EUROMOD baseline report

De Poli, Silvia et al. Working Paper EUROMOD baseline report JRC Working Papers on Taxation and Structural Reforms, No. 05/2023 Provided in Cooperation with: Joint Research Centre (JRC), European Commission Suggested Citation: De Poli, Silvia et al. (2023) : EUROMOD baseline report, JRC Working Papers on Taxation and Structural Reforms, No. 05/2023, European Commission, Joint Research Centre (JRC), Seville This Version is available at: https://hdl.handle.net/10419/280874 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ EUROMOD baseline report JRC Working Papers on Taxation and Structural Reforms No 5/2023 De Poli, S., Gil-Bermejo Lazo, C., Leventi, C., Maier, S, Papini, A., Ricci, M., Serruys, H. with Almeida, V., Christl, M., Cruces, H., De Agostini, P., Grünberger, K., Hernández, A., Jędrych Villa, M., Manios, K., Mazzon, A., Navarro Berdeal, S., Palma, B., Picos, F., Tumino, A., Vázquez Torres, E. This publication is a report by the Joint Research Centre (JRC), the European Commission’s science and knowledge service. It aims to provide evidence-based scientific support to the European policymaking process. The contents of this publication do not necessarily reflect the position or opinion of the European Commission. Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use that might be made of this publication. For information on the methodology and quality underlying the data used in this publication for which the source is neither Eurostat nor other Commission services, users should contact the referenced source. The designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part of the European Union concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Contact information Name: JRC-EUROMOD Team Address: Edificio EXPO, c/ Inca Garcilaso 3, E-41092 Sevilla Email: [email protected] Tel.: +34 9544 88713 EUROMOD website: https://euromod-web.jrc.ec.europa.eu EU Science Hub: https://joint-research-centre.ec.europa.eu JRC132899 Seville: European Commission, 2023 © European Union, 2023 The reuse policy of the European Commission is implemented by the Commission Decision 2011/833/EU of 12 December 2011 on the reuse of Commission documents (OJ L 330, 14.12.2011, p. 39). Except otherwise noted, the reuse of this document is authorised under the Creative Commons Attribution 4.0 International (CC BY 4.0) licence (https://creativecommons.org/licenses/by/4.0/). This means that reuse is allowed provided appropriate credit is given and any changes are indicated. For any use or reproduction of photos or other material that is not owned by the EU, permission must be sought directly from the copyright holders. How to cite this report: De Poli, S., Gil-Bermejo Lazo, C., Leventi, C., Maier, S, Papini, A., Ricci, M., Serruys, H., Almeida, V., Christl, M., Cruces, H., De Agostini, P., Grünberger, K., Hernández, A., Jędrych Villa, M., Manios, K., Mazzon, A., Navarro Berdeal, S., Palma, B., Picos, F., Tumino, A., Vázquez Torres, E., 2023. EUROMOD baseline report, JRC Working Papers on Taxation and Structural Reforms No 5/2023, European Commission, Seville, Spain, 2023, JRC132899. i Contents Foreword ..................................................................................................................................................................................................................................................................... 1 Executive Summary ......................................................................................................................................................................................................................................... 3 Acknowledgements .......................................................................................................................................................................................................................................... 4 Abstract ....................................................................................................................................................................................................................................................................... 5 1 Introduction..................................................................................................................................................................................................................................................... 6 2 Poverty, inequality and the effects of the tax benefit system in the EU ............................................................................................ 7 2.1 Poverty risk and inequality in the EU ....................................................................................................................................................................... 7 2.2 The effect of taxes and benefits on the risk of poverty ........................................................................................................................... 8 2.3 The effect of taxes and benefits on inequality ........................................................................................................................................... 10 3 Breaking down the redistributive effect of the tax-benefit systems in the EU ......................................................................... 13 4 Work incentives on the intensive and extensive margins: marginal effective tax rates and net replacement rates............................................................................................................................................................................................................................................................................. 15 5 The impact of the COVID-19 pandemic in the EU and the cushioning effect of policy ..................................................... 18 6 Income distribution in perspective: levels and trends across the EU during the post-financial crisis decade (2010-2019)........................................................................................................................................................................................................................................................ 22 7 Conclusions .................................................................................................................................................................................................................................................. 26 References ............................................................................................................................................................................................................................................................. 27 List of figures ..................................................................................................................................................................................................................................................... 28 List of tables ........................................................................................................................................................................................................................................................ 29 Annexes .................................................................................................................................................................................................................................................................... 30 Annex 1. SILC datasets used to create EUROMOD input datasets used in this report........................................................ 30 Annex 2. National teams contributing to EUROMOD I5.0+ ............................................................................................................................ 31 Annex 3. Country notes: tax evasion, benefit non-take-up and full year adjustment ......................................................... 32 Annex 4. Additional tables ............................................................................................................................................................................................................. 36 Annex 5. Decomposition of the redistributive effect of the tax-benefit system ...................................................................... 44 1 Foreword This paper presents a selection of baseline results and headline indicators from the latest public version (I5.0+) of EUROMOD, the tax-benefit microsimulation model for the EU. The model was previously maintained, developed and managed by the Institute for Social and Economic Research (ISER) at the University of Essex, and since 2021 these responsibilities were taken over by the Joint Research Centre of the European Commission (Unit JRC.B2) in collaboration with Eurostat and 27 national teams. The model yearly update is financially supported by the following Directorate-Generals of the European Commission: DG EMPL, DG ECFIN, DG REFORM, DG TAXUD, JRC and Eurostat. Comments should be sent to Mattia Ricci (matti[email protected]uropa.eu). 2 3 Executive Summary  EUROMOD is a tax-benefit microsimulation model for the European Union that enables researchers and policy analysts to calculate, in a comparable manner, the effects of taxes and benefits on household incomes and work incentives for the population of each country and for the EU as a whole.  The scope of EUROMOD simulations includes direct taxes and social insurance contributions (SICs), as well as non-contributory in-cash social benefits. The lack of information on individual contributory history in the underlying microdata prevents the simulation of most contributory benefits and pensions, with the exception of unemployment benefits.  This report presents the key baseline results from EUROMOD version I5.0+. The analysis covers the years 2019-2022 and focuses on income poverty, inequality and work incentives indicators. Despite being based on the same source of data (i.e. EU-SILC), EUROMOD-based indicators might not coincide with ESTAT indicators for a number of reasons, such as the differences between simulated and reported variables, modelling of non-take-up and tax evasion, differences in definitions of households incomes as well as differences in the release version of the data used.  In the base year 2019, EUROMOD-based estimates of at-risk-of-poverty rates are the highest in Bulgaria, followed by Romania, Hungary, Latvia, Spain and Italy (all above 20%). At the other extreme, the lowest poverty rates are registered in Czechia and Croatia, followed by Denmark, Slovakia and Finland (all below 11%). In Romania, child poverty reaches 28%, followed by Bulgaria, Hungary, Spain and Italy (above 25%).  Looking at the effect of tax-benefit policies on poverty rates across the EU during 2019-2022, we observe that public pensions play the largest anti-poverty effect among the various instruments of the tax-benefit systems. These are followed by non-means tested and means tested benefits, whose effects on poverty are, however, a third of pensions. Taxes and SICs play in general a smaller role in reducing poverty.  If we analyse the redistributive impact of tax-benefit systems (excluding pensions) in terms of relative income inequality, we observe a heterogeneous picture. Countries with the strongest redistributive effect are mostly Nordic and Central European countries, while the weakest impact is typically found in Eastern European countries. Further heterogeneity can be observed in the drivers of the redistributive impact. Some countries achieve large redistributive effects with highly progressive systems (Ireland), while others rely more on high levels of taxation (Denmark).  EUROMOD also calculates Marginal Effective Tax Rates (METRs) for all individuals with earned income. They represent the proportion of a hypothetical marginal increase in earnings that would be “taxed away” due to social insurance contributions, taxes and loss of benefit entitlements. Therefore the METRs provide a measure of labour market incentives at the intensive margin (i.e. working more, earning a somewhat higher income). According to EUROMOD simulations, in the base year 2018 Belgium exhibits the highest mean METR by far (54%), followed by Denmark, Germany and Lithuania, all above 44%. The lowest mean METRs are observed in Cyprus, Bulgaria and Estonia (all below 25%).  Given the extraordinary impact of the COVID crisis on the labour market, standard EUROMOD simulations using 2019 income information do not accurately reflect the impact of 2020 and 2021 policies. This can be overcome by using EUROMOD’s Labour Market Adjustment (LMA) add-on, as a way to simulate transitions to unemployment and short-term compensation schemes. When accounting for labour market transitions, we observe that the majority of countries experience a drop in both market income and disposable both in 2020 and 2021. When zooming on disposable income, we observe that European tax-benefit systems are able to absorb a significant proportion of the market income loss caused by adverse labour market transitions, with Slovakia, Ireland, Croatia, and Belgium being the countries with the strongest cushioning effect.  The analysis of the evolution of income distribution in the post-financial crisis decade (2010-2019) shows that both market income and disposable income growth have occurred at very similar rates across income percentiles. The main exception is the growth of market income for the poorest 5%, which appears far more volatile. However, this volatility does not translate to the growth of disposable income pointing to the role of the tax-benefit system in absorbing these fluctuations. 4 Acknowledgements This report would not be possible without the many people who contribute and have contributed to the development of EUROMOD. We are particularly indebted to the EUROMOD National Teams that make the annual update of the model possible and to the Eurostat colleagues that collaborated on the production of the EUROMOD input data: Sébastien Chami, Albane Gourdol, Olga Moraru and Anastasija Norkuviene. We would also like to acknowledge the support provided by Eurostat for providing access to microdata from the EU Statistics on Incomes and Living Conditions (EU-SILC) made available under the agreement RPP 189/2019-ECHP-LFS-EU-SILC-HBS. We would also like to thank the National Statistical Institutes of Austria, Belgium, Bulgaria, Croatia, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Luxembourg, Malta, Netherlands, Poland, Slovakia, Slovenia, Spain and Sweden. None of the aforementioned data providers bears any responsibility for the analysis or interpretation of the data reported here. 5 Abstract This report provides a selection of baseline results and headline indicators from the latest public version (I5.0+) of EUROMOD, the tax-benefit microsimulation model for the EU. We begin by presenting indicators for income inequality and at-risk-of-poverty. We then provide a comparative decomposition of the redistributive effect of the tax-benefit systems across the EU. We study how different Member States achieve various degrees of redistribution through different combinations of progressivity and size of their tax-benefit systems. We then analyse various work incentive indicators both at the intensive and the extensive margin, discussing how effective marginal rates of taxation and replacement rates vary across countries. The report also describes the way EUROMOD can be used to simulate economic shocks leading to labour market transition through the LMA (Labour Market Adjustment) add-on. We illustrate this by simulating the impact of the COVID-19 pandemic and the cushioning effect of policy measures taken by EU Member States. Finally, we present the evolution of the income distribution over the post-financial crisis decade and we compare living standards across EU countries at the top and the bottom of the income distribution. . 12 Table 3. Effects of tax-benefit components on Gini coefficient rate, baseline year 2019 Country Disposable Income (DPI) DPI less meanstested DPI less non-means- tested DPI plus direct taxes DPI plus SIC Market income Market income plus pensions AT 0.25 0.28 0.28 0.31 0.26 0.49 0.36 BE 0.23 0.25 0.25 0.31 0.25 0.49 0.35 BG 0.40 0.41 0.41 0.41 0.40 0.53 0.43 CY 0.29 0.32 0.30 0.32 0.29 0.44 0.35 CZ 0.23 0.24 0.25 0.27 0.25 0.43 0.30 DE 0.31 0.33 0.33 0.37 0.32 0.52 0.40 DK 0.25 0.29 0.29 0.31 0.25 0.45 0.36 EE 0.30 0.31 0.34 0.34 0.31 0.47 0.38 EL 0.30 0.33 0.31 0.34 0.31 0.53 0.37 ES 0.31 0.34 0.33 0.36 0.31 0.50 0.39 FI 0.25 0.30 0.28 0.30 0.27 0.51 0.36 FR 0.28 0.34 0.31 0.32 0.29 0.53 0.39 HR 0.28 0.29 0.28 0.30 0.30 0.46 0.33 HU 0.31 0.32 0.33 0.31 0.32 0.46 0.34 IE 0.28 0.36 0.31 0.36 0.30 0.52 0.45 IT 0.32 0.34 0.32 0.37 0.33 0.52 0.39 LT 0.34 0.35 0.36 0.37 0.36 0.50 0.41 LU 0.26 0.28 0.29 0.33 0.26 0.50 0.37 LV 0.34 0.34 0.36 0.37 0.35 0.48 0.39 MT 0.30 0.32 0.31 0.34 0.30 0.46 0.37 NL 0.27 0.31 0.29 0.33 0.28 0.42 0.37 PL 0.26 0.28 0.28 0.28 0.27 0.45 0.31 PT 0.31 0.32 0.32 0.36 0.32 0.52 0.39 RO 0.33 0.34 0.34 0.34 0.37 0.52 0.40 SE 0.25 0.28 0.31 0.30 0.26 0.47 0.35 SI 0.24 0.26 0.26 0.27 0.26 0.45 0.32 SK 0.21 0.22 0.23 0.23 0.22 0.39 0.26 Source: EUROMOD version I5.0+ 13 3 Breaking down the redistributive effect of the tax-benefit systems in the EU In this section we decompose the redistributive effect of the tax-benefit system in the EU-27 countries, for the baseline year 2019, using the Kakwani decomposition framework. We start by looking into the overall redistributive effect (RE) of the tax-benefit system as modelled in EUROMOD (excluding pensions), and by disentangling the roles played by relative progressivity (measured by the Kakwani index) and the relative size of the policies in relation to disposable income (level effect). Furthermore, we decompose the overall redistributive effect to identify the role played by each of the tax-benefit components. The methodologies used are based on Kakwani (1977), Reynolds-Smolensky (1977) and the adaptation and generalisation proposed by Onrubia et al. (2014). The formalisation of the indicators and details on the income and policy components included can be found in Annex 5. In Figure 3 we plot the Kakwani decomposition of the redistributive effect for all EU member states. The redistributive effect of a policy (or in this case, of the whole tax-benefit system, except pensions) is the product of its relative progressivity (measured by the Kakwani index, and plotted in the horizontal axis) and its level (relative size with respect to disposable income, plotted in the vertical axis), minus a re-ranking effect. 5 In order to easily compare countries, we also plot"iso-redistribution curves” 6 that represent the multiple combinations of progressivity and level of redistribution that lead to the same redistributive effect. 7 Figure 3. Progressivity (x), level (y) and redistribution of the tax-benefit system before re-ranking (position w.r.t. curves) Source: EUROMOD version I5.0+ Note: The Kakwani, level and RE displayed in this figure refer to the net effect of taxes and benefits modelled in EUROMOD baseline 2019. This consists of personal income taxes and cash benefits. 5 The re-ranking effect in redistribution analysis refers to changes in the relative ranking of individuals when the income distribution is changed by a policy. For example, an individual A with lower market income than an individual B may end up with a higher disposable income because he/she is entitled to a specific benefit and B is not. 6 This way of plotting the Kakwani decomposion was inspired by López Laborda et al. (2022) 7 Note that we plot the values before re-ranking effect, to keep consistence between the pairs of progressivity and level value and total redistribution. 14 Countries with the strongest redistributive effect (i.e. those that are further away from the axis origin in Figure 3 and, therefore, closer to the darker curves), are mostly Nordic and Central European countries (Ireland, Denmark, Belgium, the Netherlands, France and Austria). This relatively strong redistributive effect is, however, obtained through different policy designs. At one extreme is Ireland, with a very progressive taxbenefit system which is however relatively low in terms of level (low burden over disposable income). In other words, the overall impact of the tax-benefit system on household income is low on average, but it is very progressive in the way it redistributes from the most to the least well off. At the other extreme is Denmark, featuring one of the lowest progressive tax-benefit systems, but which achieves a redistributive effect very similar to Ireland due the high level (i.e. a very high burden over disposable income, of about 60%). Among countries with the lowest redistributive effect are Poland, Hungary and Bulgaria (and, typically, Central and Eastern European countries), while Southern European countries (Spain, Greece, Malta, Cyprus, and Portugal) tend to feature an intermediate level of redistribution compared with their European peers. In Figure 4, we decompose the total redistributive effect by the tax-benefit system components. Countries are ranked from left to right with respect to the total redistributive effect of their tax-benefit system. Four clear patterns emerge. First, the countries with the strongest redistributive effect (those located on the righthand-side of the figure) largely achieve it through means-tested benefits (in particular this is the case of Ireland, France, Finland and Denmark) and direct taxes (this is particularly the case of Belgium and Luxembourg). Second, social benefits (as a whole) are the main drivers of the redistributive effect across the EU-27. Third, direct taxes are the main driver of the redistributive effect in many of the countries which achieve an intermediate level of redistribution, mostly Southern European countries. Fourth, social insurance contributions play a very minor redistributive role, with the exception of Romania where their magnitude, combined with their progressive nature, makes their redistributive effect larger than any other component. Figure 4. Redistribution of the tax-benefit system by component Source: EUROMOD version I5.0+ Note: The decomposition of the redistributive effect (RE) displayed in this figure refers to the net effect of taxes and benefits modelled in EUROMOD baseline 2019. This mainly consists of personal income taxes and cash benefits (pensions, consumption and wealth taxes, as well as in-kind benefits are excluded). 15 4 Work incentives on the intensive and extensive margins: marginal effective tax rates and net replacement rates. EUROMOD can be used to calculate the effects of the tax and benefit systems on work incentives. In this section, we present and discuss two indicators that provide valuable insights in this regard: the marginal effective tax rates and the net replacement rates. The first indicator measures the part of the extra pay that the tax-benefit system takes away from individuals when their earnings increase, in terms of both increased taxes and lost benefits, and it is often used as a measure of work incentives at the intensive margin (i.e. how much labour to supply). The second indicator, instead, measures households replacement income when one of its members lose her earnings (e.g. because of lay-off) and it is often taken as a measure of work incentives at the extensive margin (i.e. whether to participate or not in the labour market). Figures are reported for the baseline year 2019, while in the Annex 4 we present the series for the policy years 2019 – 2022. We consider first the marginal effective tax rate (METR). METRs are calculated for all individuals with earned income based on the increase in disposable income out of an increase in 3% earnings. Specifically, the METR is the share of the employment income growth that does not translate in disposable income because of the increase in tax liabilities and benefit withdrawal. In Table 4 we present results for individuals of working age (18-64) who have more than one unit of national currency of monthly earnings. We exclude from our calculations the top percentile of the METR distribution if the value is above 150% and the lowest percentile if the value of METR is negative. These exclusions are made for average METR to be less sensitive to “outliers”, although such values are in principle plausible. Furthermore, we assume full take-up of benefits and full tax compliance in all countries. Hence, all of the marginal earnings are assumed to be earned in the official economy and are subject to taxes, contributions and benefit withdrawal, under full compliance. METRs are therefore to be considered as indicators of the effects of the design of the tax-benefit system on marginal earnings that are retained rather than calculations of the marginal return to additional work in practice. Table 4 shows that Belgium exhibits by far the highest mean METR (54%), followed by Denmark, Luxembourg, Germany and Finland, where METRs range between 44% and 46%. The lowest mean METRs are observed in Cyprus, Estonia and Bulgaria (below 25%). As we can be seen in Annex 4, Table A4.4, the ranking of countries remains largely the same when ranked by the median METR instead of the mean. This also shows which countries have made reduced disincentives to labour market participation over the period considered, and which ones have worsened in the ranking. Looking at mean METR, Hungary is the country with the largest decrease in disincentives between 2019 and 2022 (more than 7 percentage points). That is because of the introduction of two main reforms on income tax in 2022; specifically an income tax allowance for young taxpayers (those aged 25 years or less) and a tax refund for families with children. In Table 4, further presents average METR decomposition in three main components: (1) taxes, representing the average increase in taxes paid at the household level as a proportion of the increase in individual gross earnings; (2) social insurance contributions, including changes in both employee, self-employed and other social insurance contributions paid by the individual; and (3) benefits, representing the average reduction in benefits and pensions paid at the household level as a proportion of the increase in earnings. Despite a wide variation across countries, the graph shows that the tax component is usually the most important. Its size varies significantly across countries and range from relatively low values in Cyprus, Bulgaria and Romania to relatively high values in Denmark and Belgium. In Denmark, almost all of the average METR is accounted for by taxes. While in Belgium the share of taxes is lower but still accounting for most of the average METR. Nordic countries together with Germany, Luxembourg and Belgium also have the highest METR due to taxes in absolute terms (all over 27%), while taxes seem to offer less disincentive to work at the margin in Cyprus, Bulgaria and Croatia, countries which are also characterized by a relatively flat tax system. Countries where the contribution of SIC to METR is the largest are instead Hungary, Romania, Lithuania, Slovenia and Slovakia, in all cases above 17% (27% in Romania). At the other end of the spectrum, in Spain, Estonia, Ireland and Denmark, the SIC contribution to METR is the lowest, below 5 percentage points (in Estonia, for example, most of SICs are paid by employers). In a few countries, the contribution of benefits is also relevant to the mean METR, however to a minor extent if compared to SIC and especially to taxes. 16 Table 4. Mean Marginal effective tax rates by component, 2019 Country Taxes SIC Benefits Total METR AT 20.7 16.4 3.5 40.6 BE 34.2 16.7 3.4 54.4 BG 7.9 13.1 1.1 22.2 CY 6.3 10.1 3.6 20.0 CZ 16.6 11.4 1.3 29.4 DE 26.8 15.8 2.7 45.2 DK 43.3 0.0 2.2 45.6 EE 19.3 3.2 2.4 24.9 EL 15.9 15.9 1.0 32.7 ES 19.3 4.1 2.8 26.2 FI 18.6 11.2 10.4 40.2 FR 10.0 15.4 0.6 26.0 HR 15.4 17.5 0.1 33.0 HU 27.2 4.8 4.8 37.0 IE 28.0 9.5 2.4 40.0 IT 18.7 21.0 0.7 40.4 LT 28.0 11.4 5.3 44.6 LU 19.0 10.7 0.8 30.4 LV 17.5 6.2 3.4 27.1 MT 21.8 11.8 5.1 39.1 NL 16.2 10.9 0.6 27.7 PL 21.5 11.1 1.3 33.8 PT 8.0 27.2 2.0 37.3 RO 26.2 6.1 3.1 35.4 SE 16.7 18.1 4.9 39.7 SI 13.4 17.1 2.0 32.5 Source: EUROMOD version I5.0+ Finally, we consider the Net Replacement Rate (NRR). Table 5 provides the NRR by country as well as its breakdown by component. Recall that the NRR represents the ratio between household income when one of its member loses her income (i.e. the replacement income), as opposed to the situation when she does not. The NRR breakdown highlights the importance of each tax-benefit component as well as of market income (of other members of the household) in the replacement income. Looking at the overall NRR, countries featuring the highest replacement rates are Denmark, Luxembourg, Finland, France and Portugal. Note that Denmark and Luxembourg are also the countries with the highest replacement on the account of social benefits. Across countries, market income together with social benefits appear to account for most of the replacement income. However, while social benefits depend on the very rules in force in the tax-benefit system, the market income component has more to do with the household structure as well as with the labour market participation of particular groups of the population such as women, the youngest and the oldest. With exception of countries with large marginal rates of income taxation, such Denmark, Sweden and Finland, the contribution of taxes to NRR is generally small. Similarly, and with limited exceptions (e.g. Netherlands and Romania), the contribution of SIC is also small. 17 Table 5. Net Replacement Rate by component, 2019 Country Taxes SIC Market income Benefits Total NRR AT -6.6 -8.8 52.6 40.0 77.2 BE -13.6 -7.7 57.7 39.0 75.4 BG -4.5 -7.0 53.0 36.5 78.1 CY -1.9 -4.9 50.7 26.9 70.7 CZ -4.0 -6.6 51.8 24.7 65.8 DE -8.9 -8.4 51.8 42.1 76.5 DK -37.6 -1.1 57.8 58.5 77.6 EE -6.3 -1.3 40.9 39.3 72.6 EL -7.9 -8.4 47.0 40.1 70.8 ES -5.2 -4.0 48.2 32.0 70.9 FI -18.4 -4.5 47.2 53.8 78.1 FR -12.4 -5.1 48.2 52.5 83.2 HR -2.4 -10.2 55.9 27.7 71.0 HU -6.8 -3.0 49.2 27.3 64.7 IE -10.1 -2.2 53.6 29.4 70.8 IT -10.0 -6.8 46.8 37.7 67.7 LT -8.6 -13.5 56.4 38.5 72.9 LU -12.0 -11.6 44.9 66.0 87.3 LV -7.6 -5.1 48.5 33.4 69.2 MT -4.6 -5.0 53.3 20.7 64.4 NL -10.9 -29.3 60.2 55.3 75.3 PL -9.3 -7.5 54.2 20.6 58.0 PT -5.3 -5.6 52.7 41.9 83.7 RO -4.9 -18.1 59.2 27.9 64.1 SE -19.0 -5.6 46.1 52.1 73.7 SI -6.5 -16.4 61.5 32.2 70.9 SK -4.1 -12.6 60.7 28.6 72.5 Source: EUROMOD version I5.0+ 18 5 The impact of the COVID-19 pandemic in the EU and the cushioning effect of policy EUROMOD allows users to design and implement labour market transitions. The transitions are made operational through the Labour Market Adjustment (LMA) add-on and allow for the simulation of policies triggered by changes in the labour market status of individuals. The add-on runs from policy year 2020 onwards, on all 27 EU member states and with all input datasets. In its original form, it covered the transition from employment to unemployment (short-term or long-term), and the transition from unemployment to employment. Following the COVID crisis, the LMA add-on has been modified to also cover transitions to monetary compensation schemes. A detailed analysis of the effectiveness of those schemes during the first year of the COVID pandemic can be found in Christl et al. (2022). Intuitively, the LMA add-on modifies the values of specific socio-demographic variables of observations eligible for transitions in order to reflect their new labour market status. These include variables such as earnings, months in work, labour market characteristics, etc. Detailed information can be found in the “Summary note for the EUROMOD Labour Market Add-on” and the note "Simulating labour market transitions in EUROMOD", included in the EUROMOD model documentation. 8 The modelling of those transitions is performed using a random allocation based on aggregate statistics included in all models in a uniform way. Two main sources of data are used: administrative data collected by national teams and EUROMOD developers and data provided by Eurostat. 9 Information about the source of data by type of transition is included in the EUROMOD Country Reports. 10 For example, to simulate transitions to monetary compensation schemes, we use the above-mentioned aggregate statistics to define the share of employees/self-employed (disaggregated by gender and sector of activity) who move to monetary compensation along with the duration of this transition. We then randomly select in our microdata individuals to experience this transition until the target statistics are met. For those selected individuals, the LMA Add-on will adjust their labour market status, job characteristics and income variables. Figure 5 shows the share of employed people (both employees and self-employed) transiting to monetary compensation schemes in 2020 and 2021, based on the data included in EUROMOD. In 2020, the highest share of people entering monetary compensation schemes is observed in Cyprus, France, Italy, Croatia, Greece and Luxembourg (more than 30% of the total workforce). On the other hand, in Sweden and Finland we observe a lower share of people entering in those schemes (less than 10% of the workforce). The latter is also the case for Latvia, Bulgaria, Hungary and Ireland. As expected, in 2021 the share of people transiting to monetary compensation schemes is lower than in 2020 for all EU countries. Still, in counties such as Malta, Cyprus and Greece more than 20% of employed people undergo this labour market transition. 8 Available at https://euromod-web.jrc.ec.europa.eu/resources/model-documentation. 9 In Eurostat data, labour transitions are produced by Eurostat, using detailed distributional information on the loss of jobs and shortterm work schemes from the Labour Force Survey and administrative data. The impact across different categories of individuals, the duration of unemployment/absence and percentage of hours worked are modelled using the EU-LFS longitudinal and quarterly transitions as target. For more information please consult the methodological note available here. For cases where national administrative data are used, please check the corresponding Country Reports. 10 At the time of preparing the public release of EUROMOD (version I5.0+), no statistics were available for Ireland for 2021. 19 Figure 5. Share of people transiting to monetary compensation schemes in 2020 and 2021, % of total workers Source: EUROMOD version I5.0+ To examine how the impact of those transitions translate into changes in household income, we compare market and disposable incomes after the transitions (LMA add-on switched on) with respect to the baseline simulations (LMA Add-on switched off), for both 2020 and 2021. Figures 7 and 8 show percentage changes in market income and disposable income for the entire population, whereas Table 6 shows the percentage changes in disposable income for quintile groups, as well as for the entire population. We observe that the majority of countries experience a drop in market income when labour market transitions are accounted for. This applies for both 2020 and 2021. The fall in market income widely varies among countries and is due to two types of transitions: from employment to monetary compensation schemes and from employment to unemployment. In 2021, we observe increases in market income in a small number of countries (namely Luxembourg, Poland and Finland); these are due to the prevalence of transitions from unemployment to employment in those Member States. When focusing on disposable income, we observe that European tax-benefit systems are able to absorb a significant proportion of the market income loss caused by adverse labour market transitions. In 2020, a very strong cushioning effect can be observed in Slovakia, Ireland, Croatia, and Belgium. Looking at income quintiles, we observe that the decreases in disposable income usually follow a progressive pattern (i.e. they become more pronounced as we move from the poorest to the richest quintiles of the distribution). The result aligns with the existence of upper thresholds or lump-sum components in the amounts of monetary compensation schemes (i.e. components that are not connected to individuals’ previous earnings), the progressivity of European tax systems and the stronger presence of means-tested benefits at the bottom of the income distribution. Finally, is worth noting that in a number of countries, disposable income is estimated to increase in some quintiles of the income distribution, and especially the poorest one. The main reason for this increase is the existence of monetary compensation schemes as the ones described above and of social benefits (such as unemployment and social assistance benefits) that are able to more than offset the effect of adverse labour market transitions. 11 11 Please note that in countries where the monetary compensation scheme has a minimum amount based on the minimum wage, we might overestimate the compensation for individuals who, according to SILC data, earn less than the minimum wage. 20 Figure 6. Percentage change (%) in market income and disposable income in 2020 Source: EUROMOD version I5.0+ Note: Order of countries according to decreasing disposable income loss. Figure 7. Percentage change in market income and disposable income in 2021 Source: EUROMOD version I5.0+ Note: Order of countries according to decreasing disposable income loss. -17,0 -15,0 -13,0 -11,0 -9,0 -7,0 -5,0 -3,0 -1,0 1,0 IE IT BE MT CY EL HR ES AT SK CZ PL FR SE PT LT LV DE EE HU RO NL FI BG LU DK SI Market income Disposable income -12,0 -10,0 -8,0 -6,0 -4,0 -2,0 0,0 2,0 BE SK EL CY LT IT LV AT MT HR HU CZ EE DE NL SE SI BG FR RO DK FI PL PT ES LU Market income Disposable income 21 Table 6. Change in mean equivalised disposable income with labour market changes by quintile, w.r.t. baseline (%) Mean equivalised disposable income Country Policy year Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 All AT 2020 -0.40 -0.84 -1.43 -1.25 -2.69 -1.64 AT 2021 -0.08 -0.41 -1.27 -0.98 -1.11 -0.90 BE 2020 1.17 -1.08 -2.99 -3.70 -5.27 -3.13 BE 2021 0.54 -0.96 -2.32 -2.55 -3.83 -2.34 BG 2020 0.02 -0.06 -0.04 -0.07 -0.19 -0.11 BG 2021 0.02 -0.07 -0.06 -0.06 -0.18 -0.11 CY 2020 0.00 -1.68 -3.10 -2.88 -4.22 -3.01 CY 2021 0.82 -0.28 -1.21 -0.91 -2.01 -1.15 CZ 2020 0.16 -0.46 -0.96 -1.21 -2.58 -1.35 CZ 2021 0.36 -0.18 -0.13 -1.07 -0.69 -0.48 DE 2020 4.31 0.25 -0.70 -1.04 -1.91 -0.67 DE 2021 1.89 -0.24 -0.62 -0.65 -0.48 -0.31 DK 2020 0.29 0.04 -0.09 -0.03 -0.28 -0.08 DK 2021 0.59 0.47 0.00 0.06 -0.05 0.13 EE 2020 0.54 -0.50 -0.30 -0.49 -1.09 -0.58 EE 2021 0.63 -0.18 -0.47 -0.35 -0.88 -0.45 EL 2020 -0.09 -1.28 -1.79 -2.66 -4.81 -2.97 EL 2021 0.51 -0.56 -0.99 -1.71 -1.79 -1.29 ES 2020 -0.03 -1.09 -1.50 -1.52 -2.30 -1.66 ES 2021 7.88 0.70 0.61 -0.10 -0.66 0.48 FI 2020 1.83 0.53 -0.47 -0.32 -0.77 -0.13 FI 2021 2.72 0.78 0.06 0.00 -0.29 0.33 FR 2020 2.64 0.46 -0.53 -1.59 -3.17 -1.31 FR 2021 2.16 0.47 -0.30 -0.29 -0.69 -0.11 HR 2020 0.82 -0.71 -1.81 -2.19 -3.77 -2.22 HR 2021 0.54 -0.14 -0.71 -0.94 -1.44 -0.84 HU 2020 1.56 -0.62 -0.76 -0.91 -0.64 -0.58 HU 2021 -0.22 -1.08 -0.71 -1.00 -0.38 -0.67 IE 2020 -0.73 -0.88 -2.95 -4.43 -6.92 -4.31 IE 2021 n/a n/a n/a n/a n/a n/a IT 2020 -1.33 -2.53 -3.02 -3.07 -3.97 -3.22 IT 2021 -0.39 -0.91 -1.08 -1.08 -1.08 -1.01 LT 2020 0.01 -0.15 -0.66 -0.69 -1.41 -0.84 LT 2021 -0.03 -0.26 -1.31 -1.38 -1.34 -1.10 LU 2020 4.28 2.34 0.05 -0.68 -1.85 -0.09 LU 2021 4.35 3.67 0.74 0.13 -0.21 1.00 LV 2020 -0.51 -0.22 -0.45 -0.75 -0.92 -0.68 LV 2021 0.48 -0.19 -0.79 -1.08 -1.59 -0.99 MT 2020 -1.28 -2.09 -2.43 -2.68 -4.26 -3.02 MT 2021 0.36 -0.55 -0.11 -0.76 -1.77 -0.90 NL 2020 2.25 -0.36 -0.59 -0.46 -0.65 -0.26 NL 2021 1.40 -0.30 -0.62 -0.51 -0.38 -0.25 PL 2020 -0.10 -0.52 -0.96 -1.35 -2.19 -1.34 PL 2021 1.60 0.99 0.37 0.23 -0.02 0.41 PT 2020 2.41 -0.07 -0.43 -1.02 -1.86 -0.84 PT 2021 4.16 1.93 0.46 0.09 -0.70 0.42 RO 2020 3.46 -0.04 -0.43 -0.41 -1.15 -0.40 RO 2021 3.77 0.54 0.18 -0.17 -0.66 0.05 SE 2020 0.66 -0.66 -1.11 -1.27 -1.21 -0.94 SE 2021 2.33 -0.12 -0.51 -0.59 -0.42 -0.16 SI 2020 5.54 1.51 -0.61 -1.07 -1.26 0.08 SI 2021 3.45 0.76 -0.69 -1.08 -0.85 -0.16 SK 2020 0.05 -0.79 -1.35 -1.89 -2.02 -1.45 SK 2021 0.14 -0.67 -1.26 -2.14 -1.91 -1.42 Source: EUROMOD version I5.0+ 28 List of figures Figure 1. Poverty risk and the role of public pensions and non-pension benefits and taxes (2019 incomes and policies) ......................................................................................................................................................................................................................................9 Figure 2. Income inequality (Gini coefficient) and the role of public pensions and non-pension benefits and taxes (2019 incomes and policies) ...................................................................................................................................................................... 11 Figure 4. Redistribution of the tax-benefit system by component ................................................................................................ 14 Figure 5. Share of people transiting to monetary compensation schemes in 2020 and 2021, % of total workers .................................................................................................................................................................................................................................. 19 Figure 6. Percentage change (%) in market income and disposable income in 2020 ...................................................... 20 Figure 7. Percentage change in market income and disposable income in 2021 ............................................................... 20 Figure 8. Market and disposable income in the EU (€ per year in PPP), 2010-2019. Selected income percentiles............................................................................................................................................................................................................................ 24 Figure 9. Market and disposable income growth 2010-2019. EU average. Selected income percentiles. .......... 25 29 List of tables Table 1. EUROMOD poverty and inequality statistics, baseline year 2019 ..................................................................................8 Table 2. Effects of tax-benefit components on at-risk-of-poverty rate, baseline year 2019...................................... 10 Table 3. Effects of tax-benefit components on Gini coefficient rate, baseline year 2019 ............................................ 12 Table 4. Mean Marginal effective tax rates by component, 2019 ................................................................................................. 16 Table 5. Net Replacement Rate by component, 2019 ........................................................................................................................... 17 Table 6. Change in mean equivalised disposable income with labour market changes by quintile, w.r.t. baseline (%) ........................................................................................................................................................................................................................ 21 Table A2.1. National teams and team leaders by country ................................................................................................................. 31 Table A3.1. Summary of tax compliance, benefit non-take up and full year adjustments in EUROMOD I5.0+, 2019-2022 systems ..................................................................................................................................................................................................... 32 Table A4.1 EUROMOD poverty and inequality statistics: 2019-2022 ......................................................................................... 36 Table A4.2 Effects of tax-benefit components on poverty risk (60%): 2019-2022 ......................................................... 38 Table A4.3. Effects of tax-benefit components on Gini coefficient: 2019-2022 ................................................................ 40 Table A4.4. Mean and median marginal effective tax rates: 2019-2022 ............................................................................... 43 Table A4.5. Income concepts used for the decomposition of the redistributive impact ................................................. 44 30 Annexes Annex 1. SILC datasets used to create EUROMOD input datasets used in this report Country Base dataset for EUROMOD AT EMSD = UDB (C20_release_21_09 rev.1) + National SILC BE EMSD = UDB (C20_release_21_09 rev.1) + National SILC BG EMSD = UDB (C20_release_21_09 rev.1) + National SILC CY EMSD = UDB (C20_release_21_09 rev.1) + National SILC CZ EMSD = UDB (C20_release_21_09 rev.1) + National SILC DE EMSD = UDB (C20_release_22_03 rev.3) DK EMSD = UDB (C20_release_21_09 rev.1) + National SILC EE EMSD = UDB (C20_release_21_09 rev.1) + National SILC EL EMSD = UDB (C20_release_21_09 rev.1) + National SILC ES EMSD = UDB (C20_release_21_09 rev.1) + National SILC FI EMSD = UDB (C20_release_21_09 rev.1) + National SILC FR EMSD = UDB (C20_release_22_03 rev.1) + National SILC HR EMSD = UDB (C20_release_21_09 rev.1) + National SILC HU EMSD = UDB (C20_release_21_09 rev.1) + National SILC IE EMSD = UDB (C20_release_21_09 rev.1) + National SILC IT EMSD = UDB (C20_release_22-03_rev.1) + National SILC LT UDB (C20_release_21_09_rev.1) + National SILC LU UDB (C19_release_20_09) LV EMSD = UDB (C20_release_21_09 rev.1) + National SILC MT EMSD = UDB (C20_release_21_09 rev.1) + National SILC NL EMSD = UDB (C20_release_21_09 rev.1) + National SILC PL UDB (C20_release_22_03) + National SILC PT UDB (C20_release_22_03) RO UDB (C20_release_21_09_rev1) SE EMSD = UDB (C20_release_21_09 rev.1) + National SILC SI EMSD = UDB (C20_release_21_09 rev.1) + National SILC SK EMSD = UDB (C20_release_21_09 rev.1) + National SILC 31 Annex 2. National teams contributing to EUROMOD I5.0+ Table A2.1. National teams and team leaders by country Country National team – team leader AT European Centre for Social Welfare Policy and Research - Michael Fuchs BE University of Antwerp – Gerlinde Verbist K.U. Leuven – André Decoster BG University of National and World Economy (UNWE) – Ekaterina Tosheva CY Ministry of Labour, Welfare and Social Insurance - Costas Stavrakis CZ CERGE-EI – Daniel Münich DE ifo Institute – Leibniz Institute for Economic Research at the University of Munich – Mathias Dolls DK Roskilde University – Bent Greve EE PRAXIS Center for Policy Studies – Merilen Laurimäe and Kelly Toim EL Athens University of Economics and Business (AUEB) – George Economides ES Institute for Fiscal Studies – Noemí Villazán Pellejero FI Research Department of the Social Insurance Institution of Finland (KELA) – Tapio Räsänen FR Aix-Marseille University – Alain Trannoy HR Institute of Public Finance – Ivica Urban HU TÁRKI Social Research Institute – Péter Szivós IE Economic and Social Research Institute (ESRI) – Karina Doorley IT Milan University – Carlo Fiorio University of Eastern Piedmont – Francesco Figari LT Vilnius University – Jekaterina Navickė LU LISER – Nizamul Islam LV Baltic International Centre for Economic Policy Studies (BICEPS) – Anna Pluta MT Ministry for Finance and Employment – Stephanie Vella NL Stichting Centerdata – Klaas de Vos PL Center for Economic Analysis (CenEA) – Michał Myck PT Lisboa School of Economics & Management – Carlos Farinha Rodrigues Institute of Public Policy – Joana Vicente RO National Research Institute for Labour and Social Protection – Eva Militaru SE SOFI - Stockholm University – Rense Nieuwenhuis SI Institute for Economic Research (IER) – Nataša Kump SK Ministry of Finance of the Slovak Republic – Martin Mikloš 32 Annex 3. Country notes: tax evasion, benefit non-take-up and full year adjustment Table A3.1. Summary of tax compliance, benefit non-take up and full year adjustments in EUROMOD I5.0+, 2019-2022 systems Country Benefit take-up adjustment (BTA) 2019-2022 Tax compliance adjustment (TCA) 2019-2022 Full year adjustment (FYA) 2019 2020 2021 2022 AT - - - - - - BE on - - - - - BG - on - off off off CY - - off off - - CZ - - - off off off DE - - - - - - DK - - - - - - EE on - off off off EL on on off off off off ES on - - off - - FI on - - off off off FR on - - - - on HR on - - - - - HU - - - off off off IE on - - - - - IT - on - on on - LT - off - on on - LU - - - - - - LV on - - - - - MT - - - - - - NL - - - - - - PL - - - - - - PT on - - - - - RO on on - - - - SE - - - - - - SI on - - - - - SK on - on - on on Source: EUROMOD version I5.0+ Note: “on” (“off”) indicates that the adjustment is available and switched on (off) by default; “-“ indicates that no adjustment is available. Tax evasion For Bulgaria tax evasion adjustments have been made because of oversimulation of taxes and social insurance contributions. The adjustment is based on a comparison between net and gross employment incomes. Under this approach, it is assumed that an individual is involved in the shadow economy if her (positive) net and gross employment incomes are equal. Such an individual is assumed to be a full tax evader and hence, no income tax and social insurance contributions are simulated for her. Furthermore, for the simulation of the income test for child and social assistance benefits, the earnings of a tax evader are not taken into account because it is assumed that they will not be reported and thus, will not be part of the income test. No correction for individuals with self-employment income has been done. These adjustments lead to more accurate simulations of the tax and benefit instruments. For Greece tax evasion adjustments have been made on the basis of external estimates for the extent of average income underreporting by income source (earnings, self-employment income from farming and nonfarm business). Assuming that net incomes reported in SILC reflect true incomes, two sets of gross incomes have been derived – one under the assumption of full compliance and the other assuming that everyone have underreported a given income source to the tax authority by the same proportion. A user can choose which 33 assumption is utilised for calculating disposable incomes, and the model automatically draws on the relevant set of gross incomes. Adjustments for tax evasion are used by default for the baseline scenarios. For Italy self-employment income has been calibrated in order to take into account tax evasion behaviour. Since we implement our own net-to-gross procedure (starting from net incomes reported in SILC data), we split the recorded self-employment income into two components: the first component declared to the tax authorities (and hence grossed up) and the second component not declared (but still included in the definition of disposable income). The coefficient used to separate the two components allows us to get a total aggregate gross self-employment income corresponding to the aggregate amount of reported selfemployment income as reported in the official statistics. For Romania all self-employed in agriculture living in rural areas and with a self-employment income below the average wage are assumed to evade taxes. Full compliance is assumed for both income taxes and social insurance contributions for the rest of the countries. Benefit non-take-up For Belgium we employ a simple non-take-up correction of the main means-tested benefits by applying the take-up proportions estimated on a caseload basis. In particular, we adjust for the non take up of benefits with a simple random non take-up correction by applying the take-up proportion estimated as the ratio between the caseload recipients reported by the Official Statistics and those simulated to be entitled by EUROMOD. Take-up probabilities are applied at the household level (so that people entitled to the same benefits within a household exhibit the same take-up behaviour), for each benefit separately. For Croatia, non-take-up is simulated for subsistence benefit on the assumption that small entitlements (i.e. smaller than 3% of the average net wage) are not claimed. Full take-up is assumed for all other simulated means-tested benefits. For Estonia non-take-up is simulated for social assistance on the assumption that small entitlements (either in absolute or relative to other household income) are not claimed. Full take-up is assumed for all other simulated means-tested benefits. In Finland eligibility for income support is assessed at the family level (rather than at the household level). For example, adult children can apply separately from their parents. In practice, however, this happens rarely. Therefore, in the model we account for non-take-up by simulating income test at the household level. Also, the households where the head is self-employed are excluded from eligibility (as they rarely apply for income support). For France non-take-up correction of the main means-tested social assistance benefit (RMI/RSA)13 is simulated to be randomproportions of non-take-up -separately by active and inactive units (for RSA) taken from external data. For Greece a random non-take-up correction is simulated for unemployment assistance benefit for longterm unemployed and child benefit. Full take-up is assumed for all other simulated means-tested benefits. For Ireland, non-take-up is simulated for the Working Family Payment (formerly known as Family Income Supplement), applying external estimates on the caseload. Full take-up is assumed for all other means-tested simulated benefits. For Latvia non take up is simulated for paternity benefit based on the benefit receipt observed in the data. For Poland, the eligibility of housing benefit, due to significant differences between the number of recipients simulated by the model (assuming full take up) and reported in official statistics, is conditional on receipt being reported in the input database. Furthermore, due to lack of information on assets that are necessary for the means-test, the eligibility for temporary social assistance is simulated conditional on an estimated expected probability to be eligible. Moreover, by law the central government is obliged to pay just a share of the total benefit amount. The rest (or part of it) may be paid by the local government. In EUROMOD, we assume that only the central government pays its part. 34 For Portugal full take up is assumed in the simulation of all means-tested benefits. However, given the inability of simulating all eligibility conditions for the social solidarity supplement for the elderly, the simulation of this benefit overestimates the number of recipients and aggregate amounts. Thus, the beneficiaries were calibrated to guarantee consistency with the official statistics. For Romania non-take-up is simulated for the minimum guaranteed income, which under full take-up is overestimated by a factor of 4. The calibration is based on the assumption that households headed by a person under 26 do not claim for they are students. For Slovenia a non-take-up correction is simulated in the years 2017-2021 for social assistance only if older input data (based on SILC 2018 or SILC 2019) are used. Baseline simulations of the years 2019-2022 do not correct for non-take-up because input data based on SILC 2020 do not require such a correction. For Slovakia a non-take-up correction is simulated for the material need benefits. The take-up rate is calculated as the ratio between the actual expenditure based on administrative data and the expenditure simulated by EUROMOD without correcting for non-take-up. In Spain a non-take-up adjustment is simulated for the national and regional minimum income schemes. These benefits are overestimated in EUROMOD due to (i) the non-simulation of some eligibility conditions, because of lack of relevant information in EU-SILC, (ii) the non-take-up by potential beneficiaries, and (iii) the existence of different regional budget constraints and bureaucratic procedures across regions. The calibration aligns both the simulated number of beneficiaries and total expenditure by region with the figures obtained from official statistics. Full take-up is assumed for all simulated means-tested benefits for the remaining EU countries. Full year adjustments For Cyprus for employees’ and employers’ contribution to the General Health System in 2019 and 2020. For Czechia in 2020 and 2022 for the change in the Minimum Living Standard index, and in 2021 for the change in the amount of the Child Allowance. For Estonia in 2007 for child allowance and allowance for families with 3+ children. In 2009 for unemployment insurance benefit, employer social insurance contribution, credited social insurance contribution, employee social insurance contribution and self-employed social insurance contribution. In 2013 for child allowance and needs based family benefit. In 2017 for parental allowance for families with 7+ children / many children. In 2020 for unemployment insurance benefits. In 2021 and 2022, for pension contribution payments (2nd pillar). For Finland since 2020, several benefits amounts are increased in August. The full year adjusments calculate the monthly average taking into account the increase of the benefits amounts in August. For France in 2022 several benefit amounts and pensions increased in July, as well as the SMIC in August, as response to rising consumer price inflation. For Greece in 2019 and 2022 for employees’ and employers’ social insurance contribution for supplementary pensions. In 2020 for employees’ and employers’ social insurance contribution for unemployment. For Italy in 2020 for a reform of the bonus "IRPEF". In 2021 for the introduction of the Children Universal Allowance. In the baseline, both in 2020 and 2021, the full year adjustment extension is set to on. For Lithuania in 2017 for unemployment insurance benefit, in 2020 to take into account the increase in the social assistance benefit, and in 2021 for the single person benefit. For Netherlands in 2015 for Social Assistance Benefit (net). For Portugal in 2012 the equivalence scale used for Social insertion income changed in August. For Slovakia in 2019, 2021 and 2022 for several changes introduced within these years with regards the Child Benefit and the Tax Credit on Dependent Children. 35 For Spain in 2015 for Personal Income Tax. In 2018 for self-employed SIC. In 2020 for the simulation of the new nation-wide minimum income scheme. No full-year adjustments are applied for the remaining EU countries. 36 Annex 4. Additional tables Table A4.1 EUROMOD poverty and inequality statistics: 2019-2022 Poverty risk Poverty risk (60%) Country Policy year 50% 60% 70% age<18 age>=65 Poverty threshold EUR/year Gini coefficient AT 2019 6.8 13.7 20.7 16.5 13.7 15884 0.248 AT 2020 6.3 13.3 20.4 15.8 13.8 16571 0.243 AT 2021 6.2 13.4 20.3 16.3 13.1 16766 0.244 AT 2022 6.7 13.0 19.4 16.2 13.2 18324 0.238 BE 2019 5.7 12.4 21.6 13.7 15.8 14689 0.228 BE 2020 5.3 11.8 21.2 12.6 15.6 14828 0.225 BE 2021 5.5 12.0 21.5 13.4 15.6 15247 0.228 BE 2022 5.6 12.8 22.3 12.7 20.8 16275 0.230 BG 2019 16.8 24.1 31.2 27.9 39.6 2799 0.398 BG 2020 16.6 24.4 31.4 28.0 40.9 3042 0.400 BG 2021 16.8 24.5 31.2 27.6 41.5 3400 0.400 BG 2022 17.0 24.4 31.4 26.9 42.3 3692 0.399 CY 2019 5.6 14.8 24.1 18.1 21.4 10120 0.292 CY 2020 5.5 15.8 24.1 18.4 27.1 10183 0.292 CY 2021 5.7 15.6 23.9 18.0 27.0 10308 0.293 CY 2022 5.7 15.6 23.8 17.6 27.9 10404 0.293 CZ 2019 4.5 9.1 18.0 11.0 13.6 6319 0.235 CZ 2020 3.9 8.2 16.4 10.1 10.7 6411 0.228 CZ 2021 4.7 9.9 18.8 11.8 14.4 7281 0.239 CZ 2022 3.9 8.0 16.2 10.3 9.4 8202 0.227 DE 2019 11.8 18.5 25.9 19.9 20.3 15199 0.308 DE 2020 11.1 17.9 25.5 18.1 19.4 15399 0.303 DE 2021 11.5 18.2 26.0 18.0 20.9 15906 0.308 DE 2022 12.2 18.9 26.5 19.2 22.5 17214 0.310 DK 2019 6.0 12.2 20.9 9.6 12.4 19344 0.253 DK 2020 6.1 12.5 21.2 9.5 14.0 19604 0.255 DK 2021 6.2 12.5 21.3 8.7 16.0 19534 0.260 DK 2022 6.3 12.7 21.2 8.8 16.3 19912 0.261 EE 2019 11.0 19.6 27.6 13.5 40.6 7268 0.303 EE 2020 10.0 18.8 27.0 13.6 37.6 7486 0.300 EE 2021 11.2 19.3 27.9 13.5 40.0 7883 0.305 EE 2022 10.8 19.1 27.7 13.6 39.0 8316 0.306 EL 2019 10.2 16.7 23.9 19.7 12.9 5512 0.301 EL 2020 11.3 17.8 25.5 19.6 16.1 5601 0.309 EL 2021 10.8 17.4 25.1 19.4 15.0 5623 0.312 EL 2022 10.4 16.9 24.2 18.7 14.4 5695 0.307 ES 2019 14.2 20.9 27.4 27.0 19.1 9571 0.314 ES 2020 14.3 20.5 27.7 27.3 15.9 9483 0.312 ES 2021 14.1 20.5 27.3 26.2 19.2 9767 0.307 ES 2022 12.0 19.1 26.6 24.8 15.8 9900 0.302 FI 2019 3.3 10.7 20.9 10.8 10.2 14913 0.252 FI 2020 3.3 10.5 20.8 10.3 10.0 15142 0.247 FI 2021 3.5 10.8 21.1 10.6 10.6 15363 0.249 FI 2022 3.7 11.3 21.4 11.4 11.1 15824 0.251 FR 2019 6.0 11.4 20.4 15.9 7.5 13025 0.283 FR 2020 5.3 10.3 18.8 14.3 6.1 12854 0.277 FR 2021 5.9 11.1 20.1 15.7 7.3 13304 0.283 FR 2022 5.7 10.9 19.9 15.5 6.7 13747 0.281 HR 2019 12.8 19.3 25.9 16.7 33.6 4988 0.276 HR 2020 13.0 19.5 26.2 16.6 34.8 5099 0.280 HR 2021 13.0 19.5 26.5 16.6 34.9 5356 0.285 37 Poverty risk Poverty risk (60%) Country Policy year 50% 60% 70% age<18 age>=65 Poverty threshold EUR/year Gini coefficient HR 2022 12.9 19.2 26.2 16.2 34.7 5646 0.283 HU 2019 14.8 21.0 27.7 25.1 25.4 3621 0.312 HU 2020 14.9 21.1 27.6 25.8 27.3 3556 0.312 HU 2021 15.3 21.6 27.2 25.7 30.8 3788 0.313 HU 2022 14.9 21.6 27.3 24.3 31.2 3672 0.316 IE 2019 7.4 17.2 27.1 19.5 27.5 15227 0.283 IE 2020 9.1 17.3 26.8 20.0 20.1 15489 0.290 IE 2021 12.2 19.9 28.3 23.5 22.1 16755 0.299 IE 2022 12.7 20.5 28.8 24.1 23.4 17632 0.302 IT 2019 14.4 20.8 27.3 25.8 16.8 10604 0.319 IT 2020 13.8 20.7 27.4 25.7 17.1 10796 0.312 IT 2021 13.0 20.1 27.2 23.7 17.4 10870 0.306 IT 2022 13.2 20.0 26.9 23.7 17.1 11388 0.309 LT 2019 11.9 19.4 27.1 18.3 32.0 4917 0.337 LT 2020 9.6 16.9 24.8 13.3 30.6 5511 0.322 LT 2021 10.7 18.7 25.4 16.6 31.7 5906 0.330 LT 2022 10.5 18.2 25.5 16.4 30.1 6658 0.329 LU 2019 3.6 13.1 21.5 17.0 6.9 24277 0.258 LU 2020 3.6 12.9 21.5 16.1 7.2 24730 0.258 LU 2021 3.2 12.4 21.3 15.5 7.2 25126 0.257 LU 2022 2.7 11.7 21.3 14.5 5.8 25825 0.256 LV 2019 14.2 20.6 27.5 15.1 37.8 4995 0.337 LV 2020 14.1 20.2 27.3 15.2 36.6 5310 0.334 LV 2021 13.7 20.2 27.4 13.6 38.3 6073 0.327 LV 2022 13.2 19.8 27.2 13.3 37.4 6561 0.322 MT 2019 7.9 15.4 23.4 15.2 29.0 9892 0.300 MT 2020 7.3 14.3 23.4 14.5 25.1 9753 0.293 MT 2021 7.2 14.2 23.3 14.5 24.8 10017 0.294 MT 2022 7.0 14.4 23.1 14.8 25.6 10648 0.294 NL 2019 5.4 12.1 20.3 14.0 7.4 15530 0.266 NL 2020 5.4 11.9 20.1 13.2 7.8 16119 0.266 NL 2021 5.4 12.0 20.2 13.1 8.3 16620 0.264 NL 2022 5.3 11.5 19.6 12.7 7.5 16732 0.262 PL 2019 8.1 14.3 21.6 11.9 18.0 4737 0.265 PL 2020 8.0 14.3 21.9 11.1 20.2 4918 0.261 PL 2021 8.2 14.6 22.2 11.9 20.8 5199 0.263 PL 2022 7.8 13.8 21.6 11.5 18.2 5475 0.256 PT 2019 9.6 16.4 24.0 17.2 19.7 6681 0.308 PT 2020 9.7 16.4 24.1 17.0 20.2 6774 0.308 PT 2021 9.7 16.4 23.9 16.8 20.6 6827 0.308 PT 2022 9.7 16.8 24.1 16.6 22.6 7096 0.309 RO 2019 15.7 22.7 29.9 28.0 24.5 2602 0.328 RO 2020 15.5 22.9 29.4 29.5 22.9 2772 0.327 RO 2021 15.3 23.1 29.4 29.7 22.4 3056 0.325 RO 2022 13.4 20.8 27.9 29.2 13.4 3386 0.313 SE 2019 8.0 14.7 23.8 18.1 11.8 14886 0.254 SE 2020 8.2 14.6 23.9 18.5 10.4 15675 0.256 SE 2021 8.6 14.7 24.2 18.5 10.7 16573 0.256 SE 2022 8.7 14.8 24.1 18.7 10.2 16053 0.255 SI 2019 4.8 11.5 20.2 9.3 15.7 8338 0.236 SI 2020 5.2 12.0 20.6 10.1 15.9 8726 0.235 SI 2021 6.1 12.7 20.8 11.2 16.9 9155 0.238 SI 2022 5.5 12.0 20.2 10.8 14.3 9456 0.234 SK 2019 7.2 11.7 18.4 17.4 8.9 5198 0.211 SK 2020 7.1 11.4 18.0 16.6 8.5 5468 0.209 44 Annex 5. Decomposition of the redistributive effect of the tax-benefit system Following Kakwani (1977), the redistributive impact of the tax-benefit system can be decomposed as follows: 𝑅𝐸 =YI−𝑌𝐷          Y𝐷    ∗ΠY𝐼,Y𝐷 K−𝑅 where Y𝐼 is initial income (original + pensions in our case) Y𝐷 is disposable income (initial income + benefits - taxes - social insurance contributions) Y𝐼−𝑌𝐷           Y𝐷     is the level ΠY𝐼,Y𝐷 K is the progressivity (Kakwani index) of the tax-benefit system as a whole, which is in turn the difference between the concentration index of the aggregated tax-benefit components (sorted by initial income) minus the Gini coefficient of initial income (C𝑌𝐼−𝑌𝐷−GY𝐼) R is a re-ranking effect, i.e. the Gini coefficient of disposable income minus the concentration index of the same variable, but sorted by initial income (GY𝐷−CY𝐷). Figure 3 depicts the values of ΠY𝐼,Y𝐷 K (x axis) and Y𝐼−𝑌𝐷           Y𝐷     (y axis) for all EU member states. The position in the graph in relation to the curves is determined by Y𝐼−𝑌𝐷           Y𝐷     ∗ΠY𝐼,Y𝐷 K (𝑅 is not considered for the graphical representation). This redistributive effect without re-ranking is usually referred to in the literature as Reymond-Smolensky index (see, e.g. Verbist and Figari 2014). Following the generalisation of Onrubia et al (2014) for taxes, we propose the following formula to decompose the impact by tax-benefit component: 𝑅𝐸 = ∑ Ci  Y𝐷    ∗ΠY𝐼,Y𝐼+Ci K m i=1 −𝑅 where Y𝐼 is initial income (original + pensions in our case) Y𝐷 is disposable income (initial income + benefits - taxes - social insurance contributions) Ci is each of the m components (taxes and benefits) added/subtracted to initial income Ci    Y𝐷     is the level of each component (average component over disposable income) ΠY𝐼,Y𝐼+Ci K is the progressivity (Kakwani index) corresponding to component i, which is the difference between the concentration index of the component (sorted by initial income) minus the Gini coefficient of initial income (CCi−GY𝐵) R is a re-ranking effect, i.e. the Gini coefficient of disposable income minus the concentration index of the same variable, but sorted by initial income (GY𝐷−CY𝐷). Figure 4 depicts the values of the redistributive impact ( Ci    Y𝐷     ∗ΠY𝐼,Y𝐼+Ci K) of each of the following components: means-tested benefits, non-means tested benefits, taxes and social insurance contributions. Additionally, it shows the overall re-ranking effect −𝑅. Table A4.5 lists the income concepts used for the abovementioned computations. Table A4.5. Income concepts used for the decomposition of the redistributive impact Concept Corresponding EUROMOD income list Initial income = market income + pensions (Y𝐼) ils_origy + ils_pen Means-tested benefits ils_benmt Non-means-tested benefits ils_bennt Taxes ils_tax Social insurance contributions paid by the individual ils_sicdy Disposable income (Y𝐷) ils_dispy = ils_origy + ils_benmt + ils_bennt - ils_tax - ils_sicdy GETTING IN TOUCH WITH THE EU In person All over the European Union there are hundreds of Europe Direct information centres. 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