The distributional implications of the euro area crisis : evidence from macroeconomic adjustment in Greece and Portugal.
Date: 2025-01-29
Institutions: European Stability Mechanism
Subjects: Greece, Portugal, economic consequence, euro area, fiscal policy, household income, monetary crisis, rescheduling of public debt, social inequality

Working Paper Series  | 67 |  2025

The distributional implications of the euro 
area crisis: Evidence from macroeconomic 
adjustment in Greece and Portugal     

This paper explores the distributional effects of economic crises and the 
resulting economic adjustments on household income in Greece and 
Portugal.

Misina Cato    
European Stability Mechanism  

Olga Francova    
European Stability Mechanism

Valentin Lang      
University of Mannheim   

Disclaimer
This working paper should not be reported as representing the views of the 
ESM. The views expressed in this Working Paper are those of the authors and 
do not necessarily represent those of the ESM or ESM policy.

 
 
Working Paper Series  |  67  |  2025

The distributional implications of the euro 
area crisis: Evidence from macroeconomic 
adjustment in Greece and Portugal  

Misina Cato  European Stability Mechanism     
Olga Francova 1 European Stability Mechanism   
Valentin Lang  University of Mannheim   

Abstract
We  study  the  social  and  distributional  implications  of  the  euro  area  (EA)  sovereign 
debt crisis in countries that received external financial assistance for macroeconomic 
adjustments. Combining granular data from about one million survey interviews with 
households across euro area states carried out between 2004 and 2019, we show how 
disposable income and its components evolved across different social groups before, 
during  and  after  the  EA  crisis  and  the  associated  adjustment  programmes.  Using 
difference-in-difference  and  triple-difference  methods,  we  compare  distributional 
outcomes  in  countries  with  large  financial  assistance  programmes  (Greece  and 
Portugal) to euro area countries with similar pre-crisis trajectories and macroeconomic 
fundamentals. The results show that incomes for most social groups in these countries 
declined during the crisis, but income losses were not uniform and differed across groups 
in terms of gender, age, and income level. Overall, public transfers mitigated increases 
in  inequality  of  market  incomes,  yet  both  the  timing  and  the  magnitude  of  changes 
in market income and public transfers varied across income groups. While reforms of 
public transfer policies in the early macroeconomic adjustment period placed a greater 
burden on poorer income groups than on richer ones, reforms in later stages reduced 
inequality by supporting poorer households, especially in the case of Greece.

Keywords: Sovereign debt crises, economic adjustment, fiscal policy, inequality
JEL codes: D31, E62, F32, F34

1 o.francova@esm.europa.eu

Disclaimer
This  Working  Paper  should  not  be  reported  as  representing  the  views  of  the  ESM.  The  views 
expressed in this Working Paper are those of the authors and do not necessarily represent those of 
the ESM or ESM policy. No responsibility or liability is accepted by the ESM in relation to the accuracy 
or completeness of the information, including any data sets, presented in this Working Paper.

© European Stability Mechanism, 2025 All rights reserved. Any reproduction, publication and reprint in the form of a 
different publication, whether printed or produced electronically, in whole or in part, is permitted only with the explicit 
written authorisation of the European Stability Mechanism.

ISSN 2443-5503                                          doi: 10.2852/3911215
ISBN 978-92-95223-63-9                         EU catalog number: DW-01-24-009-EN-N        

The distributional implications of the euro area crisis: 

Evidence from macroeconomic adjustment in Greece and Portugal 

Misina Cato 

Olga Francova 

Valentin Lang 

ESM 

ESM 

University of Mannheim 

January 2025 

 
 
 
 
 
 
 
 
 
Contents 

Non-technical summary 

Introduction 

1.  Empirical setting 

2.  Data 

3.  Descriptive evidence 

Mean disposable household income and income inequality 

Income by decile group 

Income by type 

4.  Distributional effects across social groups: Evidence from DID 

Dynamic difference-in-difference design 

Results for average income 

Heterogeneity by household characteristics 

5.  Distributional effects across income groups: Evidence from Triple-DID 

Disposable household income 

Income from social safety nets 

Disaggregating social safety nets 

Conclusion 

References 

Appendix 

Appendix A 

Appendix B 

Appendix C 

Appendix D 

Appendix E 

Appendix F 

Appendix G 

Appendix H 

Appendix I 

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2 

 
 
 
 
 
 
The distributional implications of the euro area crisis: 

Evidence from macroeconomic adjustment in Greece and Portugal* 

Abstract 

We study the social and distributional implications of the euro area (EA) sovereign debt 
crisis  in  countries  that  received  external  financial  assistance  for  macroeconomic 
adjustment.  Combining  granular  data  from  about  1  million  survey  interviews  with 
households across euro area  states  carried out  between 2004 and 2019, we  show  how 
disposable  income  and  its  components  evolved  across  different  social  groups  before, 
during  and  after  the  EA  crisis  and  the  associated  adjustment  programmes.  Using 
difference-in-difference  and  triple-difference  methods,  we  compare  distributional 
outcomes in countries with large financial assistance programmes (Greece and Portugal) 
to  euro  area  countries  with  similar  pre-crisis  trajectories  and  macroeconomic 
fundamentals. The  results show  that  incomes  for most  social groups in these  countries 
declined during the crisis, but income losses were not uniform and differed across groups 
in terms of gender, age, and income level. Overall, public transfers mitigated increases in 
inequality of market incomes, yet both the timing and the magnitude of changes in market 
income and public transfers varied across income groups. While reforms of public transfer 
policies in the early macroeconomic adjustment period placed a greater burden on poorer 
income  groups  than  on  richer  ones,  reforms  in  later  stages  reduced  inequality  by 
supporting poorer households, especially in the case of Greece.  

Keywords: Sovereign debt crises, economic adjustment, fiscal policy, inequality 

JEL codes: D31, E62, F32, F34 

* We are grateful to Matthieu Bellon, Pilar Castrillo, Kari Korhonen, Ricardo Reis, Lorenzo Ricci, Rolf 
Strauch, Konstantinos Theodoridis, Kamila Slawinska, Matthias Gnewuch and participants at research 
seminars at the European Stability Mechanism and the International Monetary Fund for constructive 
feedback  and  suggestions.  We  would  also  like  to  thank  Karol  Siskind  for  the  editorial  review.  The 
opinions expressed in this paper are solely those of the authors and do not necessarily represent the 
views of the European Stability Mechanism or any other institution. 

3 

 
 
 
 
 
 
 
 
 
 
Non-technical summary:  

The  paper  explores  distributional  effects  of  economic  crises  and  the  resulting  economic 
adjustments on household income. It uses the euro area (EA) sovereign debt crisis as a case 
study to analyse changes in the income distribution during the periods of economic adjustment 
triggered by economic crises. It draws lessons from the experiences of both Greece and Portugal.  

Detailed data from the European Union Statistics on Income and Living Conditions (EU-SILC), 
help us look beyond conventional aggregate inequality measures. The fine-grained microdata 
allow us to examine how different income sources for various social groups evolved during the 
crisis. Thanks to this high-quality dataset, we can differentiate between groups based on age, 
gender,  education,  and  income  level.  With  multiple  empirical  methods,  we  examine  the 
distributional  impact  of  the  crisis  and  the  economic  adjustments  and  provide  a  nuanced 
understanding of these effects.  

The paper also investigates the roles of social spending and redistribution.  In both Portugal 
and Greece, we document a temporary decline in disposable household income due to the crisis 
and the adjustment programs. The observed decline is larger in magnitude in Greece than in 
Portugal. After an initial drop in the early adjustment period, income gaps compared to similar 
countries stabilise. This points to distinctive trajectories in the early adjustment period and in 
subsequent years. Notably, the gap starts to diminish in 2016 in Greece and in 2015 in Portugal, 
indicating a gradual convergence and a potential shift in income dynamics in later stages of the 
crisis. 

Our analysis confirms that different income groups depend on different income types and that 
such  variation  results  in  different  vulnerabilities  in  times  of  crisis.  For  the  most  vulnerable 
groups, our analysis documents the crucial role played by pensions and transfer systems during 
the economic adjustments in the Eurozone crisis. It points to the need to put in place effective 
and  sustainable  safety  nets  in  stable  times,  as  ad  hoc  tweaks  during  economic  crises  can 
negatively impact the well-being of the lowest income groups. We also confirm that targeted 
reforms can alleviate pressures on the poor. 

4 

 
 
 
 
 
 
 
 
 
Introduction 

How do episodes of macroeconomic adjustment during financial crises affect societies? How are the 
burdens  of  crisis  adjustment  distributed  across  households?  What  roles  do  social  spending  and 
redistribution play? These are crucial questions for both economists and policymakers, but they are 
notoriously hard to answer (Lane & Milesi-Ferretti 2012, Ramey 2016, Summers 2000, Walter 2013). 
Their  empirical  assessment  requires  fine-grained  micro-level  data  that  are  comparable  across 
countries.  Most  household  surveys,  however,  are  nationally  organised  and  thus  barely  allow  cross-
country comparisons. In addition, the countries for which comparable high-quality household surveys 
exist are mostly advanced economies where adjustment episodes are exceptional. 

We approach these questions in the context of the euro area (EA) sovereign debt crisis, which offers 
an excellent laboratory to study shifts in distributional dynamics during the crisis and the periods of 
macroeconomic adjustment. First, the EA crisis hit several years after the European Union (EU) had 
launched  its  Statistics  on  Income  and  Living  Conditions  (EU-SILC),  an  annual  survey  instrument 
producing granular and multidimensional microdata on the distribution of household incomes. These 
data are exceptional in that they offer harmonised microdata across all EU Member States and thus 
allow  tracing  and  comparing  household  incomes  across  EA  countries  before,  during  and  after  the 
sovereign  debt  crisis.  Second,  during  this  crisis,  several  euro  area  countries  with  current  account 
deficits  lost  investor  confidence  and  underwent  macroeconomic  adjustment  backed  by  external 
financial assistance. While other EA countries with current account deficits did not face the same loss 
of  investor  confidence  and  did  not  undergo  such  adjustment,  they  faced  similar  macroeconomic 
constraints  stemming  from  their  joint  membership  in  the  common  currency  area.  They  thus  lend 
themselves to comparisons with statistical methods.  

More specifically, the EA sovereign debt crisis hit Europe after the 2008 global financial crisis had 
exposed imbalances accumulated since the onset of the single currency. In the run-up to the crisis, 
the excess capital from countries with a strong savings position flowed through the euro area banking 
system and fueled housing-bubbles, debt-financed growth and current account deficits in other euro 
area economies. The financial turmoil then led to a reassessment of financial risks. As a consequence, 
the indebted borrowers with current account deficits were no longer able to access cheap funding and 
service their mounting debt. Concerns about debt sustainability triggered fears that some countries 
might have difficulty sustaining their euro area membership and countries had to embark on alternative 
policy trajectories. 

When the crisis intensified and some countries effectively lost market access, EA members, together 
with the EC and the IMF, reacted by setting up new financial assistance institutions to organise loan 
programmes  conditional on policy reforms. The financial assistance was coordinated jointly by the 
International  Monetary  Fund  (IMF)  and  the  European  Commission  (EC)  with  co-financing  from  the 
newly  established  European  Stability  Mechanism  (ESM,  and  its  predecessor  EFSF).  Official  financial 
assistance replaced market financing and provided countries leeway to reshuffle economic structures 
and  implement  alternative  policies.  The  European  strategy  is  thus  reminiscent  of  IMF-led  loan 
programmes, where loan-financed macroeconomic and structural adjustments were applied to help 
countries regain market access and gradually improve competitiveness, growth and employment. 

We study changes in the distribution of household incomes around the EA sovereign debt crisis and 
the associated macroeconomic adjustment. An inspection of the programme policy conditions leads 
us  to  focus  on  the  adjustments  in  Greece  and  Portugal,  where  conditionality  related  to  social  and 
distributional issues was significantly more far-reaching than in Cyprus and Ireland. We aim to assess 
how the macroeconomic adjustment in the context of the crisis affected the most vulnerable countries: 

5 

 
 
their social fabric, their transfer policies and the extent to which their most vulnerable social groups 
were protected. 

The  main  contribution  of  this  paper  is  to  look  beyond  aggregate  measures  and  to  identify  how 
various  income  sources  of  different  social  groups  evolved  during  the  crisis.  By  using  highly  fine-
grained  microdata,  we  differentiate  between  social  groups  in  terms  of  age,  gender,  education  and 
income  level.  We  also  distinguish  between  short  and  long  run  effects  and  determine  the  extent  to 
which the shifts were driven by changes in market incomes versus changes in different types of social 
transfers  and  pensions.  The  disaggregation  provides  a  nuanced  understanding  of  changes  in 
distributional outcomes during the adjustment period and more general lessons on the role of social 
policy for inequality in times of macroeconomic crises. 

In terms of empirical methods, we adopt multiple approaches to examine the distributional impact 
of the crisis and the macroeconomic adjustment.  We begin with descriptive analyses to document 
trends in distributional outcomes in crisis countries by disaggregating household incomes by source 
and  by  distinguishing  between  different  socioeconomic  groups.  We  then  apply  difference-in-
differences (DID) techniques to compare incomes in EA countries with loan programmes to incomes in 
similar  EA  countries  without  loan  programmes.  While  fully  isolating  the  causal  effects  of  the 
macroeconomic  adjustment  programmes  from  the  impact  of  the  crisis  is  virtually  impossible,  this 
approach allows us to study how trends in various income sources of different income groups differed 
between countries with and without financial assistance programmes that are comparable in terms of 
pre-crisis  trajectories  and  macroeconomic  fundamentals.  Recognising  the  importance  of  further 
addressing potential confounding factors that vary over time and across groups, we employ a triple DID 
specification  at  the  country-group-year  level.  This  approach  allows  absorbing  all  time-varying 
macroeconomic developments that vary across countries by means of country-times-year fixed effects. 
This brings us closer to disentangling the effects of the crisis from the effects of the financial assistance 
programmes.  

Overall, our results point to pronounced heterogeneity in income developments amid the ongoing 
crisis and the macroeconomic adjustment. The onset of the crisis and adjustment period interrupted 
the  relatively  steep  growth  of  household  income  in  countries  that  required  external  financial 
assistance.  While  incomes  for  most  social  groups  followed  a  similar  downward  trend  during  the 
adjustment, the size of the income losses differed across groups in terms of gender, age, and income 
level. Transfers mitigated increases in inequality, but early adjustment policies placed a larger burden 
on poorer income groups than on richer ones. Conversely, reforms of transfer policies in later stages of 
the  crisis  tended  to  reduce  inequality  by  supporting  poorer  households,  especially  in  the  case  of 
Greece. The intricate links between crisis effects and country-specific policy responses prevent us from 
directly linking income developments to specific policy measures. 

More  specifically,  we  reach  three sets  of  substantive  conclusions.  First,  we  document  a  decline  in 
mean  incomes  in  Greece  and  Portugal  relative  to  countries  with  similar  pre-crisis  trajectories  and 
macroeconomic fundamentals. The combined effect of the crisis and the adjustment programmes had 
a  negative  impact  on  disposable  household  income  in  both  countries,  relative  to  similar  euro  area 
countries without an adjustment programme. The observed decline is larger in magnitude in Greece 
than in Portugal. After an initial drop in the early adjustment period, income gaps compared to similar 
countries  stabilise.  This  points  to  distinctive  trajectories  in  the  early  adjustment  period  and  in 
subsequent  years.  Notably,  the  gap  starts  to  diminish  in  2016  in  Greece  and  in  2015  in  Portugal, 
indicating  a  gradual  convergence  and  a  potential  shift  in  income  dynamics  in  later  stages  of  the 
adjustment period.    

6 

 
 
Second, the distributional analysis shows that the crisis period implied income losses for all income 
groups in Greece and Portugal, but the size of the losses differed across groups. While the Gini index 
of disposable household income does not point to significant changes of overall inequality, the more 
disaggregated  analysis  reveals  different  income  dynamics  across  various  social  groups.  In  Greece, 
during the early years of the crisis and adjustment period, the poorest 10 percent experienced a larger 
decline  in  their  income  than  other  income  groups.  From  2016  onwards  however,  there  is  a  faster 
catching-up process for this group. The relative losses of the poorest compared to the richest were 
recovered by 2018. In Portugal, the dynamics are similar. Initially, there is a stronger negative effect for 
the bottom 10 percent in 2014 and 2015, but thereafter, the relative losses of the poorest diminish 
over  time  and  undergo  a  recovery  phase.  In  both  countries,  we  document  greater  vulnerability  for 
households headed by women, who experience a larger income decline. 

Third, various income sources have contributed to these distributional changes in different ways. In 
both Portugal and Greece, losses in market income – i.e. before taking into account state intervention 
with public transfers – are larger for richer households, reflecting the larger dependency on market 
incomes of this group. For poorer households, transfers and pensions play a more important role in 
supporting  household  income  during  the  adjustment  period.  Cuts  to  pensions  reduced  disposable 
income of low-income households in both countries, and these cuts were stronger than for households 
with higher incomes. In Greece, we record a substantial drop in pensions and transfers for the poorest 
10 percent between 2012 and 2016. This negative trend reverses in 2017 and the relative losses for 
this group vanish towards the end of the observation period. Reforms to social transfers at this time, 
such as the introduction of the Social Solidarity Income scheme, seem to have benefited the poorest 
and contributed to this development. The findings emphasise the importance of considering various 
income  sources  for  a  comprehensive  understanding  of  the  distributional  effects  of  economic 
adjustments.  

Our study contributes to two strands of literature. First,  research on the distributional effects of loan 
programmes from international lenders – like the IMF – that set policy conditionality in exchange for 
financial  assistance.  Several  studies  conclude  that  IMF  programmes,  on  average,  increase  income 
inequality (Vreeland 2002,  Oberdabernig 2013,  Lang 2021).  Loan  conditions,  particularly those  that 
demand social-spending cuts and labor-market reforms, have been identified as channels (Forster et 
al. 2019, Lang 2021, Stubbs et al. 2022). Such evidence is so far almost exclusively based on aggregate 
country-level  data  for  developing  countries.  We  add  to  this  literature  by  providing  evidence  in  the 
context of more advanced economies, differentiating between effects for different social groups, and 
tracing the distributional effects back to different income types. 

Second, we contribute to the literature studying the social implications of the European sovereign 
debt crisis. Studies in this strand of the literature often pinpoint the lack of social considerations in the 
programme  design,  especially  when  it  comes  to  the  conditionality  attached  to  loan  disbursements 
(Kentikelenis et al., 2016). Studies focusing on the sovereign debt crisis either describe the effects of 
the crisis on poverty and income inequality (Andriopoulou et al. 2019, Kaplanoglou, Rapanos, 2018) or 
use  static Euromod  microsimulation  models  to  bridge  gaps  in  data availability when  estimating  the 
impact  of  the  crisis  on  the  income  distribution  (Leventi  and  Matsaganis,  2016).  Official  Eurozone 
programme  evaluations (IMF IEO 2017, ESM 2020) suggest that  conditions applied across countries 
varied significantly and highlight the importance of programmes’ social sustainability. We contribute 
to  this  literature  by  analysing  and  comparing  how  the  two  selected  euro  area  financial  assistance 
programmes  fared  in  terms  of  social  and  distributional  aspects  with  an  empirical  approach  that  is 
substantially more fine-grained than what has been considered so far.  

7 

 
 
The remainder of the paper is structured as follows. Chapter 1 describes the empirical setting and 
explains the conditions under which the adjustment during the Eurozone crisis took place. Chapter 2 
describes the data we use. Chapter 3 provides a descriptive analysis of these data. Chapter 4 compares 
aggregate income levels and inequality of countries under adjustment to macroeconomically similar 
countries  and  provides  micro-level  evidence  from  difference-in-difference  and  triple-difference 
designs. Chapter 5 differentiates between income types and takes a closer look at the role of pensions 
and  social  safety  nets.  We  conclude  with  lessons  learnt  for  the  design  of  financial  assistance 
programmes that aim to avoid adverse distributional effects. 

1. Empirical setting 

In  2008,  the  euro  area  (EA)  was  hit  by  the  global  financial  crisis  (GFC)  and  the  ensuing  turmoil  in 
financial markets. To re-establish investor confidence and regain market access, five EA countries went 
through financial assistance programmes that were conditional on the implementation of economic 
reforms agreed by the governments and a partnership of European and international institutions: The 
European Commission (EC) led the policy design, the International Monetary Fund (IMF) provided its 
expertise and limited funding. At first, the newly established European Stability Mechanism (ESM) and 
its predecessor (EFSF) channeled the funding. Over time, the ESM became more involved in financial 
sector measures and monitoring.  

The financial assistance was coupled with policy conditionality introducing structural reforms intended 
to enhance competitiveness and reduce imbalances accumulated in the run-up to the financial crisis. 
The  strategy  of  making  official  financial  support  conditional  on  the  implementation  of  adjustment 
policies  is  reminiscent  of  IMF-led  loan  programmes  in  the  context of  debt  and  balance-of-payment 
crises  in  developing  and  emerging  markets.  Macroeconomic  adjustment  is  often  characterised  by 
extensive  fiscal,  financial,  or  structural  reforms  aimed  at  stabilising  economies  facing  crises  or 
challenges.  

Within  the  euro  area,  countries  participating  in  financial  assistance  programmes,  such  as  Cyprus, 
Ireland, Greece, and Portugal, experienced distinct circumstances. The reforms implemented reflected 
underlying economic problems. For instance, Cyprus and Ireland primarily focused on financial sector 
policies, while Greece and Portugal concentrated more on fiscal measures (see Appendix A. Figure 3 
and also Clancy et al. 2022). This diversity in the programme focus suggests that the implications for 
inequality and the mechanisms through which these programmes influence it can vary significantly, 
adding complexity to the assessment of their social impact. 

To address this difficulty, we initially evaluate the database of conditionalities published by the ESM. 
The data include conditionality clauses from around 60 different official reviews across all programmes 
(Moshammer and Siskind, 2020). This allows us to select the countries whose adjustment process we 
study and compare in greater detail. The ESM conditionality is summarised in the online Programme 
Database, which offers an overview of all conditions discussed by the European institutions and former 
ESM  programme  countries  (Moshammer  and  Siskind,  2020).  The  number  of  conditions  gives  an 
indication of the reform intensity. For a more nuanced understanding of the respective focus of the 
adjustment programmes, we assigned the conditions to three categories: social policy, health policy, 
and education policy (Appendix A. Table 1). This coding reflects the COFOG spending classification. As 
a country with only financial sector conditionality, Spain is excluded from our analysis.  

8 

 
 
Appendix A. Figure 2 and Appendix A. Figure 4 show the number of conditions across time, policy areas 
and shares across the overall programmes.1 They identify Greece and Portugal as the countries with a 
substantially  larger  share  with  social  policy  conditionality  when  compared  to  Cyprus  and  Ireland, 
suggesting  that  the  Greek  and  the  Portuguese  adjustment  programmes  are  systematically  different 
from  the  other  two  smaller-scale  programmes.  Besides,  the  two  countries  also  faced  the  largest 
number  of  conditions  targeting  social  spending,  the  policy  area  that  is  the  most  relevant  for  our 
analysis.2  Our  empirical  analysis  therefore  focuses  on  Greece  and  Portugal.  According  to  our 
classification,  Greece  had  the  largest  number  of  conditions  in  the  most  relevant  sectors  when 
compared to Portugal, suggesting that the potential implications of the adjustment process are likely 
to be more significant in Greece (see Appendix A. Figure 1). 

To illustrate the role of conditionality, we construct a timeline of policy measures adopted in the two 
key countries that could impact household income (Appendix G). The overview shows that conditions 
implemented in Portugal were more consistent across time and less extensive than in Greece. We also 
use  this  timeline  when  studying  the  mechanisms  driving  our  results.  Obviously,  the  intertwined 
conditions and complex economic circumstances of economic crisis make it impossible to draw causal 
inferences about the distributional effects of specific conditions. Nevertheless, the conditionality data 
and  the  timeline  show  that  many  conditions  and  reforms  coincide  with  subsequent  distributional 
changes  where  causal  connections  are  plausible,  even  if  one  cannot  empirically  discern  whether 
observed effects result from the conditions themselves or are consequences of the broader economic 
context. 

2. Data 

We rely on multiple data sources for different aspects of our research objectives. As our main data 
source, we obtained restricted-use microdata from Eurostat: the scientific use files of the EU’s Statistics 
on Income and Living Conditions (EU-SILC). For our purpose, these data constitute an ideal and unique 
source.  In  other  parts  of  the  world,  household-level  income  surveys  are  country-specific.  Different 
approaches  across  national  surveys  render  cross-country  comparisons  with  household-level  data 
notoriously difficult. EU-SILC, in contrast, provides income surveys that apply the  same  approach in 
each EU Member State at an annual frequency for the 2004-2023 period. 

EU-SILC  provides  fine-grained  information  on  income  sources  for  each  surveyed  household  and 
individual. The data differentiates between labour income, capital income, pensions, different types of 
social transfers and benefits, and different types of taxes. The dataset allows us to identify how the 
types  of  incomes  and  transfers  changed  during  the  period  of  the  macroeconomic  adjustment. 
Decomposition of disposable income sources across different deciles helps better understand changes 
in income distribution. 

1 Given that the database covers only conditionality included in the European institutions’ documents, we do not analyse conditions 
applied by other international institutions. 

2 In terms of duration, Greece exhibited the longest persistence of conditionality per year, as illustrated by Appendix A. Figure 2. Note that 
we do not take into consideration compliance with conditions. An analysis of compliance can be found in Clancy et al, 2023. In Appendix F, 
we show how government spending changed in Portugal and Greece during the adjustment period versus the rest of the euro area countries. 
It shows that in both countries, government spending was substantially reduced relative to other euro area countries. We acknowledge as a 
caveat that the usefulness of conditionality as a measure of the intensity of the adjustment programme is somewhat limited by the possibility 
that a larger number of conditions may also reflect the lack of effectiveness of earlier conditions. We abstract from the analysis of compliance 
with the conditionality.  

9 

 
 
 
A key innovation compared to the existing literature in this field relates to our usage of the restricted-
access EU-SILC data at both the household level and the individual level. For our analysis, we combine 
each survey wave for each EA country to a large individual-level dataset. We also match the individual-
level data to household-level data, by nesting individuals in households. Our final dataset consists of 
about one million observations. Details as well as caveats to be considered when using these data are 
discussed in Appendix I. 

In addition, we use aggregate data on social outcomes, public spending, and macroeconomic indicators 
from Eurostat and the World Bank (for details see  Appendix I) and the ESM conditionality database 
discussed in chapter 2. 

3. Descriptive evidence  

This chapter provides descriptive evidence on the trends of household incomes in both Greece and 
Portugal  before,  during,  and  after  the  financial  assistance  programmes.  After  initially  showing  how 
mean household income and Gini indices of inequality evolved during the macroeconomic adjustment 
process, we use EA-wide microdata to go beyond these conventional measures of income levels and 
inequality. This analysis sheds light not only on income levels across different social groups and their 
main income sources but also allows for identifying and tracking the most vulnerable groups.  

Mean disposable household income and income inequality 

To measure economic well-being, our analysis relies mainly on equalised disposable household income, 
which  reflects  the  amount  of  money  available  to  households  after  taxes,  transfers  and  other 
compulsory deductions. It sums up the gross personal income components of all individual household 
members and adds gross income components at the household level. Regular taxes on income, wealth, 
regular  inter-household  cash  transfers  paid  and  social  insurance  contributions  are  subtracted.    An 
equivalisation accounts for the household size according to the modified OECD equivalence scale.3 The 
overall level of equivalised disposable household income determines the ability of household members 
to meet their needs and enjoy a certain quality of life.  

Figure 1, panel (a) plots mean equivalised disposable household incomes in Portugal, Greece and the 
remainder  of  the  euro  area.4  While  Greek  income  recorded  steep  growth  before  the  onset  of  the 
Eurozone crisis, there was a sharp drop at the beginning of 2010. The drop in incomes takes place at 
the same time as the start of the adjustment programme indicated by a dot. Around 2013, household 
income stabilised at a lower level and recorded only marginal growth rates. Portuguese income growth 
was less strong before the crisis and the decline during the crisis is substantially less pronounced than 
in  Greece.  The  post-crisis  recovery  is  also  faster  in  Portugal  than  in  Greece.  Mean  incomes  in  the 
remainder of the euro area were growing relatively fast before the crisis, continued their growth at 
lower rates during the crisis and returned to pre-crisis growth rates toward the end of the 2010s. 

We turn to inequality in panels (b) and (c) of Figure 1. In both Greece and Portugal, pre-crisis trends in 
inequality  in  income  before  social  transfers  were  relatively  flat  and  similar  to  the  EA  average.5 
Coincident with the onset of the crisis and the financial assistance programmes, inequality in market 

3 The equivalisation divides total disposable household income by the number of “equivalent adults” in the household. It gives a weight of 
1.0 to the first adult, 0.5 to the second adult and each person aged 14 and older, and 0.3 to each younger child. 

4 In Appendix B. Figure 1 we report equivalized income reported in Figure 1 Panel (a) adjusted for inflation.  

5 Note that private pension plans are included in the Eurostat’s definition of the Gini coefficient of income without transfers. 

10 

 
 
 
incomes increased in both countries, while the EA average remained stable. This increase continued 
until  2015/2016.  Afterwards  inequality  declined.  Trends  in  net  income  inequality  i.e.  after  social 
transfers  are  remarkably  different.  Both  Greece  and  Portugal  saw  declining  levels  of  net  inequality 
before the crisis, while the decline was substantially stronger in Portugal. At the onset of the crisis, net 
inequality mildly increased in Greece and remained stable in Portugal. Starting in 2016, net inequality 
decreased in both countries.  The Gini coefficient of income after social transfers remained more stable 
as social systems played their role and mitigated the propagation of the shock. 

Figure 1 
Income and inequality  

Note: This figure reports the means of equivalised disposable household income for Greece, Portugal and the remainder of the euro area (excluding 
Cyprus and Ireland) in panel (a). Panel (b) shows the Gini coefficient of equivalised disposable income before social transfers (pensions included in 
social  transfers).  Panel  (c)  shows  the  Gini  coefficient  of  equivalised  disposable  income  after  all  social  transfers.  Dots  indicate  the  start  of  the 
adjustment programme in the two countries. See also Appendix B. Figure 1, which adjusts for inflation and shows very similar trends. 
Source: EU-SILC and Eurostat. 

The  descriptive  analysis  highlights  three  main  lessons.  First,  the  crisis  and  adjustment  episodes 
coincided  with  breaks  in  the  income  and  inequality  trends  in  both  countries,  suggesting  that  the 
distributional implications of the adjustment episodes deserve a more detailed analysis. Second, trends 
in  Greece  and  Portugal  are  different,  suggesting  that  there  is  merit  in  analysing  both  countries 
separately. Third, the tax-and-transfer systems did not fully absorb the economic shock and translated 
into different trajectories of the market and net income. Without taxes and transfers, inequality in both 
countries would have increased much more during this period. At the same time, taxes and transfers 
did not fully absorb distributional consequences either. A more detailed analysis of the role of taxes 
and transfers during the adjustment process therefore promises important insights. 

Income by decile group  

Analysing inequality solely on the basis of Gini coefficients leaves important blind spots. First, countries 
with  different  income  distributions  can  have  the  same  Gini  coefficient  (Elison  2002).  Second,  the 
measure is more sensitive to changes in inequalities in the middle of the income  spectrum than to 
changes at the bottom and the top of the income distribution. Complementing the Gini coefficient with 
a more detailed breakdown of income according to income groups derived from microdata provides a 
more nuanced understanding of the income distribution. This is why, in the following, we look at decile 
groups. For such an analysis, we assign each observed household to one of ten decile groups depending 
on the household’s relative position in the distribution of equivalised disposable household income in 

11 

 
 
 
 
each country and in each year. This way we can trace how income levels and income sources of each 
decile group varied over time in each country.6  

Figure 2 
Income by decile group   

Note: This figure reports  the  disposable income of  households in Greece and Portugal by decile. The  equivalised disposable income is the  total 
income of a household, after tax and other deductions (transfers to other households), that is available for spending or saving, the indicator reflects 
the  purchasing  power  of  households  and  their  ability  to  invest  in goods  and services or  save  for  the  future,  by  accounting  for  taxes and  social 
contributions and monetary in-kind social benefits. See also Appendix B Figure 2 , which adjusts for inflation and shows very similar trends. 
Source: EU-SILC, own calculations. 

Figure 2 shows the evolution of disposable income by decile group for Greece and Portugal. In Greece, 
the income drop starting in 2010 is visible for all income groups. The bottom 10% are affected to a 
somewhat  stronger  extent  than  the  top  90%.  They  recover  from  a  period  with  markedly  reduced 
disposable household incomes around the year 2017. Overall, trends in Portugal are similar, although 
the drop in income is substantially less pronounced. Between 2012 and 2015, declining incomes are 
observed for the top 10% and the bottom 20%, while other income groups see stagnant incomes. After 
2015, all income groups see a substantial increase in disposable household income as the crisis recedes.  

These results highlight that income losses during the crisis and adjustment processes affected large 
segments of the society. At the same time, they indicate that the poorest groups experienced stronger 
income losses than other groups and that income trends for the richest households also deviate from 
those of middle-income groups. A more detailed analysis at the decile-group level with a particular 
focus on the top and the bottom thus seems worthwhile. 

Income by type  

As  a  next  step,  we  disaggregate  between  different  types  of  income.  In  the euro  area,  income  from 
employment and self-employment is the most important income source. Most European economies, 
however, also entertain a large tax-and-transfer system. In the countries we consider, by far the largest 
amount of transfer income results from pensions and other old-age benefits. Social transfers are an 
order of magnitude smaller than pensions but are an important income source for some segments of 
society. Other income sources that are of smaller magnitude for society as a whole, while constituting 
an important income source for clearly defined groups include family benefits and capital income (see 
Appendix D. Figure 2 and Appendix D. Figure 3).  

6 Note that this exercise is based on repeated cross-sectional data (rather than panel data) such that assignment to an income group 
of a given household is not fixed over time. Instead, decile groups have the same size in each year such that the values indicate for 
each year the average income of the bottom 10 percent, the second 10 percent, etc., up until the top 10 percent. 

12 

 
 
 
 
 
 
Figure  3  disaggregates  incomes  by  type  for  selected  income  groups  in  Greece  and  Portugal  (see 
Appendix D. Figure 1 for all income groups as a share of total household income7). The graphs show 
that various income types are of different significance for each income group and each country. They 
also show that various income types evolved differently across income groups during the crisis. For the 
Greek bottom 10 percent, pensions constitute an important income source and a reduction in pension 
payments is an important driver behind the drop in disposable household incomes for this group. Rising 
social transfers in the second half of the 2010s contribute to rising incomes for this decile group. For 
decile group 5 and 10, the drop in income from employment contributes most to the observed drop in 
household incomes in the early 2010s, while income from pensions  did not substantially change for 
these groups. 

Overall  patterns  in  Portugal  are  similar.  Social  transfers,  however,  constitute  a  more  important  and 
more stable income source for poorer decile groups than in Greece.8 The drop in employment income 
is much less pronounced in Portugal than in Greece but  it is clearly visible for richer households. A 
concurrent increase in pensions for these decile groups suggests an increase in early retirement during 
the crisis, partially compensating losses of employment income for the rich. 

Figure 3 
Income by type and decile group  
(EUR)

Note: This figure reports the main income components of households’ income by decile group and over time. In the first row are depicted the results 
for Greece and in the bottom row for Portugal. Based on Eurostat definition of HY020 the income without transfers includes: gross employee cash 
or  near  cash employee  income,  company  car,  gross  cash  benefits or  losses  from  self-employment  (including  royalties),  pensions received  from 
individual  private  plans,  rental  of  a  property  or  land,  regular  inter-household  cash  transfers  received,  interests,  dividends,  profit  from  capital 
investments in unincorporated business, income received by people aged under 16 MINUS regular taxes on wealth, regular inter-household cash 
transfer paid, tax on income and social insurance contributions. See also Appendix B for summary statistics. Source: EU-SILC, own calculations. 

7 In Appendix D we replicate the figure as a share of total gross household income and in Appendix D. Figure 4 and Appendix D. Figure 5 we 
replicate  the  figure  split  by  educational  attainment.  Appendix  D.  Figure  2  highlights  relative  stability  of  the  capital  income  compared 
to employment income.  

8  This is  consistent  with Arnold and  Rodrigues (2015) who  estimate  that  non-pension benefits in  Portugal  provide  considerable 
support to the low-income households, constituting up to 20% of disposable income for bottom quintile. 

13 

 
 
 
 
The main lesson to be learned from the descriptive analysis is that for different income groups, different 
income types are important, leading to different kinds of vulnerabilities. For a holistic understanding of 
how the crisis and subsequent adjustment episode affected households across the income distribution, 
a disaggregation by income group and income type is thus necessary. In our main analysis, we will thus 
present  results  for  different  types  of  incomes  by  income  group.  Analysing  the  components  of 
disposable  household  income  provides  insights  into  the  role  of  social  support  structures  and  helps 
identify  how  market  incomes  and  tax  and  transfer  systems  interacted  to  produce  changing 
distributional outcomes. 

4. Distributional effects across social groups: Evidence from 

DID  

So far, we have studied the data in a descriptive way, providing insights into income dynamics during 
the crisis in the two countries under consideration. Yet, to what extent did the experiences in Greece 
and Portugal differ from EA countries that faced a comparable external economic environment in the 
monetary union but did not lose market access and did not engage in financial assistance programmes? 
We draw such comparisons in the following chapter.   

Dynamic difference-in-difference design 

EU-SILC has a cross-sectional data feature, where a random sample of households is surveyed in each 
year in each country. Exploiting this feature, we pool euro area microdata across survey waves between 
2004  and  2019  to  employ  a  difference-in-difference  (DID)  design  by  estimating  the  following 
specification: 

𝑇

𝑦𝑖𝑐𝑡 = ∑ 𝛽𝑡(𝐷𝑐 × 𝜏𝑡)

+ 𝛿𝑐 + 𝜏𝑡 + 𝑿′𝒄𝒕𝜆 +   𝒁′𝒊𝒕𝛼 +  𝜀𝑖𝑐𝑡                                 (1) 

𝑡≠𝑏𝑎𝑠𝑒 𝑦𝑒𝑎𝑟

The outcome variable, 𝑦𝑖𝑐𝑡, denotes various income types of household i surveyed in country c in year 
t. We initially focus on total disposable household income but subsequently also use other components 
of  income.  The  variable  D  indicates  one  of  the  two  “treated”  countries  (Greece  and  Portugal)  in 
separate regressions. Applying “event-study” specifications, D is interacted with year fixed effects, 𝜏𝑡, 
to  allow  estimating  how  differences  between  treated  and  control  countries  vary  over  time.  In  all 
regressions, we include full sets of year fixed effects, 𝜏𝑡, to absorb all time-specific shocks that hit all 
countries  in  the  sample  in  the  same  way  and  country  fixed  effects,  𝛿𝑐,  to  absorb  all  time-invariant 
country-specific  differences.  𝒁′𝒊𝒕 controls  for  micro-level  household  characteristics  such  as  the  age, 
gender,  education,  marital  status  of  the  household  head,  the  number  of  children  under  18  in  the 
household and a categorical variable for the type of household (couple with one or 2 children, couple 
with 3 children or more, mono-parental households, young single or couple without children, and older 
single or couple without children). 𝑿′𝒄𝒕 represents the control vector of country-year-level variables. 
Throughout the treatment bias” or “collider bias”, the baseline specification only includes the country’s 
age structure.  In alternative specifications reported in Appendix C, we include additional time-varying 
macroeconomic  variables  (such  as  exports  and  imports,  the  current  account  balance,  and  the 
government’s interest payments to account for differences in market pressure) to minimise differences 
between  control  countries  and  treated  countries.  The  results  from  specifications  with  and  without 
control variables are similar. In all the specifications, we use robust standard errors clustered at the 
country level.  

14 

 
 
The DID design requires us to determine an appropriate control group. A key difficulty lies in the non-
random selection of countries that undergo an adjustment programme. Our selection of the control 
group is informed by both theoretical reasoning and an auxiliary application of the synthetic control 
method as a data-driven approach. As regards to theoretical reasoning, we follow the broad consensus 
in the literature that the EA crisis was a balance-of-payments crisis that had fundamentally different 
implications  for  countries  with  current  account  deficits  than  for  countries  with  current  account 
surpluses  (for  details  see  Baldwin  et  al.  2015,  Frieden  and  Walter  2017).  Most  deficit  countries 
encountered  financial  and  economic  troubles  during  the  crisis,  while  most  surplus  countries  fared 
substantially better.  

In  Table  1  we  follow  Frieden  and  Walter  (2017)  in  ranking  EA  countries  by  their  pre-crisis  current 
account  balance.  As  is  visible  in  Table  1,  all  countries  that  were  under  any  form  of  adjustment 
programme had current account deficits before the crisis. Greece and Portugal were the countries with 
the largest current account deficits in the pre-crisis period. The table also shows that other countries 
that participated in adjustment programs (Cyprus, Ireland), received limited financial sector assistance 
(Spain), or saw sharp spikes in borrowing costs and multiple credit rating downgrades  (France, Italy, 
Malta, Slovakia, Slovenia) were countries with current account deficits (Baum et al. 2016). We exclude 
Lithuania,  Latvia,  and  Estonia  as  they  adopted  the  euro  after  the  start  of  the  financial  assistance 
programmes in some countries. Moreover, Latvia  underwent a financial assistance programme before 
joining the euro area.  

Table 1  
Definition of DID control group, current account balance 

Euro area countries 2009 

Current Account Balance (% GDP) 2004-2009 

Greece 

Portugal 

Cyprus 

Spain 

Slovakia 

Malta 

Ireland 

Slovenia 

Italy 

France 

Belgium 

Austria 

Finland 

Germany 

Netherlands 

Luxembourg 

-10.50 

-9.93 

-7.53 

-7.34 

-6.40 

-4.57 

-3.91 

-2.80 

-1.47 

-0.28 

1.57 

3.21 

3.55 

5.58 

6.05 

6.51 

Note: EA countries ranked by their pre-crisis current account balance. ESM programme countries in blue. Countries that are used for the control 
group of the DID design in orange. Spain is excluded from the control group because it had a small-scale financial-sector programme. 
Source: Eurostat. See also Frieden and Walter (2017). 

Therefore, we propose as the control group those EA countries that had a current account deficit in the 
pre-crisis period. Following this approach, the control group consists of Slovakia, Slovenia, Italy, Malta, 

15 

 
 
 
 
 
and France.9 This amounts to excluding the group of surplus countries that fared relatively well during 
the EA crisis (Luxembourg, Netherlands, Germany, Finland, Austria, Belgium). 

To combine this theory-driven selection of the control group with a data-driven approach, we apply the 
synthetic control method in Appendix E. As a donor pool of potential control countries, we use the 
entire  euro  area  excluding  only  the  countries  that  received  some  sort  of  financial  assistance 
programme.  We  then  let  the  synthetic  control  method  select  a  linear  combination  of  countries  to 
match  the  pre-crisis  trend  of  disposable  household  income  as  well  as  of  a  large  number  of 
macroeconomic and demographic country-year specific fundamentals (see Appendix E for details). For 
the data-driven construction of the counterfactuals for both Greece and Portugal, the synthetic control 
algorithm selects only countries with a negative current account balance in the pre-crisis period (Italy, 
Slovakia, and Malta). This result suggests that countries with a current account deficit during this period 
are an appropriate control group for both theoretical and empirical reasons. 

Bearing  in  mind  the  challenges  related  to  the  identification  of  the  control  group,  we  conduct  an 
additional  robustness  check.  Following  Zigraiova  and  Erce  (2024),  we  define  an  alternative  control 
group  based  on  a  comparative  dimension  that  prioritises  the  risk  of  losing  market  access.10  In  this 
exercise,  we  compare  Greece  and  Portugal  to  this  alternative  control  group  consisting  of  Slovenia, 
Slovakia and Italy. These countries are selected based on their high probability of market access loss 
prior to the sovereign debt crisis. This approach leverages countries with similar market signals and 
thus provides an alternative basis for our comparative analysis.  

With this approach, the DID design estimates how income levels of households in Greece and Portugal, 
conditional  on  covariates,  differ  from  households  in  macroeconomically  similar  countries  before, 
during and after the crisis. If the pool of control countries constitutes an appropriate control group, 
there should not be any significant differences in outcomes in the pre-adjustment period. This can be 
tested by estimating 𝛽𝑡 for all 𝑡  <   −1 and as the results below show, we do not find such statistically 
significant 𝛽𝑡s in the pre-adjustment period. 

The 𝛽𝑡 for all 𝑡  ≥  0, the adjustment and post-adjustment period, indicate the dynamic differences 
between households in the two countries under consideration (Greece and Portugal) and the control 
group,  conditional  on  time-invariant  country  characteristics,  EA-wide  time  trends  and  observable 
household and country characteristics. This DID specification assesses the aggregate effects of the crisis 
and the adjustment policies. The results should be interpreted as the combined effects of the crisis and 
the adjustment period. The estimates in this chapter provide an understanding of how, during the crisis 
and  the  following  adjustment,  household  income  trends  in  programme  countries  diverged  from 
observably  comparable  households  in  similar  countries  without  adjustment  programmes.  In 
subsequent  analyses  in  chapter  6,  we  make  use  of  a  triple  DID  design,  which  can  under  certain 
assumptions  disentangle  distributional  effects  of  changing  national  policies  from  the  crisis 
repercussions. 

Results for average income 

We  begin  by  studying  the  mean  of  disposable  household  income.  The  results  for  the  estimated 
coefficients of interest, 𝛽𝑡, and the corresponding 95% confidence intervals are plotted in Figure 4. The 
plot shows no indication of pretends as the estimates are close to zero, statistically insignificant, and 
do  not  show  any  systematic  temporal  pattern.  Before  the start  of  the economic  downturn  and  the 

9 As France’s current account deficit is close to zero, we exclude it from the control group in robustness tests (see Appendix C). The 
results are barely affected. 

10 Due to data limitations, their study excludes several countries e.g. Malta.  

16 

 
 
 
adjustment  programme,  the  disposable  income  evolved  similarly  for  households  in  the  treatment 
countries and in the control group countries, conditional on the controls. This further supports the view 
that the selected countries provide a valid control group. 

Once  the  crisis  has  propagated  into  the  real  economy  and  with  the  start  of  the  adjustment,  the 
disposable  income  of  households  in  Greece  declined  to  a  greater  extent  than  in  the  control  group 
countries. The largest difference is recorded in 2016 with 0.57 log points (statistically significant at the 
99%  level).  After  2016,  the  gap  between  Greece  and  treatment  group  countries  starts  shrinking, 
indicating a reversal. The overall pattern is similar for Portugal, but its magnitude is smaller. The largest 
difference is observed in 2015, when it reaches 0.2 log points, and by 2019 the gap declines to 0.1 log 
points. The pattern indicates that the divide between the control and treatment groups in terms of 
disposable income is on a path of reversion to the pre-crisis period. 

These results are robust to specifications with various sets of control variables. In Appendix C. Figure 
1,  we  show  regressions  without  controls  and  with  additional  macroeconomic  country-year  level 
controls.  We  also  show  that  results  remain  unchanged,  when  adjusting  income  for  inflation  and 
purchasing  power  parity  (Appendix  C.  Figure  2)  or  when  we  rely  on  a  different  control  group  of 
countries following Zigraiova and Erce (2024) as explained in the previous chapter (Appendix C. Figure 
2, Panel C). We report the full regression output in Appendix C. Table 1. 

Figure 4 
Disposable income over time  

Note: The figure shows the regression results from a pooled OLS regression following specification (1). It includes country and year fixed effects and 
robust  standard  errors  clustered at  country  level.  A  set  of  household  level  controls are  included (age,  gender,  education,  marital  status  of  the 
household head, the number of children under 18 in the households and a categorical variable for the type of household (couple with one or 2 
children, couple with 3 children or more, mono-parental households, young single or couple without children, and older single or couple without 
children)) and a country level control (the share of population older than 65 years old). The outcome  variable  in each of the panels is the total 
disposable household income and has a natural logarithmic transformation. In all panels the control group countries are defined as described in 
Table 2 and are the following: Italy, France, Slovenia, Malta and Slovakia. The dots represent the point estimate, while the lines represent the 95% 
confidence intervals. The vertical line is positioned one year prior to the start of the programme, which serves as the base year. The Y axis depicts 
the regression coefficients βt which capture differences between treated and control countries in household income in log terms for each year. 
Regression results are weighted using the cross-sectional weighting for household data. 
Source: EU-Silc, based on own calculations.  

These results corroborate the findings from the descriptive evidence. They indicate marked differences 
in the two programme countries. In Greece, we observe a strong negative effect followed by a positive 
reversal in the income trend starting from 2016 onwards. In Portugal, we observe a milder negative 
effect followed by a positive reversal in the average income trend as of 2015.11 

11 Note that these results are also in line with the auxiliary SC method shown in Appendix E. The fact that pre-trends are insignificant 
and  that  the  results  from  SC  and  DID  regressions  yield  very  similar  results  shows  that  the  DID  design  constructs  a  similar 
counterfactual as the SC method. As a result, subsequent results on distributional effects can be interpreted is the same way as 
results from an SC design. 

17 

 
 
 
 
 
Obviously, these results only indicate an aggregate estimate and do not inform about which groups of 
the population experienced a larger drop in income. This is the focus of the subsequent chapters. 

Heterogeneity by household characteristics 

As a next step, we differentiate between different types of households to see which social groups faced 
the most significant drops in income. We initially focus on the gender of the household heads, their 
age, and their education. For these analyses, we estimate the same regressions as before but split the 
sample according to these household characteristics and report separate regression results for the sub-
samples.  Separating  the  microdata  according  to  these  characteristics  allows  us  to  get  a  deeper 
understanding of how the crisis and economic adjustments propagated across society.  

We observe differences across gender of the household head (Figure 5. Panel A). Both household types 
recorded significant income drops. However, households with female heads were impacted somewhat 
more strongly in both countries. This might be driven by the sectoral employment of women in lower 
paying jobs and an intensifying gender pay gap in times of crisis.  

In terms of age differences (Panel B), individuals older than 65, mostly pensioners, at the mean do not 
appear to have suffered as much as other groups. The finding is in line with Andriopoulou et al. (2019) 
and  Arnold  and  Rodrigues  (2015),  who  report  relatively  favorable  income  positions  of  pensioner-
headed households. The economic adjustment period affected market income more than pensions. In 
addition,  older  groups  have  a  higher  probability  of  benefiting  from  alternative  income  sources. 
Nevertheless, income losses for the elderly during the crisis, which went up to 0.4 log points in the mid-
2010s, are not trivial and result mostly from substantial pension cuts. We study pensions in more detail 
in the following chapter. 

Differences according to education of the household head are small. Households headed by high-skilled 
individuals fared somewhat worse during the adjustment period. This might be caused by a higher level 
of informality for low-skilled workers in the Greek economy during the adjustment (Hassan, Schneider, 
2017; Dellas et al. 2022). But also, by the income squeeze at the top of the income distribution and a 
decrease in the education premium documented in the case of Portugal (Oliveira et al. 2023, Rodrigues 
et al., 2012).  In sum, women and the working-age population experienced a relatively larger decrease 
in income during the adjustment in both Portugal and Greece. 

18 

 
 
 
 
Figure 5 
Heterogeneity by household head characteristics  

Panel A: Gender

Panel B: Age 

Panel C: Education 

Note: The figure shows the regression results from a pooled OLS regression from Specification (1) and split into two different samples according to: 
i) gender, ii) age, iii) education. It includes country and year fixed effects and robust standard errors clustered at country level. A set of household 
level controls are included (age, gender, education, marital status of the household head, the number of children under 18 in the households and a 
categorical variable for the type of household (couple with one or 2 children, couple with 3 children or more, mono-parental households, young 
single or couple without children, and older single or couple without children)) and a country level control (the share of population older than 65 
years old). In each of the  cases, the control that is used to  spit the sample is removed. The  outcome variable  in each of  the panels is the  total 
disposable household income. The outcome variable in the regression has a logarithmic transformation. In all panels the control group countries 
are defined as described in Table 2 and are the following: Italy, France, Slovenia, Malta, and Slovakia. The dots represent the point estimate, while 
the lines represent the 95% confidence intervals. The vertical line is positioned one year prior to the start of the programme. Regression results are 
weighted using the cross-sectional weighting for household data. 

19 

 
 
 
 
 
 
 
 
5. Distributional  Effects  across  Income  Groups:  Evidence 

from Triple-DID 

To study the distributional implications of the adjustment periods in Greece and Portugal in greater 
detail, we differentiate by income decile groups. As anticipated in chapter 4, we assign each household 
to its decile group according to equivalised disposable household income. This allows us to identify 
income  groups that  saw  the  most  significant  changes  to their  income  over  time.  Such  a  distinction 
deepens  our  understanding  of  changes  in  the  overall  inequality  as  well  as  in  various  trends  across 
groups.  

To estimate these effects, we initially estimate the DID regressions at the decile-group level. They are 
specified as follows and estimated separately for each decile group g: 

2021

ln 𝑦𝑔,𝑐,𝑡 = 𝜇𝑐 + 𝜏𝑡 + ∑ 𝛽𝑔,𝑡(𝑃𝑐 × 𝜏𝑡)

+ 𝑋𝑐𝑡𝛤 + 𝜀𝑔,𝑐,𝑡  , ∀ 𝑔 ∈ 𝐺                (2) 

𝑡=2004

These regressions compare a given decile group in the respective treated country to the same decile 
group in the countries of the control group while absorbing country and year fixed effects, as well as 
the same country-year-specific macroeconomic control vector specified above. 

While these results show the changes in incomes for the different income groups relative to the same 
income  groups  in  similar  countries,  they  do  not  allow  us  to  infer  whether  the  distributional 
consequences  of  the  adjustment  period  differed  significantly  by  income  group.  Moreover,  these 
regressions  do  not  allow  us  to  disentangle  the  effects  of  the  country-specific  crisis  shock  from  the 
effects of the adjustment policies. To address these two limitations, we also run “triple difference-in-
difference” (triple-DID, Olden & Moen 2022) regressions by estimating the following specification: 

ln  𝑦𝑑,𝑐,𝑡 = ∑

𝑇
𝑡≠2009

𝛽𝑡 (𝐷𝑐 × 𝜏𝑡 × 𝛾𝑔)

+ 𝛿𝑐 + 𝜏𝑡 + 𝛾𝑔 + (𝐷𝑐 × 𝛾𝑔) + (𝜏𝑡 × 𝛾𝑔) + (𝐷𝑐 × 𝜏𝑡) + 𝜀𝑔,𝑐,𝑡       (3) 

These  are  our  most  conservative  specifications.  Importantly,  they  absorb  all  country-year-specific 
aggregate  shocks  by  including  country-times-year  fixed  effects,  𝜏𝑡 × 𝛾𝑔. 12  This  means  that  the 
aggregate income shock of the crisis effect in a given country-year is fully controlled for. What remains 
is only variation across decile groups within a given country-year. This helps disentangle crisis effects 
from adjustment effects. Nevertheless, this specification estimates the isolated distributional effects of 
the adjustment policies implemented by the two countries only under the strong assumption that the 
crisis had a homogenous aggregate effect across income groups within a given year. 

Disposable Household Income 

We plot results in Figure 6. The upper panel shows the results of the DID regressions. It depicts the DID 
estimates for all ten income groups. In Greece, the drop in disposable household income is significant 
for all income groups and remains so until the end of the observation period. The drop in the early 
adjustment period is strongest for the poorest 10 percent. The triple DID regressions in the lower panel 
show that these differences in the early adjustment period are statistically significant, suggesting that 
incomes of the bottom 10 percent were indeed more negatively affected than incomes in the middle 

12 Since country-times-year fixed effects are absorbed, we argue that we do not need to restrict the control group here and use the 
full sample of EA countries for the triple DID regressions to increase statistical power. 

20 

 
 
 
of the income  distribution.13  Thanks to the  catching-up process starting in 2017, the relative  losses 
compared to the reference groups were recovered by 2018. 

Figure 6 
DID design: disposable household income – results for specification 2 panel a) panel b) shows the triple diff 
in difference 

Note: The two graphs in the top panel show the results of DID regressions with the outcome variable disposable household income before transfers by 
decile group in Greece (left) and Portugal (right) (Specification 2). The two graphs in the bottom panel show the results of triple-DID regressions of the same 
variables (Specification 3). 

The overall pattern in Portugal shows some similarities but the magnitude of the economic shock is 
milder. At the decile-group level, individual coefficients for the early adjustment period are all negative 
but not statistically significant at conventional levels. The triple DID results show, however, that the 
bottom 10 percent are also more negatively affected than the income groups in the middle. Over time, 
DID  coefficients  increase  for  all  decile  groups  pointing  to  an  overall  economic  catch-up  in  Portugal 
relative to comparable countries. The relative losses for the bottom 10 percent also diminish over time, 
pointing to a catch-up at the bottom of the Portuguese income distribution. 

Income from social safety nets  

To  shed  more  light  on  the  income  developments  across  income  deciles,  we  disaggregate  overall 
disposable household income into various income sources. First, we examine the combined transfer 
income from all social safety nets (including all types of social transfers and pensions) and exclude all 
market  income  (from  employment,  self-employment  or  capital  investments).14  Given  that  many 
conditions of the adjustment programmes in Greece and Portugal focused on the reform of the pension 

13 Note that the regressions include all decile groups but we only plot the bottom top income groups to reduce clutter. A graph with 
all coefficients is shown in the Appendix. Decile group 5 is the left-out reference group in all regressions. 

14 In Appendix H, we also show results for market income and further differentiate between pensions and transfers. 

21 

 
 
 
 
 
 
 
and transfer system (see chapter 2, Appendix A and Appendix G), we expect the most direct effects of 
the  policies  on  these  sources  of  household  income.  In  subsequent  analyses,  we  then  disaggregate 
between income from different types of social safety nets.  

The top panel of Figure 7 shows descriptive evidence over time and suggests that richer households 
received higher income from social safety nets. This is mostly due to pension payments, which account 
for the largest share of this income type and are larger for richer households as they reflect previous 
earnings. In terms of changes over time, the most notable development is a substantial drop in pension 
and transfer payments  to the  poorest  10 percent  in Greece  between 2012 and 2017.15  In Portugal, 
there also is a drop during the adjustment period for poorer households but it is smaller than in Greece. 
At the same time, we observe an increase in pension payments to the Portuguese richest ten percent 
in 2012. 

15 Leventi and Picos (2019) link a drop in income of households headed by pensioners to the 2011 personal income tax reform which 
resulted in a considerable rise in pensioners’ solidarity  contributions; on average, half  of  this category  of households  located in 
deciles 5-10 experienced income losses of 5% or more. 

22 

 
 
 
Figure 7 
Descriprive, DID, and triple-DID: Income from Social Safety Nets (Pensions and Transfers) 

Note: The two graphs in the top panel show the evolution of income from pensions and transfers by decile group in Greece (left) and Portugal (right). The 
two graphs in the bottom panel show the results of DID regressions of the same variables. 

The results from DID regressions reported in the middle panel compare these developments to similar 
EA countries. They are generally in line with the descriptive findings: in the early crisis phase, there is 
a  substantial  drop  in  income  from  pensions  and  transfers  combined  for  the  poorest  10  percent  in 
Greece.  This  drop  is  mostly  driven  by  a  drop  in  income  from  pensions  (see  Appendix  H,  which 
differentiates  between  pensions  and  transfers).  The  negative  trend  for  the  poorest  10  percent  in 
Greece,  however,  starts  to  reverse  in  2017  and  the  relative  losses  vanish  towards  the  end  of  the 
observation period. This trend reversal is driven mostly by transfers, which increase for the poorest 10 
percent in later stages of the adjustment period (see Appendix H and below).  

These  results  are  confirmed  by  the  triple  DID regressions  in the  bottom  panel. They  show  that  the 
temporary differences between the bottom 10 percent and the middle of the income distribution are 
statistically significant for the 2012-2017 period and vanish thereafter. To the extent that the crisis in 

23 

 
 
 
 
 
 
 
 
 
Greece  had  a  homogenous  impact  on  income  from  public  transfers  across  income  groups,  these 
estimates  indicate  the  distributional  effects  of  the  adjustment  policies.  The  results  show  that 
adjustment policies temporarily reduced income from social safety nets for the poor. Distinguishing 
between pensions and social transfers suggests that this reduction is driven by pensions (Appendix H). 
Social transfers for the poor increased in later stages of the adjustment programme and contributed to 
the catching-up for the bottom 10 percent. 

For  Portugal,  the  relative  loss  in  pension  and  transfer  incomes  for  the  bottom  10  percent  is  not 
statistically  significantly  different  from  the  middle of  the  income  distribution. However, the relative 
increase in this income stream for the top ten percent  is statistically significant until the end of the 
observation period. This suggests that, also in Portugal, changes in distributional patterns of income 
from pensions and transfers contributed to inequality during the adjustment period. 

Disaggregating social safety nets  

This section delves into a more detailed analysis of different types of social transfers to understand how 
the social safety net has affected distributional outcomes during the crisis. We use the most granular 
type of EU SILC data at the individual (household-member) level, which differentiates between many 
types of social transfers. The public transfers that were considered thus far included different types of 
public  redistribution  schemes  related  to  poverty,  education, 
families,  sickness,  disability, 
unemployment, housing, etc. This analysis now differentiates between the various types. It thereby 
excludes all kinds of pensions (i.e., old-age and survivors’ benefits), since their aggregate volume is an 
order of magnitude larger and masks the nuances in levels of other targeted social transfers. As these 
different types of social transfers vary strongly across countries and are thus difficult to compare in a 
regression-based framework, we study this highly granular disaggregation in a descriptive way. 

Before  turning  to  these  microeconomic  data  on  social  transfers,  we  illustrate  the  macroeconomic 
evolution  of  social  protection  spending  in  Greece  and  Portugal  before,  during,  and  after  the  crisis. 
Appendix  F  depicts  government  spending  on  social  protection  in  the  analysed  countries.  It  shows 
dynamic growth in government spending on social protection per capita in the run-up to the financial 
assistance  programme  in  both  Greece  and  Portugal.  With  the  start  of  the  adjustment,  the  social 
protection  expenditures  drop  in  Greece,  while  they  continue  to  rise  in  Portugal.  The  Greek  social 
protection spending continuously dropped each year between 2009-2013. In 2013/4 social protection 
spending stabilised at a lower level until the end of the observation period. In Portugal, the adjustment 
process only slowed the pace of expenditure increases (see Appendix F).  

In  line  with  the  modest  change  to  aggregate  social  protection  spending  levels  during  the  crisis  in 
Portugal, we observe only limited over-time fluctuations in income from social benefits across income 
groups. There are three notable observations. First, the most substantial break in the time series is 
observable for the top 10 percent, for whom we record a large jump in income from unemployment 
benefits in 2012/3. This reflects the ability of unemployment benefits in Portugal to act as automatic 
stabilisers  when  the  crisis  hit  the  job  market.  More  generally,  the  expenditure  data  show  that 
unemployment benefits make up a large share of social protection spending in Portugal. They increase 
after the  start of the  adjustment  process and start  decreasing around 2013. The  reform timeline  in 
Appendix  G  provides  details  on  the  policy  changes  in  the  Portuguese  unemployment  benefits  and 
shows both changes in disbursed amounts and extensions of coverage. A second notable observation 
is  the  progressive  role  played  by  family  allowances,  which  are  large  for  low-income  households, 
decrease with household income and are relatively small for high-income households. A final notable 
observation from Portugal is that income support to address social exclusion is highly targeted. This 

24 

 
 
 
type of social spending mainly reaches households in the bottom 10 percent and only few households 
in richer decile groups benefit from it. 

For  Greece,  social  exclusion  benefits  were  initially  much  less  targeted.  In  the  pre-crisis  and  early 
adjustment  period,  households  from  virtually  all  decile  groups  received  similar  amounts  of  income 
from  social-exclusion  spending.  Unemployment,  disability  benefits  and  family  allowances  are  also 
similar across income groups in this time period. The most notable observation for the early adjustment 
in Greece is the substantial decline in income from social spending for all income groups. The largest 
drops are observed for households in decile groups 2-8. This shows that the cuts to social spending in 
the  early  adjustment  period  affected  both  rich  and  poor  segments  of  the  Greek  society.  As  social 
transfers constitute a relatively more important income source for poorer households (Appendix D), 
this had more severe implications for those households. In the later adjustment period, we identify a 
marked shift. The data show a strong increase in social exclusion income support for the bottom ten 
percent and a mild increase for the second decile group. This uptick coincides with the implementation 
of the Guaranteed Minimum Income (GMI) / Social Solidarity Income (SSI) in 2016/2017. In the same 
period,  we  also  observe  a  substantial  increase  in  income  from  family  allowances  for  low-income 
families. Richer families simultaneously lose income from family allowances, reflecting cuts to family 
allowances implemented in 2011/12 (see the timeline in Appendix G). Overall, the evidence shows that 
social transfers in Greece become significantly more targeted toward low-income households in the 
later stages of the adjustment.  

A key difference between Portugal and Greece is that unemployment benefits played more of a social 
safety role in Portugal than in Greece. As an example, consider the values from 2014. According to our 
calculations, the average income from unemployment benefit for households in the 5th decile was €120 
in Greece, while in Portugal it reached €578 .16 In Greece, the household income from unemployment 
benefits decreased early after the start of the economic adjustment. The short coverage and the low 
amounts  translate  into  substantially  lower  per  capita  government  spending  on  unemployment 
benefits. Conversely in Portugal, unemployment government spending per capita continued rising until 
2013 and only diminished towards the end of the adjustment (see Appendix F. Figure 3). 

16 The OECD (2013) highlighted that for 2014 only 17% of the people declared as unemployed received the unemployment benefits 
in Greece in 2009, compared to 26% in Portugal and 53% in France. 

25 

 
 
 
Figure 8 
Social benefits excluding old-age and survivors' benefits.  

(EUR) 

Note: This figure reports breakdown of social benefits excluding the old-age and survivors benefits. The reference period is only starting in 2007. 

26 

 
 
 
 
 
Conclusion 

Our research delves into the distributional implications of the Eurozone crisis and macroeconomic 
adjustment. In particular, we focus on changes in income levels across various social groups, on how 
these changes unfold over time and on the types of incomes that drive these. By employing detailed 
microdata,  our  study  moves  beyond  aggregate  measures  to  unveil  the  nuanced  evolution  of  living 
conditions among various social groups during this period of macroeconomic adjustment. Drawing on 
descriptive  analyses,  as  well  as  on  DID  and  triple-DID  regressions,  we  reach  conclusions  on  the 
evolution of aggregate household income, on distributional changes across social groups, and on the 
mechanisms behind those. 

First, average household incomes declined during the crisis in both Greece and Portugal, while the 
decrease in incomes was more pronounced in Greece than in Portugal. The aggregate income drop in 
both  countries  was  larger  than  in  a  group  of  countries  with  similar  pre-crisis  trajectories  and 
macroeconomic  fundamentals.  The  initial  drop  was  followed  by  a  reversal  from  2016  onwards  in 
Greece and from 2015 onwards in Portugal.   

Second, these income losses during the crisis were not equally distributed across social groups. When 
differentiating between households by the gender of the household heads, their education and their 
age,  we  find  that  women  and  younger  people  saw  more  substantial  income  losses  during  the 
adjustment  in both Portugal and Greece. When examining the income  developments  at  the decile-
group  level,  we  find  that  both  in  Greece  and  Portugal,  the  bottom  10  percent  were  initially  more 
negatively affected than the middle-income groups. These groups were also worse off compared to 
their counterparts in other countries with similarly weak pre-crisis economic characteristics. 

Third, studying the mechanisms that drive these changes in disposable income, we find that various 
types  of  household  income  (market  income,  pensions,  social  transfers)  reacted  differently  for 
different income groups. In Greece, we find a significant drop in income from pensions for the poorest 
10 percent between 2012 and 2017. From 2018 onwards, however, the relative loss of income for the 
poorest ten percent ends, mainly due to a gradual boost in social transfers. This shift is associated with 
the gradual roll-out of a general minimum income scheme. 

Overall, trends are similar in Portugal, but with a smaller magnitude. Here, we also observe temporary 
income losses for the poorest ten percent that are associated with decreasing incomes from pensions. 
At  the  same  time,  we  observe  a  relative  increase  in  pension  payments  to  the  richest  10  percent 
between 2012 and the end of the observation period. In Portugal, unemployment benefits played a 
major role in shielding many income groups from more substantial income losses. In both countries, 
changing distributions of income from pensions and transfers played a key role for inequality during 
the adjustment period. 

In sum, our analysis confirms that different income groups depend on different income types and 
that such variation results in different vulnerabilities in times of crisis. For the most vulnerable groups, 
our analysis documents the crucial role played by pensions and transfer systems during the economic 
adjustments in the Eurozone crisis. It points to the need to put in place effective and sustainable safety 
nets  in  stable  times,  as  ad  hoc  tweaks  during  the  economic  adjustment  can  negatively  impact  the 
economic  well-being  of  the  lowest  income  groups.  We also  find  that  well-designed  and  targeted 
reforms can alleviate pressures on the lowest income groups. 

More generally, the heterogeneous findings for different phases of the adjustment periods suggest 
that  distributional  outcomes  are  sensitive  to  the  exact  policy  design.  While  cuts  to  transfers  and 
pensions  in  the  early  adjustment  period  in  Greece  placed  a  substantial  burden  on  low-income 

27 

 
 
households, the same households benefitted from reforms that led to more targeted social policies in 
the  later  stages  of  the  programme.  This  was  achieved  without  substantial  increases  in  the  overall 
volume of spending and thus points to the need for well-targeted social policies in times of crisis.  

28 

 
 
 
 
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Appendix 

Appendix A 

Appendix A. Table 1 
Coding of Conditionality 

ESM database sector 

ESM database subsector 

COFOG classification 

Financial sector policies 

Fiscal measures17  

Expenditure measures  

Categorised based on their description   

Omitted for the analysis 

Revenue and expenditure 
measures  
Revenue measures 

Fiscal-structural18 

Education 

Categorised based on their description  

Categorised based on their description 

Categorised under COFOG 09-Education 

Healthcare reform 

COFOG classification 07-Health 

Other policies 

Structural labour market 
policies 

Structural product market 
policies 

Pension system 

Welfare system 

Social security 

Other  

Social policies  

Other  

Monitoring and Technical 
assistance 
Active Labour Market 
Policies  
Collective bargaining  

Other  

Education system  

Employment protection 
legislation  
Pension system 

Employment protection 
legislation 
Health care reform 

Education system 

COFOG classification “10-Social Protection 

COFOG classification “10-Social Protection 

COFOG classification “10-Social Protection 

Categorised based on their description 

COFOG “10-Social Protection”  

Categorised based on their description 

Categorised based on their description 

Categorised based on their description 

Categorised based on their description 

Categorised based on their description 

09-Education  

10-Social Protection  

COFOG “10-Social Protection”  

COFOG “10-Social Protection”  

COFOG “07-Health” 

COFOG “09-Education” 

Note: Own classification. 
Source: ESM Conditionality Database 

Remaining subsectors  

Categorised based on their description 

Box 1. Conditionallity assessment  
The  inspection of the  data  we coded  for  the  four  former  beneficiary  countries suggests  that 
conditionality implemented in the social policy areas mainly touched upon health care and social 
protection.  The  intensity varied  across  countries  and  across  time.  Portugal  and  Greece  score 
highest when comparing numbers of conditionalities targeting social spending. According to our 
classification, Greece had the  highest number of conditionalities  related to social protection, 
amounting to 281 conditions, while Portugal had a notably lower count of only 87. Additionally, 
in  terms  of  duration,  Greece  exhibited  the  longest  persistence  of  conditionality  per  year,  as 
illustrated in Appendix A Figure  2 . This is why our empirical analysis focuses on Greece  and 
Portugal. We do not take into consideration compliance with conditions and their effects. For a 
more detailed assessment of compliance assessment see Clancy et al, 2023. 

17 Impact government revenues and expenditures and in turn deficit. 

18 Improve expenditure and revenue efficiency. 

33 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Appendix A. Figure 1  
Total number of conditions 

Note: This figure reports the total number of conditions for Cyprus, Greece, Ireland, and Portugal based on the ESM conditionality database. 
Source: ESM Conditionality Database. 

Appendix A. Figure 2  
Social protection related conditionalities over time  
(number) 

Note: This figure reports  conditionalities for Cyprus, Greece, Ireland, and Portugal based on the ESM conditionality  database related to COFOG 
category social protection. It reports for each year the number of conditions for each country. 
Source: ESM Conditionality Database, Eurostat and own calculations. 

34 

 
 
 
 
 
 
 
Appendix A. Figure 3  
Focus of financial assistance programs 
(percent) 

Note: This figure depicts the main classification of conditionalities for Cyprus, Greece, Ireland, and Portugal.   
Source: Own coding based on ESM conditionality database. 

35 

 
 
 
 
 
 
Appendix A. Figure 4 
Conditions across countries and sectors 
(number of conditionalities) 

Note: This figure classifies the  conditionalities for Cyprus, Greece, Ireland, and Portugal. It matches data  from the  ESM conditionality database 
against three main COFOG categories related to social spending. We refer to social spending as spending on i)  health, ii) education and ii) social 
protection. The horizontal axis shows the number of conditions, and the bars report the actual number for each category. Source: Own coding based 
on ESM conditionality database. 
Source: Own coding based on ESM conditionality database. 

36 

 
 
 
 
 
Appendix B 

Summary Statistics  

The  tables  in  this  section  report  the  mean  of  each  variable:  i)  market  income  (income  from 
employment, self-employment, interest, property), ii) gross household income (which includes taxes), 
iii)  disposable  household  income,  iv)  social  transfers,  v)  taxes.  The average  for each  income  type  is 
reported for each decile. Each table reports the average for selected period.  

Descriptive statistics - mean by (decile) before 2010 - Greece 

  Market income  Gross income 
7975.05 
11465.216 
14218.814 
16920.058 
19919.287 
23996.625 
29168.698 
35632.782 
45483.464 
81708.21 

2996.442 
4971.128 
7225.795 
9515.299 
12368.88 
16004.323 
20803.19 
28464.456 
38770.532 
55616.925 

  Disposable Inc 
5195.545 
8927.593 
11236.444 
13186.044 
15458.378 
18319.692 
21712.952 
25781.875 
31764.782 
52798.640 

  Social transfers 
515.794 
714.983 
710.685 
818.323 
732.111 
817.408 
758.172 
669.231 
401.12 
453.318 

  Total taxes 
2417.345 
2095.313 
2325.925 
3007.61 
3762.662 
4646.688 
6188.292 
8651.989 
12196.072 
26389.7 

 1 
 2 
 3 
 4 
 5 
 6 
 7 
 8 
 9 
 10 

Descriptive statistics -  mean  by (decile ) between 2010 and 2016 - Greece 

  Market income  Gross income 
6756.224 
10557.467 
13512.869 
15452.938 
17850.96 
20405.397 
24649.27 
29847.77 
36927.174 
64480.443 

5373.031 
7398.491 
9901.025 
10722.247 
12505.06 
14829.635 
19530.809 
26057.117 
34382.914 
61532.981 

  Disposable Inc 
4431.691 
7768.826 
10070.397 
11581.212 
13386.836 
15243.690 
18229.725 
21692.128 
26297.475 
43077.394 

  Social transfers 
706.358 
747.455 
815.091 
760.713 
774.288 
806.384 
844.097 
732.633 
588.896 
445.939 

  Total taxes 
2324.534 
2788.641 
3442.472 
3871.726 
4464.124 
5161.707 
6419.545 
8155.642 
10629.699 
21403.049 

 1 
 2 
 3 
 4 
 5 
 6 
 7 
 8 
 9 
 10 

Descriptive statistics -  mean  by(decile ) between 2017 and 2019 - Greece 

  Market income  Gross income 
7572.651 
11188.891 
14019.379 
15959.271 
18000.954 
20554.048 
23834.543 
28005.141 
33422.323 
56306.539 

5632.093 
7713.243 
9706.093 
11032.68 
12389.487 
14918.335 
18180.238 
23045.474 
28799.32 
53034.196 

  Disposable Inc 
4690.590 
7464.122 
9388.159 
10698.117 
12174.492 
13895.888 
15929.404 
18554.947 
22129.519 
35682.218 

  Social transfers 
1234.821 
886.314 
756.806 
669.58 
673.849 
658.679 
577.59 
517.275 
395.842 
288.055 

  Total taxes 
2882.061 
3724.769 
4631.22 
5261.154 
5826.462 
6658.16 
7905.14 
9450.193 
11292.804 
20624.321 

 1 
 2 
 3 
 4 
 5 
 6 
 7 
 8 
 9 
 10 

37 

 
 
 
   
 
   
 
 
   
 
 
 
Descriptive statistics -  mean  by(decile )  before 2011 - Portugal 

  Market income  Gross income 
5318.721 
8085.568 
9415.359 
12205.962 
14660.873 
17806.508 
21251.715 
26378.853 
35778.217 
69112.132 

2532.297 
4472.659 
5199.926 
8450.587 
11129.938 
14651.921 
18855.383 
22802.041 
31990.814 
56449.28 

  Disposable Inc 
4206.828 
6872.952 
8113.907 
10396.544 
12370.582 
14705.563 
17325.335 
20826.533 
27164.146 
49693.660 

  Social transfers 
974.127 
1052.284 
984.612 
1149.458 
1201.322 
1305.937 
1074.024 
1087.356 
1019.034 
926.172 

  Total taxes 
797.682 
894.067 
899.432 
1465.132 
1854.319 
2651.363 
3552.126 
4889.613 
7980.105 
19531.376 

 1 
 2 
 3 
 4 
 5 
 6 
 7 
 8 
 9 
 10 

Descriptive statistics -  mean  by(decile ) between 2011 and 2014 - Portugal 
  Market income  Gross income 
5521.026 
8165.178 
10095.691 
12402.592 
15222.195 
17769.02 
21232.969 
26474.622 
35115.778 
65843.92 

  Disposable Inc 
4780.545 
7292.193 
8977.842 
10966.500 
13172.486 
15072.899 
17565.993 
21037.754 
26389.940 
44948.591 

3897.295 
5575.874 
6898.459 
9465.883 
13050.891 
15642.044 
19665.559 
25280.03 
33872.357 
59625.216 

  Social transfers 
1440.806 
1057.429 
1123.8 
1200.449 
1316.555 
1254.959 
979.872 
1105.455 
990.532 
955.067 

 1 
 2 
 3 
 4 
 5 
 6 
 7 
 8 
 9 
 10 

  Total taxes 
740.481 
872.985 
1117.849 
1436.093 
2049.709 
2696.121 
3666.976 
5436.868 
8725.838 
20895.329 

Descriptive statistics -  mean  by(decile ) between 2015 and 2019 - Portugal 

  Market income  Gross income 
5249.843 
8561.457 
10716.106 
13136.24 
15950.262 
19412.334 
23411.769 
28938.793 
38567.368 
69717.635 

3515.11 
5950.825 
7171.209 
10368.592 
13594.656 
17515.058 
22077.422 
27939.317 
37368.187 
59250.682 

  Disposable Inc 
4580.050 
7653.569 
9575.765 
11521.894 
13696.780 
16163.219 
18756.164 
22177.489 
27538.886 
45316.888 

  Social transfers 
1276.015 
1108.73 
1005.166 
1020.886 
1044.717 
1001.204 
855.237 
836.761 
797.343 
1371.318 

  Total taxes 
669.793 
907.887 
1140.341 
1614.346 
2253.483 
3249.115 
4655.605 
6761.304 
11028.482 
24400.748 

 1 
 2 
 3 
 4 
 5 
 6 
 7 
 8 
 9 
 10 

Appendix B. Figure 1 
Equivalised disposable household income adjusted for inflation 

Note: This figure reports the mean of equivalised disposable household income for Greece, Portugal and the remainder of the euro area (excluding 
Cyprus and Ireland)  ajdusted for inflation.  
Source: EU-SILC and Eurostat. 

38 

 
 
 
   
 
   
 
 
   
 
 
 
Appendix B. Figure 2 
Income by decile group  

Note: This figure reports the disposable income of households in Greece and Portugal by decile adjusted for inflation. The equivalised disposable 
income is the total income of a household, after tax and other deductions (transfers to other households), that is available for spending or saving, 
the indicator reflects the purchasing power of households and their ability to invest in goods and services or save for the future, by accounting for 
taxes and social contributions and monetary in-kind social benefits. 
Source: EU-SILC, own calculations. 

39 

 
 
 
 
 
Appendix C 

Appendix C. Table 1  
Regression results 

Greece  

Portugal   

(1) 

(2) 

(3) 

(4) 

No controls   With Controls   No controls   With Controls  

Treatment # 2004.year 

Treatment # 2005.year 

Treatment # 2006.year 

Treatment # 2007.year 

Treatment # 2008.year 

Treatment # 2009.year 

-0.0784 
(0.0486) 

-0.0301 
(0.0567) 

0.0018 
(0.0545) 

0.0059 
(0.0645) 

-0.0207 
(0.0129) 

-0.0400 
(0.0724) 

-0.0029 
(0.0718) 

0.0396 
(0.0609) 

0.0194 
(0.0552) 

-0.0125 
(0.0127) 

-0.0165 
(0.0399) 

0.0097 
(0.0505) 

0.0045 
(0.0474) 

0.0404 
(0.0566) 

-0.0153 
(0.0109) 

-0.0201* 
(0.0091) 

Treatment # 2010.year 

0.0214* 
(0.0091) 

-0.0040 
(0.0093) 

Treatment # 2011.year 

-0.0889*** 
(0.0051) 

-0.1368*** 
(0.0110) 

-0.0271* 
(0.0123) 

Treatment # 2012.year 

Treatment # 2013.year 

Treatment # 2014.year 

Treatment # 2015.year 

Treatment # 2016.year 

Treatment # 2017.year 

Treatment # 2018.year 

Treatment # 2019.year 

-0.2458*** 
(0.0114) 

-0.3071*** 
(0.0159) 

-0.4114*** 
(0.0264) 

-0.4371*** 
(0.0200) 

-0.4957*** 
(0.0251) 

-0.5242*** 
(0.0174) 

-0.5090*** 
(0.0289) 

-0.5505*** 
(0.0183) 

-0.5237*** 
(0.0332) 

-0.5653*** 
(0.0173) 

-0.5093*** 
(0.0336) 

-0.5412*** 
(0.0122) 

-0.4875*** 
(0.0304) 

-0.5174*** 
(0.0138) 

-0.4215*** 
(0.0218) 

-0.4467*** 
(0.0158) 

-0.0661** 
(0.0184) 

-0.0936** 
(0.0344) 

-0.1271** 
(0.0333) 

-0.1264** 
(0.0371) 

-0.0864* 
(0.0415) 

-0.0684 
(0.0419) 

-0.0626 
(0.0383) 

0.0042 
(0.0285) 

0.0463 
(0.0718) 

0.0724 
(0.0768) 

0.0710 
(0.0651) 

0.0885 
(0.0578) 

0.0153 
(0.0213) 

-0.0023 
(0.0106) 

-0.0572*** 
(0.0096) 

-0.1054*** 
(0.0118) 

-0.1322*** 
(0.0221) 

-0.2072*** 
(0.0177) 

-0.2132*** 
(0.0180) 

-0.1717*** 
(0.0158) 

-0.1569*** 
(0.0151) 

-0.1514*** 
(0.0180) 

-0.0808** 
(0.0206) 

1,048,472 
0.1171 
0.0058 
Yes 
Yes 
No 

N 
adj. R2 
Within R2 
Country FE 
Year FE 
Controls 

993,107 
0.1175 
0.0005 
Yes 
Yes 
No 
Note: The  table  reports the  regression results  from specification (1) where  the  outcome  variable  is the  household disposable  income.  Controls 
include household level characteristics and the age structure. The table reports robust standard errors clustered at country level in parentheses. * 
p < 0.05, ** p < 0.01, *** p < 0.001. 
Source: EU-Silc based on own calculations.  

927,287 
0.3119 
0.2233 
Yes 
Yes 
Yes 

870,566 
0.3072 
0.2173 
Yes 
Yes 
Yes 

40 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Appendix C. Figure 1 
Robustness checks with alternative controls 

Panel A: Specification without controls  

Panel B: Specification with selected country level controls 

Panel C: Specification with a full set of country level controls 

Note: The figure shows the regression results from a pooled OLS regression from Specification (1). It includes country and year fixed effects and robust 
standard errors clustered at country level. In all panels the control group countries are defined as described in Table 2 and are the following: Italy, France, 
Slovenia, Malta and Slovakia. The dots represent the point estimate, while the lines represent the 95% confidence intervals. The vertical line is positioned 
one year prior to the  start of  the programme.  In panel A,  there are no control variables.  In panel B, we augment the controls with household level 
variables and selected number of country controls such as: percent of population older than 65, imports, exports, current account deficit and the real 
labour productivity per person. In panel C, we add in addition to the controls in Panel B: Gini index, real GDP growth, investment as share of GDP, volatility 
of stock market index, political stability index (World Bank) and government interest expenditure as % of GDP. Regression results are weighted using the 
cross-sectional weighting for household data 

41 

 
 
 
 
 
 
 
 
Appendix C. Figure 2  
Robustness checks adjusting for inflation and purchasing power parity 

Panel A: Specification with disposable income adjusted for inflation  

Panel B: Specification with disposable income adjusted for purchasing power parity  

Panel C: Specification with different control group  

Note: The figure shows the regression results from a pooled OLS regression from Specification (1). It includes country and year fixed effects and robust 
standard errors clustered at country level. The outcome variable in each of the panels is the total disposable household income. The outcome variable in 
the regression has a natural  logarithmic transformation.  In panel  A and B  the control  group countries are defined as described in Table  2 and are the 
following: Italy, France, Slovenia, Slovakia and Malta. In panel C we include as control group countries: Slovenia, Slovakia  and Italy forllowing Zigraiova 
and Erce (2024). Due to lower number of observations in 2024 because we reduced the number of countries the starting period is 2005. The dots represent 
the point estimate, while the lines represent the 95% confidence intervals. The vertical line is positioned one year prior to the start of the programme. 
Regression results are weighted using the cross-sectional weighting for household data. 

42 

 
 
 
 
 
  
 
 
 
Appendix D 

Appendix D. Figure 1 
Income sources as percent of gross income 

Note: The figure reports the main income components of households’ income by decile group and over time as a percent of total gross income, for 
Greece and Portugal. 

43 

 
 
 
 
 
 
Appendix D. Figure 2 
Income sources, including capital income 

Note: The figure reports the main income sources, including capital income, over time for decile 1, 5, and 10.  

44 

 
 
 
Appendix D. Figure 3 
Income sources, including capital income, as percent of total 

Note: This figure reports the main income sources, including capital income, over time for decile 1, 5, and 10. The income components are reported as a share of total gross household income.  

45 

 
 
 
 
Appendix D. Figure 4 
Income sources by education group, in levels 

Note: This figure reports the main income components in Greece and Portugal over time for two groups: household that have a head with high 
educational attainment and those whose head has a lower educational attainment.   

Appendix D. Figure 5 
Income sources by education group, in percent 

Note: This figure reports the main income components in Greece and Portugal over time for two groups: household that have a head with high 
educational attainment and those whose head has a lower educational attainment.  Income components are reported as percent of total gross 
household income.   

46 

 
 
 
 
 
 
 
Appendix E 

Synthetic Control 

As  discussed  in  the  main  text,  we  study  the  aggregate  shock  on  household  incomes  during  the 
adjustment  episodes  in  Greece  and  Portugal  with  the  synthetic  control  (SC)  method.  The  intuition 
behind the SC approach is to construct a counterfactual for the “treated” country by constructing a 
synthetic combination of control countries that matches, as closely as possible, the characteristics of 
this country in the pre-treatment period. This synthetic combination is a weighted average of untreated 
countries. The calculated weights are then used to project the synthetic control into the post-treatment 
period. Because they allow different weights for each control unit, SCs often provide more appropriate 
counterfactuals for individual treated units (Abadie, 2020). In the related literature, SC methods have 
been used to study the introduction of the euro (Puzzello and Gomis-Porqueras, 2018), the effects of 
Brexit (Born et al. 2019), the integration of new EU members (Campos, Coricelli, and Moretti 2019) and 
the adjustment programme in Greece (Revuelta, 2021). 

For our application of the SC approach, we use all member countries of the euro area as the “donor 
pool” of countries from which the SCs can be constructed. We match both Greece and Portugal on pre-
treatment values of the outcome variable, mean equivalised disposable household income, and the 
following set of basic macroeconomic variables: real GDP growth, government deficit, current account 
balance, real labour productivity per hour worked, government expenditures on social protection as % 
of GDP, exports as % of GDP, imports as % of GDP, the proportion of population less than 14 years old 
and more than 65 years old. Appendix E. Figure 1 reports the set of optimal weights that enables the 
synthetic country to closely replicate each individual treatment country’s characteristics in the period 
leading up to the start of the euro crisis. The data driven algorithm of the SC method selects Italy (75%) 
and Slovakia (25%) for Greece, and Malta (93%), and Slovakia (7%) for Portugal.  

Appendix E. Figure 1 
Donor Units for Synthetic Greece and Portugal 

Note: This figure reports the donor units weighted according to importance for the synthetic Greece and Portugal that best  match the disposable 
income during the pre-crisis period. 

47 

 
 
 
 
 
 
 
Appendix F 
Government Spending   

Appendix F. Figure 1 
Government Expenditures on Social Protection  

Note: This figure reports descriptively total government expenditures on social protection  per capita for the treatment countries, Greece or Portugal 
versus the average for the euro area (the pink dashed line).  

Appendix F. Figure 2 
Government Expenditures on Social Protection by function and per capita 

Note: This figure reports government expenditures, measured per capita, by COFOG function descriptively over time for Greece and Portugal.  

48 

 
 
 
 
 
Appendix F. Figure 3 
Government Expenditures on Social Protection by function per capita, old age category not included 

Note: This figure reports government expenditures, measured per capita, by COFOG function descriptively over time for Greece and Portugal. 

49 

 
 
 
 
 
 
Appendix G 

Policy measures timeline: Greece19 

2011 

2011 

2011+2012 

Feb-12 

2012 

late 2013 

2013 

2014 

Nov-14 

Aug-15 

2015-2016 

Jul-16 

Feb-17 

May-17 

Mid 2017 

May-17 

Jan-18 

Jan-18 

Jan-19 

Expenditure side and structural measures 

National register of beneficiaries of social and welfare benefits  

Pension reform: Pensioners' Social Solidarity Allowance/Benefit (EKAS) - a new set of stricter 
eligibility criteria was introduced 
Five family benefits were abolished:  a) lump sum allowance for the third child, b) the third child 
benefit, c) the large family allowance, d) the third family benefit, e) lifelong pension to mothers 
with more than three children  
Retrenchment of contributory unemployment for wage earners  

Broadening of access to means-tested unemployment assistance for long-term unemployed 

Introduction of the means tested unified family benefit: extension of family benefit to cover all 
children no matter the family size 
Unemployment insurance extended to formerly self-employed workers 

Family support is made of two means tested programs: Large Family Benefit (reserved for families 
with 3 or more children, it has a generous income threshold and confers a disproportionately more 
generous benefit to these families, 34 % of families in the top quintile received the benefit, 49 % of 
families in the poorest quintile receive this benefit); Unified family benefit: is also a means tested 
benefit that provides all poor families with children an equitable benefit, the amount of the 
benefit is €40 per child for those in the lowest bracket and several other programs result in 
fragmented landscape. Despite being mean-tested the two main benefits cover even the 8th 
decile. The families in the poorest 40% of the population receive insufficient transfers - 71% are 
couples with 1-2 children. Such couples make up 32% of the population. 
Guaranteed minimum income program piloted in 13 municipalities until June 2015 

World Bank requested to conduct a social welfare review. The review was conducted between 
October 2015 and August 2017.  
Introduction of the humanitarian crisis package: three means-tested schemes: a) electricity 
allowance for poor families (some of them had electricity disconnected due to unpaid bills), b) rent 
subsidy (paid directly to the landlords on behalf of their tenants), c) food subsidy in the form of 
pre-paid debit card accepted at supermarkets and other groceries.  
A phased roll out of the GMI renamed SSI began mid-July 2016. Guaranteed minimum income 
program entitled Social Solidarity Income was implemented in Greece in 30 municipalities in 
Greece from July to December 2016. The total number of accepted beneficiaries represented 7.4% 
of overall population in the 30 municipalities. The average household size of accepted applications 
is 2.51, with over 50 percent of accepted applications including 2 or fewer household members, 
and about one third had 3 or 4 members.  
Social Solidarity Income (SSI or GMI) scheme, implemented 

Government legislated measures that should improve fiscal room to provide support to the 
poorest and most vulnerable 
Establishment and operation of community centers in most municipalities as one-stop-shop for all 
social programs 
Abolishment of small family/child allowances  

Consolidation of the two main family benefits into a single benefit, classification of disability 
assessment system  
Modification of disability assessment system, establishment of the single public payment authority 
for all welfare benefits (OPEKA) and IT reforms 
Introduction of new means-tested housing benefits  

19 Source: Matsaganis (2020), World Bank (2017a, 2017b), Leventi, Picos (2019), The Tax Structure of an Economy in Crisis: Greece 2009-
2017, OECD (2013). 

50 

 
 
 
 
 
 
51 

Year/area of the measure implementedPersonal income tax bands Maximum tax rate Zero tax bracket Increase in zero tax bracte due to childrenIncrease in zero tax bracket due to disability Tax allowances (TAs)/tax credits (TCs)Solidarity contributions Self-employed & liberal professions' contribution Pensioners' solidarity contributions Solidarity contribution Emergency tax on large incomes 2009540% for annual incomes over €75,000€12,000 for employees and pensioners; €10,500 for all others 1st child: €1,000; 2nd child: €2,000; 3rd child: €10,000€ 2,400Spending on private insurance, installation of eco-friendly energy systems: eligible for TA; Social Insurance contributions (SIC): fully deducted from taxable income n/a n/an/a n/a n/a April 2010 reform affected incomes earned in 2010945% (for annual incomes over €100,000)€12,000 for all 1nd child: €1,500; 2nd child: €3,000; 3rd child: €11,500no changeSpending on private insurance, installation of eco friendly energy systems: eligible for TCs; SIC no change Paid by individuals with annual taxable income above €12,000; applied to incomes above €12,000 as a levy between 1%-4%; the top rate applicable to the annual incomes exceeding €100,000; the tax rate applies to the whole income amount.€300 per yearMain pensions exceeding €1,400 per month taxed from 3% to 14%An emergency tax was introduced, paid by individuals with income exceeding €12,000 per year with contribution rates rising from 1% for incomes between €12,000 - 20,000 per year to 4%. The tax rates apply to the entire amount not only the excess over the threshold. Personal incomes over €100,000 earned in 2009 were made subject to a one-off emergency tax at 1%.October 2011: affects earnings in 2011 and 20128no change €9,000 for persons aged below 30/above 65; €5000 for all others 1st child: €2,000; 2nd child: €4,000; 3rd child: €7,000no change TCs: 50% reduced; TAs: abolished; SIC for self-employed: provided as a 10% TCno change €500 per year (in 2011), €650 per year (in 2012)Reformed and also applied to supplementary pensions n/a n/a Revenue side I 
 
 
52 

Year/area of the measure implementedPersonal income tax bands Maximum tax rate Zero tax bracket Increase in zero tax bracket due to childrenIncrease in zero tax bracket due to disability Tax allowances (TAs)/tax credits (TCs)Solidarity contributions Self-employed & liberal professions' contribution Pensioners' solidarity contributions Solidarity contribution Interest Income tax Property income taxFarming income 2013 reform: December 2012 reform affecting incomes in 2013 - 2015different for various income sources Different for various income sources; Maximum tax rate set to 42% for annual incomes over €42,000. Self-employment income from independent services through liberal profession, or a personal commercial enterprise,  taxed by a separate tax schedule with two tax brackets, maximum tax rate of 33% for annual incomes over €50,000. abolished abolished turned into a tax credit most TCs abolished; introduction of employment & pensions income TC; SIC fully deducted from taxable income no change no changeno change2015: schedule revisedRate increased from 10% to 15% in 2013Property income was taxed at 10% (33%) for incomes below (above) €12,000 per yearSeparate tax schedules for rental and farming income were also introduced.  2013: taxed according to the employment and pension tax schedule; 2014 and 2015: taxed at 13%April 2016 reform: Affecting incomes from 2016 onwards 445% for annual incomes over €40,000n/an/ano changechanges in employment and pensions' income TC; SIC: no changeThe schedule was revised in 2015 and 2016. From 2016 onwards the schedule consists of seven brackets and the maximum rate of 10% for annual incomes above €220,000, the rates apply to the amounts exceeding the thresholds as of 2016.no changeno changeSeven brackets and  maximum rate of 10% for annual incomes above €220,000; 2016 tax rates apply marginally to the income part exceeding the bracket threshold.n/a Property income was also taxed separately, using a different tax schedule with three brackets and a maximum rate of 45% for annual incomes above €35,000. Farming income taxed separately following the PIT scheduleRevenue side II 
 
 
Policy measures timeline: Portugal 20 

Summary of Changes in Social Benefits from 2010 to 2014 

2010 

2010 

2010 

2010 

2010 

2011 

2012 

2012 

2012 

2012 

2012 

2012 

2012 

2013 

2013 

2013 

2013 

2014 

2014 

2014 

Port. 1547/09: Increase in value of reference income from €4,960 to €5,022  

 [DL 70/10] Social Integration Income (RSI), Social Unemployment Benefit (SSD) and Social Parental 
Benefit (SSP): Restricting income related conditions (via rendering the equalizing scale more 
penalizing) 
[DL 72/10] Unemployment benefit: Set new limit maximum of 3× IAS (IAS is a reference measure used 
to fix, calculate and update several social benefits, such as the unemployment allowance)= = €1,258 
(before, 3×Guaranteed Monthly Minimum Remuneration (RMMG=1,425€). 

[DL 77/10] Unemployment benefit: Reduces duration and ceiling. Allowances: Eliminate bonuses for 
students from level 2 to 5 

[DL 116/10] Allowances: Eliminates brackets 4 and 5. Eliminates 25% markup on brackets 1 and 2 

 [Lei 55-A/10 – OE11]: Allowances: Creates scholarships; Pensions: Introduces CES (Extraordinary 
Solidarity Contribution) and pension freezes 

[Lei 64-B/11 – OE12]: Pensions: Suspended payment of 13th and 14th months in 2012; Changes CES 
(extraordinary pension contributions); Froze pensions with the exception of certain minimums 

[Port. 320-B/11] Pensions:  Updated minimums amounts 

[DL 64/12] SD: Introduced a 10% reduction norm for social benefits after the 6th month of payment. 
Reduced duration. Reduced maximum limit for 2.5×IAS= €1048. Reduced warranty periods. 
Introduced 10% increase for unemployed couples with children. 

 [DL 65/12] Unemployment benefit: Extends protection to Independent Workers economically 
dependent on a single entity (“false receipts green”) 

 [DL 85-A/12] Pensions: Suspended anticipation for flexibilization 

[DL 133/12] Pensions: Limited survival pensions of ex-spouses. Sickness: Amount of daily benefit 
adjusted. Allowance: Facilitates reevaluation of the condition of financial resources. Social Integration 
Income (RSI): Restricted resource condition (via a more penalizing equivalence scale and lower 
reference values), payments depend on the beginning of the conclusion of the integration contract, 
eliminated the automatic annual renewal and reinforced the beneficiaries' obligations. 

[Port. 257/12] Social Integration Income (RSI): amount changed to €189.52 

[Lei 66-B/12 – OE13] Pensions: Froze amounts with the exception of certain minimums; Changed 
Extraordinary Social Contribution. Sickness and Unemployment benefit: Fixed contributions for Social 
Security about installments (5% and 6% respectively, in force only during 2013). 

[DL 12/13]: Unemployment benefit: Extends protection to businesspeople and members of the 
statutory bodies  

[Port. 432-A/12]: Pensions: Updated of the values of determined minimums 

 [DL 13/13] Social Integration Income: Value changed to €177.15; Solidarity Supplement for the 
Elderly: Reduced reference value from €5022 to €4909. 

 [DL 167-E/13 e Port. 378-G/13]: Pensions: Advanced legal age of retirement to 66 years old and 
approved a more penalising Sustainability Factor 

[Port. 378-B/12]: Pensions: Updated values of determined minimums  

[Lei 83-C/13 e Lei 13/14 – OE14]: Pensions: Froze values with the exceptions of determined 
minimums; changed the extraordinary minimum contribution (CES) 

20  Source:  Farinha  Rogrigues,  C.,  Figueiras,  R.,  Junqueira,  V.  (2016),  Desigualdade  do  Rendimento  e  Pobreza  em  Portugal:  As 
consequências sociais do programa de ajustamento. Available at:  https://www.ffms.pt/sites/default/files/2022-08/desigualdade-
do-rendimento-e-pobreza-em-portugal.pdf 

 
 
 
 
Main changes to the Personal Income Tax  

2010 

2010 

2010 

2011 

2011 

2011 

2012 

2012 

2012 

2012 

Update of the taxable income scale by 0.8%   

The tax deductions were updated by rates between about 0.9% and 2.4%, although some may have a 
superior update corresponding to its indexation to minimum monthly pay (update from €450 in 2009 to 
€475 in 2010). 

Creation of a new tax bracket for highest incomes (above €150,000) 

Minimum tax increased from 42% to 45.88% 

The PIT brackets were updated by 2.2%. Incomes over €153,300 were taxed at a marginal rate of 46.5%. 

Increased taxation for pensioners (The tax deduction for category H applied to pensions higher than €22,500 
decreased). Introduced limits for tax benefits applied to the 7th and 8th tax bracket.  

Special surcharge of 3.5% applied to incomes exceeding minimum national salary  

Kept the brackets and tax fees of the 2011 PIT 

Reduction of maximum tax deduction for certain pensioners from €6,000 to €4,104 . 

Limits on tax benefits applied from the third tax bracket – 
from €7,410 euros per year – and ceilings on tax deductions from €66,000 euros per year (7th bracket), with 
the maximum limit of €1,100. 

2012 

Increase in tax fees to 25% applied on interest of deposits, the dividends and capital gains 

2012 

An additional fee of 2.5% applied to the income of taxable amount exceeding €153,300  

2013 

2013 

2013 

2013 

2013 

2013 

2014 

2014 

2014 

Suspension of the Extraordinary surcharge of 3.5% 

The PIT brackets were reduced from 8 to 5. 

The first bracket, with lowest income (up to €7,000 per year) subject to a fee 14.5%, instead of previous 
11.5%. 
The people with annual income over €80,000 pay the maximum PIT (previously from €153,300). At this 
level, the maximum marginal rate rises to 48%, which adds 2.5% of Solidarity Contribution and 3.5% as an 
Extraordinary Surcharge. 

Replacement of the Extraordinary Surcharge of 3.5% on income that exceed salary national minimum 

The release fee applied to the interest on deposits, dividends and capital gains increased to 28% 

Suspension of the Extraordinary Surcharge of 3.5%. 

No changes to the PIT table: no changes in tax brackets, neither applicable fees. The tax deductions and tax 
benefits kept at the 2013 limits. 

Kept the application of PIT surcharge 3.5% as in the 2013. 

Maximum taxation increased from 42% to 45.88%.  

54 

 
 
 
 
 
 
 
Appendix H 

Appendix H. Figure  1 
Pensions and Transfer: Descriptive Evidence 

Appendix H. Figure  2 
Pensions and Transfers: Difference-in-Difference Regressions 

55 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Appendix H. Figure  3 
Pensions and Transfers: Triple Difference-in-Difference Regressions 

56 

 
 
 
 
 
 
 
 
 
 
Appendix H. Figure  4 
Disposable income without transfers and pensions (“market income”) 

Note: The two graphs in the top panel show the evolution of disposable household income before  transfers by decile group in Greece (left) and 
Portugal (right). The two graphs in the middle panel show the results of DID regressions of the same variables (Specification 2). The two graphs in 
the bottom panel show the results of triple-DID regressions of the same variables (Specification 3). 

57 

 
 
 
 
 
 
 
 
 
 
 
Appendix I  
Details on the data 

Microdata from EU-SILC 

As our main data source, we obtained restricted-use microdata from Eurostat: the scientific use files 
of the EU’s Statistics on Income and Living Conditions (EU-SILC). For our purpose, these data constitute 
an ideal and unique source. In other parts of the world, household-level income surveys are country-
specific.  Different  approaches  across  national  surveys  render  cross-country  comparisons  with 
household-level data notoriously difficult. EU-SILC, in contrast, provides income surveys that apply the 
same approach in each EU Member State at an annual frequency for the 2004-2023 period. 

EU-SILC  provides  fine-grained  information  on  income  sources  for  each  surveyed  household  and 
individual. The data differentiates between labour income, capital income, pensions, different types of 
social transfers and benefits, and different types of taxes. The dataset allows us to identify how the 
types  of  incomes  and  transfers  changed  during  the  period  of  the  macroeconomic  adjustment. 
Decomposition of disposable income sources across different deciles helps better understand changes 
in income distribution. 

A key innovation compared to the existing literature in this field relates to our usage of the restricted-
access EU-SILC data at both the household level and the individual level. For our analysis, we combine 
each survey wave for each EA country to a large individual-level dataset. We also match the individual-
level data to household-level data, by nesting individuals in households. Our final dataset consists of 
about one million observations. 

The use of EU-SILC comes with several shortcomings. Compared to some national surveys, the EU-SILC 
does not provide information on expenditure details and only limited information on income details. 
The surveys might underestimate specific programme beneficiaries or sub-population groups, since 
the data does not focus on particular social support programmes but on income. EU-SILC also does not 
differentiate between means-tested and non-means tested benefits during the analysed period. We 
therefore cannot distinguish contributive and non-contributive pensions and between unemployment 
insurance  and  social  unemployment  benefits  (Rodrigues,  Andrade  2020).  Some  tax  credits  are  also 
captured incorrectly (Alves, 2012). 

More generally, incomes reported in household surveys sometimes differ from those reported to tax 
authorities  as  well  as  amounts  spent  on  the  social  transfers  (for  Greece  see  Leventi,  Matsaganis, 
Flevotomou, 2013; and for Portugal see Alves, 2012). As a result, surveys can be missing important 
income  layers.  One  reason  is  a  general  lack  of  survey  data  on  households  at  the  top  of  income 
distribution, which typically leads to an underestimation of income inequality  (Alvaredo et al. 2018, 
Piketty  et  al.  2018,  Caranza  et  al.  2021).  Income  underreporting  motivated  by  tax  evasion  seems 
prevalent across all households, especially in Greece (OECD, 2009; Leventi and Picos, 2019).  

As a caveat, we end by noting that the main advantage of our analysis – the use of household-level 
survey data – is at the same time its major disadvantage. Our findings may be affected by biases in the 
reporting  of  household  incomes,  especially  in  times  of  crisis.  In  particular,  incomes  from  informal 
employment may be reported irregularly. While estimates of the informal share of the economy are 
prone to uncertainty, we can only speculate about general trends. During the downturn, income from 
informal  employment  will  most  likely  be  underreported,  and  may  be  larger  among  lower  income 
groups. In addition, the rate of informal employment tends to fall with higher education and within 
the low-educated worker population women are more likely to be employed informally (Bonnet et al., 

58 

 
 
2019; Elgin et al., 2021; Quiros-Romero et al., 2021). This would suggest that incomes of the lower 
income groups related to informal activities might be higher than our estimates suggest.  

Our analysis compares selected countries based on EU-SILC data. In our cross-country comparison, we 
abstract  from  adjustments  of  the  Eurostat  data  sample.  While  Leventi  and  Picos  (2019)  adjust  the 
underlying data sample based on previous estimates, Hlasny and Verme (2018) test reweighting and 
replacing methods and their results confirm inconsistencies at the very top of income distribution. To 
avoid creating bias or inconsistencies, we make no changes to the underlying data.  

The results need to be interpreted with some caution when it comes to the income reference period. 
In most countries, SILC surveys take place during the first half of the year. However, the specific timing 
can vary by country. For instance, Atkinson, Marlier, and Nolan (2017) discuss the timing discrepancies 
in  income  reporting  in  SILC  surveys,  highlighting  the  potential  lag  in  the  reported  data.  For  our 
purposes, we attribute the income data to the year of the survey publication but note that this implies 
that there may be a certain time lag of up to one year in the results. 

Aggregate data on social outcomes from Eurostat 

In  terms  of  aggregate  country-level  outcomes,  we  focus  on  two  key  aspects:  average  income  and 
income  inequality. When  analysing  average  income, we  draw  on  the  mean  equivalised  net  income 
from  Eurostat.  This  metric  considers  the  size  and  composition  of  households,  allowing  for  a  more 
accurate comparison of income levels across different household types. It adjusts the total net income 
based on the household's size, taking into consideration economies of scale and differing needs. To 
examine income inequality, we use the Gini coefficient, which is the most common metric of income 
inequality and quantifies the extent of income inequality (on a scale from 0 to 100).21 We use various 
estimates  of  the  Gini  coefficients  from  Eurostat,  which  capture  either  market  income  only  or 
household income, including social transfers and pensions, allowing us to analyse the redistributive 
impact of pensions and social transfers.  

Macroeconomic indicators 

When  controlling  for  macroeconomic  developments,  we  draw  on  country-level  macroeconomic 
indicators available from Eurostat and the World Bank. We specify the set of control variables below 
when presenting our empirical approach.  

Social spending  

Data  on  social  spending  comes  from  Eurostat.  It  refers  to  general  government  expenditure  by 
economic  function  according  to  the  international  Classification  of  the  Functions  of  Government 
(COFOG).  The  data  breaks  down  the  general  government  total  expenditure  according  to  its  socio-
economic purpose and attributes the transactions within the European System of National Accounts 
(ESA 2010). It allows us to assess why and how the governments spent money. We zoom in on the 
relevant social spending categories, such as public spending on social protection (COFOG category 10), 
health (COFOG category 7) and education (COFOG category 9).  

21  0  represents  perfect  equality  (every  individual  receives  the  same  income)  and  100  represents  maximum  inequality  (a  single 
individual receives all the income. 

59 

 
 
 
 
Conditionality  

As previously mentioned, we examine the conditions for four programme countries and code the area 
of  social  policy  that  they  target.  This  coding  reflects  the  COFOG  spending  classification.  The  data 
include  conditionality  clauses  from  around  60  different  official  reviews  across  all  programmes 
(Moshammer and Siskind, 2020). We assigned the conditions to three categories — social, health, and 
education policy — depending on the COFOG categories of expenditures they relate to. 

60 

 
 
 
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