We re-examine the evolution of income inequality in Poland in the process of post-socialist transition focusing on the previously neglected problem of under-coverage of top incomes in household survey data. Multiple statistical techniques (Pareto imputation, survey reweighting, and microsimulation methods) are applied to combined household survey and tax return data in order to obtain top-corrected inequality estimates. We find that the top-corrected Gini coefficient grew in Poland by 14-26% more compared to the unadjusted survey-based estimates. This implies that over the last three decades Poland has become one of the most unequal European countries among those for which top-corrected inequality estimates exist. The highest-income earners benefited the most during the post-socialist transformation: the annual rate of income growth for the top 5% of the population exceeded 3.5%, while the median income grew on average by about 2.5% per year. This brief summarizes the results presented in Brzezinski et al. (2019).
There is a large economic literature documenting income inequality changes experienced by former communist countries during their post-1989 transformations. While in Russia and in many post-Soviet economies, inequality exploded during the transition, Poland is often perceived as a country where inequality grew rather moderately. However, these conclusions may be unreliable as they are based on inequality measures estimated using income data only from household surveys.
Many recent studies show that surveys are plagued by significant under-coverage of top incomes, which leads to a severe downward bias of the inequality estimates. Several approaches have been proposed to correct for this problem. One of them is to combine survey data with income information taken from administrative sources such as tax returns. While top-corrected inequality estimates have been produced for many advanced economies, transition countries received little attention in this context so far.
For Poland, Bukowski and Novokmet (2019) provided series of top income shares estimated using tax data. However, their estimates are constructed for gross (pre-tax) income distributed among tax units. This kind of income concept deviates considerably from the primary measure of the standard of living analysed in income distribution literature, namely disposable equivalized household income defined for the entire population. Estimates based only on tax data are not directly comparable to standard survey-based measures, which makes it difficult to decide which of the two kinds of results are closer to the underlying inequality trends and levels.
In a recent paper (Brzezinski, Myck, Najsztub 2019), we provide the first estimates of top-corrected inequality trends for real equivalized disposable incomes in Poland over the years 1994-2015. These estimates can be readily compared with standard survey-based estimates available from Statistics Poland or from Eurostat. Our analysis re-evaluates distributional consequences of post-socialist transition in Poland.
According to the standard view, the Polish transition to a market economy was an almost unqualified success story. Poland managed to achieve fast and stable economic growth (around 4.3% per year since 1994) that was at the same time broadly inclusive and shared rather equally by various social classes and segments of the income distribution. Survey-based estimates suggest that the Gini index for Poland has not increased significantly since 1989 and reached the average level among the EU countries in 2015. In contrast to the standard view, our top-corrected results show that the inequality of living standards in Poland grew sharply over 1989-2015. The adjusted Gini index grew by 4-8 p.p. to a level that ranks Poland among the most unequal European countries for which comparable estimates exist.
Data and Methods
We use data from two sources. Our survey income data comes from the representative Polish Household Survey (PHBS) conducted annually by Statistics Poland since 1957. We use the PHBS data for 1994-2015 as the pre-1994 surveys do not contain data on individual incomes (required for our microsimulation modelling) and 2015 is the last year for which estimates of tax-based inequality measures are available. We adjust the baseline PHBS survey weights to match the census-based number of males and females by age groups (population weights). We also create a further adjusted set of weights to match the number of PIT payers in each tax bracket according to the Polish tax scale (tax weights).
Our main income variable is real equivalent household disposable (post tax and transfer) income. We obtain it from the Polish microsimulation model SIMPL applied to the PHBS data. The microsimulation model allows us to construct a gross (before PIT and employee SSCs) income distribution among the tax units, which is unavailable in the raw PHBS data. This is crucial as it is the gross income distribution between tax units to which we impute top incomes estimated using tax-based statistics.
Our second data source is the series of tax-based top income shares for Poland taken from Bukowski and Novokmet (2019). To construct top-corrected inequality estimates, we follow the methodological approach of Bartels and Metzing (2019). Using the microsimulation model applied to the PHBS data we obtain the distribution of gross income among tax units (individuals). In the next step, we use data on top income shares to estimate the parameters of a Pareto distribution for gross income distribution in terms of tax units. Then, we replace the top 1% (or 5%) of tax units’ incomes with the incomes implied by the estimated Pareto distribution. The resulting imputed gross distribution is subsequently reweighted using either population or tax weights. After imputing top incomes, we again use the microsimulation approach to compute top-corrected real equivalized household net incomes.
Corrected Income Inequality Trends
Note: Vertical lines show 95% confidence
Figure 1 presents our income inequality estimates in terms of the Gini coefficient. For the period 1994-2005, we present two top-corrected series, which can be considered as lower and upper bound estimates of the “true” Gini. The results for this period are more uncertain as they are affected by the 2004 tax reform in Poland that introduced an optional flat tax for non-agricultural business income, which reduced the marginal tax rate for the highest income taxpayers from 40% to 19%. Research suggests that before the reform the problems of tax evasion and avoidance could have been more pronounced in Poland and some of the top incomes were unreported or under-reported. The upper bound series on Figure 1 corrects for the possible higher tax evasion and avoidance before 2005.
The unadjusted Gini series suggests that income inequality in Poland was rather stable over 1994-2015. On the other hand, our top-corrected series point to a very different story. Until 2005, our two correction procedures show similar inequality trends, but somewhat different levels. After 2005, our corrected series shows systematic and high divergence between unadjusted and top-corrected Ginis ranging from 4 to 8 p.p. The top-corrected Ginis increase in the range from 14 to 26% over 1994-2015. While according to the unadjusted data Poland is only moderately unequal, the comparison of top-corrected estimates shows that in 2015 Poland has higher level of income inequality than even high-inequality EU countries such as Germany, Spain or UK.
We also show that each percentile of the disposable income distribution in Poland saw income increases in absolute terms between 1994 and 2015. This implies that on average the incomes of all social groups increased during the transition to market economy. However, these gains were shared unequally. According to our adjusted estimates, the cumulative growth in real income over 1994-2015 for the top 1% of Poles reached 122-167%, while for the bottom 10% the corresponding number is at most 57%.
Redistribution and Progressivity of the Tax System
We also analyse how our correction procedures affect measures of redistribution and progressivity of direct taxation (income taxes, employees’ mandatory social security contributions, and health insurance). The top-corrected estimates show that the percentage reduction in the Gini index due to social insurance contributions and PIT has fallen from 19.2% in 1999 to 11.6% in 2015.
While the unadjusted series suggests that the progressivity of the Polish system of PIT and social insurance contributions has decreased only mildly over time, the top-corrected series points to a much steeper fall, especially during 2005-2009. Without the top-correction, the progressivity in 2015 is overestimated by 2.3 p.p. (or by 40%). Much of the decline in tax progressivity over 2005-2009 is due to the reduction from three PIT brackets and marginal tax rates to just two brackets and rates (18% and 32%) in 2009. Even in terms of the unadjusted data, Poland ranks in the recent years as the country with the lowest PIT and SICs progressivity in the EU.
Our recent paper on estimating the top-corrected measures of income inequality shows that while Poland was already a relatively unequal country in the early 1990s, it has become one of the most unequal European countries (not including Russia) among those for which comparable estimates exist. The results have important implications for the assessment of the distributional consequences of post-socialist transformations or modernization processes in emerging countries. They indicate that using income tax data and imputation or reweighting techniques to account for the problem of missing top incomes in survey data can significantly alter the conclusions about income inequality levels and trends. More reliable inequality estimates would contribute not only to a better understanding of economic transformation and modernization processes but could also shed some light on recent political turmoil in many transition and emerging countries (such as Turkey, Hungary or Poland). As suggested by some recent research, the growing distributional tensions in emerging countries of Eastern Europe and Central Asia may be associated with more distrust in governments and an increased propensity to vote for radical political parties.
The authors gratefully acknowledge the support of the Polish National Science Centre (NCN) through project number: UMO-2017/25/B/HS4/01360. For the full list of acknowledgements see Brzezinski et al. (2019).
- Bartels, C., Metzing, M. (2019). An integrated approach for a top-corrected income distribution. The Journal of Economic Inequality, 17(2), 125-143.
- Brzezinski M., Myck M., Najsztub M. (2019), Reevaluating Distributional Consequences of the Transition to Market Economy in Poland: New Results from Combined Household Survey and Tax Return Data. IZA DP No. 12734.
- Bukowski P., Novokmet F. (2019), Between Communism and Capitalism: Long-Term Inequality in Poland, 1892-2015. CEP Discussion Paper No 1628 June 2019.
To understand the nature of income inequality one needs to know how persistent the inequality is across generations. The same inequality levels could conceal different intergenerational mobility. We utilize the Russian Longitudinal Monitoring Survey (RLMS-HSE) to find out how large intergenerational mobility in Russia is as measured by income, educational and occupational mobility. We find that although a sizeable upward intergenerational educational mobility, there is a pronounced occupational immobility and a low level of intergenerational income mobility. Indeed, the position of children in the income distribution is highly correlated with the income position of their parents, especially their mothers.
Sizeable and non-decreasing inequality in Russia poses a threat to social stability and long-term sustainability. Inequality in Russia has remained high throughout the transition period, and even slightly increased in the 2000s; the Gini inequality index rose from 0.397 in 2001 to 0.416 in 2014. The ratio of average incomes of the highest decile to those of the lowest decile also increased from 13.9 to 16 during this same period. This income gap is driven primarily by the gap between incomes of the top decile and all of the others: the top decile is estimated to have thirty percent of total monetary income in the economy. Furthermore, income inequality originates in earnings inequality: the top decile of wage earners gets thirty five percent of total wage earnings in the economy.
A key question is how persistent the inequality is, given that the same inequality levels could conceal different intergenerational mobility. In particular, social stability is challenged when income inequality is stable across generations, or put differently; there is little intergenerational mobility. Economic developments of the last 25 years seem to increase the risks of getting this problem in Russia.
Data and research methodology
We employ Russian Longitudinal Monitoring Survey (RLMS-HSE) to find out how large intergenerational mobility in Russia is as measured by income, educational and occupational mobility (Denisova and Kartseva, 2016). The RLMS-HSE questionnaires in 2006 and 2011 contain questions on dates of birth, education and occupation of the father and mother of the respondent when the respondent was 15 years old.
To study occupational and educational mobility, we use the subsample of respondents of 25-55 years old and utilize the information on education and occupation of the respondent and his/her parents. We then estimate whether the parental education level predicts the probability that children have a university degree, a secondary or a junior professional degree.
To study intergenerational occupational mobility, we estimate influence of parental occupation on the probability that the child works as a manager, a professional, a technician or professional associate, a clerk, a qualified worker or an unskilled worker.
To study the child-parent income correlation based on RLMS is trickier. There is a panel component in RLMS but it is not long enough to study intergenerational mobility directly since we for most cases are not able to observe both parents and children during their working ages. To overcome the problem we impute wages for parents. In particular, we choose respondents aged 25-35 (children) in 2006 (and 2011). We then identify respondents born in the period 1945-1961 (1945-1966 for children in 2001) (‘parents’) and use the labor market information for this group as of 1995 (2001 as robustness check) to impute parental wages. We estimate a wage equation (separately for males and females) on the sample of ‘parents’ and then use the estimated returns (coefficients) and the reported age and education of respondent’s mother and father to impute wages of respondent’s parents.
We follow Björklund and Jantti (1997) to estimate the child-parent correlation of earnings based on the equation:
delta= β0 + β1X+ β2 delta_father + β3 delta_mother + ε
where delta=log(wage/average wage in respective sample), X – age, education, settlement type, region. Standard errors are clustered on primary sampling unit.
Intergenerational educational mobility
Our analysis shows that the education of parents, high professional (university) and secondary professional in particular, is a major determinant of children’s education. Moreover, there are clear signs of upward educational mobility across generations for both males and females: the coefficients in the transition parent-child matrix are significantly higher above the diagonal (Table 1).
Table 1. Father-child education matrix
The probability to have a university degree is 2.4 percentage points higher if the mother’s education is at university level (as compared to secondary school), and 2.1 percentage points higher if the father’s degree is at university level (as compared to secondary school). A secondary professional degree of parents also increases the probability of a child getting a university degree by about 1 percentage point. The probability of having secondary professional degree decreases if the father or mother has a university degree.
Intergenerational correlation of occupations
There are signs of sizeable occupational rigidity between generations, especially for the top two occupational groups (managers and professionals). The probability that a child works in the same occupational group is the highest for parents-professionals: it is 40% for fathers-professionals and 35% for mothers-professionals. Surprisingly, it is also rather high for parents employed as skilled workers – about 20%. These patterns survive controlling for other variables.
The correlation of parent-child wages measured for 2006 data are presented in Table 2. The results point to the sizeable average intergenerational rigidity of relative wages: the wage elasticity of children’s wages with respect to parental wages is about 0.4. This is at the level of the intergenerational wage rigidity in the US (Solon 1999).
There is sizeable gender asymmetry in the rigidity: we observe a high and significant correlation of son-mother wages, but an insignificant correlation of son-father wages. There is no significant correlation of daughter-parents wages.
Table 2. Parent-child income correlations, 2006
Generational poverty stemming from low intergenerational income mobility is a threat for sustainable development in any country. The economic and social development in transition seems to increase the risks of having this problem in Russia. Our estimates show that although there is sizeable upward intergenerational educational mobility in Russia, there is a pronounced occupational immobility, and low level of intergenerational income mobility. Indeed, the position of children in the income distribution is highly correlated with the income position of their parents, especially mothers. These findings are worrisome signals important for the design of policies of sustainable development.
- Björklund, Anders; and Markus Jantti, 1997. “Intergenerational Income Mobility in Sweden Compared to the United States,” American Economic Review, 87(5), 1009–18.
- Denisova, Irina; and Marina Kartseva, 2016, “Intergenerational Mobility of Russian Households”, mimeo
- Solon, Gary, 1999. “Intergenerational Mobility on the Labor Market,” Chapter 29 in Handbook of Labor Economics, Vol.3 edited by O.Ashenfelter and D.Card , 1761-1800.
Concerns about global economic inequality have become central in today’s policy debate. This brief summarizes what is known about the development of inequality globally, emphasizing the difference between the developments within countries and between countries. In the former sense, inequality has risen in most countries in the world since the 1980s, but in the latter sense inequality, has (most probably) dropped. To ensure future progress in terms of continued decreasing global inequality, fighting increasing inequality within countries is likely to be central.
In recent years, the distribution of income and wealth has emerged as one of the most widely discussed issues in societies everywhere. US President Barack Obama has called rising income inequality the “defining challenge of our time”, the topic has been on the agenda at meetings of the World Economic Forum in Davos, and studies by the IMF and the OECD (e.g., OECD, 2014, and IMF, 2014) have associated income inequality with lower economic growth. Thomas Piketty’s best-selling book “Capital in the Twenty-First Century” (2014) has placed the topic center-stage well outside academic and expert circles. At the same time, some have argued that all the talk about increasing inequality is in fact wrong and that it misses what they perceive as the more important story, namely, the decreasing global inequality. So, which is it, and what conclusions can be drawn?
Different Ways of Viewing the Facts About Global Inequality
When people talk about global income inequality there are a number of things that could be referred to. First, one might think of the inequality within countries across the world. From this perspective, the question in need of an answer would be: “How has inequality within individual countries changed globally in recent decades?” The short answer is that it has increased in most places. This is certainly the case in most of the developed world since the 1980s, while in emerging markets and developing countries (EMDCs) there are greater differences across time and regions. Looking at disposable incomes at the household level (the most commonly used measure in international comparisons) most countries in Asia and Eastern Europe have seen marked increases of inequality, while the trend seems to have been the opposite in Latin America and in large parts of Africa. In level terms, the development has been one of convergence since, on average, the countries in Eastern Europe and Asia started at much lower levels than those in Latin America and Africa. The development has resulted in that inequality levels are today on average at similar levels, with a Gini coefficient of between 0.4 and 0.45, in Africa, Asia, and Latin America (see figure 1 below and IMF, 2015) The same is true for the average across OECD countries where inequality has increased the most in percentage terms in countries starting at low levels, with the US being an exception in that inequality has increased even though the level has always been at the higher end among developed economies (e.g., OECD, 2015). The European average is today around 0.3 while the household disposable income Gini in the US is just below 0.4.
Figure 1. Change in the net Gini Index, 1990-2012
Source: IMF, 2015.
Looking at other income inequality measures, such as top income shares, the picture is similar: inequality has increased in most countries for which we have data since the 1980s. While it is important to recognize that top income shares are a very different measure of inequality, it has been shown that there is a close relationship between top income shares and the Gini coefficient in terms of capturing both level differences across countries and trends in the development (e.g., Leigh, 2007 and Morelli, Smeeding and Thompson, 2015). This together with one of the main strengths of the top income measure, namely, the length of the time series, allows us to put the recent developments in a historical perspective.
Figure 2 shows the income share of the top decile group for a number of mainly developed countries over the 20th century, illustrating the surprisingly common trends over the past 100 years (but also important level differences). On average, top shares (driven mainly by what happened in the top 1 percent) dropped from the beginning of the century until about 1980 after which it has risen in a fanning-out fashion. The point of the figure is clearly not to illustrate any individual country but rather to illustrate the overall long-run trend. For details of the historical development of income as well as wealth distribution, see Roine and Waldenström (2015).
Figure 2. Top 10 percent income share over the 20th century
Source: World top income database (WTID).
While the overall picture of rising inequality in most countries over the past decades is pretty clear, the development between countries is less so. There are two main reasons for this. First, it depends on what is considered the unit of observation and how these units are weighted. Second, it depends on what one assumes about the vast gaps in data availability, in particular in EMDCs (see e.g., Lakner and Milanovic, 2013, for more details).
As explained by for example Milanovic (2012) there are essentially three different ways in which one might think about the global distribution of income: 1) Treat every country as one observation and use a country’s GDP per capita as the measure of income; 2) do the same as in 1) but give different weight to each country according to its population; 3) Treat individuals (or households) as the unit of observation regardless of where people live. In all three cases it is possible to line up all observations from the poorest to the richest (and, hence, also to calculate a Gini coefficient). In the first way of looking at the world, we treat everyone in each country as being represented by the country’s average income and we also give the same weight to Luxemburg and India. In the second case, we recognize that more people live in India and weight it accordingly but we still, by construction, force everyone in each country to have the country average, thus ignoring within country inequality. Only in the last approach do we actually take into account both relative population size and differences in development within countries. This clearly seems the most satisfactory way to look at what has happened, but it is also the most demanding in terms of data.
In terms of the first two approaches, inequality in the world has fallen in the past decades. This is especially clear when weighting countries by population size. Rapid growth in China and India has caused average incomes in the world’s most populous and initially poor countries to increase faster than the global average, implying a reduction in global inequality. Some may think that this is not surprising and only to be expected since these countries start at such low levels, but in fact, this development marks the reversal of a 200-year trend toward increasing global inequality. Even “catch-up growth” is certainly not to be taken for granted.
Now the real question is this: What has happened to the global income distribution if we take into account the recent increasing inequality within many countries, including China and India? The answer turns out to complicated and uncertain (see Lakner and Milanovic, 2013 for details) but in the end most of the evidence points to decreasing global inequality in this sense too. As François Bourguignon puts it in a recent article in the Foreign Affairs: “…the increase in national inequality has been too small to cancel out the decline in inequality among countries” (Bourguignon, 2016, p. 14).
To understand both of these counteracting forces it is illustrative to look at real income growth across the global income distribution. Figure 3 below is taken from a presentation by Branko Milanovic, organized by SITE in 2014 (and available online here). It shows the real income growth for different percentile groups in the global distribution over the period 1988-2008. Moving from left to right the figure shows positive but modest growth for the very poorest individuals in the world, and much higher growth for the groups just above, with rates increasing toward the middle of the global distribution. In the range of about 5 dollars/day (in PPP adjusted terms) growth has been the highest. By developed-country standards, these people are still very poor, but globally they are truly the “middle class” in the sense that they make up the middle of the global income distribution. Moving further right we see a sharp drop in real income growth at a level around the 80th percentile. This part of the distribution is mainly populated by the lower middle classes of the developed world, and here income growth has been essentially zero over the past decades. Moving further right we again see a sharp increase in real income growth illustrating the large gains going to individuals in the top of the global income distribution.
Figure 3 summarizes much of what has happened: the left part showing the rapid growth of income among most of the world’s relatively poor, while the right shows the increasing inequality in the developed world, with the top of the distribution gaining the most.
Figure 3. Real income growth at various percentiles of the global income distribution, 1988-2008 (in 2005 PPPs).
Source: Lakner and Milanovic (2013).
Why This Matters and What Should Be Done About Global Inequality?
The forces that explain what has happened are of course complex and differ over time and across countries but one thing seems clear, the growth of real incomes in developing countries as well as the relative decline of incomes in the lower end of the income distribution in developed countries have at least in parts been shaped by the same intertwined processes of globalization and technological development. Overall, these processes are powerful positive developments, but at the same time it is easy to see how those who perceive themselves as losers in these developments may try to resist them using their political voice. It is important to remember that globalization is the result of a combination of technology and political decisions, and consequently not an inevitable process. After all, the globalization backlash in the period 1914-1945 did not happen because the technological feasibility of the process suddenly disappeared.
The appropriate government responses are of course also likely to be different across countries, but here there are also some common factors that stand out. In the developing world, the most challenging aspects will have to do with maintaining state capacity and the ability to tax increasingly mobile tax bases. In many developing countries taxation will also be key, but here the challenge is more about creating a capable and accountable state in the first place. As succinctly and, I think, correctly put by Nancy Birdsall in a review of Thomas Piketty’s “Capital in the Twenty-First Century”: “(I)n the developing world, the challenge is not, at least not yet, the one Piketty outlines — that an inherent tendency of capitalism is to generate dangerous inequality that if left unchecked will undermine the democratic social state itself. The challenge is the other way around: to build a capable state in the first place, on the foundation of effective institutions that are democratically accountable to their citizens.”
- Atkinson, Anthony B. 2015. “Inequality – What can be done?” Harvard University Press.
- Birdsall, Nancy. 2014. “Thomas Piketty‘s Capital and the developing world”
- Ethics & International Affairs / Volume 28 / Issue 04 / Winter 2014, pp 523-538.
- Bourguignon, François, and Christian Morrison. 2002. “Inequality among World Citizens: 1820-1992”, The American Economic Review, Vol. 92, No. 4. (Sep., 2002), pp. 727-744.
- Bourguignon, François. 2016. “Inequality and Globalization. How the rich get richer as the poor catch up”, Foreign Affairs, Volume 95, Number 1, pp. 11-16
- Lakner, Christoph, and Branko Milanovic. 2013. “Global Income Distribution: From the Fall of the Berlin Wall to the Great Recession.” WB Policy Research Working Paper 6719, World Bank, Washington.
- Leigh, Andrew. 2007. “How closely do top income shares track other measures of inequality?”, The Economic Journal, 117 (November), 589–603.
- OECD (2015), “Growth and income inequality: trends and policy implications”, OECD Economics Department Policy Notes, No. 26 April 2015.
- OECD. 2011. Divided We Stand: Why Inequality Keeps Rising. Paris: OECD Publishing.
- OECD. 2012. “Reducing Income Inequality While Boosting Economic Growth: Can It Be Done?” In Economic Policy Reforms: Going for Growth. Paris: OECD Publishing.
- Ostry, Jonathan David, Andrew Berg, and Charalambos G. Tsangarides. 2014. “Redistribution, Inequality, and Growth”, IMF SDN, February 17, 2014
- Milanovic, B. 2013. “Global Income Inequality by the Numbers: in History and Now.” Global Policy 4 (2): 198–208.
- Morelli, Salvatore, Smeeding, Timothy, and Jeffrey Thompson. 2015. “Post-1970 Trends in Within-Country Inequality and Poverty: Rich and Middle Income Countries”, Chapter in Atkinson, A.B., Bourguignon, F. (Eds.), Handbook of Income Distribution, vol. 2A, North-Holland, Amsterdam.
- Piketty, Thomas. 2014. “Capital in the Twenty-first Century”. Cambridge, Massachusetts: Harvard University Press.
- Pritchett, Lant. “Divergence, Big Time.” Journal of Economic Perspectives, Summer 1997, 11(3), pp. 3-17.
- Roine, Jesper, and Daniel Waldenström. 2015. “Long-Run Trends in the Distribution of Income and Wealth”, Chapter in Atkinson, A.B., Bourguignon, F. (Eds.), Handbook of Income Distribution, vol. 2A, North-Holland, Amsterdam.
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
There is a trade-off between two major objectives of a tax-benefit system: equity and efficiency. The tax-benefit systems that redistribute a lot of income tend to generate disincentives to work. The tax-benefit systems that create good incentives to work and earn, are less effective in mitigating poverty, social exclusion and deprivation. In this brief we argue that, when contrasted to other EU countries, the Latvian tax-benefit system is less effective in achieving either of the objectives.
There is a fundamental trade-off between the two principal objectives of a tax-benefit system – income redistribution and efficiency. On the one hand, income redistribution is desirable as it helps to mitigate socially undesirable market outcomes such as poverty and deprivation. On the other hand, more income redistribution is often associated with higher distortions to labour supply and work effort.
There is no universal prescription as to how much a government should redistribute. The answer to this question depends, among other factors, on the relative value that society (government) assigns to the welfare of different population groups, and on the individuals’ labour supply elasticity.
However, a given degree of income redistribution can be achieved at a different cost of efficiency. In this brief, we analyse the degree of income redistribution generated by the tax-benefit system and work incentives in Latvia in the context of other EU countries. In our analysis, we use the European microsimulation tax-benefit model EUROMOD (Sutherland and Figari, 2013) version G2.0, EU-SILC data, and the analysis framework developed by Jara and Tumino (2013).
Income Redistribution in the EU
EU countries differ substantially in terms of inequality of original income and in terms of the degree of redistribution generated by the tax-benefit system (see Figure 1, data on 2007 and 2013). The Gini coefficient of equivalised household original income (which consists of income from employment and self-employment, property income, private pensions, private transfers and other relatively minor components) ranges from around 0.4 (Cyprus, Netherlands) to almost 0.55 (Romania in 2007, Ireland in 2013).
Inequality of original income in Latvia in 2007 was at the EU average level (Gini coefficient of 0.47), but the degree of income redistribution generated by direct taxes, benefits and pensions was the lowest in the EU. As a result, the inequality of disposable income in Latvia in 2007 was the highest in the EU (Gini coefficient of 0.37). Part of the answer as to why the degree of income redistribution in Latvia is so low is a relatively small contribution of pensions to redistribution – it is almost half of that observed in the EU on average, despite the fact that the share of public pension recipients in the total Latvian population in 2007 was above the EU average. Another important factor was the very minor role of means-tested benefits: in the EU on average, means-tested benefits generate a reduction in Gini coefficient by about 0.02, while in Latvia the corresponding figure is just one tenth of this.
Figure 1. Gini coefficients of original equivalised household income and degree of redistribution generated by tax-benefit systems in the EU in 2007 and 2013
Source: EUROMOD statistics, authors’ calculations.
In the course of the crisis and the following recovery, the degree of redistribution in Latvia increased (see lower panel of Figure 1). An important factor behind the increase was growing number of pension recipients and an increase in the average size of pensions (both in absolute terms and relative to employment income). The increase in the number of pension recipients was not a result of changes in eligibility criteria, but was due to population ageing and the fact that more people applied for other types of pensions. The growth in the average size of pension was due to generous indexation of pensions in 2008 and compositional changes, as pensions of new pensioners until 2012 were larger than the average pension. Another reason for a growing degree of redistribution was an increase in the size and the number of recipients of means-tested benefits (mainly Guaranteed Minimum Income (GMI) benefit). This was a result of reforms in the provision of the means-tested benefits and of falling incomes from employment, which made more people eligible for the social assistance programmes. Nevertheless, despite the increase in recent years, the degree of income redistribution in Latvia remains one of the lowest in the EU.
The existence of a trade-off between income redistribution and better work incentives suggests that tax-benefit systems that ensure less income redistribution are likely to generate better work incentives. Jara and Tumino (2013) have demonstrated the existence of this trade-off in the EU countries in 2007-2010 by identifying a negative and statistically significant correlation between Gini coefficients and Marginal Effective Tax Rates (METR). The METR is a measure that is commonly used to quantify work incentives at the intensive margin. It shows what proportion of a small increase in earnings (which results from e.g. an increase in the supplied hours of work) is lost as a result of extra tax payments or foregone benefits that the person is no longer eligible for after the increase in earnings. The negative correlation identified in Jara and Tumino (2013) suggests that countries with less income redistribution (i.e., higher Gini coefficients) tend to have better work incentives (lower METRs).
In Latvia, the mean METR in 2013 was 32.2%, only slightly below the EU average (34.5%), and much higher than the average in Estonia (22.8%) and Lithuania (27.4%), despite a lower degree of income redistribution (EUROMOD statistics). Another feature of the Latvian tax-benefit system is that it is characterised by especially high METRs for poor individuals. Thus, in 2013, 94% of individuals who faced METRs in excess of 50% belonged to the two bottom deciles of distribution of equivalised disposable income. This is different from many other European countries, where distribution of high METRs is either more even across deciles or rising towards the top end of income distribution (Jara and Tumino (2013), data for 2007).
The main reason for high METRs faced by the poorest population groups in Latvia is the design of means-tested benefits (GMI and housing benefits), which generates 100% METRs for the recipients of these benefits. Namely, for each additional euro earned, the amount of benefit is reduced by one euro, which leaves the net income unchanged. This adversely affects employment incentives for the poorest individuals and increases the poverty risk.
Figure 2 illustrates mean METRs by deciles of equivalised disposable income in Latvia and shows the contribution of taxes, benefits and social insurance contributions (SICs) to the mean METRs. It clearly demonstrates that high METRs in the bottom deciles result mainly from the contribution of benefits, which disappears in the fourth decile. The contribution of SICs is slightly smaller in the bottom decile, which is due to the fact that the proportion of employed individuals is smaller in the bottom decile. For the same reason, and also because of basic tax allowances, the contribution of direct taxes is smaller in the bottom deciles, but then the contribution of taxes levels off, reflecting the Latvian flat tax rate.
Figure 2. The contribution of direct taxes, benefits and social insurance contributions (SIC) to METRs in Latvia by deciles of equivalised disposable income in 2013
Source: authors’ calculations using EUROMOD-LV
In their study on the incentive structure created by the tax and benefit system in Latvia, the World Bank (2013) pointed out the problem of bad work incentives generated by Latvian means-tested benefits. Our results, which are based on a population-representative database of incomes, also identify means-tested benefits as the major contributor to high METRs in the lowest deciles of the income distribution. Another concern expressed by the World Bank (2013) was that the problem of informal employment (either in the form of undeclared wages or work without a contract) can be exacerbated by high participation tax rates and METRs.
The Latvian tax-benefit system is characterized both by a relatively low degree of income redistribution and relatively weak work incentives, as measured by METRs. Recipients of means-tested benefits (GMI and housing benefits) are faced with 100% METRs, as benefits are withdrawn at the same rate as household income rises. This creates disincentives to increase labour supply for low-paid/low-skilled individuals, and hence creates a risk of poverty traps. Evidence from the literature suggests that the labour supply of low paid workers is particularly sensitive to the incentives generated by the tax-benefit system, hence reforms that would bring down METRs in the bottom deciles could yield positive results in terms of employment of low paid/low skilled workers.
A potential reform is to introduce either a gradual phasing out of the means-tested benefits, or to exclude a certain amount of employment income from the income test for the means-tested benefits. Such reforms would be targeted at the bottom end of the income distribution, help combat poverty, improve the incentive structure of the Latvian tax-benefit system, and positively affect the labour supply of low-skilled/low-paid workers.
- EUROMOD statistics on Distribution and Decomposition of Disposable Income, accessed at http://www.iser.essex.ac.uk/euromod/statistics/ using EUROMOD version no. G2.0, retrieved on October 14, 2014
- Jara, H. Xavier & Alberto Tumino (2013). “Tax-benefit systems, income distribution and work incentives in the European Union,” International Journal of Microsimulation, Interational Microsimulation Association, vol. 1(6), pages 27-62.
- Sutherland, Holly & Francesco Figari (2013). “EUROMOD: the European Union tax-benefit microsimulation model,” International Journal of Microsimulation, Interational Microsimulation Association, vol. 1(6), pages 4-26.
- World Bank (2013). “Latvia: “Who is Unemployed, Inactive or Needy? Assessing Post-Crisis Policy Options”. Analysis of the Incentive Structure Created by the Tax and Benefit System. Financial Incentives of the Tax and Benefit System in Latvia,” European Social Fund Activity “Complex support measures” No. 1DP//18.104.22.168.1./09/IPIA/NVA/001