COMMISSION STAFF WORKING DOCUMENT Accompanying the document COMMUNICATION FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT, THE COUNCIL, THE EUROPEAN ECONOMIC AND SOCIAL COMMITTEE AND THE COMMITTEE OF THE REGIONS Better assessing the distributional impact of Member States' policies
Date: 2022-09-28
Institutions: Directorate-General for Employment, Social Affairs and Inclusion, European Commission
Subjects: climate change policy, coronavirus disease, digital transformation, distribution of income, green economy, impact study, labour market, marginalisation, social inequality, sustainable development

EN 

  EN 

   EUROPEAN COMMISSION  Brussels, 28.9.2022  SWD(2022) 323 final  COMMISSION STAFF WORKING DOCUMENT    Accompanying the document COMMUNICATION FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT, THE COUNCIL, THE EUROPEAN ECONOMIC AND SOCIAL COMMITTEE AND THE COMMITTEE OF THE REGIONS Better assessing the distributional impact of Member States' policies {COM(2022) 494 final}  
 
 
Contents 

1. 

2. 

Introduction ........................................................................................................................................... 3 

Increasing importance of reliance on DIAs .......................................................................................... 3 

3.  An overview of DIA use in the Member States .................................................................................... 6 

4.  Key components of a good quality DIA ............................................................................................. 11 

a.  Who and when should do a DIA and at what level of granularity? ................................................ 11 

b.  What policy areas to choose for DIAs and what length of time to consider? ................................. 13 

c.  What tools to use? ........................................................................................................................... 17 

d.  What data to use? ............................................................................................................................ 23 

e.  What indicators to use? ................................................................................................................... 28 

f.  How to disseminate DIAs? ............................................................................................................. 34 

5.  Conclusion .......................................................................................................................................... 36 

Annex .......................................................................................................................................................... 37 

1 

 
 
 
 
 
 
 
 
List of abbreviations 

AROP  

at-risk-of-poverty rate 

DBP 

DIA 

Draft Budgetary Plan 

Distributional impact assessment 

EU-SILC 

European Union’s Survey of Income and Living Conditions 

HBS 

Household Budget Survey 

HFCS   

Household Finance and Consumption Survey  

GDP 

LMA 

NRP 

SCP  

Gross Domestic Product 

Labour Market Adjustment 

National Reform Programme 

Stability or Convergence Programme  

2 

 
 
 
 
 
 
 
 
 
 
 
 
 
1. Introduction  

Distributional  impact  assessment  (DIA)  can  be  referred  to  as  an  analysis,  usually 
quantitative in nature, which assesses the distributional effects of policies on the income of 
various groups across the population. More specifically, DIA refers to  the assessment of the 
impact  of  policies  on  the  income  distribution  in  a  country  by  analysing  their  impact  on 
disposable  income,  poverty  and  income  inequality  indicators  (e.g.,  at-risk-of-poverty  rate, 
income quintile share ratio S80/S20). This can be done either before the policy is implemented 
(i.e., ex ante DIA, which is the main focus of this Staff Working Document), by simulating and 
assessing  its  distributional  impact  through  microsimulation  models,  or  after  the  policy  is 
implemented  (ex  post  DIA)  to  evaluate  its  actual  performance  and  impact.  An  appropriate 
account of the impact of new measures on the income distribution contributes to evidence-based 
policy  making  and  builds  up  a  comprehensive  framework  where  costs  and  benefits  of  (to  be) 
adopted  measures  are  considered,  both  in  terms  of  their  overall  budgetary  impact  but  also 
whether they may affect, in a disproportionate way, different groups of the population.1  

This Staff Working Document (SWD) supports the Commission Communication on better 
assessing  the  distributional  impact  of  Member  States’  policies.  As  such,  it  presents  further 
considerations  on  elements  of  the  current  context  that  call  for  making  more  systematic  use  of 
DIA. It then gives an overview of the extent to which DIA is used in EU Member States. Finally, 
it provides additional information on various aspects of conducting quality DIA.  

2. Increasing importance of reliance on DIAs 

Recent  years  have  seen  an  increase  in  the  calls  for  a  better  understanding  of  the 
distributional impacts of policies. The 2008 financial crisis gave a strong push to the inequality 
and  redistribution  related  research,  including  improvements  and  wider  use  of  microsimulation 
models  for  redistributive  analyses.  The  challenges  brought  about  by  the  green  and  digital 
transitions, and the COVID-19 pandemic contributed further to that. 

Poverty and inequality-proofing of new policies in a context of growing economic inequalities   

Reliable  assessment  of  the  distributional  impact  of  the  planned  budgetary  measures  is 
particularly important in a context of overall increasing inequalities. Europeans feel strongly 
about the need to address income inequalities and for national governments to take measures in 

1 See for instance Livermore, M. A. and Rosenberg. J. S. (2014) L'analyse distributive dans les études d'impact, ou comment prendre en compte la 
répartition des conséquences d'une règlementation, Revue française d’administration publique, 149(1), 145-161.  
https://www.cairn.info/revue-francaise-d-administration-publique-2014-1-page-145.htm  

3 

 
 
 
 
 
 
                                                           
this respect as expressed in a recurrent manner in Eurobarometer surveys2. Income inequality has 
become  one  of  the  main  concerns  for  EU  citizens.  Eurobarometer  surveys  from  before  the 
COVID-19  crisis  (2017)  show  that,  overall  84 %  of  people  in  Europe  think  that  income 
inequality in their country is too high, with 81 % saying their governments should take measures 
to reduce income inequality. This highlights public perceptions of unfairness about the impact of 
public policies on inequalities, which may feed discontent and mistrust towards institutions and 
governments. 

Over the last decade, overall income inequality has been quite stable in the EU.  The 2008 
crisis  had  seen  an  increase  of  income  inequality  with  different  situations  in  EU  countries.3
Income  inequality  then  slightly  decreased  in  the  years  before  the  COVID-19  pandemic.  The 
income quintile share ratio in the EU27, which compares the share of total income received by 
the  20%  of  the  population  with  the  highest  disposable  income  to  that  received  by  the  poorest 
20%, stood at 4.90 in 2020, slightly lower than in 2011 when it was 4.99. However, inequality 
increased at the lower end of the income distribution (as captured by S50/S204) in the first half of 
2010s  and  in  spite  of  the  subsequent  decrease  in  2020  was  still  slightly  higher  than  a  decade 
earlier,  raising  concerns  about  the  inclusiveness  of  economic  growth.  The  income  share  of  the 
bottom 40% of the population has also remained rather constant at 21.4% in 2020 compared to 
21.2% in 20115. During the economic recovery preceding the COVID pandemic, labour market 
improvements  have  generally  favoured  high-income  families,  whereas  levels  of  long-term 
unemployment and inactivity remained high, and in-work poverty did not decline significantly6. 

Addressing poverty and income inequalities is important not only for fairness reasons but 
also  to  support  economic  growth.  For  instance,  an  OECD  report  (2021)7  suggests  how  well-
designed tax policies can support inclusive and sustainable growth and address the distribution of 
income and wealth. Inclusive growth involves a number of dimensions and captures whether the 
benefits  of  economic  growth  are  shared  broadly,  thereby  including  the  distributional  effects  of 
growth,  the  inclusiveness  of  labour  markets,  and  the  improvement  in  the  well-being  of  low 
income groups.  

Tackling income inequalities requires reforms by Member States in different policy areas, 
including  the  design  of  their  tax  and  benefit  systems,  wage  setting  mechanisms  and  labour 
market incentives, inclusiveness and equal opportunities in education and training (starting from 
early age), and access to affordable and quality services for all. Redistribution systems alone may 
not  suffice  to  address  the  challenge  of  increasing  income  inequality.  Fostering  growth  while 

2 Eurobarometer on Fairness, inequality and inter-generational mobility (April 2018) https://europa.eu/eurobarometer/surveys/detail/2166 
Eurobarometer on Social Issues (March 2021) Social issues - March 2021 - - Eurobarometer survey (europa.eu) 
3  See  Joint  employment  report  2022 and  EU-SILC  income  inequality  indicators,  “Income  quintile  share  ratio  S80/S20  for disposable  income” 
(ilc_di11) and “Gini coefficient of equivalised disposable income” (ilc_di12). and “Income quintile share ratio” definition and related terms for 
more information Link  
4 Income quintile share ratio S50/S20 for disposable income by sex and age group - EU-SILC survey (ilc_di11e) 
5 Income quintile share ratio S40/S100 for disposable income by sex and age group - EU-SILC survey (ilc_di11f)  
6 See Joint Employment Report 2022 (https://op.europa.eu/en/publication-detail/-/publication/316112f2-fda1-11ec-b94a-01aa75ed71a1/language-
en/format-PDF) and the SPC Annual Report 2021 (https://ec.europa.eu/social/main.jsp?catId=738&langId=en&pubId=8432&furtherPubs=yes) 
7 OECD (2021), Tax and Fiscal Policies after the COVID-19 Crisis: OECD Report for the G20 Finance Ministers and Central Bank Governors, 
October 2021, Italy, OECD, Paris, www.oecd.org/tax/tax-policy/tax-and-fiscal-policies-after-the-covid-19-crisis.htm  

4 

 
 
  
                                                           
addressing inequality concerns requires a comprehensive approach to policy design, calling for 
action  at  the  pre-market,  in-market,  and  post-market  stages.  At  the  same  time,  distributional 
impact of all reforms, whether they aim at reducing inequalities or not, should be scrutinised, so 
that if need be compensatory measures can be designed.   

Assessing  the  impact  of  new  policies  in  the  face  of  short-term  shocks  and  longer-term 
challenges 

The massive public support put in place at national and EU levels together with the strong 
automatic  stabilisation  cushioned  the  consequences  of  the  COVID-19  pandemic,  in 
particular  for  low  income  groups.  As  a  result,  according  to  Eurostat’s  flash  estimates8,  the 
average  income  quintile  share  ratio  (S80/S20)  in  the  EU  remained  stable  or  registered  a  slight 
drop  in  2020.  While  employment  incomes  are  estimated  to  have  dropped  by  10%  for  the  first 
income  quantiles  and  2%  for  the  fifth,  the  overall  impact  on  disposable  incomes  was  largely 
contained across the whole income distribution, thanks to the strong smoothing effect of the tax 
and benefit systems, including the operation of short-time work and other job retention schemes. 
However,  it  is  important  to  pay  attention  to  distributional  considerations  as  Member  States 
withdraw the emergency support measures and the current surge in prices also lead to a number 
of measures to protect poorer households.  

Such  short-term  shocks,  such  as  the  war  in  Ukraine,  compound  with  structural  changes, 
such  as  the  twin  climate  and  digital  transition,  that  should  be  taken  into  account  when 
designing  tax  and  spending  policies  for  a  resilient  and  inclusive  recovery.    For  the  digital 
transition  reskilling  and  upskilling  is  important  in  order  to  prevent  exacerbating  the  digital 
divide,  and  possible  increased  wage  differences  between  low-  and  high-skilled  workers.  All 
public policies that have distributional effects should in principle be accounted for in this respect, 
not just tax and benefit policies, such as for instance environmental and climate policies, which 
similarly to  tax and benefit policies can be more or less progressive depending on their design 
(i.e. targeting, policy instrument, etc.)9. The SWD accompanying the Commission Proposal for a 
“Council Recommendation on ensuring a fair transition towards climate neutrality”10, presented 
on  14  December  2021,  also  provides  modelling  evidence  on  some  available  tools  to  address 
potentially  adverse  distributional  effects  linked  to  environmental  and  climate  policies  and  to 
adequately support what they refer to as disadvantaged households. 

Building trust and supporting the implementation of new policies 

Better-informed  and  transparent  reforms  are  more  likely  to  be  better  implemented. 
Evidence-based  policy  design  is  important  for  reform  implementation.  Trust  in  the  decision-

8  Released  in  July  2021,  available  on  Eurostat  website.  The  full  data  for  2020  will  be  available  in  the  autumn  2022.  It  is noteworthy  that  top 
incomes/capital incomes in EU-SILC (as in all income surveys as opposed to administrative data) are underestimated, hence national inequality 
estimates are underestimated too. 
9 For examples of analysis distributional effects of environmental and climate policies see:  Vona, F. (2021), "Managing the distributional effects 
of environmental and climate policies: The narrow path for a triple dividend", OECD Environment Working Papers, No. 188, OECD Publishing, 
Paris,  https://one.oecd.org/document/ENV/WKP(2021)20/en/pdf  and  Shang,  B.  (2021).  The  Poverty  and  Distributional  Impacts  of  Carbon 
Pricing:  Channels  and  Policy  Implications.  IMF  Working  Papers,  2021(172)  https://www.elibrary.imf.org/view/journals/001/2021/172/article-
A001-en.xml  
10 https://ec.europa.eu/commission/presscorner/detail/en/qanda_21_6823  

5 

 
 
                                                           
making  process  can  be  built  by  having  a  quality  collaborative  preparatory  and  implementation 
process in place, accompanied by reliable distributional assessments which increase transparency 
and efficiency. Implementation of complex reforms also comes with difficulties associated with 
social and economic costs, which often fall on specific groups while their benefits can be more 
diffused. Challenges associated with the reform, alternative policy choices and likely trade-offs 
should be understood before implementation. This includes distributional impacts as all policies 
may not be win-win in terms of encouraging economic growth and reducing inequality but rather 
having groups seeing increases in their incomes and others losing income as a result of reforms, 
which can also lead to the adoption of measures focussed on specific groups (that will reduce or 
eliminate  the  negative  impact  on  poverty  or  the  widening  of  inequalities).The  opposition  from 
those who might lose and the perceived unfair distribution of gains can result in a lack of support 
and commitment to these reforms. Reform implementation can be affected by political economy 
factors11,  which  highlights  the  importance  of  the  assessment  of  potential  groups  affected. 
Possible compensatory measures, as well as consultation of stakeholders and general public and 
timing of implementation may improve the design of reform and their acceptability.  

3. An overview of DIA use in the Member States 

Member States differ in terms of the types and number of national budgetary documents in 
which DIA results are presented. Table 1 summarises for each Member State whether at least 
some DIA analysis was performed during the preparation of national budgetary documents, i.e. 
those  not  submitted  to  the  European  Commission,  and  whether  the  DIA  results  were  then 
presented  in  them.  The  following  types  of  budget-related  documents  at  the  national  level  are 
considered:  National  Growth  Strategy,  progress  towards  EU2020  indicators,  progress  in 
implementing  Country  Specific  Recommendations,  national  document  introducing  expenditure 
and  revenue  measures  and  documents  with  an  ex  ante  or  ex  post  evaluation  of  budgetary 
measures.  A  significant  heterogeneity  among  the  Member  States  emerges  from  the  table: 
Belgium,  Cyprus,  Luxemburg  and  Romania  did  not  perform  any  DIA  here.  Bulgaria,  Czechia, 
Germany, Italy, the Netherlands, Portugal and Slovakia presented some DIA results in no more 
than two of the types of budget-related documents considered. Austria, Denmark, France, Latvia, 
Malta, Sweden  and Slovenia had  at  least  one DIA occurrence in  at  least  five of these types of 
documents.  

11  Aphecetche,  T.,  Canton  E.,  Garrone  M.  and  Hobza  A.  (2022)  Understanding  the  Political  Economy  of  Reforms.  Lessons  from  the  EU, 
Economic Brief 70, European Commission: https://ec.europa.eu/info/publications/understanding-political-economy-reforms-lessons-eu_en  

6 

 
 
 
 
                                                           
 
Table 1 – DIA presences outside DBPs, SCP and NRP, for seven different kinds of national 
budget-related documents (2019-2020)  

MS 

National 
Growth 
Strategy 

Progress 
towards 
EU2020 
indicators 

Progress in 
implementing 
country-Specific 
recommendations 

National 
document 
introducing 
expenditure 
measures 

National 
document 
introducing 
revenue 
measures 

Document 
with an ex 
ante 
evaluation 
of an 
adopted 
budgetary 
measure 

Document 
with an ex 
post 
evaluation 
of past 
adopted 
budgetary 
measures 

AT 
BE 
BG 
CY 
CZ 
DK 
DE 
EE 
EL 
ES 
FI 
FR 
HR 
HU 
IE 
IT 
LT 
LU 
LV 
MT 
NL 
PL 
PT 
RO 
SE 
SI 
SK 

Legend: 

No DIA performed 

DIA details included in the document 

DIA analysis commented but without presenting details of the analysis. 

Source: Study on Distribution Impact Assessment by the University of Milan, financed by the Commission 
(DG EMPL): https://data.europa.eu/doi/10.2767/511644  

7 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure  1  shows  how  many  Member  States  had  some  DIA  analysis12  for  each  of  the  seven 
types  of  budget-related  documents  at  the  national  level  considered.  More  than  half  of  the 
Member  States  declared  to  have  had  at  least  one  DIA  occurrence  in  national  documents  that 
introduce  either  revenue  (20  out  of  27)  or  expenditure  (18  out  of  27)  measures.  About  half  of 
Member States had at least one DIA occurrence in national documents with an ex ante (13 out of 
27) and an ex post (12 out of 27) evaluation of an adopted budgetary measure. Having at least 
one DIA occurrence was less frequent in progress towards EU2020 indicators13 (8 out of 27) and 
in progress in implementing country-specific recommendations (6 out of 27) documents. Finally, 
only four countries (Austria, Denmark, Slovenia and Sweden) declared to have had at least one 
DIA occurrence in the National Growth Strategy document.  

Figure 1 – Number of EU Member States with at least one DIA occurrence outside DBPs, 
SCP and NRP, for seven different kinds of documents (2019-2020) (N=27) 

Source:  Study  on  Distribution  Impact  Assessment  by  the  University  of  Milan,  financed  by  the  Commission  (DG 
EMPL): Study on distributional impact assessment - Publications Office of the EU (europa.eu) 

Very few Member States use DIA for analysing the impact of reforms that might not have a 
direct bearing on the state budget. Only the Netherlands explicitly reports analysing with DIA 
reforms  that  result  in  transferring  benefits  from  one  group  to  another  without  having  a  direct 
bearing  on  the  state  budget.  In  Czechia  and  Poland,  regulatory  impact  assessments  of  new 
legislation are required by law and sometimes (but not always) include DIA analysis. 

12 Here as well, a DIA occurrence can refer to the inclusion of the DIA analysis(es) itself in the document or to only the inclusion of comments on 
the results of this/these DIA analysis/es. 
13  In  the  European  Semester,  the  European  Commission  requires  MS  to  publish  macroeconomic  forecasts  and  constantly  assesses  progress 
towards the European 2020 targets. 

8 

 
 
 
                                                           
The presentation of DIA results in Draft Budgetary Plans submitted to the Commission by 
euro area Member States remains infrequent14 in spite of Regulation (EU) 473/2013 calling 
for it where possible.  For any given budget year in the 2015-2020 period, at most 6 out of 19 
euro  area  Member  States  included  DIA  analysis  in  their  DBP15.  Only  two  included  DIA  on  a 
regular basis (IE and NL) and eight included DIA occasionally (AT, EE, FI, FR, EL, LT, LV and 
MT).  9  euro  area  Member  States  did  not  present  any  DIA  in  their  DBPs  in  that  period.  In 
general, euro area Member States do not perform separate DIAs for each budgetary measure. In 
most cases, all or most of the budgetary measures are analysed simultaneously in a single joint 
DIA,  irrespective  of  budgetary  measure  type  (revenue/expenditure)  and  area.  Their  aggregated 
impact  is  reported  by  income  level  or  by  specific  socio-demographic  groups.  Revenue  and 
expenditure  measures  are,  on  average,  included  in  roughly  equal  numbers,  even  though  some 
differences  between  the  euro  area  Member  States  and  across  time  do  exist.  Finally,  the  most 
frequent policy areas considered are those regarding welfare and social inclusion. 

Box 1: Presentation of DIA results in the 2022 Draft Budgetary Plans (DBPs) by Ireland  

The DIA results of the main budgetary measures in Ireland’s 2022 DBP are presented in a form of a short text and 
two charts16. One chart shows the distributional effects of budgetary measures in 2022 in terms of percentage change 
in equivalised disposable income by decile. Both the total effect and the separate effects are presented (for carbon 
tax, excise, direct taxes and changes in benefits). Another chart shows the impact by family type.  

A  few  Member  States  included  DIA  analysis  either  in  their  Stability  or  Convergence 
Programme (SCPs) and/or the National Reform Programme (NRPs) in the recent past. In 
terms  of  the  SCPs,  only  Croatia  included  DIA  results  in  2019,  and  no  country  included  it  in 
2020.  Regarding  the  NRPs,  four  countries  presented  DIA  analysis  in  2019  (BE,  HR,  MT  and 
PT), and four countries used DIA in 2020 (HR, IT, MT and PT). Member States also made only 
limited  use  of  DIA  in  their  National  Energy  and  Climate  Plans  (NECPs)  submitted  to  the 
Commission.  In  its  assessment,  in  September  2020  the  Commission  concluded  that  there  was 
little information on energy poverty and distributional impacts. 

14 According to ”Study on Distributional Impact Assessment” by the University of Milan 2022 https://data.europa.eu/doi/10.2767/511644 
15 See “Study on DIA” by the University of Milan 2022 above 
16 A link to the full documentation of the analyses undertaken in the budget is also provided (https://www.gov.ie/en/publication/7599a-budget-
publications/). 

9 

 
 
 
                                                           
Figure  2:    DIA  use  by  Member  States  (in  their  DBPs  and  other  budgetary  national 
documents) 

Source:  Study  on  Distribution  Impact  Assessment  by  the  University  of  Milan,  financed  by  the  Commission  (DG 
EMPL),  link.  Note:  use  in  DBPs  relates  to  budget  years  2018-19  and  use  in  other  national  documents  relates  to 
budget years 2019-2020. 

The  Figure  2  depicts  the  fact  that  reliance  on  DIA  varies  significantly  across  Member 
States, but most use DIAs in their national budgeting processes, though to varying extent17. 
Only  a  few  Member  States  appear  not  to  be  conducting  such  analysis  at  all18.  It  is  also 
noteworthy that many countries perform DIA analysis that is not included in publicly available 
documents.  

DIA  can  also  be  used  for  other  reforms/investments,  but  the  literature  reviewed  does  not 
provide a synthetic view in that respect. This typically relates to the design of reforms that may 
not have a (direct) impact on public budgets, but can have an impact on households’ incomes and 
certain  groups.  For  instance,  this  can  relate  to  reforms  of  minimum  wages19  or  of  co-payment 
features  for  healthcare.20  However,  estimating  the  distributional  impact  of  such  reforms  or 

17  According to  the  “Study  on  Distributional  Impact  Assessment”  by  the University  of  Milan  which  looks at  the  DIA  use  in the  DBPs and in 
budget-related documents at the national level, such as National Growth Strategy, progress towards EU2020 indicators, progress in implementing 
country-Specific recommendations, national document introducing expenditure and revenue measures and documents with an ex- ante or ex- post 
evaluation of budgetary measures. 
18 CY, LU and RO according to the “Study on Distributional Impact Assessment” by the University of Milan. 
19 For instance, as part of the  Semester cycle, some analysis has been done of the impact of the introduction of a minimum wage (such as in 
Cyprus, see European Commission Staff Working Document 2022, Country Report – Cyprus, accompanying the document Recommendation for 
a COUNCIL RECOMMENDATION on the 2022 National Reform Programme of Cyprus and delivering a Council opinion on the 2022 Stability 
Programme of Cyprus {COM(2022) 604 final} - {SWD(2022) 640 final}, p. 47.) 
20 See for instance Aaltonen K, Tervola J & Heino P (2021). Analysing the effects of healthcare payment policies in conjunction with tax-benefit 
policies: A microsimulation study with real-world healthcare data. INVEST working paper 34/2021. 

10 

 
 
 
                                                           
investments beyond the tax and benefit systems can also be made more difficult due to the lack 
of appropriate data and simulation tools.  

4. Key components of a good quality DIA 

a.   Who and when should do a DIA and at what level of granularity? 

Ex ante and ex post evaluations 

DIA analyses can be undertaken before a proposed policy change have taken place (ex ante 
evaluation) or after the policy change (ex post evaluation).   

Whereas DIAs can be conducted at various stages of policy design and implementation of 
policy  reforms,  carrying  out  such  assessments  ex  ante  is  particularly  useful  for  policy 
making.  Ex  ante  DIAs  allow  capturing  the  expected  effects  of  policies  on  income  of  various 
groups.  They  are  typically  carried  out  using  lagged  survey  or  administrative  data.  Uprating  of 
monetary variables and nowcasting techniques can be used to help make this lagged data more 
representative  of  the  current  (or  targeted)  fiscal  year.  As  such,  the  distributive  impact  of  the 
policy reforms are estimated in an analysis, where all changes are induced by the policy reforms.  

Running  ex  post  DIA  is  important  in  that  it  allows  understanding  the  actual  impact  of 
implemented  policy  reforms.  These  ex  post  analyses  typically  make  use  of  data  from  before 
and after the policy reform to evaluate its effect and this reduces the need for a microsimulation 
model to create counterfactual income distributions. 

The result of an ex post analysis are often contaminated by other changes that occurred – to 
incomes, demographics or policies other than the one studied  – around the same time. To 
estimate an ex post effect, sufficient time needs to pass such that the effect of the policy reforms 
on the population will have passed through to survey/administrative data sources. To conduct ex 
post DIA analysis with microsimulation models, practitioners can use decomposition methods to 
separately  identify  the  effect  of  policy  reform(s)  from  general  changes  to  market  incomes  (see 
for  instance  Bargain  and  Callan,  201021;  Paulus  and  Tasseva,  202022;  Doorley  et  al,  202123). 
Other  techniques  used  for  policy  analysis,  such  as  the  difference-in-difference  estimator,  may 
also be appropriate for ex post policy analysis.  

Ex post evaluation of the ex ante DIA is also useful, depending on the type of policy and its 
implementation framework. For instance, Fioramanti et al. (2016)24 assess the accuracy of the 

21 Bargain, O., & Callan, T., (2010). Analysing the effects of tax-benefit reforms on income distribution: a decomposition approach. Journal of 
Income Inequality. Vol. 8.pp. 1-21: https://link.springer.com/article/10.1007/s10888-008-9101-4  

22 Paulus, Alari & Tasseva, Iva V. 2020. "Europe Through the Crisis: Discretionary Policy Changes and Automatic Stabilizers," Oxford Bulletin 
of Economics and Statistics, Department of Economics, University of Oxford, vol. 82(4), pages 864-888, August: 
https://ideas.repec.org/a/bla/obuest/v82y2020i4p864-888.html  

23 Doorley, K.; Callan, T. & Savage, M. 2021. "What drove income inequality in EU crisis countries during the Great Recession?," Fiscal Studies, 
John Wiley & Sons, vol. 42(2), pages 319-343, June: https://ideas.repec.org/p/esr/wpaper/rb202102.html  
24 Fioramanti, M., González Cabanillas. L., Roelstraete, B.,and & Ferrandis Vallterra, A., (2016).   European Commission’s Forecasts Accuracy 
Revisited: Statistical Properties and Possible Causes of Forecast Errors. European Commission Discussion Paper 027: 
https://econpapers.repec.org/paper/eufdispap/027.htm  

11 

 
 
                                                           
 
European Commission’s ex ante forecast of real GDP growth, inflation and general government 
balance  (as  a  %  of  GDP)  by  comparing  ex  ante  forecasts  of  these  variables  with  ex  post 
realisations.  This  is  a  valuable  exercise  as  it  can  help  diagnose  the  accuracy  of  ex  ante 
assessments  and  aid  model  development.  Comparing  ex  ante  and  ex  post  analyses  of  policy 
reforms in DIA may be more difficult than similar comparisons using aggregates such as GDP. 
The  main  barrier  is  that  the  ex  post  effect  of  policy  changes  on  income  distribution  is  more 
difficult  to  estimate  than  an  aggregate  such  as  GDP,  which  is  measured  in  an  accounting 
framework  by  statistical  bodies  and  is  easily  available  to  researchers.  Some  academic  research 
has tried to use ex post analysis to validate the findings of ex ante analysis using microsimulation 
(Bargain and Doorley, 2017)25. However, this type of validation is not usually carried out.  

The  euro  area  Member  States  typically  perform  DIA  at  different  stages  in  the  policy 
process26. For instance, it appears that most (14) euro area Member States conduct a DIA before 
the budget approval. There are 6 of them that also conduct a DIA before the implementation of 
the policy but after the budget approval. It also appears that 10 euro area Member States conduct 
a DIA after the implementation of the policy (i.e., ex post evaluation). In this respect, there are 8 
euro area Member States in which DIA is typically performed both ex ante (before the budget’s 
approval  and/or  the  implementation  of  the  policy)  and  ex  post  (IE,  IT,  MT,  LV,  NL,  DE,  FR, 
BE). 

Box 2: Timing of conducting DIA in the Netherlands 

In  the  Netherlands  DIA  is  conducted  before,  during  and  after  the  budget  decision-making  process.  DIA  is 
performed  before  the  approval  of  the  budget  to  predict  the  policies’  effects,  but  also  during  and  after  the 
implementation  of  the  budget  to  monitor  the  policies’  implementation  and  to  assess  their  actual  distributional 
effects. 

In some Member States (e.g. FI, EL, IE), DIA is usually conducted near the adoption of the 
budget,  while  others  (e.g.  MT,  LV)  start  the  DIA  analysis  earlier  and  thus  have  a  longer 
period to conduct DIA. The difference between the two approaches is  mirrored in  the size of 
the staff involved, which is smaller in Latvia and Malta. Moreover, it should be noted that Malta 
and  Latvia  performed  DIA  in  the  DBP  only  once  over  the  period  2018-2020  (in  2018)  for 
specific measures and this fact could also explain the aforementioned differences. In some other 
Member States (e.g. LT and FR), DIA is carried out throughout the year when it is requested by 
policymakers.  

25 Bargain, O. & Doorley, K. "The Effect of Social Benefits on Youth Employment: Combining Regression Discontinuity and a Behavioral 
Model." Journal of Human Resources, vol. 52 no. 4, 2017, p. 1032-1059: https://ideas.repec.org/a/uwp/jhriss/v52y2017i4p1032-1059.html  
26 According to the “Study on Distributional Impact Assessment” by the University of Milan 

12 

 
 
 
 
 
                                                           
Granularity of DIA analysis 

An important decision to make while running DIA is whether to assess the impact of policy 
reforms separately or jointly.  In general, it is good practice to present the effect of the bundle 
of  changes.  The  impact  of  a  whole  package  of  policy  reforms  is  of  interest  to  the members  of 
national Parliaments and the public. Also the assessed impact should take into account changes 
both  in  taxes  and  benefits.  Moreover,  budgetary  changes  are  often  decided  as  a  package  with 
some  measures  designed  to  compensate  for  others.  Last  but  not  least,  there  may  also  be 
interactions between the proposed policy reforms and therefore it is more useful to present their 
combined effect.  

On top of this, it is preferable to account separately for all policies that have a significant 
impact  on  households’  incomes.  A  useful  yardstick  for  deciding  whether  the  policy  has  a 
sizeable  effect  is  to  consider  whether  it  corresponds  to  a  change  higher  than  0.1%  of  GDP. 
Beyond  measures  with  a  large  budgetary  impact,  it  also  suggested  that  DIA  be  performed  for 
those  measures  that  have  the  potential  to  redistribute  more  than  0.1%  of  GDP  in  disposable 
income between different population groups (even though those measures may have a net overall 
budgetary  impact  of  less  than  0.1%  of  GDP).  The  population  groups  to  consider  here  are 
whichever  groups  are  most  relevant  for  the  policy  at  hand.  This  will  often  refer  to  income 
deciles, but  could  equally  refer to  different  age groups, urban-rural  groups, wealth quantiles or 
indeed  other  categorisations  (e.g.  tax  brackets).  A  qualitative  judgement  in  terms  of  which 
measures to submit to DIA should complement the more quantitative considerations.  

Many  Member  States  analyse  the  impact  of  policies  both  separately  and  jointly  (BE,  CZ, 
DK, EE most of the time, EL, FR, HR, HU, IE, IT, LV most of the time, MT, NL, RO, FI, SE).  
In Portugal and Slovakia, the measures are considered separately.  When measures are analysed 
jointly,  separate  effects  might  also  be  shown  (CZ).  In  the  Netherlands,  all  the  policies  are 
combined and a single joint DIA is performed on their total impact. 

b.  What policy areas to choose for DIAs and what length of time to consider? 

Conducting  DIA  based  on  a  microsimulation  model  enables  estimating  the  effect  of  a 
number of types of policies, in particular direct taxation and various benefits, but also indirect 
taxation, non-cash benefits and wealth taxes, depending on the model and data in question.  

Standard  microsimulation  models  allow  first  and  foremost  simulating  the  impact  of 
changes  to  direct  personal  taxes  (as  well  as  social  security  contributions)  and  social 
benefits.  Almost  all  Member  States  run  DIA  on  changes  in  direct  taxes  and  social  benefits. 
Examples of specific benefits analysed include support for the elderly (BE, SE), minimum state 
old  age  pension  (LV)  support  for  persons  with  disabilities  (BE),  disability  insurance  benefits 
(BE), minimum state disability pension (LV), child benefits (BE, HR), unemployment insurance 
benefits  (BE,  SE),  sickness  benefits  (BE,  SE),  maternity  and  parental  benefits  (HR,  SE), 
minimum income reform (LV), in-work benefit (MT), social assistance  (MT), housing benefits 
(SE).     

13 

 
 
Another  major  category  of  household  taxation  is  consumption  taxes  (mostly  value-added 
tax and excise). Estimating their distributional effect requires a model integrating consumption 
data. In general, expenditure data can be imputed, or is available alongside detailed income data 
in a household budget survey, which is usually collected separately from the income and living 
conditions  surveys. The  effect  of  changes to  indirect  taxes can be  estimated across the income 
distribution and added to the effect  of changes to direct  taxes and  benefits  in  order to  give the 
distributional effect of the package of reforms. This functionality is for instance available in the 
EUROMOD Indirect Tax Tool (see Box 6). Consumption taxes are an important policy area as 
distributional impact of changes to them tends to be large and often regressive as lower income 
households  spend  a  larger  portion  of  their  income  on  goods  and  services  (see  for  instance 
Savage, 2017)27. The importance of integrated direct tax-benefit and indirect tax modelling has 
grown in the context of the green transition. Research has shown that the disproportionate effect 
of  carbon  tax  increases  on  low-income  households  can  be  offset  by  recycling  the  revenue 
generated  from  the  carbon  tax  into  tax  cuts  and  increases  in  social  welfare  rates28.  This  also 
concerns  the  Social  Climate  Fund  using  revenues  from  the  Emissions  Trading  Scheme.  At  the 
moment, only few Member States seem to be analysing indirect taxes in their DIA (DK, IE, IT, 
NL, SE). 

Ideally,  Member  States  should  also  be  able  to  perform  a  DIA  analysis  on  wealth  tax 
reforms.  Even  though  they  can  have  significant  distributional  impacts,  they  are  generally  not 
taken  into  account  in  current  models,  due  in  particular  to  the  lack  of  suitable  underlying  data. 
Estimating  the  effect  of  changes  to  wealth  tax  is  not  common  but  can  be  performed  if  the 
underlying model is linked to data with detailed wealth and asset data. Such data was first made 
available  by  the  European  Central  Bank  in  a  standardized  fashion  in  the  euro  area  with  the 
Household Finance and Consumption Survey (HFCS), which can be incorporated in EUROMOD 
to allow for wealth tax modelling.  Kuypers et al., (2017)29 outline how the Household Finance 
and  Consumption  Survey  can  be  incorporated  in  EUROMOD  (also  see  Box  6  for  EWIGE)  to 
allow for wealth tax modelling for a limited number of countries. Boone et al. (2019)30 describes 
how this is expanded to simulate wealth tax rules for 1731 EU Member States. 

Estimating the effect of changes to in-kind benefits completes DIA analysis. However, it is a 
complex  exercise  as  it  requires  the  data  underlying  the  microsimulation  model  to  contain 
(imputed) information about eligibility for these benefits (such as health status for health benefits 
or childcare usage for childcare benefits) as well as assumptions regarding the monetary value of 
the  benefits.  As  a  result,  the  redistributive  impact  of  in-kind  benefits  is  largely  overlooked,  in 
spite  of  the  fact  that  in-kind  social  benefits  represent  over  30  percent  of  the  overall  social 

27 Savage, M. (2017). Integrated Modelling of the Impact of Direct and indirect Taxes Using Complementary Datasets. The Economic and Social 
Review, 48(2), 171-205: https://ideas.repec.org/a/eso/journl/v48y2017i2p171-205.html  
28  Bercholz,  M.,  &  Roantree,  B. 
https://www.esri.ie/sites/default/files/media/file-uploads/2019-06/Carbon%20taxes%20and%20compensation%20options.pdf   
29 Kuypers, S., Figari, F., Verbist, G., & Verckist, D. (2017).  EWIGE- European Wealth data InteGration in EUROMOD. JRC Working Papers 
on Taxation and Structural Reforms No. 4: https://ideas.repec.org/p/ipt/taxref/201704.html  
30 Boone, J., Derboven, J., Figari, F., Kuypers, S., & Verbist, G. (2019). EWIGE 2- Update and Extension of the EUROMOD Wealth Taxation 
Project. JRC Working Papers on Taxation and Structural Reform No. 7: https://www.econstor.eu/bitstream/10419/202265/1/jrc-wptsr201907.pdf  
31  These  are  Austria,  Belgium,  Cyprus,  Estonia,  Finland,  France,  Germany,  Greece,  Hungary,  Ireland,  Italy,  Luxembourg,  Poland,  Portugal, 
Slovakia, Slovenia and Spain.  

and  Compensation  Options.  Budget  Perspectives 

(2019).  Carbon  Taxes 

2020 : 

14 

 
 
                                                           
benefits, as it is not systematically measured. For instance, current evidence does not show if the 
financial burden of paying for healthcare is not  shifted to those who can least  afford it: poorer 
groups  and  regular  users  of  health  services.  This  can  encourage  inefficient  patterns  of  use  of 
healthcare. Furthermore, there is a risk that poverty reduction fostered by policy changes related 
to cash transfers would be offset by accompanying changes in policies covering in-kind benefits 
(such  as  healthcare).  At  the  moment,  social  transfers  in-kind  are  taken  into  account  to  some 
extent  in  Estonia,  Ireland  and  Slovakia  (such  as  recreational  vouchers,  meal  vouchers,  free 
school  lunches),  Sweden  (such  as  fees  for  childcare  and  for  elderly  care).  Examples  of  other 
measures  analysed  by  Member  States’  DIA  include  reimbursements  of  medical  costs  (BE), 
payments for welfare services, doctors (DK) and some heating and water benefits (LT). 

In principle DIA can also be used in other areas where reforms and investments have an 
impact on households’ incomes. This can relate to a number of areas where reforms can have 
significant distributional effects, such as for instance labour market reforms (eg. reforms to the 
design and levels of minimum wages), or reforms that have an impact on prices (such as energy 
prices, as well as housing or medicines  prices). However, a lack of relevant data or simulation 
tools, and difficulties in determining the effects of such policies compared to an alternative, may 
in practice limit the possibilities to undertake such broader analyses.    

Box 3: Importance of DIA analysis of healthcare benefits 

Capturing the impact of healthcare benefits on poverty and inequality is highly pertinent. However, while the impact 
of cash benefits is well measured, there is a gap when it comes to monitoring the impact of healthcare benefits on 
poverty and inequality.  

Examples of assessing the redistributive impact of healthcare benefits remain rare. Still, evidence available shows 
that  in-kind  healthcare  benefits  have  substantial  redistributive  effects  (placeholder  for  key  messages  once  the 
Eurostat’s paper is published) and better measuring these effects would also help design health policies that further 
contribute  to  the  reduction  of  poverty  and  inequalities.  The  paper  ‘Social  transfers  for  education  and  health  –
imputation  into  EU  SILC  data’  (Grundiza,  2019)  shows  that  in-kind  health  benefits  reduce  income  inequalities  –
although the extent differs by country-, having the biggest impact on the disposable income of two bottom income 
quintile  groups.  The  Finnish  Institute  for  Health  and  Welfare  used  an  analysis  based  on  the  actual  consumption 
approach  with  a  focus  on  older  people  as  the  input  to  the  discussion  on  the  on-going  healthcare  reforms.  This 
analysis  shows  that  in-kind  health  benefits  narrow  the  income  gap  with  elderly  people  in  the  two  bottom  income 
quintiles  benefiting  most.  The  Belgian  Healthcare  Knowledge  Centre  presented  in  2020  the  assessment  of  the 
redistributive  impact  of  healthcare  benefits,  based  on  insurance-based  approach,  as  part  of  the  framework  of  the 
Healthcare  System  Performance  Assessment,  which  provides  scientific  expertise  used  to  improve  the  healthcare 
system.  This  analysis  shows  that  the  way  the  public  health  insurance  is  financed  matters  for  redistributive  effect. 
Social contributions and government subsidies that represent nearly three quarters of the revenue for public health 
insurance  have  a  progressive  effect.  However  quite  high  out-of-pocket  payments  (about  19%  in  2018)  reduce  the 
redistributive effect, affecting low income household relatively more than high income households.  

Results  of  these  studies  confirm  that  including  in-kind  healthcare  benefits  in  DIA  would  help  to  assess  the 
redistributive  role  of  changes  of  healthcare  coverage  policies  (introducing,  increasing  or  decreasing  co-payments, 
introducing  ceilings  for  costs).  WHO  work  on  affordability  of  healthcare  also  confirms  that  relying  on 
microsimulation models such as EUROMOD would allow to account for all the parameters that matter to ensure that 
healthcare is affordable and lack of healthcare access does not push people into poverty (in particular coverage of 
health  benefits  and  impact  of  out  of-pocket  payments,  particularly  for  poor  people  and  regular  users  of  health 
services; timely access to a broad range of health services without informal payments). Importantly, this also allows 
to  assess  the  impact  of  various  financing  options  (i.e.  through  taxes  and/or  social  contributions)  of  the  healthcare 

15 

 
 
system and exploring scenarios of alternative  policy  measures (e.g. related to the design of co-payment schemes). 
WHO work on affordability of healthcare also shows that sequencing of policy measures is very important.  Some 
countries will need to redesign health coverage policy at the same time as seeking additional public investment in 
the health system. DIA can help with providing feedback for such sequencing and ensuring that policy changes do 
not produce disproportional effects on the most disadvantaged groups. 

In the end, the choice  of policy area to examine beyond the standard play  of the tax and 
benefit systems is  likely to depend upon the model  and underlying data. Simulation of tax 
reforms  is  likely  to  be  more  robust  with  administrative  data  as  survey  data  is  known  to 
underrepresent  the  top  of  the  income  distribution.  Simulation  of  welfare  reforms,  conversely, 
may be more accurate with survey data as low-income households or administrative social data 
may better capture household’s structures. The examination of indirect or wealth taxation or non-
cash  benefits  requires  extra  data  which  is  not  usually  standard  in  microsimulation  models.  
Policies selected for running DIA vary between countries. Important considerations in a number 
of  countries  seems  to  be  factors  such  as  the  size  of  the  impact  of  measures  (DK,  FR,  SK,  IT, 
RO),  availability  of  data  (DK,  NL,  LV),  if  the  measures  could  be  simulated  in  a  reliable  way 
(BE, EL, LV, MT, RO, FI, SE, SI), policy priorities (IE, DE, ES, HU, PL) and time constraints 
(EE). 

Time horizon of the DIA analysis 

Most Member States focus in their ex ante DIA analysis on capturing the impact of policies 
in the following year (DE, IE, EL, ES, FR, HR, CY, LT, HU, PT, RO, SK, FI). Short-term 
effects are often the focus in static models (like EUROMOD) used for budget analysis. 

However  there  may  be  good  reasons  to  think  across  multiple  years.  Dynamic 
microsimulation  models  such  as  PENSIM,  SESIM  and  MIDAS  (Curry,  1996;  Flood,  2007; 
Dekkers  et  al.  2010),  simulate  inter-temporal  transitions  in  the  population.  This  allows 
researchers  to  project  how  socio-economic  development  will  evolve  under  current  policies. 
Dynamic microsimulation models can also be used to evaluate the future performance of various 
long-term  programmes  such  as  pensions,  education,  health  and  long-term  care,  by  analysing 
simulated future cross-sectional data. Dynamic microsimulation models are more complex than 
static models and this makes them more costly to develop with more methodological challenges. 

Some Member States (BE, CZ, DK, EE, IT, LV, MT, NL, AT, PL, SE) also take into account 
longer-term  effects.  This  could  be  more  medium-term  analysis  (4  years)  or  very  long-term 
effects  (until  2070). Some countries like Belgium, Malta and Austria have separate models for 
analyses  of  short-term  and  long-term  effects.  The  short-term  models  could  typically  analyse 
changes in tax and benefit systems, while longer term models could focus more on pensions and 
pension reforms, and any other ageing-related expenditure. 

Box 4: Microsimulation used for long-term projections in Malta   

For  Malta  the  model  PROST  analyses  pension  contributions,  entitlements,  system  revenues,  and  system 
expenditure, over a long time frame. The model can undertake simulations of the development of the current pension 
system,  analyse  various  options  for  pension  reform,  and  assess  together  future  aggregate  expenditure  levels,  the 
adequacy of individual pensions and the distribution of pension levels across the population as well as the impact of 
changes in coverage, eligibility, benefit levels, or contribution rates on these.   

16 

 
 
When simulating policy changes one has to decide on how to model changes that will have 
effect during a calendar year. One option is to pro-rata changes that are not in place for the full 
calendar year. Another option – and that followed by the EUROMOD model – is to include all 
policy changes made by 30 June in the policy system for that year. Policy changes which occur 
later in the year are modelled in the subsequent year32.  

c.  What tools to use? 

DIAs need to be based on sound models to provide an overall reliable picture of the impact 
of  policy  changes.  Therefore,  conducting  DIA  that  gives  credible  results  requires  reliance  on 
microsimulation  models,  their  extensions  and  linkages  with  labour  supply-demand  and  general 
equilibrium macrosimulation models.  

Policy changes are often considered in terms of their effects on a number of “hypothetical 
families”.  In  this  approach,  a  number  of  typical  households  are  considered  and  the  effect  of 
budgetary  changes  on  their  income  is  assessed  (see  Box  5).  Such  an  analysis  -  no  matter  how 
well  chosen  -  simply  cannot  give  an  overall  picture  of  the  distributional  impact  of  a  policy 
change on incomes and work incentives at the population level. This approach has limitations as 
households  within  a  category  might  differ  in  terms  of  income,  housing  tenure,  number  of 
children and other characteristics that affect their tax-benefit position.  

Box 5: Hypothetical household models 

The OECD Tax-Benefit Simulation Model (TaxBEN) 

The  tax-benefit  model  TaxBEN  (TaxBEN)  is  the  cross-country  tax  and  benefit  simulation  model  developed  and 
maintained by the OECD. It is a unique tool for exploring the detailed mechanics of tax-benefit policies and reforms 
on  working  age  individuals  and  their  families  across  countries.  The  scope  of  TaxBEN  includes  taxes  and  social 
benefits  that,  together,  account  for  a  large  share  of  government  budgets.  TaxBEN  produces  policy  indicators  on 
household incomes, labour costs and work incentives in different family situations and policy settings. It covers a 
broad set of income support and tax policies going back to early 2000s for more than 40 OECD member and non-
member countries. The model draws on a comprehensive library of tax and benefit policy rules that are relevant for 
working-age individuals and their families. 

Hypothetical Household Tool (HHoT) 

The Hypothetical Household Tool (HHoT) is a EUROMOD (please see below for details about EUROMOD) plugin 
for  designing  hypothetical  households  and  generating  data  according  to  the  chosen  household  and  individual 
characteristics.  The  generated  dataset  can  then  be  used  to  estimate  the  effects  of  taxes  and  benefits  on  household 
disposable income, by running EUROMOD. 

With  the  HHoT plugin  users  can  create  their  own  hypothetical  data,  which  allows  them  to  better  understand  how 
policies impact households with specific characteristics, while giving full control over the characteristics of interest 
(see for instance  Gasior and Recchia 2019). HHoT generates specified hypothetical  household input datasets. The 
flexibility  of  the  tool  allows  users  to  specify  a  broad  spectrum  of  different  hypothetical  households.  The 
specification  of  household  composition  and  other  characteristics  is  only  limited  by  the  scope  of  variables  in 
EUROMOD input data. For example, various household types can be included, as well as a variety of labour market 
statuses  and  income  sources.  HHoT  allows  users  to  compose,  save,  use  and  re-use  their  own  database  of 

32EUROMOD  does  this  as  a  general  rule,  but  has  a  feature  (Full  Year  Adjustment  extension)  that  allows  to  take  into  account  the  date  of 
implementation. In several countries this extension is used to adjust policies approved before or after 30th June. This is  available in the public 
model.  

17 

 
 
                                                           
 
hypothetical  households.  This  plugin  is  included  with  the  default  installation  of  EUROMOD,  and  can  be  found 
under the "Applications" toolbar ribbon, inside the "Tools" group.33 

Microsimulation models34 provide a suitable alternative to hypothetical household analysis 
for  conducting  DIA  (see  for  instance  Bourguignon  and  Spadaro,  200635).  They  are  based  on 
large-scale representative samples of households. This ensures that the models represent as fully 
as possible the great diversity of household circumstances relevant to tax and social benefits. 

Broadly  speaking,  there  are  two  main  classes  of  microsimulation  models  that  Member 
States use for conducting DIA. On the one hand, EUROMOD is a harmonised microsimulation 
model for the EU, currently  managed, maintained and developed  by the  Joint  Research Centre 
(JRC)  of  the  European  Commission.  The  model  is  continuously  updated  in  collaboration  with 
Eurostat  and  national  teams  from  each  EU  Member  State,  making  use  of  microdata  from  the 
European  Union  Statistics  on  Income  and  Living  Conditions  (EU-SILC).  On  the  other  hand, 
many Member States also use country-specific microsimulation models (Figure 3).  

Figure 3: Number of EU Member States by type of model(s) used for DIA 

Note: total = 26 (no information on LU). Source: questionnaire to the Member States. 

EUROMOD microsimulation model36 

EUROMOD  is  a  tax-benefit  microsimulation  model  available  for  all  EU  Member  States 
enabling  researchers  and  policy  analysts  to  calculate,  in  a  comparable  manner,  the  effects  of 
direct  taxes  and  benefits  on  household  incomes,  poverty  and  income  inequality  and  work 
incentives for the population of each Member State. Cross-country comparability is enabled by 

33 More information on HHoT can be found on the EUROMOD website: https://euromod-web.jrc.ec.europa.eu/overview.  
34 Microsimulation models are usually based on a representative set of data for a country’s population (administrative or survey data) as well as a 
detailed  modelling  of  the  tax  and  benefit  system  of  a  country.  These  characteristics make  them  suitable  for  detailed  distributional  analyses  of 
changes in tax or benefit systems. 
35 (PDF) Microsimulation as a tool for evaluating redistribution policies | Amedeo Spadaro and Francois Bourguignon - Academia.edu 
36 For detailed information about the model and access to it, check the EUROMOD website: https://euromod-web.jrc.ec.europa.eu/.  

18 

 
 
 
 
                                                           
coding the policy systems of the EU Member States according to a common framework based on 
a standard set of modelling conventions. The input microdata used in the model (based on EU-
SILC  microdata)  are  processed  according  to  a  standard  set  of  protocols.  The  EUROMOD 
platform  is  highly  flexible  but  also  documented,  validated  and  transparent.  Underpinning  the 
model  is  the  purpose-built  software  comprising  a  user-friendly  interface,  supplemented  by 
extended functionalities (plugins and add-ons) for special purpose analysis. EUROMOD is used 
by around half of the EU-27 Member States for conducting DIA analysis (AT, BE, EE, EL, FI, 
IE, HR, IT, MT, NL, LV, RO, SK). 

An annual release of a stable version of EUROMOD occurs in January each year, while two 
beta  versions  are  released  in  April  and  September.  EUROMOD  is  open  source,  free  and 
transparent  to  use.  It  can  also  be  made  compatible  with  country-specific  administrative  data, 
which is a significant advantage. It allows the automatic computation of a range of costings and 
indices relating to income distribution. While there are multiple advantages of the model, some 
substantial initial investment is required (in terms of time dedicated to  learning how to use the 
model and maintaining the skills required for regular use of the model due to the specificity of its 
programming language).  

EUROMOD  can  be  particularly  useful  for  Member  States  which  have  none  or  rather 
limited DIA practice in place. It is readily available for free to all Member States. Greece is an 
example of a country which recently successfully adopted EUROMOD as part of its budgetary 
cycle,  which  in  turn  resulted  in  presenting  DIA  outcomes  in  the  Draft  Budgetary  Plans  on  a 
regular  basis.  While  setting  up  the  DIA  process,  the  Greek  national  administration  officials 
benefited  from  the  technical  assistance  provided  by  the  JRC.  This  also  contributed  to  building 
trust in the results of the model. This has enabled the Greek authorities to provide timely analysis 
on reforms to taxes and benefits along with analysing the effect of labour market shocks. 

An  interesting  feature  of  EUROMOD  is  the  In-depth  Analysis  plugin.  It  is  a  user-friendly 
tool  that  produces  a  set  of  eighteen  fully  customisable  tables  for  analysing  the  impact  of  tax-
benefit reforms, classified in three groups: fiscal, distributional, and inequality and poverty. The 
user  can  select  and  construct  variables  for  analysis  and  variables  for  breakdowns,  restrict  the 
calculations  to  specific  target  populations,  define  specific  income  variables  for  inequality  and 
poverty  calculations,  and  change  parameters  for  those  indicators.  Additionally,  users  can  save 
their  sets  of  preferences  and  load  them  at  any  time,  so  that  they  can  replicate  the  same 
calculations  in  the  future,  with  the  same  or  different  outputs.  The  output  tables  are  shown  on 
screen,  with  the  possibility  to  save  them  in  Excel  format.  The  user  can  optionally  save  the 
microdata of the simulated scenarios in a single dataset. This plugin is included with the default 
installation of the EUROMOD software. 

EUROMOD  can  be  very  useful  for  gauging  the  distributional  impact  of  the  green 
transition  and  energy  price  shocks.  For  example,  in  an  ongoing  Commission  project  GD-
AMEDI (Green Deal - Assessing and Monitoring Employment and Distributional Impacts of the 
Twin  Transition)  EUROMOD  is  used    for  assessing  employment  and  distributional  impacts  of 
climate  and  energy  policies  across  Member  States,  regions,  sectors,  occupations,  skills, 
households  and  income  group.  As  such,  the  project  endows  the  Commission  with  instruments 

19 

 
 
and  background  to  conduct  policy  analyses  related  to  the  impacts  of  measures  taken  in 
carbon/GHG  intensive  sectors  on  the  labour  markets,  employment  and  skills  and  on  relevant 
social outcomes, including energy poverty or mobility poverty. 

Country-specific models 

Many Member States use their country-specific models. National models can take two forms. 
Some  are  standalone  models  which  were  built  separately  from  EUROMOD  and  which  are 
maintained by country experts, often located within ministries, think-tanks or central banks (e.g.  
DK,  FI,  NL  which  have  a  long-standing  tradition  of  running  DIA  analysis).  Other  national 
models use EUROMOD but extend it to the needs of the country-specific context (e.g. IE, LV).  

National  models  have  some  advantages  and  disadvantages  compared  to  EUROMOD.  On 
the one hand, the national models are often based on more detailed data (such as administrative 
records)  than  EU-SILC,  which  allows  for  more  accurate  and  detailed  simulations  but  may  not 
allow access for some users. On the other hand, EUROMOD benefits from large economies of 
scale as there are 27 national teams as well as a team at the JRC working to maintain and update 
the  model.  This  allows  for  a  harmonised  data  preparation  process,  which  tends  to  reduce  the 
likelihood  of  error.  There  is  also  extensive  documentation  and  validation  of  the  EUROMOD 
model which may happen to a much lesser extent, if at all, for national models.  

Linking microsimulation models with other tools and models 

Incorporating  behavioural  responses  and  macroeconomic  feedback37  helps  make  DIA 
results  more  realistic  but  requires  additional  tools.  Many  policies  cause  important 
macroeconomic feedback effects,  which cannot  be captured by static models which is  the case 
for  the  vast  majority  of  microsimulation  models.  One  option  is  to  use  macroeconomic  models 
that allow for some degree of heterogeneity in terms of household or labour types. For example, 
Roeger et al. (2019)38 use QUEST, a dynamic stochastic general equilibrium (DSGE) model with 
different  skills  types.  Many  computable  general  equilibrium  (CGE)  models  account  for 
considerable  disaggregation  by  household  types  (Dixon  and  Jorgensen,  201339,  provide  many 
examples).  Furthermore,  each  of  these  types  of  macroeconomic  models  can  be  linked  to  a 
microsimulation  model,  such  as  Barrios  et  al.  (2019)40  (link  of  EUROMOD  to  QUEST), 
d’Andria  et  al.  (2019)41  (link  of  EUROMOD  to  an  overlapping  generation  model)  and 
Bourguignon et al. (2010)42 (overview of linking microsimulation models to CGE models). 

It is also important to ensure consistency with other exercises. It helps when the assumptions 
and some of the main variables in the models are consistent with other relevant exercises. This 

37  Microsimulation  models  often  do  not  account  for  behavioural  responses  (such  as  the  estimated  effects  on  labour  supply  of  changes  in  the 
income  tax)  or  macroeconomic  feedback  (for  instance  increased  labour  supply  may  increase  GDP  and  government  revenues).  Incorporating 
behavioural responses and macroeconomic feedback can be done by building more advanced models taking into account the various effects (such 
as incorporating  behavioural replies into a microsimulation model or linking it to a macroeconomic model). 
38 https://www.researchgate.net/publication/356305566_The_Distributional_Impact_of_Labour_Market_Reforms_A_Model-Based_Assessment  
39 Peter B. Dixon and Dale W. Jorgenson (Ed.), Handbook of Computable General Equilibrium Modeling. Elsevier: 
https://www.sciencedirect.com/handbook/handbook-of-computable-general-equilibrium-modeling/vol/1/suppl/C  
40 https://wittneben.eu/publication/barrios-2019/barrios-2019.pdf  
41 https://joint-research-centre.ec.europa.eu/publications/edge-m3-dynamic-general-equilibrium-micro-macro-model-eu-member-states_en.  
42 https://microsimulation.pub/articles/00020  

20 

 
 
                                                           
can  be  ensured  by  applying  the  same  assumptions,  constraining  variables  to  follow  pre-
determined values, and comparing results. This would make the model results more relevant in 
the ongoing policy debate, and prevent discussions on the plausibility of underlying assumptions.  

EUROMOD  may  be  complemented  by  add-ons  or  extensions  which  enable  a  more 
comprehensive  assessment  of  the  impact  of  reforms  (see  Box  6).43  For  example  the  labour 
market  adjustment  (LMA)  tool  proved  to  be  convenient  for  modelling  employment  losses  as  a 
result  of  lockdown  policies  during  the  COVID-19  pandemic44.  This  allowed  the  assessment  of 
the  impact  of  emergency  policy  responses  in  terms  of  wage  subsidy  schemes  and  emergency 
unemployment supports.  

Box 6: EUROMOD extensions  

Indirect Tax Tool (ITT) 

This extension of EUROMOD uses EU-SILC data matched with HBS data. In collaboration with the University of 
KU  Leuven,  the  University  of  Essex  and  Praxis  Centre  for  Policy  Studies,  the  JRC  has  expanded  EUROMOD  to 
account  for  the  simulation  of  indirect  taxes  in  all  Member  States.  During  2022  this  tool  is  being  tested  with  the 
objective of incorporating it to the public version of EUROMOD distributed by the JRC in the future.  

This new tool allows to simulate tax reforms at a highly disaggregated level of consumption (in most cases at the 
commodity level, e.g. "rice"). Also it does not rely on a EUROMOD plug–in, but on a specific policy module. The 
full integration aims at increasing the model’s transparency and ease of use and no additional knowledge is required 
to  run  a  policy  simulation  including  an  indirect  tax  reform.  For  tax  reform  simulations,  three  behavioural 
assumptions  are  allowed:  constant  income  shares  of  consumption,  constant  expenditure  shares  and  constant 
quantities, which correspond to different price and income elasticities.  

European Wealth data InteGration in EUROMOD (EWIGE) 

It is an alternative version of EUROMOD that uses input data based on the Household Finance and Consumption 
Survey  (HFCS)  data,  allowing  to  simulate  wealth-related  taxes.  Indeed,  analytical  tools  are  still  underdeveloped 
when it comes to empirical analyses of different types of wealth-related taxes and policies. Distributional analysis of 
income  and  wealth  requires  information  on  disposable  income  and  wealth.  Integrating  the  HFCS  data  in 
EUROMOD makes it possible to assess the effect of different current and hypothetical wealth taxes and policies on 
the distribution of income and wealth.45 

JRC's EUROLAB labour supply-demand model 

It  is  an  EU  labour  supply-demand  microsimulation  model  built  on  EUROMOD  and  based  on  the  modelling  of 
discrete  choice  labour  supply  through  a  Random  Utility  Maximization  approach.  It  estimates  a  set  of  structural 
parameters of the utility function and applies them to estimate labour supply behaviour. The main contributions of 
EUROLAB  are  the  assessment  of:  1)  labour  supply  elasticities,  2)  changes  in  the  labour  participation  rate  and 
working  hours,  and  3)  changes  in  labour  supply  when  labour  demand  is  taken  into  account.  Other  individual  or 
household  decisions  can  be  factored  in.  Currently  it  appears  that  most  Member  States  use  their 
microsimulation  models  as  such  for  DIA  and  in  particular  do  not  link  a  macro-model  to 
their micro-model. When a Member State uses  a macro-model (not necessarily for DIA), it is 

43 The model has been linked to EUROLAB (JRC’s labour supply/demand model), QUEST (Dynamic Stochastic General Equilibrium model  of 
the European Commission’s Directorate General for Economic and Financial Affairs), JRC GEM-E3 (energy and environmental model) and JRC 
EDGE-M3 (pension and demographic model). 
44 Labour transition model based data are produced by Eurostat, using detailed distributional information on the loss of jobs and short-term work 
schemes from the Labour Force Survey and administrative data. The impact across different categories of individuals, the duration of 
unemployment/absence and percentage of hours worked are modelled using the EU-LFS longitudinal and quarterly transitions as target. 
45 https://joint-research-centre.ec.europa.eu/publications/ewige-2-update-and-extension-euromod-wealth-taxation-project_en  

21 

 
 
                                                           
often  used  separately  from  the  micro  model  although  some  Member  States  take  into  account 
macro-economic forecasts in (some of their) microsimulations (BE, DK, FR, HU, PL). 

Defining the baseline for the assessment of the distributional impacts 

An  important  question  to  answer  before  running  a  DIA  analysis  is  whether  the  policy 
reforms  will  be  compared  to  an  indexed  baseline  or  a  baseline  that  is  fixed  in  nominal 
terms.  Comparing  to  an  indexed  baseline  –  in  which  the  parameters  of  the  tax  and  welfare 
system are adjusted to account for forecast inflation or wage growth – allows to show what the 
effect of budgetary reform is compared to  a scenario in which purchasing power is maintained 
and/or  income  inequality  is  constant.  Comparing  to  a  nominally  fixed  baseline,  on  the  other 
hand,  shows  the  effect  of  policy  reform  in  nominal  terms,  without  accounting  for  inflation  or 
income growth.46  

Defining  the  baseline  is  an  important  decision  as  there  is  a  number  of  possibilities  for  a 
baseline policy to  evaluate  year-to-year changes  including indexation of tax-benefit parameters 
(or  benefit  parameters  alone)  by  wage/price  growth.  It  also  needs  to  be  decided  whether  the 
baseline  should  include  policy  changes  which  were  pre-announced  and  temporary  policies. 
Whatever decision is taken in that respect, it is crucial to clearly describe what the baseline is and 
how this affects the interpretation of results. In their budgetary plans eleven euro area Member 
States rely primarily on the previous year’s status quo as the baseline scenario. Using a projected 
baseline scenario for today (nowcasting) or a dynamic projection for the future (forecasting) is 
less  common  (with  five  and  seven  countries  using  them  respectively).  Nevertheless,  about  one 
third of countries use more than one baseline scenario when conducting DIA.       

The data used for the model baseline should be timely, whereas there is inevitably a time 
lag between a survey being conducted and the data being available. (In the case of EU-SILC, 
in  the  framework  of  Regulation  (EU)  2019/1700,  Eurostat  receives  microdata  from  Member 
States for operation year N by the end of year N, and complete datasets with final income data is 
received  by  28  February  of  year  N+1.  Country  indicators  are  published  as  soon  as  the  data  is 
validated and approved. In a period of minimal demographic and labour market change, this is 
usually not problematic. However, in a period of recession or significant labour market change, 
using older data can have significant implications for DIA. A common solution to this is to use 
uprating factors to  adjust the data to  subsequent years. The model ‘uprates’ the data to  current 
year’s price and income levels using price and wage inflation and this is  typically sufficient to 
approximate the current income distribution4748. 

Most Member States apply uprating and reweighting to the data to take into account latest 
developments  in  income  and  tax-benefits  systems  as  well  as  to  reflect  the  population's  latest 
situation/composition.  A  few  Member  States  account  for  changes  in  demography  (e.g.  use  of 

46  Bargain  and  Callan  (2010)  show  that  gross  income  inflation  is  a  distributionally  neutral  factor  that  seems  most  appropriate  for  indexed 
benchmarks: https://econpapers.repec.org/article/kapjecinq/v_3a8_3ay_3a2010_3ai_3a1_3ap_3a1-21.htm  
47 O'Donoghue, Cathal and Loughrey, Jason (2014) 'Nowcasting in Microsimulation Models: A Methodological Survey' Journal of Artificial 
Societies and Social Simulation 17 (4) 12 https://www.jasss.org/17/4/12.html  
48 Navicke, J., Rastrigina, O., & Sutherland, H. (2014). Nowcasting indicators of poverty risk in the European Union: A microsimulation 
approach. Social Indicators Research, 101-119. https://www.jstor.org/stable/24721071  

22 

 
 
                                                           
ageing  projections  in  some  Member  States)  (BE,  DK,  IE,  FR,  LT)  and  uprating  for  various 
macroeconomic developments.       

Human resources 

DIA is usually performed by a team of 3 or 4 people, consisting mostly of economists, often 
working within the ministry of finance. Some countries (Slovenia, Germany, Ireland and Latvia) 
employ external consultants (e.g. research institutes) to support them. 

In  terms  of  training  on  DIA,  on-the-job  training  is  usually  the  preferred  option.  This 
training is often complemented by the participation in the courses organised by the EUROMOD 
network. The training needs depend on the complexity of the model used. For the full model, a 
sufficient level of training is needed to use the model correctly.  

More complex models are more challenging to extend and maintain. The Member States that 
successfully  do  this  tend  to  have  solid  structures  to  ensure  the  quality  of  production.  For  the 
others, it is suggested that they maintain a team of experts for the DIA model and to ensure the 
continuity  of  DIA  expertise.  This  team  would  need  to  be  given  adequate  time  and  resources 
(such as training resources) for having reliable DIA model in place and for performing DIA of 
advanced quality using it. 

Entrusting  independent  bodies  with  doing  DIAs  can  help  make  them  more  credible  and 
accurate.  Thanks  to  their  expertise,  academia  or  research  institutes  are  well  placed  to  develop 
advanced  DIA  techniques.  Moreover,  they  are  not  affected  by  the  same  political  motives  as 
administrations  when  assessing  decisions  to  which  they  have  often  not  contributed.  The 
replication of results by  various institutions  can also  make for a more comprehensive analysis, 
and enhance its quality and credibility. 

d.  What data to use? 

Two types of data sources can be linked to a microsimulation model to conduct DIA. Survey 
data,  such  as  EU-SILC,  tend  to  be  most  straightforward  to  access  and  are  widely  used49. 
Alternatively, administrative or register data can be linked to the model. A third option combines 
the analysis based on survey data with that based on some administrative data to allow a more 
accurate simulation of incomes, benefit eligibility and tax liabilities.  

49 It is important to note that EU-SILC also has some data coming for administrative records, in particular for income data for approximately half 
of the EU countries. 

23 

 
 
                                                           
Figure 4: Number of EU Member States by type of data used for DIA 

Note: total = 26 (no information on LU). Source: questionnaire to the Member States. 

There  are  differences  as  to  the  type  of  data  feeding  into  DIA  across  countries.  Some 
Member States conducting DIA only rely on administrative data, using one or multiple datasets 
(BE, EE, ES, NL, PT, SE). Some other Member States  use administrative data  in  combination 
with  survey data (SILC or HBS data for instance) (AT, CZ, DK,  FI, FR,  IE, EL, HR,  IT, HU, 
LV, MT, PL, RO, SK). In most Member States, administrative data used for DIA are not publicly 
available, but they are easily accessible (possibly upon request) in some countries (DK, ES, IE, 
SE). 

24 

 
 
 
Figure 5 – Data used for producing DIA analysis - euro area Member States(*) (N=17) 

Note: respondents are allowed to choose multiple answers. (*) Cyprus and Luxembourg are excluded since no DIA 
is performed in these Member States. Source: Study on Distribution Impact Assessment by the University of Milan, 
financed by the Commission (DG EMPL): https://data.europa.eu/doi/10.2767/511644  

Survey data 

Survey data, such as EU-SILC, which is readily available to researchers, is widely used in 
DIA  by  academics  and  public  administrations.  Depending  on  the  national  practices,  many 
benefits in the survey data may be aggregated together50. This can make it difficult, for instance, 
to  ascertain  whether  somebody  is  in  receipt  of  unemployment  assistance  or  unemployment 
insurance  benefit,  with  implications  for  simulating  policy  changes  to  either  of  these.  Those 
countries that have their microsimulation models based on national survey data need to have in 
place  methods  to  ensure  that  the  data  are  of  sufficient  quality  and  timeliness  and  include 
adequate details on the income and living conditions of individuals and households. 

50  According  to  Commission  delegated  regulation  2020/258  supplementing  regulation  2019/1700,  from  2021  member  states  should  provide 
disaggregate  benefits  variables  as  part  of  the  yearly  mandatory  EU-SILC  variables,  i.e.  benefit  variables  are  breakdown  by  whether  they  are 
contributory or not, and if they are means-tested or not. 

25 

 
 
 
                                                           
Box 7: Sources of survey data 

EU Survey on Income and Living Conditions (EU-SILC) 

The EU Statistics on Income and Living Conditions (EU-SILC) collects comparable cross-sectional and longitudinal 
data  on  income,  poverty,  social  exclusion  and  living  conditions.  EU-SILC  provides  two  types  of  data:  cross-
sectional  data  over  a  given  time  or  a  certain  time  period  with  variables  on  income,  poverty,  social  exclusion  and 
other living conditions and longitudinal data on individual-level changes over time, observed periodically over a 4-
or  6-year  period.  Information  on  social  exclusion  and  housing  conditions  is  collected  mainly  at  household  level, 
while labour, education and health information is obtained from individuals aged 16 and over. Income variables at 
detailed component level are also mainly collected from individuals. 

EU-SILC data are used to monitor poverty and social inclusion  in the EU as part of the European Pillar of Social 
Rights  and  European  Semester  -  the  framework  for  coordinating  economic  policies  across  the  EU  –  through  the 
social scoreboard. EU-SILC data also provide quantitative evidence for monitoring the implementation of the social 
protection and inclusion dimension of the European Pillar of Social Rights, provide data for the Social Protection 
Performance Monitor and are used in the context of the Open Method of Coordination on social inclusion and social 
protection.  

Household Budget Survey (HBS) 

The  Household  Budget  Survey  (HBS)  is  a  national  survey  focusing  on  households'  expenditure  on  goods  and 
services,  giving  a  picture  of  living  conditions  in  the  EU.  It  is  carried  out  by  each  Member  State  and  is  used  to 
compile weightings for important macroeconomic indicators, such as consumer price indices (used as measures of 
inflation) and national accounts. They were launched in most EU Member States at the beginning of the 1960's and 
Eurostat has been collecting and publishing these survey data every five years since 1988. The two last  collection 
rounds (waves) were 2015 and 2020. 

Household Finance and Consumption Survey (HFCS) 

The  HFCS  collects  information  on  the  assets,  liabilities,  income  and  consumption  of  households.  The  dataset 
provides  insights  into  their  economic  behaviour  and  financial  situation  –  highly  relevant  factors  in  terms  of 
monetary  policy  and  financial  stability.  The  survey  is  based  on  84,000  interviews  conducted  in  18  euro  area 
countries, as well as Poland and Hungary, mainly in 2013 and 2014.  There have been three survey waves so far, on 
which the data were released in April 2013, December 2016 and March 2020, respectively. 

The  HFCS  questionnaire  consists  of  two  main  parts:  (i)  questions  relating  to  the  household  as  a  whole,  including 
questions  on  real  assets  and  their  financing,  other  liabilities  and  credit  constraints,  private  businesses,  financial 
assets,  intergenerational  transfers  and  gifts,  and  consumption  and  saving;  (ii)  questions  relating  to  individual 
household members, covering demographics (for all household members), employment, future pension entitlements 
and income (for household members aged 16 and over). 

Administrative data 

The main advantages of administrative data include the  larger sample size higher level of 
disaggregation and accuracy of incomes (even though in practice, analysis is often carried out 
using  synthetic  data  to  protect  taxpayer  anonymity  For  example,  BELMOD,  the  new 
EUROMOD-based  microsimulation  model  developed  in  Belgium,  incorporates  administrative 
data based on a sample of almost 9% of the total population. 

Box 8: Data used for DIA in Finland 

The SISU model used for DIA in Finland relies on administrative data representing a sample of 15% of the whole 
population, which is a high proportion by international comparison. The data based on income distribution statistics 

26 

 
 
 
 
 
 
 
 
 
 
 
collected  by  Statistics  Finland  comes  from  various  administrative  sources  such  as  the  Population  Register,  Tax 
Administration, the Social Insurance Institution, Centre for Pensions and  the Financial Supervisory Authority. The 
housing costs are imputed based on a regression model. The data is published with a lag of 1.5 years,  and Statistics 
Finland  publishes  nowcasted  and  forecasted  data  (adjustments  e.g.  to  incomes  and  employment  rates  used  in  the 
budget process). 

Some drawbacks to administrative data include the limited socio-demographic information 
they contain. Administrative data may also be skewed by tax evasion. Sometimes, the data must 
be used on the Virtual Desktop Interface (VDI) of a statistical agency with a statistician charged 
with checking that statistical disclosure rules are not violated before results are transferred to the 
user’s own PC (see Box 9 for more details on the pros and cons of using administrative data). 

Box 9: Advantages and disadvantages of using administrative data 

Opportunities of administrative data 

Using administrative data allows for more precise simulation of policy reforms, improving the accuracy of simulated 
outcomes.  This  is  due,  among  others,  to  a  larger  coverage  of  population,  availability  of  work  histories,  the 
possibility to study small subgroups (e.g. persons with disabilities) or rare events (e.g. multiple births); and a more 
accurate recording of information (no misreporting, sampling errors, etc.). Analysis based on administrative allows 
assessing  crucial  policy  matters  such  as  sustainability  of  pension  systems  or  labour  market  transitions  (especially 
atypical  workers).  Administrative  data  also  help  simulate  subnational  policies  better  as  it  can  provide  more 
geographical  details,  allowing  to  zoom  in  on  the  geographical  distribution  of  income  beyond  the  usual  regional 
disaggregation  (e.g.  at  local  level).  This  is  also  important  as  in-kind  transfers  are  often  provided  through  local 
services. 

Challenges of administrative data 

However, using administrative data has also its challenges due a number of aspects, such as gaining access can be 
difficult  and  time  consuming,  privacy  concerns  when  the  data  is  used  by  researchers,  clear  and  informative 
accompanying documentation is often missing, understanding the units  included in administrative sources may not 
be  straightforward  (and  requires  communication  with  data  provider),  policy  changes  may  lead  to  changes  in  data 
collection,  there  may  be  computational  issues  given  the  size  of  administrative  datasets,  suitable  “background” 
variables (e.g. sociodemographic characteristics) might be lacking as compared to survey data. 

Typical  national  administrative  datasets  that  are  useful  for  DIA  include  records  of  taxpayers  of  Personal  Income 
Tax, other records of taxpayers (wealth taxes, immovable property taxes, inheritance and gift taxes, etc.), databases 
of recipients of social benefits, pensioners, unemployment benefits, records of social insurance contributions paid by 
employees, self-employed and employers.  

Combining survey and administrative data 

Using  survey  data  next  to  administrative  data  can  enhance  the  quality  of  DIA  results. 
Combing a survey dataset, such as SILC, with administrative records relating to income, welfare 
and  taxation  can  often  provide  a  solution  to  some  of  the  drawbacks  of  either  survey  or 
administrative data used alone. The SWITCH model used by the Economic and Social Research 
Institute  and  government  departments  in  Ireland  is  an  example  of  a  national  microsimulation 
model based on linked survey and administrative data.  

Administrative data is best seen as a complement to surveys such as EU-SILC, rather than 
a  replacement.  Simulations  using  administrative  data  allow  for  more  accurate  modelling  of 

27 

 
 
 
 
 
 
 
social  security  contribution  history  and  certain  policies.  The  adaptability  of  EUROMOD  to  be 
compatible with national level administrative data is a significant advantage. In the Greek case, 
this  has  been  particularly  advantageous  as  EUROMOD  has  been  twinned  with  rich 
administrative tax data, which is generally considered as more robust than EU-SILC in some key 
aspects, most notably revenue estimation. 

It is also important to stress the crucial role on the national statistics institutes in providing 
timely, comprehensive and detailed data that can be used for running DIA analysis. Further 
efforts  should  be  made  in  that  respect.  Officials  in  France  and  Finland  pointed  at  the  lack  of 
variables  in  available  data  that  would  enable  DIA  to  be  carried  out  for  more  policies.  More 
precisely,  in  France,  the  issue  is  that  income  data  are  only  available  on  yearly  income,  while 
monthly  information  would  be  useful  for  performing  DIA  on  some  specific  social  benefits.  In 
Finland, the survey data on consumption lacks variables about the consumption of alcohol and 
tobacco. 

e.  What indicators to use?  

A  number  of  indicators  lend  themselves  to  illustrate  the  distributional  impact  of  policy 
reforms.  These are typically those that capture the impact of policy changes on the household 
income along the income distribution as well as on the levels of inequality and poverty51. Using 
the common indicators agreed at the EU level makes comparisons across Member States easier.  

In order to illustrate the impact on household income distribution, the decile (or quintile) 
impact  of  policy  reform  are  commonly  used  to  show  how  reforms  affect  low-income  and 
high-income households  differently.  It  is  suggested to  use mean equivalised (i.e. adjusted for 
household  composition)  disposable  income,  meaning  income  after  direct  taxes  have  been  paid 
and  benefits  have  been  received.  This  has  the  benefit  of  being  comprehensive  and  comparable 
between individuals and households. Having such clear view on the impact of a measure on the 
deciles’ mean disposable incomes also  enables to  shows the impact  of  policies on households’ 
purchasing power along the income distribution.  

DIA  analysis  should  also  show  the  impact  of  policy  reform  on  the  level  of  inequality  by 
relying on (a selection of) key indicators, such as the income quintile share ratio (S80/S20) that 
is the share of income going to the richest 20 per cent compared to the poorest 20 per cent of the 
population), which is the related European Pillar of Social Rights Scoreboard indicator (as well 
as  its  decomposition  between  the  upper  part  S80/S50  and  lower  part  S50/S20  of  the  income 
distribution). It can be complemented by the income share of the bottom 40% of the population 
along the income distribution (S40) or the Gini index (which is the most widely used aggregated 
measure  of  income  inequality,  capturing  what  percentage  of  the  domestic  income  of  a  country 
each  cumulative  percentile  of  the  population  owns  and  converting  it  into  an  index).  Such 
summary measures of income inequality can be used to illustrate the effect of policy reform on 

51 EUROMOD has embedded tools (Statistics Presenter and In-depth Analysis plugin) that can automatically calculate most of the indicators 
described. Additionally, the outcomes can be fully customised (e.g. for the population groups mentioned at the end of this section). 

28 

 
 
                                                           
 
the income distribution, but at the same time they can mask heterogeneity at different points of 
the income distribution, which decile and quantile impact assessments pick up on. 

DIA results should also capture the impact on the level of poverty. It is suggested to use the 
at-risk-of-poverty  (AROP)  rate  and  the  relative  median  at-risk-of-poverty  gap.  The  first  one  is 
calculated  as  the  share  of  persons  with  an  annual  equivalised  disposable  income  below  60  per 
cent  of  the  median  annual  equivalised  disposable  income  and  is  the  main  component  of  the 
AROPE  indicator that underpins the EU target  of reduction of poverty and exclusion by 2030. 
The  relative  median  at-risk-of-poverty  gap  shows  how  far  below  the  poverty  line  the  median 
person in poverty is as a share of the value of the poverty line.  

Box 10: Additional indicators of inequality 

Additional  measures  of  income  inequality  such  as  the  Atkinson  Index  or  Theil  Index  (or  other  general  entropy 
measure) do not display some of the known weaknesses of the Gini Index, such as being more sensitive to changes 
in the middle of the income distribution than to changes at the top or bottom, but are seldom used.  

Atkinson Index: A measure of income inequality that includes the level of aversion to inequality (measured in the 
coefficient ϵ), which weights incomes along with the distribution differently. Depending on the level of inequality 
aversion, the index becomes more or less sensitive to changes at different ends of the distribution. For example, if 
the inequality aversion is high, the marginal social welfare of an increase in income is higher for the lower end of 
the distribution. If there is no aversion (ϵ=0), the marginal social welfare for an income increase for the lower end of 
the income distribution is the same as for an increase for the higher end.  

Theil  Index:  A  measure  of  income  inequality  that  is  built  on  statistical  information  theory  and  belongs  to  the 
generalised  entropy  family  of  inequality  indices.  The  Theil  index,  like  all  members  of  the  generalised  entropy 
family, can be exactly decomposed in between and within the inequality of mutually exclusive groups. The value of 
the index can range from 0 (perfect equality) to infinity.  

Reynolds-Smolensky  Index:  The  most  commonly  used  index  of  redistribution.  It  measures  redistribution  as  the 
difference between the Gini index of income before tax and the concentration index of post-tax income. 

Kakwani Index: It uses the Gini framework to measure how progressive a social intervention is. It is equal to the 
difference between the Gini index for the social intervention, and the Gini index for incomes before imposition of 
the policy intervention. Theoretically, the Kakwani index can vary between −1 to 1; the larger the index is, the more 
progressive is the social intervention. 

The  mean  disposable  income  by  income  group  and  the  Gini  coefficient  are  the  most 
frequently  used  indicators  for  presenting  DIA  results  in  DBPs  (Figure  6).  Over  the  period 
2018-2020, three countries used the AROP rate, the relative median at-risk-of-poverty gap and 
performed  a  “winners  and  losers”  analysis52  from  the  implemented  policy  across  different 
income  groups.  Two  countries  included  the  total  share  of  income  of  the  bottom  80%  in  the 
income distribution (S80), while Theil index, Atkinson indices and wealth inequality indicators 
are used by only one country.  

52 Meaning an analysis of who and how many are those who gain or lose from the implemented policy as opposed to the situation without the 
policy. 

29 

 
 
 
 
                                                           
Figure 6 –Types of DIA analysis by euro area Member States in DBP (2018-2020) (N=9) 

Note:  The  analysis  includes  only  Estonia,  Finland,  France,  Greece,  Ireland,  Latvia,  Lithuania,  Malta  and  the 
Netherlands, as they are the only Euro Area Member States that performed at least one DIA occurrence in their DPB 
in  2018-2020.  Respondents  are  allowed  to  choose  multiple  answers.  Source:  Study  on  Distribution  Impact 
Assessment 
EMPL): 
https://data.europa.eu/doi/10.2767/511644  

the  Commission 

the  University 

of  Milan, 

financed 

(DG 

by 

by 

Outside the DBPs, the majority of Member States conducting DIA used winners and losers 
across  different  income  groups  (changes  in  income  levels  for  various  groups,  such  as 
income deciles or quintiles), Gini coefficient, average tax burden by income group, at-risk-
of-poverty rate and mean disposable income by income group to present the results (Figure 
7).  About  half  of  Member  States  included  the  total  share  of  income  of  the  bottom  80%  in  the 
income  distribution,  S80,  and  at-risk-of-poverty  gap.  Only  a  few  Member  States  based  its 
analysis  on  Kakwani  index,  wealth  inequality  indicators,  Reynolds-Smolensky  index,  Theil 
index, total share of income of the bottom 40% in the income distribution and Atkinson index. 

30 

 
 
 
Figure 7 – Types of indicators used for DIA analysis outside DBPs (2019-2020) - EU 
Member States (N=23) 

Note: Cyprus, Romania and Luxembourg do not perform any DIA outside DBPs.  For Bulgaria, information is 
missing. Source: Study on Distribution Impact Assessment by the University of Milan, financed by the Commission 
(DG EMPL): https://data.europa.eu/doi/10.2767/511644 

Besides  comparing  the  types  of  DIA  indicators  used  both  in  and  outside  DBPs,  it  is  also 
interesting  to  compare  the  number  of  those  different  indicators53  used  by  each  euro  area 
Member  State  in  and  outside  its  DBP.  Figure  8  below  shows  this  comparison  for  the  year 
2020. It appears that, overall, the euro area Member States used a larger number of different DIA 
indicators  outside  their  DBPs  than  in  their  DBPs,  with  Finland  being  the  only  exception.  This 
result is not  completely  unexpected as the DBPs are mainly centred on macroeconomic issues, 
with possibly less consideration for distributional concerns than in other types of budget-related 
documents. These results are consistent with the results shown in Figure 2 where, overall across 
the euro area Member States54, it appears that DIA was more frequently performed outside the 
DBPs than in the DBPs in the years 2019-2020.   

53 The indicators considered in the questionnaire used for the interviews are: mean disposable income, average tax burden, winners and losers, 
AROP, AROPE, Gini, S80/S20, S40, Theil index, Atkinson index, Reynolds-Smolensky index, Kakwani index, wealth groups. 

54 Considering only Euro Area Member States in this table. 

31 

 
 
 
                                                           
Figure 8 – Number of different types of DIA indicators used in the DBPs and outside the 
DBPs55 in 2020, for those euro area Member States with at least one DIA occurrence in 
2020 in their DBPs and/or outside their DBPs (N=9) 

Note: Latvia and Malta did not implement any DIA in the DBP 2020. Source: Study on Distribution Impact 
Assessment by the University of Milan, financed by the Commission (DG EMPL): 
https://data.europa.eu/doi/10.2767/511644 

55 In SCP/NRP and/or in one of the seven types of budget-related documents considered in table 3.6. 

32 

 
 
 
                                                           
Figure 9 – Types of indicators and sub-groups’ decomposition used in the DIA and various 
aspects of DIA methodology (stage at which DIA is performed and baseline used) - euro 
area Member States (*) (N=17) 

Note: respondents are allowed to choose multiple answers.  (*) Cyprus and Luxembourg are excluded since no DIA 
is performed in these Member States. Source: Study on Distribution Impact Assessment by the University of Milan, 
financed by the Commission (DG EMPL): https://data.europa.eu/doi/10.2767/511644 

DIA analysis can usefully be complemented by presenting results concerning certain socio-
economic  groups  and  the  majority  of  Member  States  consider  subgroups  in  the  analysis 
(BE, CZ, DK, DE, EE,  IE, EL, ES, FR, HR,  IT, LV,  LT, HU, MT, NL, PL, RO, SK, FI, SE). 
Subgroups considered vary between countries and depending on the actual analysis undertaken. 
Subgroups could include (children, working age, and older people), gender, connection to labour 
market  (employed,  self-employed  etc.)  and  by  level  of  education.  For  instance,  in  Belgium, 
depending  on  the  kind  of  policy  reforms,  the  subgroups  for  which  some  additional  results  are 
presented may differ (e.g. regional child benefit reforms versus unemployment benefit reforms). 
In  Denmark,  changes  in  equalised  disposable  income  are  presented  depending  on  type  of 
connection to labour market (employees, self-employed, unemployed, students, pensioners etc.). 
In  Germany,  the  assessment  of  effects  for  households  of  social  benefit  recipients  and  different 
type of households, based on criteria’s such as such as age, education and migration. In Austria, 
it is assessed whether a proposal has significant impacts on the AROPE target group. In Estonia, 
the  assessment  of  the  impact  of  benefits  on  poverty  and  other  monetary  variables  is  made  by 
different  age  groups,  number  of  children  in  the  household,  household  type  or  other  socio-
demographic  characteristics  (e.g.  impact  of  unemployment  benefits  on  unemployed  persons 
poverty  rates;  pensions  increase  on  the  pensioners  poverty).  In  Poland,  regulatory  impact 

33 

 
 
 
assessments estimate the number of households affected by a particular intervention in terms of 
size  and  type  of  households  (retirees  and  pensioners,  households  with  children,  single  parents 
with children, households with persons with disabilities); and also age groups (e.g., effects of tax 
interventions on workers under 26). In Romania, the results are usually analysed by age groups, 
type  of  household  (rural/urban,  household  size,  number  of  children,  etc.),  region,  educational 
level,  employment type.  In Sweden, estimates for subgroups are sometimes used in  the budget 
process  (age  groups,  types  of  household,  educational  level,  regional  level),  but  are  usually  not 
included when the overall effect of all reforms is presented in the budget bill. 

f.  How to disseminate DIAs? 

Apart from allowing policymakers to make informed decisions, one of the functions of DIA 
is arguably to give a credible picture to the general public of how policy reforms will affect 
them.  In  several  Member  States,  the  public  or  the  media  pay  significant  attention  to  DIA 
analysis (BE, DK, IE, EL, FR, IT, LT, NL, AT, PL, SE).  

A  well-written  press  release  or  infographic  to  accompany  the  more  detailed  report  or 
presentation is key to facilitating the reporting of DIA in the media. These communications 
should focus on the results and what they mean for the general public, without dwelling much on 
the  technical  detail  of  the  model  documented  in  the  underlying  report  or  presentation.  For 
example, the public is likely to be interested in whether budgetary changes are more beneficial to 
low-  or  high-income  households  and  the  implications  of  reforms  for  poverty  rates;  income 
inequality  and  living  standards.  An  author  of  the  DIA  –  ideally  one  who  has  received  media 
training – could also be made available to speak to the media in the event that clarifications or 
interviews are requested.  

DIA results gain to be communicated in a way that raises the interest of the media and the 
public, which is the case in a number of Member States (BE, DK, IE, EL, FR, IT, LT, NL, 
AT, PL, SE). There is also coverage of distributional considerations of programmes of political 
parties in the run-up to parliamentary elections (BE, FI). The media attention may depend on the 
size of expected distributional impact of proposed measures (CZ, AT, SK). DIA considerations 
often feature high during parliamentary debates (BE, CZ, DK, DE, FR, IE, IT, LV, LT, AT, PL, 
FI, SE).  

Where  they  are  disseminated  publicly,  DIA  results  are  usually  included  in  budgetary 
documents and other documents either proposing or implementing reforms  (CZ,  DK, EE, 
ES,  FR,  IT,  LV,  LT,  NL,  AT,  PL,  FI  and  SE).  Other  than  that,  DIA  results  are  presented  in 
annual  reports  published  by  public  institutions  sometimes  presented  during  press  conferences 
(e.g.  Study  Commission  on  Ageing  in  BE).  Greece  appears  to  be  the  only  country  where  the 
DBP is the only document where DIA results are publicly disseminated on a regular basis. DIA 
results are occasionally published as policy briefs or analytical reports (IT, LV, LT, HU, MT and 
SK).  In  the  Netherlands,  DIA  calculations  on  policy  measures  included  in  the  coalition 
agreements  are  also  published.  In  some  Member  States,  DIA  results  are  only  disseminated 

34 

 
 
internally and mainly used to guide policy discussions behind closed doors where specific reform 
scenarios are being considered (EE, PT and RO).  

DIA results presented to the public are usually accompanied by very brief comments, while 
details are presented in the form of a table or a graph. In Malta, the results are presented with 
a short explanation as  to which cohorts and deciles are expected to  be mostly impacted by the 
reform.  In  Denmark,  if  deemed  relevant,  Members  of  Parliament  can  request  more  detailed 
analysis  and comments.  No comments  are provided on the results of the  DIA analysis that are 
included in the Greek DBP. 

Member States differ in terms of the extent to which the models and the underpinning data 
are made available to researchers and analysts on the one hand and the public on the other. 
Generally  speaking,  access  seems  to  be  more  restricted  in  Member  States  that  use  country-
specific  models,  as  opposed  to  those  using  EUROMOD  which  is  by  definition  available  to  all 
users. For example, in Finland, the microsimulation models and datasets used in the analysis are 
accessible to researchers and analysts, but not to the general public. In Denmark models as such 
are  not  available  to  researchers,  media  or  Members  of  Parliament.  However,  the  latter  can 
request  to  have  results  of  (in  principle  any)  type  of  reform  proposal.  In  Sweden,  academia, 
research institutes and other state authorities than the government offices can  request  access to 
the entire model including data. 

Box 11: National examples of DIA dissemination 

In Ireland, the Department of Finance regularly publishes DIA results on the day the budget package is announced. 
Ireland has committed to a multi-year budgetary package of increasing carbon taxes, so there is an onus to produce 
DIA to show that the direct tax and welfare system is offsetting the regressive pattern of losses induced by carbon 
taxes. The Ministry also produces backward-looking analysis examining how tax changes affect measures such as 
the Gini coefficient as well as hypothetical household examples. The Irish Economic and Social Research Institute 
publishes its own DIA results in the days following the presentation of the budget by the government. Hereby ESRI 
acts in its role of an independent analyst and commentator. The ESRI also undertakes a public briefing on their DIA.  

In Greece, outputs produced by the Ministry of Finance each year for their DBP are standardised to save time and 
allow  automation.  When  communicating  the  results,  the  interpretation  and  analysis  by  the  Ministry  remains 
minimal. Media coverage of this analysis is relatively extensive.  

An interesting example of a succinct form of presentation is the key output from the  Dutch model MIMOSI, is a 
boxplot  of  the  purchasing  power  growth  year-to-year.  The  boxplot  is  shown  for  all  households,  by  gross  income 
quintile  and  for  those  working,  pensioners  and  social  welfare  recipients  separately.  Households  with  and  without 
children alongside singles, double-earner and single-earner households are also shown. A scatterplot of purchasing 
power growth is also shown. This gives more detailed insights as each data point in the plot represents a household. 

35 

 
 
 
 
5. Conclusion  

Various  considerations  need  to  be  taken  into  account  while  conducting  DIAs  and 
disseminating their results.  Obtaining accurate  DIA results  hinges on using a  well-developed 
microsimulation model, which ideally goes beyond the static analysis and includes behavioural 
responses or a longer term perspective. For the model to deliver accurate results, it needs to be 
fed with comprehensive and timely data. It is hence essential to make such data available both to 
various  public  administration  entities  but  also  to  external  researchers,  also  to  allow  that  the 
analysis can be replicated. To the extent that data allow, the choice of policy reforms on which 
DIAs are  run can be  gradually extended beyond  the tax-benefit measures, which is  the core of 
DIA. The dissemination of the analysis can take different channels, but it is important to ensure 
that  the  main  results  are  made  available  in  a  synthesised  and  easy  to  read  manner.  The 
information provided above constitutes a snapshot of existing tools and methods, but is important 
to  note  that  these  should  and  can  be  further  developed  in  order  to  provide  sound  DIA  results 
disseminated to a broader audience, including social partners and civil society.  

36 

 
 
 
 
Annex 

Discussions with the Council Committees 

During the preparation of the Communication on increasing the use of ex ante DIAs by Member 
States, the Commission engaged in a dialogue on the issue with the Employment Committee and 
Social  Protection  Committee  (advisory  committees  for  the  Employment  and  Social  Affairs 
Council)  as  well  as  the  Economic  Policy  Committee  (an  advisory  committee  of  the  Economic 
and Financial Affairs Council). During the dedicated discussions that took place in  March and 
April 2022, the relevance of conducting  ex ante  DIAs of budgetary measures and reforms  was 
highlighted  and  its  potential  contribution  to  reducing  inequality  and  poverty  was  duly 
acknowledged. Members agreed that DIAs were important for the Committees’ work in view of 
the various major transitions at play. The discussions confirmed the obstacles to more systematic 
use of DIA (in particular, tight timelines and lack of sufficient human resources as well as lack of 
a specific request) but also pointed at the problem of missing sufficiently granular data (among 
others due to data protection rules). There was an interest among the Member States to exchange 
more  on  using  DIA,  in  particular  in  the  context  of  the  poverty  reduction  target  as  well  as  the 
transitions,  energy  prices  hikes  and  associated  compensatory  measures.  Discussions  on  new 
developments in terms of DIA methodologies would be appreciated.  

Call for evidence on the Commission initiative on DIA 

In April 2022, the Commission launched a call for evidence regarding the guidance to Member 
States  on  increasing  the  use  of  ex  ante  DIAs  and  as  a  result  received  35  contributions  coming 
from  12  Member  States  and  Iran.  More  than  half  of  them  were  submitted  by  EU  citizens  (see 
Figure  10  below),  whereas  non-governmental  organisations  (representing  various  sectors  of 
society) were responsible for almost a quarter of the contributions. There were also submissions 
by public authorities and trade unions’ organisation.  

Figure 10: Response to the Call for Evidence by category of respondents 

EU citizen

Non-governmental organisation

Other

Public authority

Trade union

Non-EU citizen

37 

 
 
 
Consulted  parties  acknowledged  DIAs’  useful  contribution  to  evidence-based  policymaking 
and stressed the importance of conducting DIAs with a view to stepping up public investment in 
social infrastructure. They also stressed that DIAs were of particular importance in the context of 
economic inequalities and social vulnerabilities that were rising across the continent. At the same 
time,  the  importance  of  supporting  economic  growth  for  reducing  inequality  and  poverty  was 
also underlined.  

DIAs  should  cover  not  only  economic  impacts  of  policy  reforms,  but  also  include  social  and 
environmental  considerations.  However,  combatting  social  and  economic  inequalities  should 
not  be  viewed  only  as  an  underpinning  dimension  of  the  green  and  digital  transitions,  or  as  a 
bottleneck  to  economic  growth,  but  as  a  key  objective  in  its  own  right.  DIAs’  relevance  was 
recognised in the context of scaling back the emergency measures adopted to cushion the impact 
of the pandemic. 

It  was  stipulated  that  DIAs  should  acknowledge,  among  others,  the  contribution  of  social 
integration enterprises to the reduction of poverty and inequality. It was also suggested that 
DIAs  could  help  tackle  administrative  barriers  to  accessing  social  benefits.  It  was  also 
highlighted that using DIA was of particular importance for regional governments to inform their 
decision-making  processes.  The  importance  of  ex  post  DIAs  was  also  noted  as  it  could  set  a 
strong foundation for a potential ex ante DIA of another similar project. 

Disseminating  DIA  results  was  pointed  at  as  a  crucial  aspect.  It  was  underlined  that  these 
results  should  not  be  presented  only  in  budgetary  documents,  which  usually  fall  outside  the 
scope of the interest  of the public And that efforts  should be made to  communicate the results 
using fora and language easily accessible to European citizens and in particular the youth.  

Some organisations called for making DIA analysis more uniform across Member States so 
that they could be  easier to compare. At the same time this should be done acknowledging the 
specific  national  contexts.  The  importance  of  providing  for  independence  to  those  conducting 
DIA analysis was also stressed so that the results would be deemed credible. The issue of access 
to data as well as their quality and timeliness was pointed at as a major enabler of  robust DIA 
results.  A  call  was  also  made  for  making  models  and  data  easily  accessible  to  researchers, 
thereby allowing replicability which in turn could enhance the credibility of results. A suggestion 
was  also  made  to  set  aside  some  European  funds  for  supporting  organisations  involved  in 
conducting DIAs.   

Several respondents called for the Commission to provide further support to Member States to 
assist them in developing their DIA processes, in particular by identifying best practices and 
facilitating  exchanges  between  national  experts.  Moreover,  it  was  mentioned  that  the 
Commission  guidelines  should  make  clear  and  explicit  that  DIAs  should  be  streamlined  and 
applied in all policy fields. An idea of establishing a dedicated scientific committee was also put 
forward, which would bring together researchers and decision-makers who would be in charge of 
reviewing, supplying templates, sharing good practices, and hosting workshops. 

A  view  was  expressed  that  the  Commission’s  guidance  issued  by  means  of  a  Communication 
was  not  as  effective  as  binding  targets  or  a  directive/a  regulation.  Therefore,  the  envisaged 

38 

 
 
monitoring process of the implementation of DIAs by the Member States by the Commission 
could  be  usefully  strengthened  beyond  regular  information  provided  to  the  Social  Protection 
Committee  and  the  Employment  Committee  and  regular  reviews  of  DIA  practice  in  Member 
States  and  mutual  learning  events  aimed  at  further  improving  tools  and methodologies  used  to 
conduct DIAs.  

39