  Statistics authorities should develop and maintain a catalogue of data 

assets in their national data ecosystem, and make the catalogue publicly 
available (86% of respondents agree or strongly agree) (or 176 out of 204 
respondents) 

  Statistics authorities should provide professional advice to organisations 
within their ecosystem on issues related to data and data processing, such 
as quality, data reuse, intellectual property, confidentiality, security and 

60 

 
metadata (85% of respondents agree or strongly agree) (or 173 out of 204 
respondents) 

  Statistics authorities should assess the quality of statistics made available 
to the public by other organisations (75% of respondents agree or strongly 
agree) (or 152 out of 204 respondents)  

  Statistics authorities should mediate among organisations interested in 
data sharing and reuse (61% of respondents agree or strongly agree) (or 
124 out of 204 respondents) 

3.3 Targeted consultations 

In-depth stakeholder interviews  

ICF, preparing the study in support of this impact assessment, conducted 30 interviews 
between  October  and  November  2022  primarily  to  inform  use  cases  and  to  understand 
statistical  users’ data needs and the potential benefits  associated with  enhanced  B2G4S 
data sharing (see chapter 6 and 7 of the impact assessment). The contractor interviewed 
10 data producers (NSIs), 3 private data holders, 16 statistical users’ organizations and 1 
individual expert [ICF, Annexes 2 and 3]. 

Online survey 

The  online  survey,  equally  conducted  by  ICF,  targeted  NSIs  and  private  data  holders. 
NSIs  were  reached  by  Eurostat  through  the  Directors  of  Methodology  (DIME/ITDG) 
network.  ICF reached out to 69 individual companies and 81 business associations with 
a focus  on European affairs (who were asked to share the survey  with  their members). 
The survey received a total of 27 replies, 18 from NSIs and 9 from private data holders. 

Integrated results of targeted consultations 

The direct or indirect benefits as identified by various stakeholders in various use cases 
are summarised in Table 7:  

Table 7: Results of targeted consultations 

Use case 

Stakeholder type 

Direct/indirect benefits 

Mobile network operators 

Society 

Less  overall burden on businesses as automated data sharing mechanisms replaces 
lengthy surveys 

Statistical users 

Fine-tuning  facilities  and  services;  better  decision-making 
understanding of interaction between territories, human behaviour, spatial planning 

including  better 

NSIs 

Better data in terms of quantity, quality, granularity, and timeliness; 
Enhanced reputation 

Private data holders 

Compensation: a three-year contract (total value EUR 300.000), subsequent annual 
contracts (EUR 24.000);  
Involvement in research and scientific publications; 
Sharing of methodological expertise; 
Increased data quality might lead to monetisation and commercialisation of data; 
Enhanced reputation; 

Financial transaction data 

Society 

Reduce response burden for the business sector by saving up to 3.400 hours of work 

61 

 
Use case 

Stakeholder type 

Direct/indirect benefits 

Statistical users 

Production  of  short-term  statistics  about  the  retail  trade  sector  and  certain  B2C 
service industries 

Larger coverage and bigger transaction datasets 

NSIs 

Cut up 20,000 individual questionnaires per year; 
Higher data timeliness and frequency 

Private data holders 

(Vipps) Compliance with national Statistics Act 

Smart meters 

Statistical users 

(Fable) Supporting the company’s mission  
Bringing some “gravitas” to their datasets as a result of collaboration with respected 
public institutions 

Better understand changing patterns of energy demand and consumption; 
To produce better overviews of specific markets and fill knowledge gaps; 
Better city planning, including residence and mobility 

NSIs 

Faster access to more accurate data;  
Smart  meter  data  go  far  beyond  what  would  be  possible  to  achieve  through 
traditional data gathering methods  

Private data holders 

- 

Smart devices (IoT) 

Statistical users 

To understand levels of pollution within and across regions;  
Identify and improve issues within a large city 

NSIs 

Frequent and more detailed data, improving accuracy;  
innovation  

Private data holders 

- 

Collaborative economic 
platforms (tourism) 

Statistical users 

Filled information gap related to knowledge of short-term rentals, allowing a more 
accurate debate about tourism;  
Better understanding on how travelling is developing and how is impacting tourism 

NSIs 

Private data holders 

- 

- 

Equally  the  following  Table  9  gives  a  summarized  overview  of  various  stakeholders 
estimate of costs in various use cases: 

Table 8: Stakeholders’ estimate of costs in various use cases 

Use case 

Stakeholder type 

One-off/recurrent costs 

Comments 

Mobile network 
operators 

Statistical users 

- 

(If data sharing did not occur): information gaps 
leading to poorer decision-making; additional time 
to find alternative resources; additional 
procurement to access commercial databases 

NSIs 

Time/human resources for data processes 
and analysis (one-off) 
Compensation to MNOs (one-off/recurrent) 

NSIs need appropriate resources to handle data 
coming from MNOs due to significant pre-
processing and cleaning before data is usable to 
generate official statistical purposes 

Private data holders 

“Minimal” human resources for data 
processes and analysis (one-off) 

62 

 
 
 
 
 
Use case 

Stakeholder type 

One-off/recurrent costs 

Comments 

Financial 
transaction costs 

Statistical users 

- 

NSIs 

Private data holders 

“Very low” cost in setting up data sharing 
(one-off); 
Time dedicated to establish dialogue with 
company and explain the legal basis: 2/3 
months full time work by a team of 5/6 
individual (one-off) 

(Vipps) Development costs: one full week 
(one-off) 
Daily data/report delivery: 1-2 months full 
time work (recurrent) 

(Fable) Long time to set up the 
collaboration (one-off) 
“Minimal” technological infrastructure (i.e. 
server) costs (recurrent) 

Smart meters 

Statistical users 

- 

NSIs 

“Low” personnel costs, significantly 
“minimal” in comparison to collecting such 
data in a traditional way (recurrent) 

Hiring new data scientist (recurrent) 

Low initial costs setting up a server (one-
off) 

Private data holders 

Statistical users 

- 

- 

Smart devices 
(IoT) 

NSIs 

“Low” costs associated with personnel time 
in establishing partnership (one-off). Team 
was later expanded into small number of 
full-time roles (recurrent). 

Private data holders 

- 

Statistical users 

- 

Collaborative 
economic 
platforms 
(tourism) 

NSIs 

- 

Private data holders 

“Manageable costs” (if there is one data 
portal, one API, and one format) (recurrent) 

63 

(If data sharing did not occur): information gaps 
leading to poorer decision-making; additional time 
to find alternative resources; additional 
procurement to access commercial databases 

Data sharing was pre-existing so previous 
technological infrastructure was in place 

(Vipps) Data sharing was pre-existing so previous 
infrastructure was in place 

(Fable) Costs might increase if data sharing grows 
in complexity, involving third parties, etc. 

(If data sharing did not occur): information gaps 
leading to poorer decision-making; additional time 
to find alternative resources; additional 
procurement to access commercial databases 

(If data sharing did not occur): information gaps 
leading to poorer decision-making; additional time 
to find alternative resources; additional 
procurement to access commercial databases 

NSI noted that securing certain types of data from 
private owners can be “prohibitively expensive” 

- 

(If data sharing did not occur): information gaps 
leading to poorer decision-making; additional time 
to find alternative resources; additional 
procurement to access commercial databases 

NSI commented that NSIs should not pay to reuse 
privately held data 

Costs become “unmanageable” if the company had 
to share the data with each Member State or share 
it at very short intervals 

 
 
 
 
 
 
 
 
 
 
 
3.4 Online stakeholder workshop 

The  objective  of  the  online  stakeholder  workshop,  which  took  place  on  8  November 
2022, was to seek stakeholders' validation of the findings of the preceding consultation 
activities.  The  invitees  were  therefore  overlapping  with  the  stakeholders  having  been 
subject  of  the  targeted  stakeholder  interviews  and  the  online  survey.  The  workshop 
included an audience interaction using Mural where the participants could express their 
support  for  various  policy  options  and  measures  and  discuss  their  potential  impacts, 
including on SMEs and on public authorities and their budgets. 

When looking at the potential effectiveness of the envisaged policy options, stakeholders 
participating  in  the  validation  workshop  suggested  that  policy  option  1  of  the  impact 
assessment would have the highest level of effectiveness to achieve the objectives of the 
revision (56% voted ‘High’ and 28% ‘Very high’). When looking at perceived efficiency 
and perceived coherence, it was  also policy option 1 that was considered as having the 
highest  level  of,  respectively,  efficiency  to  achieve  the  objectives  of  the  revision  (50% 
voted  ‘High’  and  11%  ‘Very  high’)  and  coherence  in  relation  to  other  EU 
initiatives/regulation  in  this  field  (50%  voted  ‘High’  and  22%  ‘Very  high’).  However, 
when  looking  at  the  perceived  EU  added  value  of  the  envisaged  policy  options, 
stakeholders  participating  in  the  validation  workshop  suggested  that  policy  option  2 
would  have  the  highest  level  of  EU  added  value  compared  to  Member  States  acting 
separately to achieve the same results (44% voted ‘High’ and 39% ‘Very high’). Policy 
option 1 was also deemed as having a high level of EU added value as 44% of workshop 
participants voted ‘High’ and 33% voted ‘Very high’. 

3.5 Other targeted consultations  

In  the  anticipation  of  the  adoption  of  the  Data  Act  proposal,  the  topic  of  access  to 
privately  held  data  for  statistical  purposes  was  discussed  within  the  ESS  Committee  at 
several  occasions  and  led,  amongst  others,  to  the  adoption  of  a  position  paper  on  the 
importance of access to privately held data for official statistics in June 202155.  

On  20  and  21  October  2022  the  Conference  of  European  Statistics  Stakeholders  took 
place at  the University of  Rome, “La Sapienza”.  This  conference was organised  by the 
European  Statistical  Advisory  Committee  (ESAC),  the  European  Central  Bank  (ECB), 
the Federation of European National Statistical Societies (FENStatS), and Eurostat. The 
Expert Group on facilitating the use of new data sources for official statistics presented 
its recommendations, and Eurostat presented the initiative to revise Regulation (EC) No 
223/2009.  The  recommendations  of  the  Expert  Group  and  the  initiative  to  revise 
Regulation (EC) No 223/2009 were very well received.  

3.6. Under the French Presidency a high-level meeting was organised in Lyon on 7-8 
April 2022 on ‘Making the European Statistical System fit for the future’. Subsequently 
a  dedicated  meeting  of  the  Presidents  and  Directors-General  of  the  NSIs  was  held  in 
Luxembourg on 18 and 19 May 2022 to discuss the conclusions.  

55  Cf.  “European  Statistical  System  (ESS)  position  paper  on  the  future  Data  Act  proposal  ¬  Access  to 
privately  held  data  is  urgently  needed  for  producing  new,  faster,  more  detailed  official  statistics”,  June 
2021. 

64 

 
                                                 
At the meeting in Lyon, a large consensus emerged on a number of topics to be covered 
by the revision of the Regulation, including the provision of a legal basis for sustainable 
access  to  privately  held  data  for  official  statistics,  the  use  of  new  technologies  and  the 
development  of  experimental  statistics,  the  recognition  of  the  possibility  for  NSIs  to 
assume new roles and tasks, and the fostering of data sharing in the ESS. 

The  objective  at  the  subsequent  meeting  of  the  ESS  Committee  in  Luxembourg  on  18 
and  19  May  2022  was  to  identify  more  precisely  how  the  proposed  changes  could  be 
addressed  at  operational  level  and  be  effectively  implemented  for  two  issues,  namely 
sustainable  access  to  privately  held  data  and  data  sharing  in  the  ESS  for  producing 
European statistics.  

During  the  discussions,  the  urgent  need  to  set  up  a  proportionate,  limited,  predictable 
framework  for  making  data  available  for  the  compilation  of  official  statistics  was 
highlighted: the objective is to set up a mechanism that is solid from a legal point of view 
and  that  will  effectively  oblige  data  holders  to  make  their  data  available  to  NSIs  and 
Eurostat,  under  appropriate  safeguards  and  conditions.  The  importance  to  address 
privately held data as another data source was also emphasized. At the same time, it was 
recognised that not all details of data sharing arrangements should be set in the law. 

It was also considered that data sharing within the ESS should be strengthened, notably 
in  relation  to  cross-border  phenomena,  even  though  different  views  were  expressed  on 
how to best achieve this objective with some participants noting that current Regulation 
(EC)  No  223/2009  already  allows  for  data  sharing  on  a  voluntary  basis  and  the 
possibility  to  address  this  issue  in  sectoral  legislation  was  mentioned  as  an  alternative. 
Views however broadly converged on the need to ensure that any strengthening of data 
sharing should be limited and purpose oriented, based on high security controls within a 
delineated  common  data  space  and  built  on  modern  data  access  technologies  such  as 
privacy enhancing technologies. 

ANNEX 3: WHO IS AFFECTED AND HOW? 

1.  PRACTICAL IMPLICATIONS OF THE INITIATIVE 

Depending  on  the  specific  objectives  and  related  measures,  different  types  of 
stakeholders would be affected by the initiative.  

Specific  stakeholders  can  have  various  roles,  e.g.,  businesses  can  be  respondents 
providing data to the statistical system but can also be users of official statistics products. 
The  following  list  stakeholders  therefore  elaborates  on  their  (possible)  role  as  this 
determines the impact of the measures on them. 

As main stakeholders, we distinguish between the  

-  The European Statistical System (ESS), 

 i.e., Eurostat, the national statistical authorities (including the NSIs and the other 
national authorities) that produce European statistics. 

65 

 
 
-  The European System of Central Banks, whose members produce monetary, 

banking and finance European statistics. 

-  The European businesses and their organisations distinguished according to 

their size into SMEs and large enterprises; 

-  The key institutional users  at EU, national and regional level that make informed 

decisions and policies  

-  The academic sector and researchers  
-  Media, which take the role of informing the society and different parts thereof; 
-  European citizens and the society at large. 

The European Statistical system (ESS) 

The problem identified and measures to address them have a direct impact on ther daily 
work  of  the  producers  of  European  statistics.  They  collect  data  from  different  relevant 
data  sources,  process  the  data  and  produce  European  and  national  official  statistics, 
which they disseminate to the public at large. In addition, they further develop European 
statistics and under strict conditions provide access to detailed but anonymized statistical 
data for research purposes. In the context of the revision of the Regulation on European 
statistics,  Eurostat  will  be  enabled  to  increase  substantially  its  capability  to  provide 
insights  and  actively  supports  the  political  decision-making  at  EU  level,  sustains  and 
fosters  policymaking  in  the  context  of  the  EU  political  agenda,  facilitates  the 
implementation  of  EU  policies  and  legislation,  and  promotes  transparency  and 
democratic accountability on EU and national policymaking to the public at large. 

The European System of Central Banks 

National central banks and the European Central Bank are part of the European System 
of Central Banks. They have similar tasks and roles as statistical authorities within their 
field  of  competence.  They  act  as  producer  of  European  statistics  but  do  also  use 
European  statistics  for  economic  and  financial  analysis  to  base  financial  measures  on 
sound data. They will benefit from increased richness and quality of European statistics. 
The central banks will substantially profit from higher agility of the statistical system in 
times of crises.  

European businesses and their organisations 

European  businesses  have  a  double  role  in  official  statistics.  On  the  one  hand,  they 
participate  in  the  data  collection  process  as  respondents.  On  the  other  hand,  they  are 
using official statistics as part of their decision-making process. As respondents, they are 
affected by any increase or decrease of response burden. It is in their interest to minimize 
and automate data requests. As users of official statistics, they will profit from receiving 
more  timely  and  detailed  data.  Use  of  new  data  sources  will  affect  a  small  portion  of 
businesses  disposing  of  large  data  assets,  such  as  MNOs,  banks,  energy  providers,  or 
internet  platforms.  They  might  incur  additional  costs  or  might  have  certain  additional 
obligations  in  order  to  enable  statistical  authorities  accessing  their  data.  The  very  large 
majority  of  businesses  will  rather  use  improved  statistics  based  on  these  new  data 
sources.  They  will  benefit  from  faster  data  availability  in  cases  of  crisis.  Multinational 
businesses holding large datasets will profit from burden reduction via a one-stop shop, 
i.e., one statistical authority assessing data for the ESS. They will also benefit from data 
stewards  in  statistical  authorities  who  are  able  to  professionally  manage  data 
partnerships.  The  other  businesses  will  benefit  because  new  data  sources  have  the 

66 

 
potential to diminish response burden through reducing reporting obligations. Data assets 
are  rather  held  by  large  enterprises  also  considering  the  scaling  effects  of  providing 
services via the internet. Data start-ups and data intermediaries might be of smaller size 
and  might  be  affected  disproportionally  by  requests  for  data  access  for  the  purposes  of 
official statistics. However, these requests are rather unlikely due to the limited capacity 
of these enterprises for generating and managing large data assets that would qualify for 
reuse  by  official  statistics.  On  the  contrary,  they  might  be  partners  in  developing  and 
implementing  data  analysis  and  processing  routines  in  data  reuse  scenarios  with 
compensation for their services. 

Key institutional users 

Key  institutional  users  and  Policy  makers  need  high  quality,  i.e.,  timely,  accurate, 
comparable, harmonised data to be able to design, execute, monitor and evaluate policies 
and  to  take  informed  decisions.  They  will  benefit  from  increased  offer  of  data  through 
use of privately held data, intensified data sharing within the ESS, and higher agility in 
case of urgent  demands  in  times of crises. Statistical  authorities provide statistics in  an 
impartial way, which enables policy makers to assess and balance burden and benefits in 
their policy decisions. Dedicated data stewards are able to professionalize and rationalize 
reuse  of  data  in  the  public  sector.  In  addition,  staff  in  governments  will  benefit  from 
higher data literacy through training and education programs by statistical authorities. On 
the other hand, they have an interest in reducing administrative burden on businesses, and 
in  reducing  government  spending.  This  can  be  achieved  through  efficient  data  sharing 
mechanisms, adjusting demands for data to their intended use, intensifying data sharing 
within the ESS, etc.   

Public administrations as data providers 

Public  administrations  can  act  as  data  providers  (of  so-called  administrative  data)  for 
statistical  authorities  and/or  use  official  statistics  for  their  purposes.  In  recent  years, 
administrative data has  become more important  as source  for official statistics with  the 
potential  of  minimizing  burden  on  businesses  and  citizens.  This  development  will 
become even more important with increasing degree of digitalisation of the public sector. 
In case of reuse of administrative data, burden on public administrations might increase 
with increased use of their data for the purposes of official statistics. On the other hand, 
statistical  authorities  will  contribute  to  the  professional  management  of  the  data  and 
increase  quality  of  these  data  through  data  stewardship  functions.  Staff  in  public 
administrations  might  also  benefit  from  training  and  education  programs  organised  by 
statistical offices as part of their possible functions within the public sector. Like policy 
makers,  public  administrations  will  benefit  from  timelier,  more  frequent  and  more 
granular data. 

Academic sector and researchers 

Researchers are users of data disseminated by statistical authorities but also contribute to 
further  develop  methods  for  processing  and  analysing  data,  which  are  applied  by 
statistical  offices.  Researchers  will  benefit  from  enhanced  statistical  information  with 
improved quality. 

Media 

67 

 
The media are intermediaries between organisations producing data and information and 
citizens  consuming  that  information.  The  media  will  benefit  from  increased  offer  of 
timely,  adequate,  granular,  accurate  and  harmonised  statistical  data,  which 
is 
disseminated  impartially.  This  aspect  is  of  utmost  importance  for  public  policy  debate 
and for fighting fake information. There is also a strong demand for more granular data 
to  satisfy  the  demand  of  media  to  serve  specific  users.  Developing  new  tools  and 
methods for communication and dissemination of statistical data following the changing 
habits  of  citizens  are  crucial  in  this  respect.  Media  will  specifically  benefit  from 
mechanisms responding to urgent user demands, e.g., in cases of crisis. 

Citizens and society at large 

There is a risk that official statistics will lose relevance for citizens through focussing too 
much  on  averages,  larger  populations  and  on  national  or  European  levels  and  thus  not 
reflecting the data to daily experiences and living conditions of citizens. The citizens will 
benefit  from  more  timely  and  granular  information  offers  through  increased  reuse  or 
privately  held  and  administrative  data  by  statistical  authorities  together  with  tailored 
communication services that might be delivered through private companies. Their role of 
data  users  is  contrasted  with  their  role  of  providers  of  data,  either  in  traditional  ways 
through  statistical  surveys  or  as  users  of  digital  services  producing  data  assets  at  the 
service  provider.  These  data  are  often  personal  and  very  detailed.  Statistical  offices 
ensure anonymization and ethical use of these data by legislation and by application of 
ethical codes of practice. Through developing and integration of privacy enhancing and 
preserving technologies in the statistical production process, misuse of personal data can 
be  ruled  out  on  technical  basis.  Increased  reuse  of  new  data  sources  will  diminish 
response  burden  on  citizens  while  ensuring  anonymity  of  the  resulting  statistics. 
However,  increased  data  sensitivity  calls  for  increased  participation  of  citizens  in  the 
process of prioritisation of user demands for specific statistical products. 

2.  SUMMARY OF COSTS AND BENEFITS 

The application of use cases for the calculation of costs and benefits 

The  summary  of  the  costs  and  benefits  is  based  on  the  analysis  and  the  developed 
assessment methods described in Annex 4, analytical methods56. Costs and benefits incur 
with  implementations  of  the  additional  policy  measures  (use  cases)  based  on  the 
amended framework regulation. These include cases of reuse of new data sources, which 
have emerged as by-products of digital services, of additional data collections based on 
the urgent user demand motivated by crises, and mandatory mechanisms for internal ESS 
data  sharing.  Some  of  the  additional  cases  of  reuse  are  assumed  to  be  implemented  at 
European  level,  in  which  Eurostat  would  access  data  of  data  holders  which  operate  at 
European-wide level, centrally to produce European statistics.  

In  order  to  quantify  the  impact  of  the  different  policy  options,  we  assumed  for  each 
option  a  specific  number  of  use  cases  that  would  be  realised  within  a  time  span  of  10 
years and that would improve existing statistics or would produce new statistical output. 
The  quantification  follows  the  specific  measures  that  are  proposed  under  each  of  the 
options. In the case of crisis response, we assume that measures would be of temporary 

56 Annex 4, page 81 contains further references to literature used in this context. 

68 

 
                                                 
character,  i.e.  direct  costs  and  benefits  would  only  occur  during  the  time  the  measures 
would be executed. Indirect savings and benefits, e.g. through better treatment of crises 
and  containment  of  negative  effects  on  society  and  economy  would  not  be  limited  in 
time. The quantification depends on the assumed number and type of use cases for each 
option.  Although  the  numbers  that  are  assumed  are  based,  among  other  things,  on 
extensive experience with pilot projects and partnerships between NSIs and data holding 
businesses, the assumptions are essentially informed expert guesses that may be too high 
or  too  low.  This  is  unavoidable,  given  the  fact  the  Regulation  (EC)  No  223/2009  is  a 
framework regulation and the actual decisions on use cases are taken in the context of the 
Annual Work Programme as described in chapter 8. While the assumptions have a degree 
of uncertainty, they are realistic, as it is not so much the realisation of the given numbers 
of  use  cases  that  is  uncertain,  but  rather  whether  this takes  more  than  10  years  or  less. 
The description of the impact of the policy options and their comparison (chapters 6 and 
7, respectively) takes this into account.  

It  is  important  to  note  that  the  ESS  will  have  to  invest  in  methodological  and  quality 
frameworks as well as in providing staff of statistical offices with the required knowledge 
and skills set to use new data sources together with traditional sources and integrate them 
into the production of European statistics. Those investments in the quality aspects and in 
skills reinforcements are needed in all three options. 

Categories of costs and benefits57 

Organisational costs include costs related to entering into agreements, the monitoring of 
their  execution,  as  well  as  additional  consultations  regarding  logistics  associated  with 
data transfers, support, adjudication mechanisms, and other matters.  

Methodological development costs relate to familiarisation with the dataset and relevant 
metadata on an ongoing basis, and the development and maintenance of methodologies to 
facilitate and enable the transformation processes from source data to statistics.  

Infrastructure  development  costs  cover  mainly  computing 
the 
implementation  of  IT  solutions:  network  configuration,  hardware  and  software  tools 
necessary for the handling of the new data and the processes that utilise them, including 
the writing of code and carrying out computations in end-to-end tasks.  

resources, 

Operational costs are related to the implementation of the production process, to quality 
assurance and dissemination activities, as well as to  post-production product  awareness 
and user support.  

Upfront  costs  relate  to  efforts  entailing  all  kinds  of  frontloaded  costs,  including 
exploratory  and  investigative  research,  as  well  as  resources  for  negotiations,  subject 
matter, legal, and ethical considerations and preparations.  

Compensation of the data source relate to the fact that shared data may come from a 
wide variety of sources and may be put to various uses.  This can lead to compensation 
requests. Their exact nature or the negotiated terms cannot be known a priori. Therefore, 

57 See: Sciadas G and Stavropoulos P., Methodological support to impact assessment of using privately held data by 
official statistics, Literature review and model, December 2021 

69 

 
                                                 
the  model  allows  for  extensions  to  incorporate  compensation  to  the  source  as  and  if 
necessary.  

Improved  quality  of  the  existing  statistical  outputs  refer  to  improving  quality 
parameters,  such  as  relevance,  comparability,  accuracy  or  timeliness  as  an  effect  of 
exchanging data or adding additional data sources to compute statistical outputs.  

Extending  the  existing  line  of  output  or  producing  new  statistical  products  may  be 
based on use of new data sources or as a result of additional data sharing within the ESS. 
Here, new data sources are likely to be used in a multi-purpose approach, e.g. electricity 
smart  meter  data  may  be  used  for  producing  statistics  on  energy  consumption  at  very 
granular level but also contribute to tourism statistics (occupation of secondary homes) or 
be used in the context of population and housing statistics.  

Savings refer to savings caused by reducing survey sizes due to including data from new 
data sources.  

The transformation of the shared data to products by the statistical office will lead to the 
realisation  of  societal  benefits,  while  costs  are  calculated  assuming  an  end-to-end 
production process under the control of the statistical office. The data holders may also 
participate in the production process. To the extent that they assume an assigned role in 
the overall production due to pushing out computation for a variety of reasons, they may 
be compensated. This will have no effect on the computation of the total costs; it will just 
determine  their  fair  apportionment  among  cooperating  entities.  There  will  be  upfront 
costs, which will be amortized over time and therefore our conceptualization focuses on 
recurring costs. However, upfront costs are included in the model.  

Costs occur at both sides, the statistical offices as well as on the side of the  businesses. 
They are distributed on the basis of estimated ratios, e.g., preparation costs are distributed 
equally  between  businesses  and  statistical  offices,  while  costs  for  methodological 
developments  are  mainly  paid  by  statistical  offices.  Savings  for  statistical  offices  and 
effects  of burden reduction due to  smaller sample sizes are equally distributed between 
statistical  offices  and  businesses.  Compensations  are  costs  for  statistical  offices  and 
benefits  for  the  businesses.  Compensation  for  data  holders  should  partially  compensate 
for additional costs at the data holder for preparing data for re-use by statistical offices 
according to agreed standards. 

Who will be affected by reuse of innovative data sources 

The European Statistical System has performed a number of projects to prepare for reuse 
of  innovative  data  sources.  In  the  ESSnet  Big  Data  I  and  II58,  which  ran  from  2018  – 
2021,  national  statistical  institutes  developed  methodological  and  quality  frameworks, 
and  prepared  prototypes  of  reuse  of  innovative  data  sources  for  improving  current  or 
producing new statistics.  As possible data sources, the projects  identified smart  meters, 
automatic identification systems data of vessels and airplanes, mobile network operators’ 
data,  earth  observation  data,  financial  transaction  data,  data  from  internet  platforms, 
smart  farming.  In  all  for  the  mentioned  examples,  the  number  of  enterprises  holding 
significant amounts of data is quite small. For example, Eurostat has committed a study 

58 Cf. https://ec.europa.eu/eurostat/cros/content/essnet-big-data-i_en, 
https://ec.europa.eu/eurostat/cros/content/essnet-big-data-1_en  

70 

 
                                                 
on relevant platforms offering jobs online. Across Europe, approximately 1000 potential 
websites  were  identified.  Currently,  data  is  retrieved  from  600  websites  within  the 
European  Economic  Area  and  Switzerland.  The  number  of  websites  indicates  the 
maximum  number  of  enterprises  running  those  sites.  For  mobile  network  data,  3 to 4 
communication  service  providers  cover  the  national  market  within  Europe59,  which 
makes up 81-108 enterprises across the European Union. According to Eurostat structural 
business statistics, 90 providers of wireless telecommunication services covered 92% of 
the total turnover of all enterprises within the European Union in 202160. Following these 
typical  examples,  it  can  be  assumed  that  less  than  10,000  relatively  large  enterprises 
across  Europe  will  be  affected  by  mandatory  data  sharing  requests  as  reflected  in  data 
from European business statistics. 

The baseline option (policy option 0 - PO0) 

Based  on  experiences  within  the  ESS,  we  assume  one  case  of  intensified  data  sharing 
among  the  members  of  the  ESS.  The  assumption  is  motivated  by  the  introduction  of 
Intrastat, the statistics in intra EU trade, which started in 2012 with a prototype project61  
and took almost a decade to be fully implemented in the European context, where a series 
of  organisational,  methodological,  procedural  and  governance  issues  had  to  be  solved, 
and appropriate solutions had to be developed and implemented at Member States as well 
as at European level.  

In addition, we assume one case of reuse of privately held data realized at national levels 
and that 18 out  of 27 Member States would be successful in  reusing new data sources. 
High  initial  investments  on  the  side  of  NSIs  constitute  an  obstacle  to  B2G4S,  which, 
however, can be lowered through coordination and support at ESS level. This assumption 
is based on ESS experience with reusing platform data in the context of the collaborative 
economy,  where  methodological  constraints  limit  the  use  of  the  data  for  producing 
European statistics. 

Finally, we assume one case of crisis response, in which Eurostat would directly collect 
data  from  enterprises  to  produce  European  statistics  and  distribute  intermediate  data  to 
the  members  of  the  ESS  (data  hub  function).  Data  production  initiatives  during  the 
COVID-19  crisis  have  revealed  the  complexity  of  coordinating  national  inputs  with 
different  levels  maturity  of  data  to  produce  new  statistics  at  required  quality  level  as 
regards coverage, coherence or comparability. 

The development of guidelines is financed as part of the expenditures on methodological 
developments of Eurostat. Incentives to increase data sharing are as well financed as part 
of the annual budget of Eurostat and the members of the ESS.  

59  Cf.  GSM  Association:  Mobile  market  structure  and  performance  in  Europe,  February  2020, 
https://www.gsma.com/publicpolicy/wp-content/uploads/2020/01/GSMA-Mobile-Market-Structure-and-
Performance-in-Europe_February20.pdf  
60 Enterprise statistics by size class and NACE Rev.2 activity (from 2021 onwards) 
[SBS_SC_OVW__custom_5134279], extraction 17/12/2022, 
https://ec.europa.eu/eurostat/databrowser/bookmark/3a05894b-145c-4d6a-809a-73cd585e4c71?lang=en  
61 Cf. https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Intra-EU_trade_-
_exchange_of_micro-data#Towards_an_EU_regulation_and_national_implementations 

71 

 
                                                 
EU data 
sharing

Reuse of 
privately held 
data (B2G4S)

Crisis response

1 case

1 case at national 
levels

1 new statistical 
product at EU level

Figure 3: PO0: Application of statistical products / use cases 

A 

Direct cost and 
benefit in Million EUR 

B 

C 
B2G4S 

D 
Crisis response 

E 

at national level  

ESS 

Businesses  ESS 

at EU level (Eurostat as 
hub) 
Businesses 

1 Stat. products / 
domains 

1 Stat. products / 
domains 

1 

2 

3 

4 

F 

G 

Total direct benefits 
and costs 

Total 

ESS 

Businesses 

Preparation costs 

5 
6  Organisational 

1.8 

2.2 

1.8 

0.5 

0.0 

0.0 

7 

8 

Meth. Development 

9.7 

1.1 

0.2 

Infrastructure 

9.5 

4.1 

0.2 

0.0 

0.0 

0.0 

0.1 

1.8 

2.2 

9.9 

9.7 

1.8 

0.6 

1.1 

4.1 

0.1 

7.6 

7.7 

0.2 

3.2 

0.2 

0.7 

11 

12.6 

30.7 

10.7 

9  Operational 
10  Total costs 
Quality 
improvements 
12  Savings /burden red. 
13  Additional outputs 
14  Compensation 
15  Total benefits 
16  Net benefits / costs 
21.7 
Table 9: Cost and benefit for PO0: Reuse of new data sources and crisis response 

55.8 

24.5 

31.4 

32.4 

24.2 

54.9 

31.5 

12.7 

31.5 

11.6 

31.5 

10.8 

-0.2 

0.9 

0.3 

0.0 

0.1 

0.9 

0.8 

0.0 

31.5 

0.9 

32.4 

21.5 

3.3 

10.9 

The overall balance for the two cases is positive with a net benefit of EUR 24.5 million 
(F16) for the statistical system and EUR 21.5 million (G16) for businesses. The benefits 
for businesses mainly result from burden reduction of the B2G4S case due to decreasing 
sample  sizes  affecting  the  entire  business  sector,  while  it  is  assumed  that  the  crisis 
response  mechanism  would  produce  new  statistics,  which  would  not  lead  to  burden 
reduction.  For  both  cases,  data  holders  would  face  additional  burden,  which  is  at  least 
partially compensated by the public sector. The crisis response mechanism would create 
additional cost of EUR 0.2 million (E16) on data holders. 

A 

1  ESS data sharing 
2 
3  Burden reduction 
4  Savings through running a central system 

1 Stat. products / domains 

B 

Million EUR 

39 
3 

72 

 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
  
  
 
 
5  Costs of running a central system 
6  Total 

-0.7 
41.3 

Table 10: PO0: Cost and benefit: Voluntary ESS data sharing 

The  voluntary  data  sharing  for  one  statistical  data  collection  would  result  in  burden 
reduction of EUR 39 million (B3). The burden reduction effects are due to  the reduced 
number of enterprises in the data collection and less information asked to enterprises. In 
the  case  of  EU  Intratrade  statistics,  most  enterprises  are  only  asked  for  their  exports 
instead of exports and imports. The total savings are estimated at EUR 41.3 million (B6).  

In conclusion, the baseline option would not realise the benefits through intensified data 
sharing,  the  mechanisms  for  responding  to  urgent  user  demands,  and  the  reuse  of 
privately held data to cope with the increasing demand for European statistics. Statistical 
offices would tend to intensify use of administrative data for statistical purposes and try 
to  increase  the  number  of  surveys.  As  a  likely  effect,  statistical  offices  would 
increasingly suffer from already declining response rates to surveys, which would have in 
return a strong negative impact on the quality of the produced statistics introducing bias 
and lessen the quality dimension ‘accuracy’. Statistical offices would continue investing 
in automation of surveys, i.e., use tools for automated data collection from enterprises or 
smart  devices  for  supporting  social  survey  data  collection.  There  would  be  a  need  for 
investments  in  the  coming  years  for  these  purposes  while  the  benefits  would  be 
considerably lower than for the preferred option.  

These negative developments will likely be somewhat reduced because of the increase in 
the  availability  of  open  data  and  voluntary  data  sharing  that  may  result  from  the 
implementation of the European Data Strategy. However, this strategy does not foresee 
and  is  not  expected  to  result  in  the  sustainable  and  harmonised  reuse  of  privately  held 
data  for  official  statistics,  which  is  needed  for  the  cost-benefit  ratio  to  improve  and  to 
contribute to meeting user needs. 

Policy option 1 (PO1) 

For policy option 1, a total of 20 cases of reuse of privately held data (B2G4S) and cases 
of crisis response would be realised within the next 10 years. Out of these 20 cases, 15 
would  be  B2G4S  use  cases  and  5  would  be  cases  of  urgent  user  demands  in  times  of 
crises. Out of the total of these 20 cases, 5 would be realised at European level of which 
2 are cases of crisis responses. We assume that 9 cases would produce completely new 
statistical products and in 11 cases existing statistical products would be enhanced. Only 
for  the  latter  cases,  there  would  be  burden  reduction  on  the  enterprise  sector.  New 
statistical products would be produced for all of the 5 crisis response cases. During the 
last 6 years, the European statistical system has explored and prepared a number of use 
cases for reuse of new data sources, such as reuse of mobile network metadata, financial 
(bank account and card) transactions, smart meter on energy and water consumption, real 
estate  transactions,  web  platforms  (accommodation,  job  advertisements,  new  forms  of 
labour,  mobility),  mobility  data  related  to  different  means  of  transport,  smart  personal 
data,  smart  farming  data,  environmental  sensor  data,  which  could  be  implemented 
depending  on  a  cost  benefit  analysis  and  following  the  safeguards  articulated  in  the 
policy measures of PO1. 

The  number  of  crisis  response  actions  depends  on  the  number  of  crises  that  will  occur 
during the next decade. The current assumption is based on experiences from the current 

73 

 
 
crisis (Russian war of aggression against Ukraine and its effects on economy and society) 
and the COVID-19 crisis.  

In addition, it is assumed that 4 cases of mandatory data sharing within the ESS would be 
implemented within 10 years after entry into force of the amended regulation. There are a 
certain number of existing statistical products, which could benefit from EU data sharing 
and  for  which  there  is  a  demand  by  users,  such  as  balance  of  payments,  foreign  direct 
investments or migration. 

EU data 
sharing

4 cases

Reuse of privately held data

Crisis response

(15 cases)

(5 cases)

at national 
levels
(12 cases, of 
which 2 new 
products)

at EU level with 
EU data hub
(3 cases, of 
which 2 new 
products) 

at national level
(3 cases, of 
which 3 new 
products)

at EU level with 
EU data hub
(2 cases, of 
which 2 new 
products)

Figure 4: PO1: Application of statistical products / use cases 

A 
Direct cost and benefit in 
Million EUR 

B 

C 

D 

E 

F 

G 

H 

I 

J 

K 

B2G4S 

Crisis 

Total direct benefits 
and costs 

at national level  

at EU level 
(Eurostat as hub) 

at national level  

at EU level 
(Eurostat as hub) 

Total 

ESS 

Businesses  ESS 

Businesses  ESS 

Businesses  ESS 

Businesses  ESS 

Businesses 

12 Stat. products / 
domains 

3 Stat. products / 
domains 

3 Stat. products / 
domains 

2 Stat. products / 
domains 

1 

2 

3 

4 

5  Preparation costs 
6  Organisational 
7  Meth. Development 
8 

Infrastructure 

30.6 

36.7 

165.2 

160.7 

30.6 

9.2 

18.4 

68.9 

0.6 

0.7 

3.2 

3.2 

0.6 

0.2 

0.4 

1.4 

1.6 

1.9 

8.7 

8.5 

1.6 

0.5 

1.0 

3.6 

0.0 

0.0 

0.2 

0.2 

0.0 

0.0 

32.9 

39.4 

0.0  177.4 

0.1  172.5 

32.9 

9.9 

19.7 

73.9 

9 

10 

Operational 

128.5 

55.1 

2.5 

1.1 

6.8 

2.9 

0.2 

0.1  138.0 

59.1 

Total costs 

11  Quality improvements 
12  Savings / burden red. 
13  Additional outputs 
14  Compensation 
15  Total benefits 
16  Net benefits / costs 

521.7 

195.3   

441.0 

259.2   

895.5 

373.8 

182.1 

10.2 

3.6 

27.6 

9.6 

0.7 

0.2  560.3 

195.5 

2.8   

5.7   

441.0 

3.5 

3.5   

9.2   

32.4   

15.3   

456.3 

15.5 

274.2 

5.3 

0.3   

3.8 

0.2 

38.1 

10.4 

0.8   

0.8 

-8.8 

0.1   

0.8   

0.9 

0.3 

203.9   

444.5 

301.6   

0.0   

0.0  950.0 

-0.2  389.7 

444.5 

16.4 

460.9 

265.4 

Table 11: PO1: Estimated differential cost and benefit for data reuse and crisis response62 

62 As compared with the baseline option (PO0) 

74 

 
 
  
  
  
  
  
  
  
  
                                                 
The  additional  benefits  for  reuse  of  privately  held  data  and  the  crisis  response  as 
compared  to  the  baseline  option  outweigh  the  costs  for  both  statistical  offices  and 
businesses  as  compared  to  the  baseline  option.  The  net  benefits  of  EUR  389.7  Million 
(J16) for the statistical system and EUR 265.4 Million (K16) for the business sector are 
higher than for the baseline option due to more intensive use of the measures. The direct 
costs for the data holders for the cases producing new statistics exceed their benefits as 
there is no burden reduction effect for the business sector. 

A 

4 Stat. products / domains 

1  ESS data sharing 
2 
3  Burden reduction 
4  Savings through running a central system 
5  Costs of running a central system 
6  Total 

B 

Million EUR 

116 
23 
-0.7 
138.3 

Table  12:  PO1:  Estimated  differential  benefits  and  costs  of  the  measure  on  mandatory 
ESS data sharing63 

I. Overview of Benefits (total for all provisions) – Preferred Option 

Description 

Amount 

Direct benefits 

Comments 

Quality 
outputs 

improvements  of  statistical 

Volume of statistics is increasing 

204  million  (Table  11, 
J11) 
302  million  (Table  11, 
J13) 

More granular statistics 

more statistical outputs 

Increase in timeliness of statistics 

- 

not  quantified,  but  estimated  big  effects  in 
times of crises 

to 

More  data  available 
purposes 
Central production of statistics leads to 
increased coherence  

research 

not quantified 

not quantified 

Indirect benefits 

Efficiency  gains  through  better  policy 
decision 

not quantified 

Society  overall  would  benefit  from  direct 
European  actions  and  data  sharing  due  to 
better  quality  (granularity  and  timeliness)  of 
informed  policy 
statistics  enabling  better 
decisions.  

63 As compared with the baseline option (PO0) 

75 

 
 
  
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
                                                 
Efficiency  gains 
data governance and stewardship 

through 

improved 

not quantified 

Efficiency  gains  for  businesses  due  to 
better informed economic decisions 

not quantified 

More  efficient  data  sharing  and  increased 
interoperability between data spaces leading to 
increased quality of statistics 

More  statistical  output  and  improvements  in 
quality  (time  and  granularity)  can  be  used  by 
businesses  for  taking  informed  decisions.  All 
this  effect, 
enterprises  will  benefit  from 
especially  SMEs  as  they  will  usually  not  be 
able to reuse data from new sources. 

Administrative cost savings related to the ‘one in, one out’ approach 

Burden reduction on businesses due to 
mandatory data sharing 

116 million (Table 12, B3) 

Lower  sample  sizes  result  in  reduction  of 
burden on businesses and citizens 

Savings  for  the  ESS  due  to  lower 
survey sizes 
Burden reduction on businesses due to 
lower survey sizes 

445  million  (Table  11, 
J12) 
445  million  (Table  11, 
K12) 

Savings  for  the  ESS  due  to  running  a 
central  system  induced  by  mandatory 
data sharing 

23 million (Table 12, B4) 

Elimination  of  duplicate  data  collections 
across member States 
Data  will  be  processed  at  central  servers 
instead  of  national  data  processing.  This  type 
of cost savings is also included in B2G4S and 
urgent  user  demands.  In  these  cases,  savings 
are  hypothetical  as  related  systems  are  newly 
created.  Cost  efficiencies  could  be  quantified 
in  comparison  to  implementations  in  each 
Member State. 

Table 13: Overview of benefits – preferred option64  

II. Overview of costs – Preferred option 

B2G4S (national 
implementaitons) 

B2G4S (European 
implementations) 

Urgent Demand (national 
implementation) 

Urgent Demand (European 
implementations) 

Cost for mandatory data 
sharing 

Direct adjustment costs 

Direct administrative costs 

Direct adjustment costs 

Direct administrative costs 

Direct adjustment costs 

Direct administrative costs 

Direct adjustment costs 

Direct administrative costs 

Direct administrative costs 

64 As compared with the baseline option 

76 

Businesses 

Statistical Offices 

One-off 

Recurrent 

30.6 

- 

0.6 

1.6 

- 

0.0 

- 

18.4 

133.1 

0.4 

2.6 

1.0 

7.0 

0.0 

0.2 

One-
off 

30.6 

- 

0.6 

- 

1.6 

- 

0.0 

- 

2.4 

Recurrent 

165.2 

325.9 

3.2 

6.4 

8.7 

17.3 

0.2 

0.4 

1.4 

 
  
  
  
  
 
 
  
  
  
  
                                                 
Indirect costs 

- 

- 

- 

- 

Costs related to the ‘one in, one out’ approach 

Direct adjustment costs  

32.9 

162.7 

Total   

Indirect adjustment costs 

Administrative costs (for 
offsetting) 

Table 14: Overview of costs – preferred option65 

The 
of  
figures 
Table 14 are calculated on the basis of the model that produces the figures in Table 11 by 
categorising them as adjustment or administrative costs. Costs related to preparations and 
methodological  development  are  considered  as  adjustment  while  the  other  costs 
(organisational,  operational  and  infrastructure)  are  categorized  as  administrative  costs. 
The costs and benefits between statistical offices and the business sector are distributed 
according to the following Table 15. 

NSI / Eurostat 

Data holder 

Preparation costs 
Organisational 
Meth. Development 
Infrastructure 
Operational 
Compensation 
Quality improvements 
Savings / burden red. 
Additional outputs 

adjustment 
administrative 
adjustment 
administrative 
administrative 
administrative 

Benefits 

0.5 
0.8 
0.9 
0.7 
0.7 
0 
 1 
0.5 
 1 

0.5 
0.2 
0.1 
0.3 
0.3 
1 
0  
0.5 
 0 

Table 15:  PO1 – Distribution of costs and benefits between statistical offices and data 

holders 

Policy option 2 (PO2) 

For  policy  option  3,  a  total  of  5  cases  of  reuse  of  privately  held  data  (B2G4S)  and  10 
cases  of urgent  user data demands  in  times crises would be  realised within the next  10 
years. Out of the total of these 15 cases (5 B2G4S and 10 urgent user demands in times 
of  crises),  6  would  be  realised  at  European  level  of  which  4  are  cases  of  urgent  user 
demands  in  times  of  crises.  We  assume  that  8  cases  would  produce  completely  new 
statistical products and in 7 cases existing statistical products would be enhanced. Only 
for the latter cases, there would be burden reduction on the enterprise sector. In contrast 
to  PO1, the majority of  use cases under PO2 relates  to  the policy measures that aim to 
address  urgent  user  demands  in  times  of  crises.  The  increase  is  motivated  by  the  latter 
mechanism.  The  number  of  10  cases  seems  to  be  plausible  based  on  recent  experience 
with an increase in the demand of urgent user needs to provide statistics for high priority 

65 As compared with the baseline option 

77 

 
  
  
  
  
  
  
  
  
  
  
  
 
  
  
  
  
 
                                                 
policy  areas.  This  increase  as  compared  to  PO1  is  motivated  by  measure  3.5,  which 
allows initiating statistical actions in response to urgent user demands other than crises. 
In contrast to PO1, we would expect that data collections due to this mechanism would as 
well lead to improving existing statistical products having a temporary effect of burden 
reduction  on  businesses.  We  estimate  the  cases  of  reuse  of  privately  held  data 
considerably lower than for PO1 because of the missing consultation phase, which would 
lead to better quality of data reuse demands by statistical offices, and the missing dispute 
resolution mechanisms, which would lead to more court cases due to complaints by data 
holders. Moreover, businesses may be troubled by the lack of safeguards and incurrence 
of  costs  without  compensation.  There  is  evidence  for  such  a  likely  development  in  the 
stakeholder interviews66. 

In addition, it is assumed that 6 cases of mandatory data sharing within the ESS would be 
implemented  within  10  years  after  entry  into  force  of  the  amended  regulation.  We 
assume  a  higher  number  of  EU  data  sharing  activities  as  this  data  sharing  would  also 
contribute  to  improved  quality  of  European  statistics  based  notably  on  coherence  and 
comparability across countries. 

EU data 
sharing

Reuse of privately held data

Crisis and urgent user demands

(5 cases)

(10 cases)

6 cases

at national 
levels

at EU level with 
EU data hub

at national 
level

at EU level with 
EU data hub

(3 cases, no 
new products)

(2 cases, of 
which 2 new 
products) 

(6 cases, of 
which 3 are 
new products)

(4 cases, of 
which 3 are 
new products)

Figure 5: PO2 - Application of statistical products / use cases 

A 
Direct cost and benefit 
in Million EUR 

B 

C 

D 
B2G4S 

E 

F 

G 

I 
Crisis and urgent user demands 

H 

J 

K 

Total direct benefits 
and costs 

1 

2 

3 

4 

at national level   at EU level (Eurostat 

at national level   at EU level (Eurostat 

Total 

ESS 

Businesses 

ESS 

as hub) 
Businesses 

ESS 

Businesses 

ESS 

as hub) 
Businesses 

ESS 

Businesses 

3 Stat. products / 
domains 

2 Stat. products / 
domains 

6 Stat. products / 
domains 

4 Stat. products / 
domains 

5  Preparation costs 

6  Organisational 

7  Meth. Development 

8 

Infrastructure 

9  Operational 

6.3 

6.3 

34.0 

27.0 

24.0 

6.3 

3.1 

3.8 

20.3 

17.8 

0.4 

0.4 

2.2 

1.8 

1.6 

0.4 

0.2 

3.2 

3.4 

0.2  17.5 

1.2  14.6 

1.0  12.2 

3.2 

1.5 

1.9 

9.7 

8.1 

0.0 

0.0 

0.1 

0.1 

0.1 

0.0  10.0 

0.0  10.2 

0.0  53.8 

0.1  43.5 

0.1  37.8 

10.0 

4.8 

6.0 

31.3 

27.0 

66 This development can be observed in the case of the new Norwegian statistics regulation, which misses 
this mechanism. 

78 

 
 
 
  
  
  
  
  
  
                                                 
14  None-compensation 

0.8   

0.8 

10  Total costs 

97.7 

51.3 

6.3 

3.1  50.9 

25.3 

0.3 

0.2  155.2 

79.8 

11  Quality improvements 

44.1   

12  Savings / burden red. 

138.6 

81.9   

13  Additional outputs 

37.8   

15  Total benefits 

16  Net benefits / costs 

220.5 

122.8 

81.9 

30.6 

1.4   

8.0   

9.4   

3.1 

17.0   

34.0 

42.1   

93.2 

-3.1  42.3 

0.2   

62.7   

22.7 

0.8 

0.6  173.5 

105.1 

-0.1   

87.8   

22.7 

-2.6 

1.0 

0.7 

0.6  324.0 

0.3  168.8 

105.1 

25.3 

Table  16:  PO2  –  estimated  differential  costs  and  benefits  for  data  reuse  and  crises 
response67 

The additional total net benefits of PO2 for the cases of reuse of privately held data and 
responses to user demands in times of crises as compared to the baseline option are much 
lower (168.8 Million (J16) for statistical offices and 25.3 Million (K16) for businesses) 
than  those  of  PO1.  This  is  mainly  due  to  the  lower  number  of  cases.  In  addition,  the 
relatively higher number of cases in which new statistical output is produced does result 
in  lower  burden  reduction  of  the  business  sector.  Finally,  the  data  holders  are  in  this 
scenario not compensated for any cost related to making data available for reuse.  

adjustment 
administrative 
adjustment 
administrative 
administrative 

Preparation costs 
Organisational 
Meth. Development 
Infrastructure 
Operational 
Additional outputs 
Quality improvements  Benefits 
Savings / burden red. 

NSI / ESTAT 

Data holder 

0.5 
0.7 
0.9 
0.6 
0.6 
 1 
0.6 
 1 

0.5 
0.3 
0.1 
0.4 
0.4 
0  
0.4 
 0 

Table 17:  PO2 – Distribution of costs and benefits between statistical offices and data 

holders 

In PO2, the distribution of cost and benefits puts a higher share of cost on data holders 
due  to  additional  obligations  on  the  data  holders,  such  as  the  appointment  of  data 
stewards, additional requirements regarding metadata and quality documentation, and the 
lack  of  consultations.  At  the  same  time,  statistical  offices  would  have  higher  benefits 
from improved data documentation. Finally, all costs of data access have to be carried by 
the data holders. Compensation mechanisms are not foreseen. 

A 

B 

67 As compared with the baseline option (PO0) 

79 

 
  
  
  
  
  
  
  
  
 
 
  
  
  
  
 
 
 
                                                 
6 Stat. products / domains 

1  ESS data sharing 
2 
3  Burden reduction 
4  Savings through running a central system 
5  Costs of running a central system 
6  Total 

Million EUR 

151 
27 
-1.3 
176.7 

Table 18:  PO2  -  Estimated  differential  benefits  of the measure on mandatory ESS data 
sharing68 

Due  to  the  mandatory  of  participation  in  ESS  data  sharing  activities,  we  assume  50% 
more  cases  of  data  sharing.  However,  we  assume  a  lesser  increase  in  savings  due  to 
burden reduction, which are based on the assumption that mirror data collections can be 
avoided or at  least  reduced. We assume that the ESS would concentrate  on those cases 
with highest burden reduction. Hence, the more data sharing cases are implemented the 
lower are the efficiency gains per additional case. 

PO2 contains a measure providing access to the data shared by data holders for research 
purposes. This is an additional burden on statistical offices, which would anyhow process 
micro data, which is used to produce aggregate statistics. The following Table 19 shows 
the  additional  cost  for  statistical  offices  due  to  this  measure.  It  is  assumed  that  the 
number of access requests is 50 per year per statistical office for these kinds of data. The 
assumptions  are  motivated  through  experience  of  Eurostat  executing  this  service  for 
traditional  statistics.  The  estimated  access  requests  of  this  category  are  12.5%  of  the 
actual total of annual requests69. 

Access to microdata for 
research  

(1000 EUR) 

Staff cost 

Infrastructure cost 

Other cost 

Total 

NSIs  Eurostat  Total 

1283 

48  1330 

675 

192 

25 

700 

7 

200 

2150 

80  2230 

Table 19:  PO2 - Estimated costs of additionally providing access to shared raw data for 

research purposes 

68 As compared with the baseline option (PO0) 
69 The assumptions are motivated by the figures in the impact assessment for the proposal of the Data  Act 
and  by  experiences  of  Eurostat  related  to  supplying  a  service  of  data  access  to  confidential  data  for 
research.  
European  Commission,  impact  assessment  Report  accompanying  the  Proposal  for  a  Regulation  on 
harmonised rules on fair access to and use of data (Data Act), SWD(2022) 34 final, page 106 

80 

 
  
 
 
 
                                                 
3.  RELEVANT SUSTAINABLE DEVELOPMENT GOALS 

The  preferred  PO1  covers  various  measures  to  improve  the  EU  level  data  evidence 
potentially  relevant  for  several,  or  even  all,  sustainable  development  goals  (SDGs). 
However, impacts in terms of actual progress towards any of the SDGs stemming from 
improved data evidence are naturally indirect and hard to assess given that the initiative 
at  hand  is  a  framework  regulation  intended  to  improve  the  data  availability, 
responsiveness  and  data  sharing  generally,  which  will  only  manifest  itself  in  sectoral 
legislation. For instance, if used for improved SDG policy-making, actual progress would 
largely depend on dispersed contextual factors and on the sensitivity of policy impacts on 
the availability and quality of data evidence. Based on the use cases in Annex 6, Table 20 
below identifies SDGs that could potentially benefit from this initiative.  

Table 20: Overview of relevant Sustainable Development Goals – Preferred Option 

III. Overview of relevant Sustainable Development Goals – Preferred Option(s) 

Relevant SDG 

Comments 

Expected 
progress 
towards the 
Goal 

1 – No poverty 

8  –  Decent  work  and 
economic growth 

9 – Industry, innovation and 
infrastructure 

11  –  Sustainable  cities  and 
communities 

17  –  Partnership  for  the 
goals 

  Improving data from farms  and the 

agricultural sector 

  Improving  data  the  labour  force, 
data 
living 
income 
conditions  and  data  from  national 
accounts.  

and 

on 

  Improving 

regarding 
statistics 
goods,  services  and  foreign  direct 
investments,  as  well  as  regarding 
business  statistics  and  digital  trade 
and cross-border data flows 

  integrating  geospatial  data  and 
statistical  quantitative  data,  mostly 
at a regional and subregional level. 
regional 

urban 

or 

  improving 
statistics. 

  improving  population  grids  of 
importance for policy areas such as 
transport,  mobility 
the 
environment. 

or 

  increasing availability of sound data 
and  measures 
the 
accountability, which can serve as a 
model 

to  promote 

81 

 
 
ANNEX 4: ANALYTICAL METHODS 

Regulation (EC) No 223/2009 is a framework regulation, the revision of which will have 
its  main  effect  through  subsequent  other  legislation,  at  both  the  EU  and  national  level. 
The analytical methods of assessing the impact of the revision take this into account. Not 
only will the revision have elements that need to be worked out subsequently (see chapter 
8), but it will also not identify the data sources that will actually be reused for statistical 
purposes. That will be done through the Annual Work Programme for European statistics 
(AWP). Every time a statistic is added to the AWP that makes use of privately held data, 
either  as  its  main  source  or  as  a  supplementary  source,  this  will  require  a  convincing 
justification,  of  which  an  assessment  of  the  costs  and  benefits  to  society,  with  a 
breakdown  to  groups  of  stakeholders,  will  be  part.  When  executing  the  AWP,  specific 
data holders will be identified and requested to enable the reuse of the data held by them. 
Such requests must also be explicitly justified.   

For assessing the impact of the revision under these conditions, a use case approach has 
been taken. Based on earlier research (see Annex 1.4), a number of use cases of potential 
use of new data sources for official statistics were derived. This was already done in the 
context of the impact assessment for the Data Act. For some use cases, it was possible to 
make  an  assessment  of  the  associated  costs  and  benefits.  Depending  on  assumptions 
about  the  eventual  extent  of  using  new  data  sources  in  official  statistics  and  their 
similarity to use cases already assessed, total costs and benefits for regulating the use of 
new  data  sources  for  official  statistics  could  be  estimated,  albeit  assumption-based  and 
with  a  large  margin  of  error  (see  Annex  3).  The  use  cases  that  fed  into  the  impact 
assessment for the Data Act were also input to the study carried out by ICF, which used 
them, together with additional use cases, for their in-depth interviews with stakeholders 
(see Annex 2), to get a more concrete view on the obstacles, the potential costs to data 
holders  and  producers  of  statistics,  and  the  potential  benefits  for,  among  other  things, 
policymaking. The remainder of this annex describes the analytical methods of the ICF 
study and the analytical approach taken to the assessment of costs and benefits of Annex 
3, i.e., of what has been used from the impact assessment for the Data Act. 

Analytical methods of the ICF study 

The analytical methods used by ICF in their study are described in [ICF, Annex 5]. All 
the  data  collection  tools  in  that  study  were  designed  to  collect  both  quantitative  and 
qualitative evidence. In particular, questions for relevant cost items were included in the 
online  survey  to  aim  for  a  full  standard  cost  model  (SCM).  An  SCM  is  achieved  by 
estimating the impact on costs on the relevant population of relevant stakeholders. This 
impact corresponds to the formula PQT: Price (in EUR of the additional cost/burden) * 
Quantity  (population  of  products,  people,  etc.)  *  Time  (one-off  or  recurring).  This 
modelling  approach  requires  extensive  granular  data  on  the  specific  segment  of  the 
population and/or business sectors, monetary values of costs or additional FTEs. The ICF 
methodology was designed to follow the traditional steps of an SCM: 

1.  identifying the additional burdens/costs; 
2.  identifying target group and/or sector(s);  
3.  identifying the frequency of costs and parameters;  
4.  design survey to gather all the aspects in steps 1 to 3; 
5.  collect data and prepare template for the analysis and reporting; 

82 

 
6.  reporting. 

However, the data gathered in the online survey were not sufficient to feed the model to 
estimate the costs. Although the questionnaire included questions on quantitative impacts 
to  allow  for  quantification,  respondents  were  mostly  unable  to  provide  quantitative 
figures.  It  is  known that some of the challenges  related to  the cost  and benefit analysis 
usually include: 

  Small sample size for robust estimations; 
  Stakeholders unable to provide coherent quantifications of cost items; 
  Selection bias of responses; 
  Difficulty in  establishing  causal  links between costs and benefits  because of the 

broad nature of policy options; 

  Consistency across results with testable assumptions. For example, if the sample 
is skewed towards a specific category of stakeholders, should the first item in the 
list  render  a  re-sampling  impossible,  then  generalising  assumptions  would  be 
unfeasible. 

While  the  ICF  study  was  designed  to  avoid  these  issues,  they  still  emerged  during  the 
data  collection.  Interviews  for  use  cases  provided  some  quantitative  estimates  of  costs 
and  benefits,  but  hardly  generalizable  as  they  were  dependent  on  use  cases’ 
characteristics,  namely  geography,  stakeholders  involved,  timing,  digital  infrastructures 
and business processes. The targeted survey was designed to gather both quantitative and 
qualitative  estimates,  but  stakeholders  again  could  not  directly  provide  quantitative 
figures  on  costs  and  benefits.  While  respondents  could  not  quantify,  they  provided  a 
systematic  assessment  (using  a  1-10  Likert  scale)  of  impacts,  which  was  used  for  the 
qualitative  appraisal.  Moreover,  the  small  sample  size  (N=27)  would  have  made  any 
quantitative estimate not entirely robust. Therefore, costs and benefits for this study had 
to  rely  on  qualitative  evidence,  including  and  especially  the  anecdotal  figures  coming 
from the use cases.  

An  important  example  of  qualitative  cost  and  benefit  information  is  the  use  case  on 
financial  transactions  data.  In  this  use  case,  the  Norwegian  NSI  has  an  ongoing  data 
sharing  activity  with  a  company  called  Vipps.    The  aim  is  to  collect  debit  card 
transactions  data  with  the  objective  of  gathering  information  about  the  retail  sector  in 
Norway. The cost-benefit analysis conducted by the Norwegian NSI70 found that despite 
the total costs for the private data holder in terms of development and data delivery (1 or 
2  months  of  full-time  work),  the  data  sharing  would  reduce  the  overall  burden  on  the 
Norwegian business sector by up to 3.400 hours of work, equalling almost 23 months of 
cumulative work. This example clearly shows that, in this specific use case, the benefits 
of  mandatory  data  sharing  (for  all  stakeholders,  including  the  business  sector,  when 
considered in its entirety) clearly outweigh the costs.  

Analytical  methods  of  the  costs  and  benefits  analysis  used  as  input  to  the  impact 
assessment for the Data Act 

70 Norway is one of the few countries that has a statistics act that mandates data collection from private data holders. 
According  to  the  Norwegian  Statistics  Act,  the  Norwegian  NSI  needs  to  conduct  a  cost-benefit  analysis  before 
compelling private data holders to deliver data. 

83 

 
                                                 
The study on “Methodological support to impact assessment of using privately held data 
by  official  statistics”  mentioned  earlier71  offered  an  indicative  example  of  data  sharing 
with  MNO  data  in  the  context  of  Covid-19  and  scanner  data72.  It  argued  that  reusing 
privately held data by public producers of official statistics yields benefits for NSIs, for 
society in general and for the business community.  

Figure  6:  Visual  representation  of  B2G  data  sharing  chains  for  official  statistical 
production, and of the benefits and the B2B data sharing chains they generate 

The study stated that smarter lockdown measures would result from reusing mobility data 
coming from MNOs: “If the use of MNO data could make things efficient and save one 
week  of  work  it  would  reduce  the  damage  by  roughly  0.2%  of  GDP,  which  is 
substantial.”  Moreover,  if  an  NSI  could  institute  a  long-term  established  process  with 
recurring  outputs,  costs  can  be  as  low  as  EUR  1  million,  “which  would  be  a  tiny  and 
insignificant  fraction compared to  the benefits  – particularly  under COVID conditions” 
(p23). For scanner data, the study estimated that the balance of benefits and costs would 
even be higher. 

The study also proposed two alternative methods for quantifying B2G4S: bottom-up and 
top down. Using the bottom-up approach and the above estimates on scanner data73, the 
study  suggested  that  this  type  of  data  sharing  for  12  data  sources  in  each  of  the  27 
Member  States  could  add,  in  total,  approximately  6.7%  to  the  data  economy,  which  is 
estimated at 3.5% of GDP. Employing a bottom-down approach, the study calculated that 
if the amount of official statistics is increased by 20% because of B2G data sharing, this 

71 Sciadas G and Stavropoulos P., Methodological support to impact assessment of using privately held data by official 
statistics, Literature review and model, December 2021.  
72  It  also  pointed  out  that  “conclusive  modelling  of  costs  and  benefits  will  undoubtedly  have  to  wait  for  sufficient 
practical experiences to accumulate” (p3).   
73  As  well  as  the  open  data  literature,  which  indicates  that  induced  benefits  can  be  higher  than  direct  benefits  by  a 
factor between 20-50 times. 

84 

 
 
                                                 
will  bring  an  added  value  to  the  economy  of  EU27  +  UK  of  between  EUR  4.3  billion 
(20% of 21.5) and EUR 12.3 billion (20% of 61.3) until 2030. 

To put the quantification in this  impact assessment in perspective, a few other, broader 
studies can me mentioned. The “Study to support an impact assessment on enhancing the 
use  of  data  in  Europe”  concluded  that  the  economic  impact  of  the  policy  measures  to 
foster data reuse as  compared to  the baseline scenario could  imply  an increase in  GDP 
with  273  billion  EUR  (representing  an  additional  1.98%  of  GDP)74.  Another  study 
analysed the size of the data market assuming data products created from processing and 
reusing  data  are  exchanged.  The  impact  of  the  data  economy  is  considered  the  sum  of 
direct,  indirect  and  induced  impacts.  The  study  estimated  an  overall  impact  of  the  data 
economy  on  the  EU27  GDP  of  2.1%  (EUR  243  billion)  in  2016  and  2.2%  (EUR  267 
billion) in 2017. Further projections stated that, by 2025 and under certain assumptions, it 
could grow as high as 5.4% of GDP75. 

ANNEX 5: LEGAL CONTEXT 

The revision of Regulation (EC) No 223/2009 takes place in the following legal context. 

The European Data Strategy put forward by the Commission in February 202076  aims 
to speed up the development of the European economy and to harness the value of data 
for the benefit of the European society. It provides the overall policy context under which 
the  Regulation  (EC)  No  223/2009  has  to  be  analysed  and  the  fitness  of  the  European 
statistics for the digital age has to be assessed.  

The proposal for a Data Act77 adopted by the Commission on 23 February 2022 is one 
of the main  pillars of the European  Data Strategy.  Its  main objective is  to set  the right 
conditions for better control and conditions for data sharing for citizens and businesses. 
The Data Act covers both business-to-business (B2B) and business-to-government (B2G) 
data sharing and sets up rules regarding the use of data generated by Internet of Things 
(IoT) devices. The Data Act proposal introduces the possibility for public sector bodies 
to  access  and  use  data  held  by  the  private  sector  that  is  necessary  for  specific  public 
interest purposes but under strict conditions and only in cases of emergencies and other 
exceptional needs.   

The Data Act proposal follows essentially an internal market logic (as illustrated by its 
legal basis i.e., Article 114 TFEU, whose objective is the establishment and functioning 
of the internal market) and clearly states that it is  without prejudice to rules addressing 
needs specific to individual sectors or areas of public interest and that it does not affect 
obligations laid down in Union or national law for the purposes of reporting, complying 

74  Deloitte,  The  Lisbon  Council,  JIIP,  GovLab,  Timelex,  Odi  (2022)  “Study  to  support  an  impact  assessment  on 
enhancing the use of data in Europe”.  
75 The European data market study update  
76 
age/european-data-strategy_en  
77  Proposal  for  a  Regulation  of  the  European  Parliament  and  of  the  Council  on  harmonised  rules  on  fair 
access to and use of data (Data Act). 

https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/europe-fit-digital-

See 

85 

 
 
                                                 
with  information  requests  or  demonstrating  or  verifying  compliance  with  legal 
obligations.  

The  Data  Governance  Act  (DGA)78   is  another  important  pillar  of  the  European  Data 
Strategy  and  will  be  applicable  from  September  2023.  It  is  a  cross-sectoral  instrument 
that aims to promote the availability of data and build a trustworthy environment for data 
sharing  to  facilitate  the  use  of  data  for  the  creation  of  innovative  new  services  and 
products  as  well  as  research.    It  also  aims  to  encourage  the  development  of  technical 
solutions,  such  as  anonymization,  pseudonymization  or  accessing  data  in  secure 
processing  environments  (e.g.,  data  rooms)  supervised  by  the  public  sector.  In  that 
context,  a  number  of  'common  European  data  spaces'  will  be  established,  such  as  for 
health,  the  Green  Deal,  and  energy.  They  will  facilitate  data  pooling  and  sharing  by 
creating  infrastructure  and  governance  frameworks  to  promote  data-driven  innovation. 
The DGA also establishes a European Data Innovation Board, which will address issues 
such as data interoperability between data-sharing organisations. Creating the necessary 
tools and processes for data sharing could be expected to benefit data sharing regardless 
of its direction, whether it is G2B or B2G.  

The  Interoperable  Europe  Act  proposal79  recently  adopted  by  the  Commission,  is  an 
act which aims to strengthen cross-border interoperability and cooperation in the public 
sector  across  the  EU.  The  goal  of  the  Act  is  to  move  beyond  the  current  voluntary 
cooperation  on  interoperability  in  the  EU  by  establishing  a  structured  EU  cooperation 
where  public  administrations,  supported  by  public  and  private  actors,  come  together  in 
the  framework  of  projects.  The  Act  also  establishes  mandatory  interoperability 
assessments  to  evaluate  the  impact  of  changes  in  information  technology  systems  on 
cross-border interoperability in the EU, foresees a revision of the Europe Interoperability 
Framework and will promote share and reuse of solutions, often open source, through an 
Interoperable Europe Portal.  

The  INSPIRE  Directive80,  establishing  an  infrastructure  for  spatial  information  in 
Europe  to  support  Community  environmental  policies,  and  policies  or  activities  which 
may have an impact on the environment, entered into force in May 2007 with a view to 
ensuring  that  the  spatial  data  infrastructures  of  the  Member  States  are  compatible  and 
usable in a Community and transboundary context. The Directive requires that common 
Implementing  Rules  (IR)  are  adopted  in  a  number  of  specific  areas  (Metadata,  Data 
Specifications,  Network  Services,  Data  and  Service  Sharing  and  Monitoring  and 
Reporting). 

The ongoing discussions on the proposal for an ePrivacy Regulation are also relevant 
for this initiative. One of the main objectives of the proposal is to guarantee privacy rules 
for  new  players  providing  electronic  communications  but  also  for  communications 
content  and  metadata.  Additionally,  the  Regulation  is  expected  to  foster  innovation  as 

78 Regulation (EU) 2022/868 of the European Parliament and of the Council of 30 May 2022 on European 
data governance and amending Regulation (EU) 2018/1724.  
79  Commission  proposal  for  a  Regulation  of  the  European  Parliament  and  of  the  Council  laying  down 
measures  for  a  high  level  of  public  sector  interoperability  across  the  Union  (Interoperable  Europe  Act), 
COM(2022) 720 final, adopted on 18 November 2022. 
80 Cf. Directive 2007/2/EC of the European Parliament and of the Council of 14 March 2007 establishing 
an Infrastructure for Spatial Information in the European Community (INSPIRE), OJ L 108, 25.4.2007, p. 
1. 

86 

 
                                                 
once  consent  is  given  for  communications  data  to  be  processed,  traditional  telecoms 
operators will have more opportunities to provide additional services and to develop their 
businesses. This could support the use of data analytics and data sharing for the purpose 
of the production of official statistics as well. 

With the General Data Protection Regulation (GDPR)81 and the ePrivacy Directive82, 
the EU has put in place a solid and trusted legal framework for the protection of personal 
data.  More  specifically,  Article 5(1),  point  (b),  of  the  GDPR  provides  that  the  further 
processing  of  personal  data  for  scientific  or  historical  research  purposes  or  statistical 
purposes  should, in  accordance with  Article 89(1) of Regulation  (EU) 2016/679,  not  be 
considered to be incompatible with the initial purposes.  

The revision of Regulation (EC) No 223/2009 should ensure that any B2G data-sharing 
partnership  for  the  sake  of  compiling  official  statistics  that  will  include  personal  data 
shall be carried out in full compliance with GDPR. Namely that individuals will continue 
to be empowered to take control of their data by exercising their rights under the GDPR, 
namely  giving  and  revoking  consent,  requesting  data  access  and  erasure  of  data  and 
requesting  data  portability.  In  2018,  the  European  Commission  published  a  set  of 
principles on B2G data sharing in its communication ‘Towards a common European data 
space’83 and accompanying staff working document ‘Guidance on sharing private-sector 
data  in  the  European  Data  Economy’84.  These  documents  defined  six  principles  that 
could support the supply of private-sector data to public-sector bodies under preferential 
conditions  for  reuse,  such  as,  proportionality,  purpose  limitation,  transparency  and 
accountability. These principles are aligned with the ones provided by the GDPR.  

Finally,  the  Single  Market  Emergency  Initiative  (SMEI)85  aims  to  put  in  place  a 
flexible  and  transparent  mechanism  to  respond  quickly  to  emergencies  and  crises  that 
threaten  the  functioning  of  the  single  market.  It  aims  to  ensure  the  coordination, 
solidarity  and  coherence  of  the  EU  crisis  response  and  protect  the  single  market’s 
functioning.  The  instrument  would  complement  other  policy  tools  to  anticipate  and 
prevent  disruptions,  where  possible,  and  would  also  prepare  for  and  respond  to 
unavoidable  crises,  which  have  important  cross-border  effects  and  threaten  the 
functioning of the Single Market. The proposal currently foresees the possibility for the 
Commission to invite or request under certain conditions, targeted information from the 
economic operators in crisis-relevant supply chains, for the management of the SMEI or 
for compiling relevant official statistics. 

81  Regulation  (EU)  2016/679  of  the  European  Parliament  and  of  the  Council  of  27  April  2016  on  the 
protection of natural persons with regard to the processing of personal data and on the free movement of 
such data, and repealing Directive 95/46/EC (General Data Protection Regulation). 
82  Directive  2002/58/EC  of  the  European  Parliament  and  of  the  Council  of  12  July  2002  concerning  the 
processing of personal data and the protection of privacy in the electronic communications sector.  
83 See https://digital-strategy.ec.europa.eu/en/news/communication-towards-common-european-data-space  
84 See https://digital-strategy.ec.europa.eu/en/policies/private-sector-data-sharing  
85  See  Proposal  for  a  Regulation  of  the  European  Parliament  and  of  the  Council  establishing  a  Single 
Market emergency instrument and repealing Council Regulation No (EC) 2679/98.  

87 

 
 
 
                                                 
ANNEX 6: ASSESSED USE CASES 

Introduction 

This  annex  describes  the  results  of  extensive  in-depth  interviews  by  ICF  with  various 
stakeholders: mainly private data holders, institutional users of statistics, and NSIs. With 
each  of  the  data  sources  limited  explorative  experiments  had  been  carried  out  in 
partnerships between data holders and NSIs. The assessment includes the following five 
digital data sources: 

1.  data from Mobile Network Operators (MNOs) 
2.  financial transactions data  
3.  data from smart meters  
4.  data from smart devices (mainly from the IoT), and 
5.  data from collaborative economy platforms 

Before summarising the results, it is important to understand the relationship between the 
role  of  various  categories  of  stakeholders  and  costs  and  benefits.  Figure  5  provides  a 
model of stakeholders [ICF, section 2.5]: 

Figure 7: Model of stakeholders related to the ESS [source: ICF] 

Benefits  and  costs  are  incurred  by  different  types  of  stakeholders  and  relate  to  actions 
taken by those stakeholders (not necessarily the same ones). For European statistics, the 
process  starts  with  the  private  holders  of  data  (Figure  5,  top  left),  who  incur  costs  for 
complying  with  the  requests  from  the  statistical  office;  this  interaction  may  result  in 
benefits as well. The national statistical authority produces statistics, which incurs costs. 
The users of European statistics a heterogeneous  group, of which every  user can  enjoy 
direct benefits and incur costs by making use of these statistics. These uses, in particular 
to the extent that they influence or even shape policies, will influence all stakeholders in 

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society  that  are  subject  of  those  policies,  leading  to  (indirect)  benefits  and  costs 
pertaining to those stakeholders. 

Summary of the results of the five use cases 

Types of data and scope  

The  use  cases  provided  valuable  examples  on  the  type  of  new  digital  data  sources  that 
can be re-used to generate official statistics: 

  MNOs:  network signalling data  were used to  understand population  distribution 
across  the  national  territory  in  the  context  of  Covid-19;  MNO’s  data  were  also 
used to produce mobility statistics on commuting and tourism; 

  Financial transaction data: debit card transactions were used to produce statistical 

indicators on the retail sector in one country; 

  Smart meters: energy data coming from smart meters informed the total amount 
of electricity being consumed and, when combined with other sources, provided 
insights into new residential construction, the energy efficiency of buildings; they 
were also used to better understand where more work is needed to install meters 
and to move residential dwellings away from solid fuel use.  

  Smart devices (IoT): traffic, air quality, and noise data gathered from IoT devices 

were used to understand levels of pollution within and across regions; 

  Collaborative economy platforms (tourism): accommodation bookings aggregated 

at the city level were used to better understand trends in tourism. 

Operational implementation  

There  were  several  themes  emerging  from  the  five  use  cases  on  the  operational 
implementation of these data sharing activities: 

  The heterogeneous nature of these B2G4S activities makes it difficult to highlight 
commonalities  in  their  operational  implementation.  An  assessment  of  each  use 
case separately was provided by ICF [ICF, chapter 6]. 

  Different  legal  frameworks were used to  implement  B2G4S  activities, including 
specific  public  contracts,  mandatory  data  delivery  based  on  a  national  statistics 
law,  a  cooperation  agreement  and  a  non-disclosure  agreement.  This  extreme 
variety  of  legal  frameworks  reiterates  the  above  point  about  the  current 
heterogeneity of data sharing activities, reinforcing the argument on the need of 
further harmonization in the EU.  

  The  level  of  effort  required  to  implement  the  data  sharing  activities  was 
dependent  on  the  novelty  of  the  collaboration:  in  one  use  case  (financial 
transaction  data),  the  operational  implementation  required  less  efforts  because 
stakeholders  had  already  previously  collaborated  and  had  already  the  technical 
infrastructure in place; in another use case (MNOs), the data sharing required the 
private  data  holders  to  develop  a  series  of  algorithms  with  a  guidance  from  the 
NSI. One smart meter use case showed that it could take up to five years for data 
sharing  collaborations  to  be  established  and  ensure  they  meet  all  the  legal 
obligations and safeguards (especially in relation to data protection). 
In  one  use  case  (financial  transactions  data),  the  NSI  needed  to  produce  a  cost-
benefit analysis before persuading the company to deliver the data. This analysis 
had to show evidence that benefits of data sharing outweighed its costs. 

 

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  Stakeholders  always  put  strong  emphasis  on  secure  data  sharing  and  data 
anonymity (to ensure users’ data were not identifiable), suggesting these are key 
factors in the implementation of any data sharing activity. For NSIs this is already 
standard practice. 
In at least two use cases, the data sharing agreement was facilitated by Eurostat, 
highlighting  the  potential  role  of  the  institution  in  coordinating  any  future  data 
sharing activity with the objective of understanding cross-border EU phenomena. 

 

Incentives and benefits 

Use  cases  underlined  that  all  main  stakeholders  (statistical  users  and  society,  NSIs  and 
private data holders) can benefit from B2G4S data sharing, although in different ways.  

Overall, stakeholders found it difficult to quantify benefits, especially when considering 
more  indirect  ones  such  as  improved  decision-making.  However,  one  useful  indicator 
could  be  the  number  of  direct  data  collections  (administrative  forms  and/or  surveys) 
directed at business and households that can be replaced by data sharing collaboration.   

Statistical users 

When  new  data  sharing  activities  were  able  to  fill  current  knowledge  gaps,  statistical 
users improved their decision-making, ultimately designing better policies for the benefit 
of society and the public good.  

For  example,  data  sharing  with  online  tourism  platforms  can  help  understand  how 
travelling is  resuming post  Covid-19, gaining a  more solid knowledge of the impact  of 
tourism on local, regional, national and European levels.  

In  another  example  on  mobile  network  data,  mobility  indicators  generated  with  MNO 
data  were  extremely  important  to  better  understand  interactions  between  territories  and 
the  daily  behaviours  of  individuals,  which  helped  informing  public  policies  on  spatial 
planning and land use. 

NSIs 

When data sharing activities were built on a solid foundation, they brought clear benefits 
in terms of quantity, quality, granularity, frequency and timeliness of data. For example, 
one NSI assured that these data allow to go far beyond what would be possible to achieve 
through  traditional  data  gathering  methods  such  as  surveys,  primarily  due  to  their 
connected and digital aspect.  In addition to faster access, the data is in some cases also 
more accurate, moving away from estimations and customer submitted entries. This can 
increase the overall reputation of NSIs, if this means an increased capacity to fill current 
information gaps.  

Furthermore,  (automated)  data  sharing  can  also  reduce  the  burden  on  NSIs,  businesses 
and society if these can replace filling lengthy paper or online surveys. For example, one 
NSI was able to cut up to 20,000 individual questionnaires per year, each of them taking 
approximately 10 minutes to deliver. This would reduce response burden for the business 
sector by up to 3.400 hours. 

Private data holders  

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Interactions  with  competent  statistical  officers  can  improve  companies’  internal 
methodologies and overall data quality, which might lead to the possibility of using these 
data  for  commercial  purposes.  In  one  use  case  (financial  transactions  data),  the  data 
sharing was also considered to benefit the company’s commercial interest as being able 
to  tell  their  (paying)  clients  that  they  are  working  with  Eurostat  (or  other  public  sector 
authorities) brings some “gravitas” to their dataset. 

In one use case, MNOs were compensated, but this case was unique among all other case 
studies.  In  general,  there  is  disagreement  among  stakeholders  regarding  what  the  right 
approach should be regarding financial incentives. 

Other important benefits may include compliance with national laws (when data sharing 
is already mandatory) and participation to research projects.  

Companies’  social  corporate  responsibility  managers  could  also  rightly  claim  their 
companies are contributing to improving societies by offering data which help producing 
better policies, which ultimately strongly enhance their reputation. 

Costs 

As  for  the  costs,  quantification  and  generalisation  is  hard,  mainly  because  there  are 
multiple  factors  to  consider  when  designing  and  implementing  a  data  sharing 
collaboration.  For  example,  data  sharing  activities  between  entities  that  have  already 
collaborated  (hence  with  already  established  procedures,  infrastructures,  and  dedicated 
staff) will have much lower costs compared to a data sharing collaboration starting from 
scratch.  

While stakeholders found it difficult to quantify costs, one could use as an indicator the 
number  of  work  hours  each  employee  logged  in  setting  up  and  implementing  the  data 
sharing activity. Some use cases presented anecdotal evidence going in this direction. 

Use  cases  generally  featured  examples  of  data  collaborations  where  all  parties  covered 
their  own  costs.  However,  in  one  case  private  data  holders  were  compensated  for  their 
data processing and analysis services: a three-year contract was stipulated for a value of 
EUR  300.000,  with  subsequent  annual  contracts  worth  EUR  24.000.  Many  NSIs  are 
sceptical about a model in which they would have to pay to reuse privately held data. The 
ESS  is  in  favour  of  introducing  “adequate  mechanisms  and  incentives  to  deal  with  the 
initial investments required and marginal costs that might be incurred by the data holders 
in processing, especially aggregating or running algorithms on the primary data to make 
them ready for use in official statistics”86. 

NSIs generally agreed that costs on their side would be low or manageable, even though 
this  might  vary  according  to  the  level  of  ambition  of  the  data  sharing  activity.  For 
example,  one  NSI  (financial  data  transactions)  needed  a  team  of  5/6  people  for  2/3 
months to set up the data sharing activity, but this was possible because the collaboration 
(with the same company) had already taken place in the past. Another NSI using smart 
meter data reiterated that personnel costs were minimal in comparison to collecting data 

86 ESS position paper on the future Data Act proposal. 

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in  a  traditional  way.  On  the  other  hand,  still  another  NSI  noted  that  a  lack  of  existing 
competences  and  skills  within  the  organisation  meant  they  had  to  hire  a  data  scientist. 
Finally,  NSIs  need  to  have  the  appropriate  resources  to  handle  the  data  coming  from 
private  data  holders  as  there  is  significant  pre-processing  and  cleaning  before  data  are 
useful for statistical purposes. 

Private data holders could incur investment, development and data delivery costs, which 
were anecdotally and occasionally “quantified” in terms of full-time employees involved 
in the data sharing. One company estimated the total costs to be equal to 1 or 2 months of 
full-time work, but this is clearly influenced by the fact that the collaboration (with the 
same  NSI)  had  already  taken  place  in  the  past.  Interviewed  private  holders  usually 
thought costs were low as well. One private data holder in the economy platform use case 
stated  that  costs  are  manageable  if  there  is  one  data  portal,  one  API,  and  one  format. 
Nonetheless, they could become unmanageable if the data holder had to share data with 
each Member State or share it at very short intervals. Generally, use cases showed that 
there  are  companies  which  are  “readier”  (have  the  technological  know-how,  the 
infrastructure,  the  personnel  etc.)  to  engage  in  data  sharing.  For  these  companies,  it 
would be easier to  start  and sustain  a data sharing compared to  other companies which 
are less tech savvy.   

Challenges 

Use  cases  highlighted  several  challenges  that  need  to  be  overcome  to  implement 
successful data sharing collaborations: 

  The absence of a clear legal framework, coupled with the complexity of national 
legislative systems, were considered obstacles for effective data sharing. One NSI 
wanting to access smart thermostat data was unable to overcome the legal issues 
in access. Whilst the private company was keen to collaborate, the absence of a 
clause  within  the  customer  contract  detailing  the  sharing  of  data  for  statistical 
purposes, made data access impossible.  

  Data privacy and security were the most pressing issues across all use cases, with 
private data holders often referring to privacy obligations towards their customers 
as a reason to start or end data collaborations. Companies wanted to avoid giving 
the  impression  that  they  establish  data  sharing  activities  with  public  authorities 
for  the  purpose  of  tracking  individuals.  The  reputational  risk  they  face  if  data 
were  leaked  is  often  seen  as  very  high.  Depending  on  the  nature  of  the  data 
sharing  activity,  other  potential  legal  issues  highlighted  were  related  to  data 
ownership and copyright. 

  Commercial interest was another frequently cited issue, with companies fearing to 
lose their competitive edge when data is shared with a public authority, if 1) their 
technical innovations are revealed/leaked in the process, 2) they already sell data 
commercially.  Interestingly,  one  company  in  the  financial  transactions  data  use 
case  suggested  that  their  commercial  interest  is  not  hurt  as  the  data  they  sell  to 
their clients serve very different purposes.  

  The  compatibility  of  concepts,  measures,  and  methods  could  become  a  big 
obstacle if not harmonized among stakeholders. If data collection definitions and 
methodologies are not transparent, official statistics cannot be produced. 

  Data  quality  could  become  an  issue  when  the  shared  data  are  not  known  to  be 
representative of the whole population; moreover, these types of data often lack 

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socio-demographic  context  (as  opposed  to  surveys,  which  can  collect  those 
contextual data) which limit their potential applications. 

  One-off  or  limited-in-time  data  sharing  activities  were  seen  as  problematic  by 
NSIs, which often require time to ensure that the production of official statistics 
comply with the appropriate standards and regulations. On the other hand, private 
data holders in the mobile network data use case have expressed concerns against 
the  prospect  of  other  public  authorities  asking  them  for  data,  which  over  time 
would create additional burdens on their internal work. 

ANNEX 7: DATA GAPS IN TERMS OF CONTENT, FREQUENCY, 
TIMILINESS AND GRANULARITY OF EUROPEAN STATISTICS  

This  annex  provides  a  summary  of  the  gaps  identified  in  European  statistics.  This  is 
based  on  the  extensive  in-depth  interviews  by  ICF  with  various  institutional  users  of 
statistics. Certain direct effects of the gaps are also summarised per area of policymaking 
as well as instances of potential uses of private data sources that could help closing these 
gaps and their perceived benefits. 

Identified gaps and their effects 

Certain  gaps  could  be identified in  the statistical needs of all  users consulted and these 
generally follow the next trends: 

  Statistics  on  labour  mobility  or  employment  on  digital  platforms,  which  are 
necessary  for  decision-making  in  the  area  of  employment  are  insufficient.  The 
need for this type of data on a more regular basis was highlighted. Here, for some 
of the  existing statistical  series (e.g., SILC), the  biggest  issue reported  was  also 
timeliness of the data. A scarcity of available breakdowns was also reported as an 
issue especially in  the case of policy  areas related to  migration, labour  mobility 
and the digital economy. 

Perceived effects: Recent crises have highlighted a need for more frequent monitoring as 
the  social  situation  is  quickly  developing.  Policy  decisions  need  to  be  taken  based  on 
information,  which  is  accurate.  For  example,  social  statistics  inform  the  European 
Semester reports and country specific recommendations, and their accuracy depends on 
the evidence available. 

  Statistics on public transport is another major data gap. A daytime population grid 
would be very useful to understand where people spend most of their time during 
the day. That will inform flows, exposures to risks to pollution, etc. and will also 
give  a  lot  of  information  on  transport  needs,  transport  flows.  Good  data  on 
residential energy consumption is also lacking. 

Perceived  effects:  Funding  is  generally  targeted  towards  less  developed  regions  or 
Member States, and unfortunately, less developed areas are typically the ones where data 
is often entirely missing. For policy makers, it would be much easier to target support to 
areas where it is needed if they could measure the current offer. Moreover, more funding 
is needed to provide alternatives, to fund household surveys, general population surveys 

93 

 
 
or  enterprise  surveys,  or  to  buy  commercial  data.  This  kind  of  surveys  can  amount  to 
costs ranging from 3 to 5 million euros a year. Due to lack of data on residential energy 
consumption, investments in energy efficiency are done in a relatively blind manner. The 
cohesion  policy  directs  a  budget  of  approx.  €350  billion,  which  is  one  third  of  the  EU 
budget as  a  whole, so the effects  of inappropriate policy decisions would be  extremely 
costly. Conversely, even if it is just a marginal improvement of 1%, when compared to 
such budgets, the benefits would be a big consideration. 

  Another major issue is access to disaggregated data, especially at micro level as 
for  market  monitoring  and  crisis  management,  this  is  extremely  important. 
Timeliness of the data is also often reported as an issue.  

Effects  perceived:  When  evidence  and  underpinning  data  is  missing,  the  most  direct 
effect is that policymakers cannot fully show or demonstrate why certain policy options 
are the best  to pursue. Similarly, when the data is too  aggregated it’s difficult  to  know 
what the policy will generate at a micro level. Delays of one to two years that sometimes 
occur  have  a  significant  effect  on  policymaking  which  relies  heavily  on  markets 
monitoring and crisis management.  This has also been identified in a recent audit by the 
European  Court  of  Auditors87,  where  they  have  been  looking  at  the  use  of  big  data  in 
agriculture. Their report concluded that the European Commission has not capitalised on 
the  potential  of  big  data  for  analysing  and  subsequently  designing  the  EU’s  Common 
Agricultural  Policy.  As  a  result,  the  Court  considers  the  Commission  as  not  having 
enough  evidence  to  comprehensively  assess  the  CAP’s  needs  and  impact.  (The  CAP 
accounts  for  more  than  a  third  of  the  EU’s  budget  –  €408  billion  between  2014  and 
2020.) 

The extra time needed to find alternative data sources is also an important downside of 
existing gaps in statistics. Another effect is the need to make significant investments in 
procuring  commercial  data.  However,  procurement  is  often  unstable  because  of 
terminating  or  changing  contracts,  impinging  on  business  continuity  and  the  analytical 
work. 

  Certain  data  on  tourism  are  often  missing  such  as  same-day  tourism,  data 
collections  on  the  destination  level,  local  conditions,  the  satisfaction  of  the 
residents, and other types of aspects at the local level. 

Effects perceived: Crises such as the Russian aggression in Ukraine increased the need to 
understand how this impacted tourism and data was not available as quickly as needed. 
The  alternative  was  data  provided  by  transport  companies  which  helped  to  get  an 
understanding given that there was no official data on both the concrete impacts on the 
travelling  and  tourism.  Nevertheless,  unofficial  sources  limit  what  policy  makers  can 
officially  do  to  inform  the  public,  their  decision-making  processes  and  the  knowledge 
basis. 

  The  frequency  at  which  labour  market  statistics  or  data  on  employment  are 
provided sometimes impacts policymaking, especially in a crisis environment. 

87 See https://www.eca.europa.eu/en/Pages/NewsItem.aspx?nid=16713 

94 

 
                                                 
Effects perceived: the analysis done by financial and economic institutions is less precise. 
This  can  lead  to  worse  monetary  policy  decisions  or  decisions  in  the  area  of  financial 
stability. 

  More data on market concentration and on market power is needed, more granular 
information on price levels for products and services, and more statistical data on 
market dynamics (e.g., granular business statistics). 

Effects perceived: More up-to-date and granular statistics would allow policy makers to 
better assess the impacts on competition. 

 

In the area of trade, the digital dimension of service trade or intra-firm trade for 
goods  have  been  highlighted  as  having  significant  gaps  in  official  statistics. 
International  data  flows  between  different  countries  or  regional  blocks  are  also 
difficult  to  assess.  E-commerce  is  also  becoming  more  and  more  important  and 
various trends among consumer habits or unfair practices are hard to measure and 
to address without accurate statistics on cross-border e-commerce in particular. 

Effects  perceived:  Insufficient  evidence to  back trade agreements  can be  a major issue. 
There is a substantial risk of committing errors or deciding on wrong priorities. When it 
comes  to  e-commerce,  surveys  on  ICT  use  are  not  very  effective  as  respondents  rarely 
remember many details about their habits. It has been shown that these traditional means 
of gathering data are often ineffective, and without clear and accurate data on consumer 
habits and cross-border sales, it is very difficult to identify harmful practices or to better 
target consumer protection measures. 

 

In the area of defence policies, certain gaps identified were data on the supply and 
demand side of the defence market (defence and security companies). 

Effects  perceived:  Statistics  that  are  currently  available  do  not  always  help  creating  a 
rigorous basis for the decision-making in this area, which forces users to employ several 
workarounds to inform their work. 

 

In the area of transport, data on passenger mobility, especially by car and public 
transport is one of the gaps highlighted. These data are collected on a voluntary 
basis but there are many gaps (almost half of the countries are missing). Data for 
missing countries need to be estimated using different sources. 

Effects perceived: In order to have complete data at EU level, this requires filling in the 
gaps with data from other sources or studies or estimates, which means that the indicators 
are  not  fully  comparable.  Collecting  them  through  studies  or  surveys  requires  more 
financial resources. 

Benefits of new or improved statistics that make use of private data sources to 

close the information gaps   

Among  the  most  identified  benefits  of  new  or  improved  statistics  is,  of  course,  better 
policymaking.  Generally  speaking,  statistical  users  report  that  having  access  to  better 
evidence would always improve the quality of the analysis and the quality of the policies 
that are designed. 

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Access to more granular privately held data could also enhance the knowledge of policy 
makers about the trends that they observe and assess and as a result, they can better target 
their investments and policies. 

Moreover, statistical users argue that the main benefit for them would be to use real time 
data  to  inform  policymakers  about  “real  issues”  during  a  crisis.  More  generally, 
collecting  data  from  new  sources  would  be  beneficial  to  suggest  when  policy  should 
intervene  and  correct  market  failures,  but  also  to  know  when  there  is  the  risk  of 
government  failure.  There  will  be  clear  societal  benefits  if  they  could  understand  the 
extent  of  certain  policy  issues  or  to  know  that  emerging  technologies  are  not  causing 
harm. 

Several instances have also emerged where data coming from the private sector are seen 
as beneficial for statistical users and policy makers: 

  Private  data  would  be  useful  to  monitor  income.  For  this,  various  information 
from the banking sector could be of interest, such as transaction data coming from 
credit  cards  or  debit  cards.  This  kind  of  data  could  provide  useful  insights  on 
consumption and therefore anticipate some trends on incomes as well. 

  Data  on  digital  platform  employment  can  come  directly  from  platforms 
themselves, if B2G4S data sharing is possible. This is an aspect that sits high on 
the political agenda of the European Commission as part of the Digital Economy 
and Digital Transition topics. 

  Data on general trends about the location of public transport stops and departures 
and  arrivals  is  extremely  useful  for  transport  policy.  A  variety  of  public  and 
private actors could provide access to those data. Population daytime grids would 
be  very  useful  to  understand  where  people  spend  most  of  their  time  during  the 
day. That will inform flows, exposures to risks to pollution, etc. and will also give 
a  lot  of  information  on  transport  needs,  transport  flows.  Any  kind  of  data  that 
mobile phones could provide that give the length of trips, the number of trips or 
the  origin  and  destination  for  trips  would  be  a  huge  benefit  for  understanding 
where  more  transport  investments  are  needed.  Data  on  passengers  using  cars 
could also be improved with the help of GPS data from MNOs and could improve 
policymaking  in  the  area  of  transport.  This  kind  of  data  from  the  private  sector 
would help policy makers understand the impact of the green transition, which is 
extremely relevant for society. 

  Getting data on the amount  of energy  consumed, which could  come from  smart 
meters would be very useful in  order to  know how much electricity is  fed back 
into  the  grid  coming  from  solar  panels,  for  example.  Again,  this  would  have  a 
huge  positive  impact  on  the  green  transition  measures.  The  coupling  of  energy 
data  with  census  data  would  be  most  helpful  to  better  understand  matters 
including  the  structure  of  families,  the  energy  performance  of  buildings,  and 
patterns  of  energy  consumption.  This  would,  in  turn,  be  a  significant  benefit  in 
helping  to  provide  evidence  for  certain  policy  priorities  including  climate 
mitigation  and  wider  environmental  policies.  Moreover,  having  access  to  such 
data would be of particular help for cohesion policy.  

  Another  example  is  data  on  the  value  of  the  housing  or  the  cost  of  rent.  Right 
now,  Eurostat  collects  that  data  in  a  few  different  ways,  but  access  to  more 
comprehensive  subnational  data  would  be  extremely  helpful  to  policy  makers. 
Some private companies have access to a large set of transactions on the housing 
market and with that information, one can understand those patterns much better. 

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  Data coming from farms would be extremely beneficial for policymaking in the 
area  of  agriculture,  as  farms  are  better  and  better  tracked.  Inputs  on  fertiliser, 
pesticides, water, supply chains and livestock would be game changers for policy 
in this area. Certain new technologies, such as AI, as well as having an integrated 
digital reporting mechanism would make this kind of tracking cost-effective and 
cost-efficient,  because  on  the  medium  to  long  term,  it  would  reduce  reporting 
burdens on farms and businesses. 

  For  tourism  statistics,  people  who  travel  and  who  use  travelling  and  tourism 
services  but  don't  necessarily  spend  a  night  in  accommodation,  are  not  always 
visible. Here  the possibility  appears to  use mobile phone data, the possibility to 
use  credit  card  data,  to  supplement  the  gaps.  These  types  of  data  would  help 
policy  makers  to  recognise  more  clearly  the  economic  impact,  value  of  tourism 
and the flows of people between the countries for tourism and travelling. Having 
access  to  privately  held  data  would  help  to  have  more  extensive  and  timely 
knowledge  of  what  is  happening  in  travelling,  tourism.  That  would  help  policy 
makers to recognise if there are some sudden trends or if there have been shocks, 
to recognise what are the impacts of those shocks. The data would also help them 
to  understand  better  what  specific  support  might  be  needed  for  tourism  because 
this is a very regional phenomenon, and this would benefit the tourism ecosystem 
and Europe as a whole. 

  Statistics produced with the help  of data from  labour platforms provide detailed 
information on the existing vacancies, which is very timely and very useful. In the 
pandemic crisis, seeing a close and near real-time observation of labour markets 
was,  of  course,  the  key  to  understanding  how  economic  growth  is,  how 
employment, and unemployment is developing. 

  The main benefits  of business metrics  coming from  the private sector would  be 
the  added  credibility,  increased  timeliness  and  the  ability  to  account  for  short-
term and structural changes when assessing productivity and competitiveness, for 
example, when necessary to analyse and prepare for very big structural changes 
(i.e., exogenous shocks such as Covid-19). More comprehensive and harmonised 
databases  on  goods,  trade  and  investments  (covering  the  EU  but  also  other 
countries) could create a “complete picture” for trade negotiations. 

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ANNEX 8: SME TEST 

The initiative is considered as relevant for SMEs.  

Step 1/4: Identification of affected businesses 

A  distinction  is  needed  between  businesses  as  data  users,  data  survey  respondents  and 
data holders. 

The more timely and detailed statistics that the initiative will bring about, will benefit all 
businesses, including SMEs, in their role as data users. 

The use of by-product of digital services for European statistics will minimize the need 
for data collection via business surveys. As such, the initiative will benefit all businesses 
through a lower response burden, but since SMEs make up the majority of respondents in 
business surveys, this category of businesses will benefit more than other categories. 

Still,  businesses  that  are  also  data  holders,  will  also  be  negatively  affected  by  the 
initiative  because  of  requests  for  access  to  their  data.  However,  those  businesses  will 
almost  exclusively  be  large  businesses.  This  is  because  of  the  market  concentration  of 
digital  services  (mobile  network  operators,  banks  (financial  transaction  service 
providers), smart meter operators, web portals, etc.). 

Step 2/4: Consultation of SME Stakeholders 

The consultation covered SMEs with respondents in the survey being asked to assess the 
overall impact of each policy option on the operation and competitiveness of SMEs and 
micro-enterprises  in  particular.  All  stakeholders  preferred  policy  option  1  to  policy 
option 2, but SMEs expressed concerns in case they would receive requests for access to 
their  data,  since  this  would  likely  increase  their  costs,  given  their  limited  resources. 
However,  SMEs  would  welcome  burden  reduction  if  data  sharing  by  large  enterprises 
would reduce the need for surveys.  

Moreover,  the  use  of  big  data  sources  will  improve  the  timeliness  of  the  statistical 
production, which could increase the competitiveness of SMEs. 

Stakeholders  further  discussed  the  impact  of  the  policy  options  on  the  operation  and 
competitiveness  of  SMEs  and  micro-enterprises  in  the  online  stakeholder  workshop. 
While participants did not generally provide additional evidence, one NSI suggested that, 
in  the long  term, SMEs  could  expect  a reduction of the burden as a  result  of receiving 
fewer requests to complete surveys or other administrative forms (if mandatory access to 
privately-held data is introduced). 

The  membership  of  the  Expert  Group  on  facilitating  the  use  of  new  data  sources  for 

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official  statistics  was  chosen  from  different  stakeholder  groups,  including  SMEs.  The 
unanimous  conclusions  of  this  Expert  Group  are  an  important  input  to  the  initiative  to 
revise Regulation (EC) No 223/2009 and have been used in this impact assessment. 

Step 3/4: Assessment of the impact on SMEs 

The  baseline  option  of  PO0  does  not  directly  change  the  burden  on  SMEs.  However, 
there  is  also  no  lowering  of  the  burden  on  SMEs  through  alternative  ways  of  data 
collection. As such, this policy option is neutral in terms of both burden and benefits. 

Under  PO1  the  distribution  among  businesses  of  costs  and  benefits  would  be  uneven. 
Data holders would incur costs, but since SMEs will only rarely be holders of data that 
will  be  in  scope  for  requests  for  access,  this  will  normally  not  affect  the  SMEs. 
Moreover,  PO1  includes  a  blanket  exemption  of  mandatory  data  sharing  for  small  and 
micro enterprises. Turning to the benefits of PO1, the reduction of burden will affect all 
sizes  of  businesses,  including  SMEs.  The  estimations  show  that  the  savings  due  to 
decrease  of  samples  are  more  than  10  time  higher  than  the  additional  burden  on 
enterprises  due  to  new  data  demands,  and  SMEs  would  particularly  benefit  from  PO1, 
since they make up the majority of respondents in business surveys.  

Under PO2, the distribution among businesses of costs and benefits would be uneven, as 
was  the  case  for  PO1,  and  as  for  PO1  SMEs  will  only  rarely  be  subjected  to  data 
requests.  On  the  benefit  side,  PO2  is  also  comparable  to  PO1,  where  SMEs  would 
particularly benefit, since they make up the majority of respondents in business surveys. 
However, the response reduction is estimated to be considerably less for PO2 compared 
to PO1. 

Still, under PO2 more enterprises will be affected in terms of burden, compared to PO1, 
by requests to provide data in cases of urgent user demands. 

Step 4/4: Minimising negative impacts on SMEs 

Some measures have been considered to mitigate the impacts on SMEs. These are:  

  Micro and small enterprises deserve extra safeguards that they will not incur costs 
due to  mandatory data sharing.  Therefore, in  the preferred option a threshold  in 
terms of the size of businesses will apply to mandatory data sharing. For surveys 
the decision on thresholds for businesses as data providers is taken in the context 
of the AWP on the basis of its effect on the contents and quality of the statistics 
concerned and the associated public benefits. However, for holders of private data 
as  by-products  of  digital  services,  a  blanket  exemption  for  micro  and  small 
enterprises  is  included  in  the  preferred  option,  PO1.  This  is  justified,  since  it 
provides the strongest safeguard conceivable. 

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