A common presumption in economics is that a carbon tax is regressive – that the tax disproportionately burdens low-income households. However, this presumption originates from early research on carbon taxes that used US data, and little is known about the factors that determine the level of regressivity of carbon taxation across countries. In this policy brief, I explore how differences in income inequality may determine the distribution of carbon tax burden across households in Europe. The results indicate that carbon taxation will be regressive in high-income countries with relatively high levels of inequality, but closer to proportional in middle- and low-income countries and in countries with low levels of income inequality.
Climate change is one of the main challenges facing us today. To reduce emissions of greenhouse gases, and thereby mitigate climate change, economists recommend the use of a carbon tax. The environmental and economic efficiency of carbon taxation is often highlighted, but the equity story is also of importance: who bears the burden of the tax?
How the burden from a carbon tax is shared across households is important since it affects the political acceptability of the tax. For instance, the “Yellow Vests” protests against the French carbon tax started due to concerns that the tax burden is disproportionately large on middle- and working-class households. Research in economics also shows that people prefer a progressive carbon tax (Brännlund and Persson, 2012).
In this brief, I explore what we know about the distributional effects of carbon taxes and analyze the link between carbon tax regressivity and levels of income inequality in theory and in application to Sweden as well as other European countries.
Carbon Tax Burden Across Households
It is a common finding in the economics literature that carbon taxes are, or would be, regressive (Hassett et al., 2008; Grainger and Kolstad, 2010). However, most of the earlier literature is based on US data, and the US is unrepresentative of an average high-income country in terms of variables that are arguably important for carbon tax incidence. Compared to most countries in Europe, income in the US is high but unequally distributed, carbon dioxide emissions per capita are high, the gasoline tax rate is low, and the access to public transport is poor. If we want to understand the likely distributional effects of carbon taxes across Europe, we thus need to look beyond the US studies.
A recent study by Feindt et al. (2020) examines the consumer tax burden from a hypothetical EU-wide carbon tax. They find that the distributional effect at the EU-level is regressive, driven by the high carbon intensity of energy consumption in relatively low-income countries in Eastern Europe. At the national level, however, carbon taxation in Eastern European countries is slightly progressive due to car ownership and transport fuel being luxuries. Conversely, in high-income countries – where transport fuel is a necessity – carbon taxation is slightly regressive.
That the incidence of carbon and gasoline taxation varies across countries with different levels of income, has been found in numerous studies (Sterner, 2012; Sager, 2019). To understand the source of this variation, we need to identify the determinants of the incidence of carbon taxes.
The Role of Income Inequality
In a recent paper, I, together with Giles Atkinson at the London School of Economics, present a simple model where the variation in the carbon tax burden across countries and time can be determined by two parameters: the level of income inequality and the income elasticity of demand for the taxed goods (Andersson and Atkinson, 2020). The income elasticity specifies how the demand for a good, such as gasoline, responds to a change in income. If the budget share decreases as income increase, we refer to gasoline as a necessity. If the budget share increases with income, we refer to gasoline as a luxury good. Our model predicts that rising inequality increases the regressivity of a carbon tax on necessities. Similarly, we will see a more progressive incidence if inequality increases and the taxed good is a luxury.
To mitigate climate change, a carbon tax should be applied to goods responsible for the majority of greenhouse gas emissions: transport fuel, electricity, heating, and food. To estimate the distribution of carbon tax burden, we must then first establish if these goods are necessities or luxuries, respectively. Gasoline is typically found to be a luxury good in low-income countries but a necessity in high-income countries (Dahl, 2012). Food, in the aggregate, is consistently found to be a necessity. A carbon tax on food would, however, mainly increase the price of red meat – beef has a magnitude larger carbon footprint than all other food groups – and red meat is generally a luxury good, even in high-income countries (Gallet, 2010). Lastly, electricity and heating are necessities, with little variation across countries in the level of income elasticities. A broad carbon tax would thus likely be regressive in high-income countries, but more proportional, maybe even progressive, in low-income countries. The overall effect in low-income countries depends on the relative budget shares of transport fuel and meat (luxuries) versus electricity and heating (necessities). A narrow carbon tax on transport fuel has a less ambiguous incidence: it will be regressive in high-income countries where the good is a necessity and proportional to progressive in low-income countries where the good is a luxury.
The income elasticities of demand, however, only provide half of the picture. To understand the degree of regressivity from carbon taxation, we also need to take into account the level of income inequality in a country. Our model predicts that a carbon tax on necessities will be more regressive in countries with relatively high levels of inequality. And increases in inequality over time may turn a proportional tax incidence into a regressive one.
To test our model’s prediction, we analyze the distributional effects of the Swedish carbon tax on transport fuel and examine previous studies of gasoline tax incidence across high-income countries.
Empirical Evidence from Sweden
The Swedish carbon tax was implemented in 1991 at $30 per ton of carbon dioxide and the rate was subsequently increased rather rapidly between 2000-2004. Today, in 2021, the rate is above $130 per ton; the world’s highest carbon tax rate imposed on households. The full tax rate is mainly applied to transport fuel, with around 90 percent of the revenue today coming from gasoline and diesel consumption.
Figure 1. Carbon tax incidence and income inequality in Sweden
Using household-level data on transport fuel expenditures and annual income between 1999-2012, we find that the Swedish carbon tax is increasingly regressive over time, which is highly correlated with an increase in income inequality. Figure 1 shows the strong linear correlation between the incidence of the tax and the level of inequality across our sample period. The progressivity of the tax is measured using the Suits index (Suits, 1977), a summary measure of tax incidence that spans from +1 to -1. Positive (negative) numbers indicate that the tax is overall progressive (regressive) and a proportional tax is given an index of zero. The level of income inequality, in turn, is summarized by the Gini coefficient (0-100), with higher numbers indicating higher levels of inequality.
In 1991, when the Swedish carbon tax was implemented, income inequality was relatively low, with a Gini of 20.8. If we extrapolate, the results presented in Figure 1 indicate that the tax incidence in 1991 was proportional to slightly progressive. Since the early 1990s, however, Sweden has experienced a rise in inequality. Today, the Gini is around 28 and the carbon tax incidence is rather regressive. This can be a potential concern if people start to perceive the distribution of the tax burden as unfair and call for reductions in the tax rate.
Empirical Evidence Across High-Income Countries
Figure 2 presents the results of our analysis of previous studies of gasoline tax incidence across high-income countries. Again, we find a strong correlation with inequality; the higher the level of inequality, the more regressive are gasoline taxes. In the bottom-right corner, we locate the results from studies on gasoline tax incidence that have used US data. The level of inequality in the US has been persistently high, and the widespread assumption that gasoline and carbon taxation is regressive is thus based to a large part on studies of one highly unequal country. Looking across Europe we find that the tax incidence is more varied, with close to a proportional outcome in the (relatively equal) Nordic countries of Denmark and Sweden.
Figure 2. Gasoline tax incidence and income inequality in OECD countries
A carbon tax is economists’ preferred instrument to tackle climate change, but its distributional effect may undermine the political acceptability of the tax. This brief shows that to understand the likely distributional effects of carbon taxation we need to take into account the type of goods that are taxed – necessities or luxuries – and the level and direction of income inequality. Carbon taxation will be closer to proportional in European countries with low levels of inequality, whereas in countries with relatively high levels of inequality the carbon tax incidence will be regressive on necessities and progressive for luxury goods.
This insight may explain why we first saw the introduction of carbon taxes in the Nordic countries. Finland, Sweden, Denmark, and Norway all implemented carbon taxes between 1990-1992, and income inequality was relatively, and historically, low in this region at the time. Policymakers in the Nordic countries thus didn’t need to worry about possibly regressive effects. Looking across Europe today, many of the countries that have relatively low levels of inequality have either already implemented carbon taxes or, due to the size of their economies, have a low share of global emissions. In countries that are responsible for a larger share of global emissions – such as, the UK, Germany, and France – inequality is relatively high, and they may find it to be politically more difficult to implement carbon pricing as the equity argument becomes more salient and provides opportunities for opponents to attack the tax.
To increase the political acceptability and perceived fairness of carbon pricing, policymakers in Europe should consider a policy design that offsets regressive effects by returning the revenue back to households, either by lump-sum transfers or by reducing tax rates on labor income.
- Andersson, Julius and Giles Atkinson. 2020. “The Distributional Effects of a Carbon Tax: The Role of Income Inequality.” Grantham Research Institute on Climate Change and the Environment Working Paper 349. London School of Economics.
- Brännlund, Runar and Lars Persson. 2012. “To tax, or not to tax: preferences for climate policy attributes.” Climate Policy 12 (6): 704-721.
- Dahl, Carol A. 2012. “Measuring global gasoline and diesel price and income elasticities.” Energy Policy 41: 2-13.
- Feindt, Simon, et al. 2020. “Understanding Regressivity: Challenges and Opportunities of European Carbon Pricing.” SSRN 3703833.
- Gallet, Craig A. 2010. “The income elasticity of meat: a meta-analysis.” Australian Journal of Agricultural and Resource Economics 54(4): 477-490.
- Grainger, Corbett A and Charles D Kolstad. 2010. “Who pays a price on carbon?” Environmental and Resource Economics 46(3): 359-376.
- Hassett, Kevin A, Aparna Mathur, and Gilbert E Metcalf. 2009. “The consumer burden of a carbon tax on gasoline.” American Enterprise Institute, Working Paper.
- Sager, Lutz. 2019. “The global consumer incidence of carbon pricing: evidence from trade.” Grantham Research Institute on Climate Change and the Environment Working Paper 320. London School of Economics.
- Thomas, Sterner. 2012. Fuel taxes and the poor: the distributional effects of gasoline taxation and their implications for climate policy. Routledge.
- Suits, Daniel B. 1977. “Measurement of tax progressivity.” American Economic Review 67(4): 747-752.
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.
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.
This brief finds that whereas in the 1980s richer women had fewer children than women near the middle of income distribution in the US, it is no longer true today. It argues that the rise in inequality is the main driver for this change. Greater income inequality enables high-income families to outsource household production to lower-income people. Changes to minimum wage laws are thus likely to affect the fertility and career decisions of the rich.
“I have frequently been questioned, especially by women, of how I could reconcile family life with a scientific career. Well, it has not been easy.”
– Marie Curie, 1867-1934
Much has been made of women “leaning in” at work at a cost to their families. Indeed, this discussion has become more prevalent as women have surpassed men in higher education in most developed countries, and have entered prestigious careers en masse, a fact reinforced by public policy. For example, in 2012 the European Commission published a special report on women in decision-making positions, suggesting legislation to achieve balanced representation of women and men on company boards. One natural question to ask is, how high is the cost of a woman’s career to her family? This is a difficult, multifaceted, and even sexist question to ask.
High-income women have historically had fewer kids (Figure 1 for the year 1980). Social scientists’ leading explanations rely on the difficulty of combining children and a career. Under this view of the world, as more women focus on their careers, they have fewer children. On the other hand, the evidence shows that more educated (or wealthier) women produce more educated children. Given these two regularities, the majority of children are born to poorer mothers, and thus receive an inferior education. Moreover, this creates a feedback loop that depresses the average education through time making us question our ability to sustain a satisfactory average level of education.
Figure 1. Fertility rates by income deciles, 1980 and 2010
Notes: Calculated using Census and American Community Survey Data. The sample is restricted to white, non-Hispanic married women. Fertility rates are hybrid fertility rates, constructed by age-specific deciles. Deciles are constructed using total household income.
However, the negative relationship between family income and fertility ceases to hold after the 2000s. Figure 1 shows that for the year 2010, the cross-sectional relationship between income and fertility has flattened or even become a U-shape. Today, high-income women have higher fertility rates than those of women near the middle of income distribution. This is a result of a substantial increase in fertility among women in the 9th and 10th decile of family income: they increased their fertility by 0.66 & 0.84 children, respectively. The rise in fertility of high-skilled females was first documented in Hazan and Zoabi (2015), discussed in a previous FREE Policy Brief. The implications are profound; children are more likely to be born to wealthier or more educated mothers than in the past. This has a far-reaching impact on the future composition of the population.
How can we understand the change in fertility patterns over time? We argue that rising wage inequality played an important role. Data for the years 1980 and 2010 show that average real hourly wages, quoted in 2010 $ grew from $28 ($51) to $50 ($64) for women (men) in the 10th decile of the income distribution. This increase was accompanied by stagnant wages for women (men) in the 1st decile, precisely the people who are most likely to provide services that substitute for household chores (Figure 2). Thus, growing wage inequality over the past three decades created both a group of women who can afford to buy services that help them raise their children, and a group who is willing to supply these services cheaply. In a recent paper, we found that the increase in wage inequality from 1980 and 2010 can actually explain the rise in high income fertility (Bar et al. 2017). Moreover, this rise in inequality has resulted in a large increase in college attendance through the changing patterns of fertility. This is because more children are now born to highly educated mothers.
Figure 2. Wives’ Wage by Income Decile 1980 & 2010
Notes: Calculated using Census and American Community Survey Data. The sample is restricted to white, non-Hispanic married men. Deciles are constructed age-by-age, using total household income. Representative wages for each decile is the average of these decile-specific wages from ages 25 to 50.
Our new understanding of the interrelation between income inequality, the relative cost of home production substitutes, fertility pattern and educational choice induces us to rethink some typical economic debates. For instance, consider the minimum wage. The typical debate about the minimum wage is focused on how it affects lower wage individuals in terms of income and their ability to find work. However, if people who earn the minimum wage are disproportionately also those who help raise wealthier families’ children, or simply make running a household easier, then a higher minimum wage can make home production substitutes more expensive for high wage women, making it harder for them to afford both a family and a career. While indirect, this effect can be significant. Figure 3 shows the distribution of the real wage, relative to the minimum wage, both for the industries of the economy associated with home production substitutes and other sectors of the economy. The figure clearly shows that workers in industries associated with home production substitutes are concentrated around the minimum wage and thus are much more likely to earn wages that are close to the minimum wage.
Figure 3. The distribution of real wages, relative to the effective real minimum wage in each state and year, by sector of the economy
Notes: Data from Current Population Survey, 1980–2010, using all workers.
Interestingly, we calculate a change in the cost of home production substitutes following an increase of the Federal minimum wage from $7.25 to $15/hour, as suggested by Bernie Sanders during the 2016 presidential election. It turns out that this increase in the minimum wage would increase the cost of market services that substitute for household chores by about 21.1%. Indeed, the minimum wage has a strong impact on the average wages of workers producing home production substitutes. However, how does this increase affect the economy?
According to our theory, higher costs of home production substitutes would affect women’s choice of how to allocate their time between labor force participation and home production, including raising children. The higher cost of these substitutes induces women to buy less of them and spend more of their time producing home production goods. Indeed, we find that the increase in the minimum wage decreases fertility and increases mothers’ time at home, and more so for higher income households. The magnitudes are large. A 10th (5th) decile household decreases fertility by 12.8% (9.4%), while the mother spends 9.7% (2.5%) more time at home. Notice that these numbers are calculated under the assumption that women can adjust fertility. What about those who are “locked in” their fertility choice? We recalculate changes in mother’s time at home for these mothers using the model’s fertility in 2010 with the increased cost of market services that substitute for household chores. A 10th decile mother increases time at home by 25.9%, while a 5th decile mother increases it by 13.1%. These numbers are larger as the family has not had a chance to scale back fertility. The short run effect on labor supply is also very large. The average reduction in labor supply by women in the 9th and 10th deciles is 3.5%.
Whether an increase in the minimum wage is good or bad for the society is a big question. Not only does it lie beyond the scope of our theory, but also beyond the scope of social sciences. However, the one modest contribution we try to make is in observing that an increase in the minimum wage heightens the rivalry between a woman’s career and family. As such, it forces women to forgo one in order to opt for the other.
The sexist nature of our question lay in the implicit assumption that it is the mother’s responsibility to look after the children or home production in general, rather than the father’s. While once this was a nearly universal attitude, it is now increasingly common for fathers to take a more central role in childcare rather than leave everything to the mother. How does this change in gender roles affect our analysis? In modern times, both spouses’ careers are potentially affected by children, as both parents take a role in child care. Fathers are now facing the same tradeoffs as mothers did in the traditional gender role story: children vs. careers. As a result, marketization is more important than ever for career oriented parents.
Talk to a high wage family and no doubt that they’ll readily tell you how important their ability to purchase daycare, prepared food, or other help at home is to their success as parents. Perhaps parents don’t realize that the price of these goods are so intricately linked to inequality or the minimum wage, but the policy maker should bear in mind that these are key factors for career women and the family.
- Hazan and Zoabi (2015), “Do Highly Educated Women Have Smaller Families” The Economic Journal
- Bar, Hazan, Leukhina, Weiss, and Zoabi (In progress) “Is the Market Pronatalist? Inequality, Differential Fertility, and Growth Revisited”
The Value Added Tax (VAT) is the main source of revenue for the public budget in Poland. Though issues regarding VAT rates or tax settlement mechanisms are brought into the public debate in Poland on a regular basis, little is still known on the distribution of the VAT burden among Polish households. In this brief, we analyze the VAT relation to household income, consumption and demographic structure in Poland. We find that the VAT burden is inversely related to income, with the bottom ten percent of the population paying on average 16.3% of their income in VAT and the top income group paying only 6.8%. Larger households, such as those with children, pay about 11%-15% more VAT due to higher spending. However, as a result of different spending structures, the additional VAT burden of families with children is independent of the number of children and only marginally dependent on their age. These differences in the tax burden should be taken into consideration in the current debate on the possibility of unifying the VAT rates in Poland.
Inequality is considered to be a serious detrimental factor for societies’ development. It has been shown to undermine the health of the population, cause civil unrest, and slow down countries’ economic growth. Nizalova’s (2014) paper shows that the focus on the purely monetary component in the studies of inequality is too narrow. In Ukraine, which has had almost no change in income/wage inequality since 1994, the inequality in other workplace dimensions has soared. Nizalova finds that workers in establishments paying higher hourly wages have enjoyed (i) relatively greater reductions in the total workplace injury burden, (ii) greater retention of various benefits/amenities, and (iii) relatively larger increases in wage payment security (de-creased wage arrears). These findings document a high degree of an unequal shift away from work-centered provision of social services, not counter-balanced by the government, and highlight the importance of timely policy intervention as a possible cause of societal disturbances.
Inequality in income, health, and political rights has been on the agenda of many governments and international organisations. It has been shown to lead to tensions in society that can grow into civil unrest, and is named one of the top global risks in the World Economic Forum Global Risk Report, 2013. Country-level comparisons by epidemiologists have documented that more unequal countries have (i) higher rates of mental illness, drug use, and homicide, (ii) a larger incarceration rate, (iii) a larger share of obese population, (iv) higher school drop-out rates, lower socio-economic mobility, lower child wellbeing, and (v) a lower level of trust (Wilkinson and Pickett, 2010). At the macro level, inequality has also been shown to impede sustainable growth (Ostry and Berg, 2011).
Yet, in Ukraine, in spite of a number of continuing severe problems with population health, labor markets, infrustructure, etc., inequality has not been high on the agenda, except for occasional concerns raised by some international organisations and researchers. In our view, there are at least three reasons for this.
First of all, most of the attention in inequality discussions is paid to income inequality. However, in Ukraine after a significant increase in this indicator by the mid-nineties, there has been hardly any dynamics, with the exception of extreme increases in incomes/wealth of a few oligarchs.
Second, and this relates to inequality in any dimension, when people in power are predominantely concerned with self-enrichment, and citizens are not showing their dissatisfaction, or the government has “effective” means of dealing with this dissatisfaction (imprisonment, physical elimination, etc.), as has been the case in Ukraine for many years, those at the lower end of the income distribution have the least chances to attract attention.
Finally, we believe that the reason international organisations have not given much attention to Ukrainian inequality must be related to the fact that the situation in many areas of life has been so dire, i.e. the level of “well-offness” is so low throughout the distribution that the overall level was considered more important than the distribution.
A recent paper by Olena Nizalova (2014) examines the importance of the non-monetary dimensions of work in studies regarding inequality in total returns to work. Nizalova’s paper exploits a unique data set collected by the International Labour Office in Ukraine to study whether there has been a significant change in the non-monetary components of inequality. If this is the case, it can explain the growing tensions in society where the changes in income/wage inequality have been limited.
Non-monetary aspects of inequality
A few academic studies have explored the issue of income/wage inequality in Ukraine and Russia (Ganguli and Terrell, 2006; Galbraith, Krytynskaia, and Wang, 2004; Gorodnichenko, Peter, and Stolyarov, 2010; Lokshin and Ravallion, 2005), and found that, if anything, the change in inequality after 1995 has been quite modest. These results are in line with the dynamics of wage inequality in Ukraine presented in Figure 1, which pictures the ratio of wages in 2nd, 3rd, and 4th quartiles of the wage distribution against those in the 1st quartile.
Figure 1. Log Differences in Hourly Wages Relative to the Lowest Paying Quartile
Source: The authors own calculations based on Ukrainian Labour Flexibility Survey for the period 1994-2004.
However, the measures used in the earlier studies may not reflect the true inequality levels in the society. Indeed, they are omitting the contribution of the non-monetary dimension of work to the overall inequality.
The study of non-monetary working conditions is important for several reasons. First, work is central to people’s lives not only because a major share of household income in most countries comes from labor earnings (Guerriero, 2012), but also because individuals spend a considerable part of their time at work. Thus, earnings inequality can inappropriately reflect the true level of the total inequality in the labor market.
Second, the importance of this direction of research is further highlighted by the development of the ILO “Decent work agenda”. One of its aims is to promote both inclusion and productivity by ensuring that women and men enjoy working conditions, which satisfy several criteria. These criteria include that working conditions are safe, allow adequate free time and rest, take into account family and social values, provide for reasonable compensation in case of lost or reduced income, and permit access to adequate healthcare.
Lastly, inequality in working conditions, and in particular workplace injuries, may directly translate into income and wealth inequality, and, indirectly, affect inequality in future generations.
Ukraine: Inequality in Non-Monetary Work Dimensions Matters
The analysis in Nizalova (2014) shows that establishments that pay higher wages, tend to provide safer and, in general, better working conditions than establishments that pay lower wages. In addition, the latter are much more likely to experience difficulties with the payment of wages and have a higher percentage of workers with severe (more than 3 months) wage arrears. This suggests that the wage inequality may be further exacerbated by the inequality in non-monetary work dimensions.
A further distributive analysis demonstrates that the inequality in non-moneraty work dimensions has been changing noticeably over time. In particular, Figure 2 shows that the burden of workplace injuries, measured as total work days lost due to injuries per 100 Full Time Equivalent (FTE) employees, over time has shifted from being concentrated in the top part of the wage distribution to the lowest part (the way to interpret Figure 2 and all subsequent figures is as follows: the diagonal line in all figures corresponds to the equal distribution of the mentioned workplace characteristic across the wage distribution. The further the actual distribution curve (in red) is from the diagonal, the more unequal it is, with the curve below the diagonal indicating a concentration of the characteristic among higher paying enterprises and the curve above the line – concentration of the characteristic in the lower end of the wage distribution).
Figure 2: Concentration Curves – Total Injury Burden by Year
Source: The authors own calculations based on Ukrainian Labour Flexibility Survey for the period 1994-2004.
Moreover, the distribution of employer-provided benefits has also changed from being almost equally spread across the wage distribution to being more concentrated in the upper part (Figure 3).
Figure 3: Concentration Curves – Amenity Scores by Year
Source: The authors own calculations based on Ukrainian Labour Flexibility Survey for the period 1994-2004.
Notice that this result is not driven by any one particular amenity – it is observed across the whole range of indicators (for example, see Figures 4-6).
Figure 4: Distribution of Transportation Subsidy Provision by Year
Figure 5: Distribution of Kindergarden Subsidy Provision by Year
Figure 6: Distribution of Health Service Provision by Year
Similarly, wage arrears’ (non-payments) concentration has changed from being almost equally distributed across all wage levels to being more concentrated among lower paying establishments (Figure 7).
Figure 7: Distribution of Wage Arrears by Year
Further, the analysis of distributional shifts in the establishment characteristics over the corresponding period shows significant changes only with respect to firm size, export status, and some sectoral shifts.
Overall, the findings of the paper document an emergence of sizeable inequality in the workplace characteristics in the Ukrainian labor market: workers in poorly paying establishments are facing disproportionately larger risks of on-the-job injury, worse provision of amenities, as well as less security in timely payments of earning.
Although further research on causes of growth in multidimensional inequality in returns to work is required, this study provides two important lessons for the research community and policy makers.
First of all, it highlights the importance of a multi-dimensional approach to labor market returns, since a focus on monetary compensations only may significantly underestimate the true inequality in a society.
Secondly, it draws attention to the need of developing adequate governmental policies to address the inequality of workplace-centered provisions of social services during the transition to market economy. By prioritizing measures to facilitate provision of affordable housing, health care, kindergartens, as well as training opportunities, the government could mitigate increasing inequalities. This would allow the government to avoid significant tensions and conflicts in society, which is an important pre-requisite for ongoing sustainable development.
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