In economic literature the effect of minimum wage on the labour market and its relevance as an anti-poverty, equality-enhancing policy tool, is a matter of vigorous debate. The focus of this policy brief is a hypothetical effect on poverty rates, particularly among women, following an increase in the minimum wage in Georgia. A simulation exercise (Babych et al., 2022) by the ISET-PI research team shows that, in Georgia, a potential increase in the minimum wage is likely to result in an overall positive albeit small reduction in poverty rates in general. At the same time, women are likely to gain more from such minimum wage policy than men. The findings are consistent with the literature claiming that a minimum wage increase alone may not result in meaningful poverty reduction. Any minimum wage increase should thus be enhanced by other policies such as training programs increasing labor force participation among women.
Many countries around the world have enacted minimum wage laws. According to the International Labour Organization (ILO) “Minimum wages can be one element of a policy to overcome poverty and reduce inequality, including those between men and women” (ILO, 2023). In economic literature, the minimum wage debate has been particularly acute, with pros and cons of the minimum wage increases, their effect on the labor market, and their relevance as an anti-poverty and equality-enhancing policy tool fiercely contested in empirical studies and simulation studies. In this policy brief, we focus on the effect of a minimum wage increase in Georgia on poverty rates, and in particular poverty rates among women.
Minimum Wage Effects
According to the European Commission (2020) a number of benefits is associated with the introduction of minimum wage. These benefits include a reduction in in-work poverty, wage inequality and the gender pay gap, among others.
International evidence, however, cautions against considering an increase in minimum wage as the silver bullet to end poverty. A 2019 report by the International Labour Organization (ILO, 2019) shows that the incidence of poverty among the working poor is comparable to the incidence of poverty among individuals outside of the labor market. Therefore, even if an increase in minimum wages would lift all working poor out of poverty, a substantial number of poor would remain.
Moreover, minimum wage can have a potential adverse effect on employment of the most vulnerable by deterring firms from hiring low-wage, low-skilled labor (Neumark, 2018). The adverse employment effect will be stronger if current wages correspond more closely to the real productivity of labor. In such scenario companies would lose by retaining low-productivity workers and, likely respond to the increase in minimum wage by laying off workers, resulting in the loss of wages, rather than in their increase. On the other hand, if salaries are lower than the real productivity of the less productive workers, companies might still be able to profit from employing them and will not be forced to lay them off, resulting in a wage increase for low-wage workers.
Whether – and to what extent – the introduction of a minimum wage reduces poverty and/or assists low-income households then depends on how many individuals are going to lose their jobs, how many workers will maintain their jobs and receive a higher wage, and where these winners and losers are positioned along the distribution of family incomes.
With regard to employment effects, the results are not perfectly homogeneous. On the one hand, a large body of evidence suggests that minimum wages do lower the number of jobs accessible to low-skill employees (Sabia, Burkhauser and Hansen, 2012; Sotomayor, 2021; Neumark, 2018) On the other hand, some scholars argue that once the study design is changed to take into account the non-random distribution of minimum wage policies in different parts of the country in question, the “disemployment effect” of minimum wage policies (considering the example of United States) largely disappear (Allegretto et al., 2013; Dube et al., 2010).
With regards to poverty, a number of studies look at minimum wage as an anti-poverty policy tool for developing countries and consider its effectiveness in reducing poverty and/or inequality. For example, a study by Sotomayor (2021) suggests that poverty and income inequality in Brazil decreased by 2.8 and 2.4 percent respectively within three months of a minimum wage increase. Effects diminished with time, particularly for bottom-sensitive distribution measures, a process that is consistent with resulting job losses being more frequent among poorer households. The fact that the subsequent yearly increase in the minimum wage in Brazil resulted in a renewed drop in poverty and inequality shows that possible unemployment costs might be outweighed by benefits in the form of higher pay among working persons and – potentially – by positive spillover effects such as increased overall consumption.
Minimum Wage and Female Poverty
As in the case of poverty in general, there is some discrepancy in the literature on whether a minimum wage increase would help reduce poverty among women. Single mothers have been the focus of research in this regard since they are typically the most vulnerable low-wage workers, likely to be hurt by the loss of employment following an increase/ introduction of a minimum wage. Burkhauser and Sabia (2007) argue that the minimum wage increases in the U.S. (1988-2003) did not have any effect on the overall poverty rates, on the poverty rates among the working poor, or on poverty among single mothers. They argue that an increase in Earned Income Tax Credit (EITC), which provides a wage subsidy to workers depending on income level, tax filing status, and the number of children, would have a higher impact on poverty, in particular among single mothers.
In the meantime, Neumark and Wascher (2011) find that EITC and minimum wage reinforce each other’s positive effect for single women with children (boosting both employment and earnings), but negatively affects childless single women and minority men. Another study on the U.S. (Sabia, 2008) looked at the effect of minimum wage increases on the welfare of single mothers, finding that most of them were unaffected as they earned above-minimum wage. Single mothers with low-education levels did not see an increase in net incomes due to the negative effect on employment and hours worked: for low-skilled individuals, a 10 percent increase in minimum wage resulted in an 8.8 percent decline in employment and an 11.8 percent reduction in hours worked.
Yet another study (DeFina, 2008) focus on child poverty rates and show that minimum wage increases have a positive (reducing) impact on child poverty in female-headed families. The effect is small but significant (a 10 percent increase in the minimum wage decreases child poverty rates by 1.8 percentage points), controlling for other factors.
Ultimately, the effect of minimum wage on poverty among women or female-headed households is somewhat ambiguous. It depends on the poverty threshold used, other policy instruments (such as the EITC), existing incentives to enter employment and how, in the specific country of interest, labor laws may affect the employer’s cost of hiring (e.g. for France, see Laroque and Salanie, 2002).
The discussion is however relevant for countries like Georgia, where the wage gap between men and women is quite large, and where more women than men tend to work in low-wage and vulnerable jobs. While the overall poverty gap between men and women in Georgia is insignificant (mainly because poverty is measured at the household level), the gap becomes apparent when comparing female-headed households to male-headed ones. The poverty rates in the former case are nearly 2 percentage points higher in Georgia (20 percent vs. 18.3 percent in 2021). The poverty rates are the highest among households with only adult women (39.3 percent for all-female households vs. 20.1 percent overall in 2018).
A Simulation of a Minimum Wage Raise in Georgia
The Georgian minimum wage legislation dates back to 1999. The presidential decree N 351 from June 4, 1999 states that the minimum (monthly) wage that is to be set in Georgia is equal to 20 GEL (with some specific exceptions in the public sector). This is a non-binding threshold. Therefore, one has to think carefully what consequences might arise from raising the minimum wage to a much higher level. In addition to previously discussed aspects, one issue to keep in mind is the different average wages across different regions in Georgia. For example, a national minimum wage increase might have more of an impact in poorer regions, where both wages and incomes are lower, while it may still be non-binding in Tbilisi.
The ISET-PI research team (Babych et al., 2022) use Georgian micro data from the Labor Force Survey (LFS) and the Household Integrated Expenditure Survey (HIES), to simulate the effect of instituting a nation-wide minimum wage on both employment and poverty rates in different regions of Georgia. One focus area of the study was to analyze the effects of a minimum wage increase on female poverty. As with any exercise using a simulation approach, this study is subject to limitations imposed by the assumptions used, e.g. how much labor demand would respond to changes in the minimum wage, etc. The study considered two hypothetical thresholds of the minimum wage; 250 and 350 GEL respectively.
Figure 1. Share of private sector employees earning below certain thresholds, by gender, 2021.
The expected household income after the minimum wage increase was calculated and then compared to the poverty threshold (for each household in a standard way, using the “adult equivalence” scale). According to this methodology, any person who lives in a household which falls below the poverty threshold is considered to be poor. A “working poor” household is defined as a household below the poverty threshold where at least one adult is working.
Figure 1 shows that there is a substantial share of both men and women whose monthly wage income falls below the hypothetical minimum wage thresholds. In addition, women are more than two times as likely to be earning below these thresholds. However, the possible impact from an increased minimum wage on female vs. male poverty is not clear-cut. Since many women are part of larger households which include adult males, their possible income losses/gains may be counterbalanced by income gains/losses of male family members, leaving the overall effect on household income ambiguous.
In addition, poverty rates are not likely to be much affected by a minimum wage increase if most poor households are “non-working poor” (where adult family members are either unemployed or outside of the labor force), a consideration particularly relevant for Georgia. The share of poor individuals who live in “working poor” households (with at least one household member employed) is just 41 percent nationally (and 35 percent in rural areas), meaning that close to 60 percent of poor individuals nationwide (and 65 percent in rural areas) are not likely to be directly affected by minimum wage increases.
Female vs. Male Poverty: Scenarios Following a Minimum Wage Increase
As one can see in Figure 2, increased minimum wages tend to reduce poverty, but the impact is not larger than one percentage point. Not surprisingly, females benefit more than males (0.3 and 0.8 percentage points vs. 0.2 and 0.9 percentage points poverty reduction for men and women respectively, under different threshold scenarios). The maximum positive impact on poverty reduction is observed under a higher minimum wage threshold.
Figure 2. Estimated impact on poverty rates, based on the national subsistence minimum.
The impact of an increased minimum wage on the expected median consumption of households doesn’t exceed a few percentage points either, as illustrated in Figure 3.
The impact is greatest in urban areas other than Tbilisi (between a 2.5 percent and a 4.2 percent increase in median consumption relative to the status quo). The lower impact in Tbilisi is most likely driven by relatively higher wages, while the low impact in rural areas is likely driven by lower participation in wage employment.
In the hypothetical case of Georgia, an impact of a minimum wage increase on poverty rates is expected to be limited, in line with the literature. In our study this finding is mostly driven by the fact that only a relatively small share of poor individuals live in “working poor” households (about 40 percent, nationally). The remaining 60 percent of poor individuals will be unaffected by the reform.
The quantitative impact on female and male poverty is estimated to be low, although the female poverty rate reduction is somewhat larger than among males.
It is important to note that the analysis doesn’t consider possible differential impacts on different groups of vulnerable families, such as families with small children and single mothers with small children. Some reasons to why groups of households may or may not be affected by the hypothetical minimum wage increase, based on their employment status and other factors, have been discussed above.
Another important point is that our exercise should not be seen as an argument against an increase of the minimum wage in Georgia. Instead, it suggests that such a reform would not have much of an impact if done in isolation. Indeed, the existing literature on minimum wage seems to be in consensus on the fact that minimum wage policies would be more impactful if supplemented by the following measures:
- Maintain and expand targeted social assistance to groups that do not benefit or that are losing jobs/incomes as a result of the minimum wage changes
- Have job re-training programs in place to help laid-off workers
- Have human capital investment programs in place to increase workers’ productivity, in particular for low-productivity sectors
- Consider other support instruments targeted toward the most affected groups of the population such as single working mothers etc.
These recommendations should be incorporated in the policy making regarding minimum wages in Georgia.
We are grateful to Expertise France for financially supporting the original report (Babych et al., 2022), which features some of the results and points raised in this policy brief.
- Allegretto, S., Dube, A., Reich, M., & Zipperer, B. (2017). Credible Research Designs for Minimum Wage Studies: A Response to Neumark, Salas, and Wascher. ILR Review, 70(3), 559–592. https://doi.org/10.1177/0019793917692788
- Babych, Y., Pignatti, N., Chapichadze, A., Lobzhanidze, G. and Shubitidze, E. (2022). Report on Minimum Wage in Georgia. ISET Policy Institute. Unpublished manuscript.
- Belman, D. and Wolfson, Paul J. (2014). What Does the Minimum Wage Do? Kalamazoo, MI: W.E. Upjohn Institute for Employment Research. https://doi.org/10.17848/9780880994583
- Burkhauser, R. V. and Sabia, J. J. (2007). The effectiveness of minimum‐wage increases in reducing poverty: Past, present, and future. Contemporary Economic Policy, 25(2), 262-281. https://doi.org/10.1111/j.1465-7287.2006.00045.x
- DeFina, R. H. (2008). The impact of state minimum wages on child poverty in female-headed families. Journal of Poverty, 12(2), 155-174. https://doi.org/10.1080/10875540801973542
- Dube, A., T.W. Lester, and M. Reich. 2010. Minimum Wage Effects Across State Borders: Estimates Using Contiguous Counties. The Review of Economics and Statistics, 92(4), 945–964. https://doi.org/10.1162/REST_a_00039
- European Commission. (2020). Proposal for a directive of the European parliament and of the council on adequate minimum wages in the European Union. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52020PC0682GEOSTAT
- International Labour Organization (ILO). (2023). https://www.ilo.org/global/topics/wages/minimum-wages/definition/lang–en/index.htm
- International Labour Organization (ILO). (2019). The working poor or how a job is no guarantee of decent living conditions chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://www.ilo.org/wcmsp5/groups/public/—dgreports/—stat/documents/publication/wcms_696387.pdf
- Geostat. (2021). https://www.geostat.ge/en
- Laroque, G. & Salanié, B. (2002). Labour market institutions and employment in France. Journal of Applied Econometrics, 17(1), 25-48. https://doi.org/10.1002/jae.656
- Neumark, D. & Wascher, W. (2011). Does a higher minimum wage enhance the effectiveness of the Earned Income Tax Credit? ILR Review, 64(4), 712-746. https://doi.org/10.1177/001979391106400405
- Neumark, D. (2018). Employment effects of minimum wages. IZA World of Labor 2018: 6. https://wol.iza.org/articles/employment-effects-of-minimum-wages/long
- Sabia, J. J., Burkhauser, R. V. & Hansen, B. (2012). Are The Effects Of Minimum Wage Increases Always Small? New Evidence From A Case Study Of New York State. Sage Publications, 350-376. https://doi.org/10.1177/001979391206500207
- Sabia, J. J. (2008). Minimum wages and the economic wellbeing of single mothers. Journal of Policy Analysis and Management, 27(4), 848-866. https://doi.org/10.1002/pam.20379
- Sotomayor, O. J. (2021). Can the minimum wage reduce poverty and inequality in the developing world? Evidence from Brazil. World Development 138. https://doi.org/10.1016/j.worlddev.2020.105182.
Disclaimer: Opinions expressed during events and conferences are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
This brief summarizes the insights shared in the online workshop “Dimensions of Well-being“, where participants presented and discussed their latest research relating to the dimensions of well-being. The two-day workshop was organized by the Stockholm Institute of Transition Economics (SITE) as part of the Forum for Research on Gender Economics (FROGEE) and took place on 28-29 June, 2021.
It has been roughly 18 months since the first cases of Covid-19 were reported in Europe. So far the total number of deaths worldwide has passed 4.4 million (John Hopkins University, 2021), unemployment is trending upward in most countries (ILOSTAT, 2021), roughly half of the world’s students have been affected by school closures (UNESCO, 2021), and an alarming increase in domestic violence has been reported across the globe (UN Women, 2020).
It is safe to say that this pandemic crisis has had a multifaceted impact on our lives. Identifying what factors contribute to overall well-being and understanding how they interact with one another is central in designing and implementing solid and effective recovery policies.
Stockholm Institute of Transition Economics invited international experts to an online workshop where they discussed and presented their recent research relating to the dimensions of well-being. The workshop was organized as part of the Forum for Research on Gender Economics (FROGEE).
Well-being in a Pandemic
The government response policies intended to contain the spread of Covid-19 have undoubtedly had a major impact on society. However, estimating the overall effect of these policies on individuals’ well-being is not necessarily straightforward. Economic support policies likely have a positive effect on income and decrease poverty. But at the same time, other responses such as lockdowns and mobility restrictions may not only have an opposite effect on these outcomes but also influence other known determinants of well-being such as social life or education.
Anthony Lepinteur, researcher at the University of Luxembourg, presented his recent work on the well-being consequences of the pandemic policy responses in Germany, France, Spain, Italy, and Sweden. Lepinteur and co-authors link survey data on subjective well-being measures to data on government economic policy and stringency indices. The former index records financial policies such as income support, furlough schemes, and debt relief while the latter measures the strictness of Covid-19 containment and closure policies. The results show that more stringent policies reduce life satisfaction, and this negative effect is stronger for women, the unemployed, and those with relatively high incomes. Economic support policies are found to have no significant impact on reported life satisfaction.
As many countries have experienced major disruptions in many sectors of their economy, concerns have been raised about deteriorating labor markets and the effect this might have on living conditions and, ultimately, the well-being of individuals. Knar Khachatryan, associate professor at the American University of Armenia, shared research studying the impact of Covid-19 on multidimensional deprivation from labor market opportunities in Armenia. Knachatryan and co-authors base their analysis on two surveys from 2018 and 2020. To measure labor market opportunities, they adopt the “Alkire-Foster method” to develop a multidimensional index of labor market deprivation – a basket of indicators explaining an individual’s degree of labor market opportunities (e.g. education, employment status, income, type of work contract, and union membership). With respect to this index, they find that education is the most important determinant of multidimensional labor market deprivation – those having less than a bachelor’s degree are very likely to be deprived in terms of labor market opportunities. The results also show that the pandemic has widened the gender gap in labor opportunities. The number of people classified as deprived has increased more for women than men during the pandemic. This is primarily because women experienced stronger income reductions and more frequent job losses.
Thesia Garner, researcher at the U.S. Bureau of Labor Statistics, discussed how ex-ante levels of well-being have affected the outcomes of economic support policies during the pandemic. More specifically, her study investigates the role of individual’s well-being in determining their reported use of economic impact payments (EIP) in the U.S. Garner and co-author assess well-being using both objective measures (e.g. income sources, employment status) and subjective ones (e.g. depression, financial difficulty, expectations about job-loss or eviction). The findings show that those who report lower levels of subjective well-being are more likely to use the EIP to pay off debt, and this likelihood increases as the well-being measures worsen. Respondents who report having experiences of financial difficulty and negative expectations about the economy are more likely to spend the stimulus on nondurables and tend to allocate it to a wider range of spending categories.
In contrast to the U.S. and most other countries in the world, Belarus’ government offered very little support to its citizens during the pandemic. Lev Lvovskiy, researcher at BEROC, presented findings on how different sectors of the Belarusian economy and society were affected by the pandemic. Using the BEROC/Satio survey data, Lvovskiy and co-authors examine that the country still had sharp drops in mobility and economic shocks mainly caused by lockdowns of major trade partners. The pandemic significantly increased the probability of income reductions and they show that financial distress associates with the incidence of depression of Belarusians.
Gender and Wellbeing
Another central topic discussed at the workshop concerned the gender aspects of well-being and other related topics from gender economics.
An essential channel through which gender differences in well-being can arise is unequal representation in politics. Sonia Bhalotra, professor at the University of Warwick, presented a study on the relationship between maternal mortality and women’s political power in 174 countries. Maternal mortality is the leading cause of death and disability for women aged 15-44, and significantly higher in low-income countries – at levels similar to what high-income countries had in the early 1900s. Bhalotra and co-authors document that the costs of providing access to prenatal health services, antibiotics, and skilled birth attendance are relatively low. They therefore argue that there are likely other barriers to adopting these solutions. Male policymakers might have a weaker preference for preventing maternal mortality or less information on its prevalence and treatment. To gain insight, the authors use a staggered event-study approach and study the effect of gender quota implementations on the maternal mortality ratio (MMR, maternal mortality per birth). They find that, in countries that adopted quotas, the MMR declined by 10% following implementation, and this effect is stronger for larger quotas. Focusing on the mechanisms, the results show that gender quotas lead to a 5-8 percentage point (p.p.) increase in skilled birth attendance, a 4-8 p.p. increase in prenatal care utilization, 6-7 % decline in birth rates, and an increase in girl’s education by 0.5 years.
Elizaveta Pronkina, researcher at Université Paris-Dauphine, also shared findings relating to gender and politics but from a historical perspective. Her research studies historic institutional differences across communist regimes and women’s work experiences. The paper focuses on Lithuania and Poland, two countries that experienced different gender policies under a communist regime. After the second world war, Lithuania was controlled by the central government of the Soviet Union while Poland’s government was able to preserve its independence although being part of the Soviet bloc. Based on anecdotal evidence, the two countries had the same religious and political policies but different enforcement – Lithuania faced a hard and Poland a soft form of communism. To isolate the impact of the Soviet policies on women’s life decisions and account for differences in the countries’ pre-communist era, the authors only include regions that were part of the Russian empire until the end of the first world war. The findings show that women living under the Soviet regime were more likely to educate themselves and have on average two additional years of work experience (by 50 years of age).
A productive environment and reliable social interactions at work are also likely to be formative elements of people’s well-being, and gender might factor in here. Yuki Takahashi, PhD candidate in economics at the University of Bologna, presented his paper on how being corrected by others affects one’s willingness to collaborate with them in future work, as well as gender differences in these responses. Takahashi conducts a quasi-experimental design in which roughly 3000 participants individually and collectively solve a puzzle. The setting allows the researcher to observe individual ability, number of corrections, as well as whether the corrections were good (i.e., a mistake was corrected), or bad (i.e., a good move was corrected). The study analyzes how the different factors affect an individual’s likelihood of being selected as a collaborator in a last puzzle-solving stage where both participants win cash earnings based on joint performance. The results show that both genders respond negatively to a correction, but women more so than men. Men are less likely to collaborate with a person who has corrected their mistake, particularly men with high ability. The gender of the corrector is found not to matter.
Domestic violence (DV) is another gender aspect of well-being that has become particularly concerning during the pandemic. For many victims, lockdowns and curfews have meant more exposure to their perpetrator. Mobility restrictions have also implied more social isolation from family members and friends as well as increased economic distress, two other factors known to exacerbate DV. In a preliminary study presented by Damian Clarke, associate professor at the University of Chile, he and co-authors address the relationship between DV and quarantines in Chile. They use longitudinal data on police DV hotline calls and use of women’s shelters to measure DV incidence, criminal complaints of DV to police to measure reporting, and mobile phone data to measure mobility. Exploiting municipal variation in the timing of lockdown entry and exit, the study shows that lockdowns lead to more DV incidence and less reporting. DV shelter use increased on average by 11% with entry and reversed with exit. DV calls to the police hotline increased by 86% and persists after lockdown exit. DV crime reports decrease by 5% and increases by 10% with exit. Moreover, the authors document that lockdowns activate both DV mechanisms – increased economic distress and decreased mobility. In municipalities where lockdowns had a stronger impact on unemployment and mobility, they also find larger changes in DV.
Expectations About the Future and Parenthood
Two other studies presented at the workshop discussed the relationship between future expectations and well-being. Claudius Garten, researcher at the Technical University of Dortmund, presented findings on the role of homeownership. Garten and co-authors utilize individual-level survey data from 2007 covering 14 European countries. It contains information on homeownership status and wellbeing measures expressed as respondents’ expectations about future living standards five years from today. They find that expectations about future living standards are higher among homeowners relative to renters and strongly associated with the value of housing assets, suggesting that material security through housing ownership works as a channel for future wellbeing. Garten further argued that since most countries included in the sample have experienced rising house property prices and increased rents since 2007, the divergence between renters and owners is likely to be even more significant today, especially in urban areas.
The second presentation that discussed expectations about well-being in later life was by Alina Schmitz, researcher at the Technical University of Dortmund. Unlike housing, which is seen as a form of material security, Schmitz’s study focuses on the role of health infrastructure quality. Availability of care services may be seen as a safety net in case of illness and care dependency and should thus have a positive effect on wellbeing. The study performs a multilevel analysis on the individual, regional and, country level using micro-survey data on individuals’ life satisfaction and macro-data on the availability of long-term care beds, covering 96 regions from six European countries in 2015. The results show that the quality of care infrastructure is significantly related to the wellbeing of those aged above 50. Moreover, care infrastructure is particularly important for the wellbeing of those with health limitations (i.e. those who require that infrastructure either now or in the future).
Parenthood is another factor that is commonly thought of as a source of happiness. Contrary to this idea, European populations are aging rapidly and the young today have fewer children than the generations before them. The reason why people choose to have few children could be several – e.g. high opportunity costs and/or low benefits of having a large family. Is the fertility rate we see in the developed world today a result of the well-being-maximizing decisions of individuals? This is the main question asked in the paper presented by Barbara Pertold-Gebicka, assistant professor at the Institute of Economic Studies at Charles University. Her study utilizes European survey data to investigate the effect of having an additional unplanned child in five developed countries. To measure the effect of an additional unplanned child and deal with the fact that happy individuals tend to have more children, Pertold-Gebicka and co-author compare people who had twin births in their second pregnancy with parents of two children. Apart from life satisfaction, the most common wellbeing measure, the authors construct a second measure of wellbeing denoted as the happiness index – normalized value summarizing five questions about feelings over the last 5 months, interpreted as the relative frequency of positive feelings. They find no significant effect of having a third child on the well-being of parents. However, when separately looking at groups divided by age of children, they find that the effect of having an additional child on well-being is negative for fathers of younger children and positive for those of teenagers. For the parents of younger children, they show that the negative effect of having a third child is likely driven by increased feelings of nervousness and problems relating to accommodation.
Measuring Inequality and Social Deprivation
Some aspects of wellbeing such as feelings of unfairness or social connections can be quite ambiguous to study as they depend on context and are hard to quantify.
Nicolai Suppa, researcher at the Centre for Demographic Studies at the UAB, presented his research aimed to improve the measurement of deprivation in social participation (DSP) and complementing previous work with an additional outcome variable measuring a different dimension of deprivation. The study uses German survey data to measure how often common social activities are performed and then uses an intersectional approach (similar to the “Alkire-Foster method”) to assign individuals as deprived based on if and how often they practice these activities. The findings show that while the DSP measure correlates positively both with income poverty and material deprivation measures, it identifies a different sample of individuals. Being deprived in terms of social participation is associated with a significant loss of life satisfaction, a magnitude comparable to the loss of being unemployed.
Ingrid Bleynat, researcher at Kings College London, also discussed how to improve measurement but presented a study focusing on a different dimension of well-being, inequality. While quantitative approaches may give little account of the detailed mechanisms of inequality and its multidimensionality, qualitative studies often focus on a subset of the population which make results difficult to generalize. Bleynat and co-authors suggest a mixed approach, combining quantitative and qualitative assessments of inequality. They utilize neighborhood-level data on average household income in Mexico City to randomly select five households in each decile of the income distribution and conduct semi-structured interviews in these households to better understand the nuances of inequality. Based on these interviews they construct two qualitative measures. The first is called inequality of lived experiences and measures qualitative experiences in work, education, and health services across the income distribution. The second is called lived experiences of inequality, and measures feelings of stigma, discrimination, and social hierarchy across gender, ethnicity and location. The quantitative data confirms that Mexico City is highly unequal across the income distribution in terms of not only income but also social factors such as housing, health and food security. The results concerning the qualitative measures, such as inequalities in lived experiences or lived experiences of inequality confirm the existing understanding – e.g., that households belonging to the lower deciles are more likely to be mistreated in the public health sector, have a hostile school environment, and worse working conditions, or that women across the income distribution bear most of the childcare responsibilities, – but provide nuanced details on the interaction between material inequality and the reported experiences.
There is no doubt that the impact of Covid-19 on our well-being has been many-sided, and the presentations of the workshop have clearly demonstrated the broad spectrum of related problems and concerns, as well as their variation across institutional, social, political, economic, and cultural contexts.
Although we are well underway, further research and comprehensive data collection on how people have coped with and responded to the pandemic is needed to design sensible recovery policies and incentivize governments to implement them.
List of Participants
- Sonia Bhalotra (University of Warwick)
- Ingrid Bleynat (King’s College London)
- Damian Clarke (University of Chile)
- Thesia Garner (US Bureau of Labor Statistics)
- Claudius Garten (TU Dortmund)
- Barbara Pertold-Gebicka (Charles University)
- Knar Khachatryan (American University of Armenia)
- Anthony Lepinteur (University of Luxembourg)
- Lev Lvovskiy (BEROC)
- Elizaveta Pronkina (University Carlos III)
- Alina Schmitz (TU Dortmund)
- Nicolai Suppa (Centre for Demographic Studies at the UAB)
- Yuki Takahashi (University of Bologna)
Part 1 | Online Workshop on Dimensions of Well-being
Part 2 | Online Workshop on Dimensions of Well-being
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.
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.
Over the last five years, Polish families with children have been entitled to a relatively generous benefit of approximately €110 per month and child. Initially granted for every second and subsequent child in the family regardless of income and for the first child for low-income families, the benefit was made fully universal in 2019. With the total costs amounting to as much as 1.7% of Poland’s GDP, the benefit reaches the parents of 6.7 million children and significantly affects these families’ position in the income distribution. Its introduction has led to a substantial reduction in the number of children living in poverty. However, since families with children are more likely to be among households in the upper half of the income distribution, out of the total cost of the benefit, a proportionally greater share ends up in the wallets of high-income families. While the implementation of the benefit has significantly changed the scope of public support to families in Poland, there are many lessons to be learnt and some important revisions to be undertaken to achieve an effective and comprehensive support system.
One of the principal commitments in the 2015 Polish parliamentary elections of the then-main opposition party – Law and Justice (Prawo i Sprawiedliwość, PiS), was introducing a generous child benefit. The purpose of this benefit was to support families and encourage higher fertility, which had been one of the lowest in the European Union for a long time. Following PiS’s electoral victory, the new government introduced a semi-universal child benefit of approximately €110 per month (exactly 500 PLN per month, thus the Polish nickname of “the 500+ benefit”) in April 2016. Initially, the benefit was granted for every second and subsequent child in the family regardless of income and for the first child in low-income families. Since July 2019 (nota bene three months before the next parliamentary elections), it was made universal – all parents with children under the age of 18 are entitled to 500PLN per month for every child. The benefit is relatively generous (for comparison, it accounts for 17.9% of the minimum wage in Poland in 2021), and universal coverage implies substantial costs for the government budget, totalling about 41bn PLN per year (1.7% of the Polish GDP).
Over the last five years, a number of analyses of the consequences of the benefit’s introduction have been conducted. These have encompassed a variety of socio-economic outcomes for Polish families with children – from a comprehensive assessment of these consequences (Magda et al. 2019) to analyses focused on specific effects of the benefit, such as the impact on women’s economic activity (Magda et al. 2018, Myck 2016, Myck and Trzciński 2019) or poverty (Brzeziński and Najsztub 2017, Szarfenberg 2017). The fifth anniversary of the benefit’s implementation seems to be a good opportunity for a summary and update of previous evaluations of the distributional consequences and financial gains for households resulting from this policy (an overview of all the previous CenEA analyses of the child benefit can be found in CenEA 2021). The results presented in this brief are based on analyses conducted using the Polish microsimulation model SIMPL on data from the 2019 CSO Household Budget Survey (more details in Myck et al. 2021). It should be noted that the analyses do not account for the impact of the Covid-19 pandemic on the material situation of households, as the data was collected before the outbreak. As previous studies suggest, the consequences for households of the pandemic and the series of resulting lockdowns varied greatly depending on various factors, such as the sources of income, sector, and form of employment, thus making it impossible to estimate precisely (Myck et al. 2020a).
The Child Benefit on Household Incomes
Due to its universal character, the distributional consequences of the child benefit payments are directly related to the position of households with children aged 0-17 in the income distribution relative to those without. As households with children are more likely to be in the upper half of the distribution (taking into account the demographic structure of households through income equivalisation), out of the total budget expenditure on the benefit, a proportionally greater share goes to high-income families (Table 1). Families with children in the two highest income decile groups (i.e., belonging to the 20% of households with the highest income) currently receive almost 25% of the total annual expenditure on the child benefit. On the other hand, among the 20% of households with the lowest incomes, families with children receive only 11.7% of the total annual cost of the benefit.
Table 1. Household gains resulting from the child benefit by income decile groups
Compared to the poorest 10% of households, families with children in the highest income decile receive 2.5 times more of the total funds allocated to the benefit.
It is also worth noting that the proportion of benefit in the disposable income is relatively evenly distributed if one considers all households in a given decile (with and without children). The proportional benefits in the first nine income deciles are in the range of 3.4% and 5.3% and only fall to 1.9% in the highest income group. A significant differentiation of the benefit in proportional terms can only be seen when accounting solely for households with children within each income decile. The benefit amounts to as much as 26.9% of the disposable income of households with children in the first decile, and the effect falls in subsequent groups – from 18.9% and 16.4% in the second and third deciles, to only 4.1% in the top decile.
The Child Benefit and the Position of Families With Children in the Income Distribution
Taking into account the magnitude of the policy, the position of families with children in the income distribution relative to other households may, to some extent, be the result of receiving the benefit itself. It is, therefore, reasonable to ask what role the benefit plays in shaping this relative position in the income distribution. Figure 1 presents the number of children under 18 in households by income decile groups when the benefit is included in total household income (left panel) and in a hypothetical scenario when the child benefit payment is withdrawn (right panel). As we can see, the withdrawal of the benefit would cause a substantial change in the relative position of families with children in the income distribution, significantly increasing the number of children in the lowest income groups. While in the current system, the poorest 10% of households include 342 thousand children aged 0-17, this number would be 553 thousand in a system without the benefit. However, the benefit also changes the relative position of high-income households with children. In the current system, the richest 10% of households include 762 thousand children. Subtracting the benefit from their household income would reduce this number to 687 thousand.
Figure 1. The child benefit and its impact on the position of families with children in the income distribution
Thus, even when taking into account the income distribution without the benefit, the number of children among the richest 10% of households is almost 25% higher than the number of children in the poorest 10% of households. Looking at the income distribution after including the benefit, there are more than twice as many children in the richest 10% of households than among the poorest 10%. This, in turn, inevitably means that, out of the total cost of the benefit, over twice as much money is transferred to households belonging to the richest deciles as compared to the funds transferred to families belonging to the poorest 10% of households.
With the total costs amounting to 1.7% of Poland’s GDP, the child benefit introduced in April 2016 substantially raised the level of direct financial support for families with children. As shown in this brief, the benefit reaches the parents of 6.7 million children aged 0-17 and significantly affects the position of these families in the income distribution. While, on the one hand, a large proportion of families with children have incomes high enough to be in the highest income groups even without this support , the lowest decile group would include over 200 thousand more children in the absence of the benefit. This confirms that the child benefit alone contributes to a significant improvement in the material conditions of families with children and to a significant reduction in poverty (cf. Brzezinski and Najsztub, 2017; Szarfenberg, 2017). However, the scale of this reduction is modest given the size of the resources involved. This is not surprising given that the bulk of the total costs of the benefit comes from the 2019 program extension to cover all children regardless of family incomes, which largely ended up in the wallets of higher-income families (Myck et al. 2020b). One of the key goals of the benefit upon introduction was to increase the number of births in Poland by easing the material conditions of families with children. Yet despite a radical increase in the level of support, the number of births in Poland over the period 2017-2020 has essentially remained the same as that forecasted by the Central Statistical Office in its long-term population projection of 2014 (Myck et al. 2021). It is thus difficult to consider the benefit a success in terms of this major objective. Moreover, the withdrawal of the income threshold has largely eliminated the negative disincentive effects of the benefit with regard to employment (Myck and Trzcinski 2019). However, it is unclear whether the post-pandemic economic situation will allow for an increase in female labour force participation, which declined following the introduction of the benefit in 2016 (Magda et al., 2018).
The effects of every socio-economic programme should be assessed by comparing cost-equivalent alternatives. Despite all gains the “500+” child benefit has brought to millions of families in Poland over the last five years, the flagship programme of the ruling Law and Justice party does not fare well in this perspective. The need for change seems much broader than the reform of the benefit alone. The benefit was introduced on top of two other financial support mechanisms focused on families with children, namely family allowances and child tax credits, and the three elements have been operating in parallel since 2016. A number of suggestions on creating a streamlined, comprehensive system have been made a long time ago (e.g., Myck et al. 2016). However, a major restructuring of the entire support system with clearly defined socio-economic policy goals in mind seems all the more justified now, when many families may require additional assistance due to the difficult financial situation related to the Covid-19 pandemic.
This Policy Brief draws on the CenEA Commentary published on 31.03.2021 (Myck et al. 2021). It has been prepared under the FROGEE project, with financial support from the Swedish International Development Cooperation Agency (Sida). The views presented in the Policy Brief reflect the opinions of the Authors and do not necessarily overlap with the position of the FREE Network or Sida.
- Brzeziński, M., Najsztub, M. 2017. The impact of „Family 500+” programme on household incomes, poverty and inequality”, Polityka Społeczna44(1): 16-25.
- CenEA 2021. Childcare benefit 500+ in CenEA analyses. https://cenea.org.pl/2021/04/06/childcare-benefit-500-in-cenea-analyses/
- Magda, I., Brzeziński, M., Chłoń-Domińczak, A., Kotowska, I.E., Myck, M., Najsztub, M., Tyrowicz, J. 2019. „Rodzina 500+– ocena programu i propozycje zmian”. (“Child benefit 500+: the evaluation of the programme and suggestions for changes”), IBS report.
- Magda, I., Kiełczewska, A., Brandt, N. 2018. “The Effects of Large Universal Child Benefits on Female Labour Supply”, IZA Discussion Paper No. 11652.
- Myck, M. 2016. “Estimating Labour Supply Response to the Introduction of the Family 500+ Programme”. CenEA Working Paper 1/2016.
- Myck, M., Król, A., Oczkowska, M., Trzciński, K. 2021. “Świadczenie wychowawcze po pięciu latach: 500 plus ile?”(„The child benefit after 5 years – 500 plus what?”), CenEA Commentary 31/03/2021.
- Myck, M., Kundera, M., Najsztub, M., Oczkowska, M. 2016. „25 miliardów złotych dla rodzin z dziećmi: projekt Rodzina 500+ i możliwości modyfikacji systemu wsparcia” („25 billion PLN to families with children: Family 500+ programme and possible modifications of the family support system”), CenEA Commentary 18/01/2016.
- Myck, M., Oczkowska, M., Trzciński, K. 2020a. “Household exposure to financial risks: the first wave of impact from COVID-19 on the economy”, CenEA Commentary 23/03/2020.
- Myck, M., Oczkowska, M., Trzciński, K. 2020b. „Kwota wolna od podatku i świadczenie wychowawcze 500+ po pięciu latach od prezydenckich deklaracji” („Tax credit and child benefit 500+ after five years since electoral declarations”, in PL), CenEA Commentary 22/06/2020.
- Myck, M., Trzciński, K. 2019. “From Partial to Full Universality: The Family 500+ Programme in Poland and its Labor Supply Implications”, ifo DICE Report 17(03), 36-44.
- Szarfenberg, R. 2017. “Effect of Child Care Benefit (500+) on Poverty Based on Microsimulation”, Polityka Społeczna 44(1): 25-30.
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.
Most reports on the labor-market effects of the first wave of COVID-19 have pointed to women, low-skilled workers and other vulnerable groups being more affected. Research on the topic shows a more mixed picture. We contribute to this discussion. Using monthly official unemployment data in Sweden we find that across wage levels, occupations with lower salaries display higher increases in unemployment, and low-wage occupations are also more difficult to do from home. The job loss probability is also higher in sectors with a higher concentration of workers born outside of the EU and those aged below 30. But we find no evidence of a gender unequal impact in Sweden. Overall, our results point to higher effects for low-wage groups but small gender differences overall.
The ongoing Covid-19 pandemic has affected the health of millions of people worldwide. But it has also had an enormous impact on economic and living conditions through government policies aimed at containing the spread of the infection. While, at the onset of the pandemic, government officials, mainstream media, and even celebrities labeled COVID-19 “the great equalizer” (Mein, 2020), the reality has proven quite different, with the most vulnerable groups of the population appearing to be the most harmed by both the health and the economic crises (see, for instance, The World Economic Forum, Joseph Stiglitz in this IMF article, and The World Bank). In this brief, we focus on one specific economic impact of the pandemic, namely its effect on unemployment status, and we study the extent to which this impact has been unequal across different groups of the Swedish society. Our analysis uses administrative data and segments the population by wage, gender, age, and foreign-born status.
Covid-19 and Inequality in the Labor Market
An extensive review of the emerging literature on the effect of the pandemic on different kinds of inequality is beyond the scope of this brief. However, a number of studies are especially relevant to put our analysis in context, as they are focused on the unequal labor market impacts of the crisis and study real-time data. Based on these studies, a number of patterns emerge. First, the effect of the pandemic on the increased probability of job loss appears stronger for low-skilled workers, as proxied by education level (see e.g., Adam-Prassl et al., 2020, Gaudecker et al. 2020, Casarico and Lattanzio 2020). Gaudecker et al. (2020) also observe that in the Netherlands the negative education gradient has been mitigated by the government identifying some sectors of the economy as essential since some of these sectors are characterized by a high concentration of low-educated workers. Second, the evidence of unequal gender impacts on the probability of job loss is mixed. While survey information from the UK and the US reveals that labor market outcomes for women have more severely deteriorated during the crisis (Adams-Prassl et al., 2020), there is no evidence of unequal impacts by gender in Germany (Adams-Pras et al., 2020) and Italy (Casarico and Lattanzio, 2020). Other papers confirm that the effect on labor-market outcomes by gender varies across contexts (see, e.g., Hupkau and Petrongolo, 2020).
Analysis of Labor Market Data From Sweden
Our analysis of the Swedish labor market provides a valuable contribution to the existing findings for a number of reasons. First, despite rising inequality over the past decades, Sweden is characterized by relatively low income inequality (e.g. OECD, 2019), high participation of women in the labor market, and high level of society inclusiveness (e.g. Gottfries, 2019, OECD 2016) among OECD countries. Second, unlike the majority of countries worldwide, throughout the pandemic, Sweden has not adopted stay-at-home orders that would have separated sectors of the economy between “essential” and “non-essential”. As a result, sectors that were typically shut down in other countries, for instance, the hospitality industry, were not ordered to close during the first wave of the pandemic and have then only faced partial limitations during the second wave. Importantly, schools below the secondary level were never closed. Third, as we will describe in more detail below, the availability of administrative information on unemployment claims on a monthly basis allows studying the “real-time” development of unemployment throughout the pandemic for the universe of employees in the Swedish labor market.
We use data from the registry of unemployed individuals kept by the Swedish Public Employment Service (Arbetsförmedlingen), the government agency responsible for the functioning of the Swedish labor market. The incentives for laid-off individuals to register with the Employment Service are high since the registration is directly connected to the right to claim various (relatively generous) unemployment benefits. As such, the data arguably includes a large share of employees who lost their job over the period studied. Based on the high incentives to register as unemployed, we also assume that the probability to register does not differ the segments of the population that we consider. The data does not include some self-employed who for various reasons choose not to register, but this group is not believed to be significant. Also, furloughed workers do not count as unemployed. This group was significant, especially in the very early stages of the pandemic, but still small relative to all unemployed. As of July 2020, they represented 13% of the total pool of unemployed individuals in Sweden (Swedish Agency for Economic and Regional Growth, 2021).
The population-wide coverage is the main advantage of our data vis-à-vis the survey information used in many recent studies of the labor market throughout the pandemic (other studies using administrative data are Casarico and Lattanzi, 2020, studying the Italian labor market, and Forsythe et al., 2020, who analyze the US case).
We consider everyone registered as unemployed/seeking employment each month from January 2019 to July 2020. The data is grouped by 4-digit occupational classification (there are about 440 occupations at this level) and each occupational group is further broken down by sex, age, and foreign-born status (specifically, Sweden born, foreign EU born, and foreign non-EU born.) We then merge this data with information on the average wage by occupational group and gender in 2019, as reported by Medlingsinstitutet and publicly available at Statistics Sweden. This measure, although not being at the individual level, allows us to develop a relatively precise proxy of wages by occupation that we use to rank unemployment by wage deciles.
With the data described above, we build the following measure of the change in job-loss probability (JLP) between February and July 2020, adjusted for seasonality:
where u is the number of workers in 4-digit occupational sector who registered as unemployed in a month over the average number of employed in the same sector in 2017 and 2018 (data available at Statistics Sweden). Put it simply, ΔJLP is a sector-level indicator of the change in job loss probability due to the pandemic; it measures the change in chances of job loss between February and July 2020, i.e. between five months after the start of the pandemic and the month before its onset, as compared to the equivalent change the year before. We thus account for seasonal factors by differencing out the job loss probability during the same months of 2019, when the pandemic was neither occurring nor anticipated. Below we use ΔJLP to show differences in the impact of the pandemic on the chances of job loss for different groups of the Swedish society.
Job loss probability by wage deciles. We leverage information on sector-level average wages and the number of employees to partition occupational sectors into (approximate) wage deciles. The purpose of such a partition is to rank sectors as being typically “low-” or “high-” wage within the Swedish context. As we document in Figure 1, the pandemic has increased the probability of job loss across all sectors of the economy; however, this increase in percentage points is higher the lower is the average sector wage, with the category of least-paid workers being the most likely to lose their job. This category includes occupations such as home-based personal care and related workers, cleaners and helpers in offices, hotels and other establishments, or restaurant and kitchen helpers. Considering that the pre-pandemic probability of becoming unemployed was already largest for this group (19.7% compared to the average 6% in 2019), the existing inequality in the labor market has been exacerbated by the Covid-19 crisis. In our regression analysis that is available by request, we also find that accounting for an index of the share of tasks that can be performed from home, defined at 2-digit occupational level, does not explain away the negative and significant relationship between wages and job loss probability. Although, we confirm previous evidence that the probability of losing jobs is lower among occupations that can be performed from home. The substantial contraction in economic activity in some sectors of the economy seems to be the driver of the unequal distribution of job losses.
Figure 1. Change in job loss probability by wage decile between February and July
Job loss probability by gender. Figure 1 also documents that, even though the change in job loss probability is higher in sectors dominated by women, the likelihood of men losing jobs has increased more in these sectors. As a result, in the regression analysis we find that there is no significant association between the share of women in a sector and the sector-level change in job loss probability.
Job loss probability by foreign status and age. We find that workers who are born outside of EU countries are significantly more likely to transition into unemployment during the pandemic (see Figure 2). The difference is striking. Based on our indicator, considering male workers the pandemic has raised the probability of job loss by roughly 7 p.p. more for non-EU citizens as compared to non-Swedish EU citizens, and by 9 p.p. more compared to Swedish citizens. These differences are only slightly smaller for women. Another group particularly affected is that of workers in the age group below 30 (result available upon request). Such patterns are due to foreign-born and younger workers being more concentrated in those low-wage sectors that also appear, based on our analysis, to be more impacted by the pandemic in terms of job loss probability
Figure 2. Change in job loss probability by foreign status between February and July 2020
Our analysis of administrative monthly data on the number of workers who register as unemployed in Sweden confirms previous evidence that the Covid-19 crisis has not been “the great equalizer”. While the pandemic has increased the probability of losing jobs across all sectors, the most affected in Sweden are those workers in occupations where the lowest wages were paid before the pandemic. Considering other demographic characteristics, vulnerable groups that were most impacted by the crisis are workers born outside of the EU and workers aged below 30. However, we do not find evidence of a gender-unequal impact of the pandemic in terms of the probability of job loss. There may of course be many other aspects to the issue along gender lines. For example, on one hand, there might be gender-unequal effects that we cannot observe in our data, for instance in the number of hours worked, temporary unemployment, and level of stress due to increased childcare responsibility. On the other hand, since schools in Sweden stayed open throughout the pandemic, the concerns related to increased childcare responsibility, which have led to identifying mothers as most vulnerable in other countries, do not necessarily apply to the Swedish context.
Sweden has adopted a number of measures to shield workers from the worst effects of the pandemic. As the country plans the recovery, special attention should be devoted to the opportunities for re-employment for the most vulnerable groups. Absent such focus, the economy emerging from the crisis might be less inclusive and equal than it has been before the pandemic, with important consequences for many societal outcomes that are generally linked to labor market inclusiveness.
- Adams-Prassl, A., Boneva, T., Golin, M., & Rauh, C. (2020). ”Inequality in the Impact of the Coronavirus Shock: New Survey Evidence for the UK”. Journal of Public Economics, 189, 104245.
- Casarico, A., & Lattanzio, S. (2020). ”The heterogeneous effects of COVID-19 on labor market flows: Evidence from administrative data”. Covid Economics, 52, 152-174.
- Forsythe, E., Kahn, L. B., Lange, F., & Wiczer, D. (2020). ”Labor demand in the time of COVID-19: Evidence from vacancy postings and UI claims”. Journal of Public Economics, 189, 104238.
- Gottfries, N. (2019). “The labor market in Sweden since the 1990s”. IZA World of Labor 2018: 411.
- Hupkau, C., & Petrongolo, B. (2020). ”Work, care and gender during the Covid‐19 crisis”. Fiscal studies, 41(3), 623-651.
- OECD (2016). ”Promoting Well-being and Inclusiveness in Sweden”, Better Policies, OECD Publishing, Paris.
- OECD (2019). ”OECD Economic Surveys: Sweden 2019”, OECD Publishing, Paris.
- Swedish Agency for Economic and Regional Growth, Database, 10 Mar. 2021.
- Von Gaudecker, H. M., Holler, R., Janys, L., Siflinger, B., & Zimpelmann, C. (2020). ”Labour supply in the early stages of the CoViD-19 Pandemic: Empirical Evidence on hours, home office, and expectations”. IZA Discussion Paper No. 13158.
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”
In August 2017, the Latvian parliament adopted a major tax reform package that will come into force in January 2018. This reform was a long-awaited step from the Latvian authorities to make the personal income tax more progressive. Some of the elements of the adopted reform, e.g. the changes in the basic tax allowance are estimated to help reducing the tax wedge on low wages and help addressing the problem of high income inequality. At the same time, the way the newly introduced progressive tax rate is designed will effectively lead to a reduction in the tax burden on labor and will hardly introduce any progressivity to the system.
In recent years, reducing income inequality has become one of the top priorities of the Latvian government. Income inequality in Latvia is higher than in most other EU and OECD countries, and the need to address this issue has been repeatedly emphasized by the Latvian officials, the European Commission, the World Bank and OECD.
The main reason for high income-inequality is a low degree of income redistribution ensured by the tax-benefit system. The personal income tax (PIT) has been flat since the mid-nineties. While the non-taxable income allowance introduces some progressivity to the system, the Latvian tax system is characterized by a very high tax burden on low wages, compared to other EU and OECD countries.
Since the beginning of 2017, the government has worked on an extensive tax reform package that was passed in the parliament in August and will become effective as of January 2018.
Two years ago, we wrote about the tax reform of 2016. In this brief, we estimate the effect of the 2018 reform on the tax burden on labour and income inequality. We will only consider changes in direct taxes on personal income – the changes in enterprise income tax and excise tax are outside the scope of our analysis. Parts of our estimations are done using the tax-benefit microsimulation model EUROMOD (for more details about the EUROMOD modelling approach, see Sutherland and Figari, 2013) and EU-SILC 2015 data.
Tax reform 2018
We focus our analysis on four elements of the reform that are expected to affect income inequality and that are described below. In our simulations, however, we take into account all changes in the PIT rules.
First, the flat PIT rate of 23% will be replaced by a progressive rate with three brackets: 20% (applied to annual income not exceeding 20,000 EUR), 23% (for annual income above 20,000 EUR and below 55,000 EUR) and 31.4% (applied to income exceeding 55,000 EUR per year).
Second, the maximum possible PIT allowance will be increased and the structure of the PIT allowance will be made more progressive. Latvia has a differentiated allowance since 2016, which means that individuals with lower incomes are eligible for a higher tax allowance. Figure 1 shows the changes in the non-taxable allowance that will be introduced by the reform. Another important change is that the differentiated allowance will be applied to the taxable income in the course of the year. The current system foresees that, during a calendar year, all wages are taxed applying the lowest possible allowance (60 EUR per month in 2017), but workers eligible for a higher allowance have to claim the overpaid tax in the beginning of the next year.
Figure 1. Basic PIT allowance before (2017) and after (2018-2020) the reform, EUR
Third, the rate of social insurance contributions will be increased by 1 percentage point. Social insurance contributions are capped and the cap will be increased from 48,600 EUR per year to 55,000 EUR per year, i.e. to the same income threshold that divides the top PIT bracket.
Finally, the reform will modify the solidarity tax – a tax, which was introduced in Latvia in 2016 and which is paid by top income earners. When this tax was initially introduced, one of its objectives was to eliminate the regressivity from the tax system caused by the cap on social insurance contributions. Hence, the rate of the solidarity tax was set at the same level as the rate of social insurance contributions and was effectively replacing social insurance contributions above the cap. The reform foresees that part of the revenues from the solidarity tax (10.5 percentage points) will be used to finance the top PIT rate. This element of the reform implies that after January 2018 those falling into the top PIT bracket will, in fact, not face a higher PIT rate than those falling into the second income bracket – the introduction of the top rate will be offset by the restructuring of the solidarity tax.
There are four main findings. First, the reform will reduce the tax wedge on labor income, whereas the tax wedge on low wages will remain high by international standards. Second, most of the PIT taxable income earners (93.5%) will fall into the bottom income bracket. Hence the reform will effectively reduce the tax burden, while the effect on progressivity is very limited. Third, the (small) increase in tax progressivity is ensured mainly by changes in the tax allowance, while the effect of changes in the tax rate on progressivity is negligible: Even those few PIT payers that fall into the top tax bracket will not experience any increase in the tax burden due to a compensating change in the solidarity tax. Finally, it is mainly the households in the middle of the income distribution that will gain from the reform.
Effect on tax wedge
We start with a simple comparison of the average labor tax wedge in Latvia and other OECD countries for different wage levels before and after the reform. The tax wedge measures the share of total labor costs that is taxed away in the form of taxes or social contributions payable on employees’ income.
Table 1. Average tax wedge for single wage earners without dependents in Latvia and other OECD countries, before and after the reform
67% of average worker’s wage
100% of average worker’s wage
167% of average worker’s wage
|OECD average in 2016, % (a)||32.3||36.0||40.4|
|Latvia 2016, % (a)||41.8||42.6||43.3|
|Latvia’s rank in 2016* (a)||6||11||16|
|Latvia 2018, % (b)||39.4||42.3||42.6|
|Latvia 2019, % (b)||39.1||42.1||42.6|
|Latvia 2020, %(b)||39.0||41.9||42.8|
Source: (a) OECD and (b) authors’ calculations. Note: * Ranking across 35 OECD countries. Higher ranking implies higher tax wedge relative to other countries.
Table 1 shows that the tax wedge on low wages (67% of an average worker’s wage) in Latvia is pretty high. In 2016, it was the 6th highest across OECD countries, while the tax wedge on high incomes (167% of the wage) is much closer to the OECD average.
While the reform will slightly reduce the tax wedge for low wage earners (from 41.8% to 39.0% in 2020), it will still remain high by OECD standards. Despite an increase in PIT rate for high-income earners, the reform will also lower the tax wedge for those who earn 167% of the average wage. Why? The explanation comes from the income thresholds for the tax brackets. The income of those earning 167% of the average wage is estimated to fully fall into the first tax bracket in 2018–2019 and only slightly exceed the income bracket for the second PIT rate by 2020. This means that most of the incomes of people earning 167% of the average wage will be taxed at the rate of 20%, which is lower than the current flat rate of 23%. Moreover, in 2020, only a small share of their income will be taxed at 23% – the same rate that these individuals would have had faced in the absence of the reform. Hence, we observe a reduction in the tax wedge for high-income earners.
Generally, only a very small share of taxpayers will fall into the middle and the top income brackets. According to our estimations, as many as 93.5% of all PIT taxable income earners will fall into the lowest income bracket, and only about 6.5% will fall into the second income bracket and about 0.5% will face the top PIT rate.
Apart from the progressive PIT schedule, the reform envisages important changes in the solidarity tax. As explained above, part of the revenues from the solidarity tax will be used to finance the top PIT rate. Therefore, even those (very few) taxpayers whose income will exceed the threshold for the top PIT rate, will not experience any increase in the tax burden because of the compensating change in the solidarity tax. Therefore, the reform will effectively reduce the tax burden on labour with very little effect on progressivity.
While lowering the tax burden is generally welcome, the motivation for applying the top rate to such a small group of taxpayers is not clear. For example, in their recent in-depth analysis of the Latvian tax system, the World Bank (World Bank, 2016) came up with a tax reform proposal that envisaged a considerably lower threshold for the top PIT rate, which, according to our estimations, would cover about 12% of the taxpayers. Given the limited budget resources and an especially high tax wedge on low wages, a more targeted reduction in the tax burden would be preferable. Similar concerns about insufficient reduction in the tax burden on low-income earners are expressed in the latest OECD economic survey of Latvia (OECD, 2017).
Effect on income distribution
Below we present the results from the tax-benefit microsimulation model EUROMOD. Figure 2 shows the simulated change in equivalized disposable income by income deciles compared to the baseline “no-reform” scenario in 2018-2020.
Figure 2. Change in equivalized disposable income by income deciles caused by the reform compared to “no-reform” scenario, %
The first thing to note is that these are mainly households in the middle of the income distribution who will gain from the reform – their income will increase due to both the increase in non-taxable allowance and the introduction of the progressive rate.
The gain in the bottom of the income distribution is smaller for several reasons. First, the proportion of non-employed individuals (unemployed and non-active) is larger in the bottom deciles. Second, individuals with low wages are less likely to gain from the reduction in the tax rate and the increase in the basic allowance, since they might already have most of their income untaxed due to the currently effective basic allowance. The same applies to pensioners who have a higher basic allowance than the employed individuals and who are mainly concentrated in the bottom of income distribution.
Our results suggest that the wealthiest households will also see their incomes grow as a result of the reform (by about 1% in 10th decile). The growth is ensured by the fact that annual income below 20,000 EUR will be taxed at a reduced rate of 20%, and, taking into account that even in the top decile only about half of the individuals get income from employment that exceeds 20,000 EUR per year, the gain from the tax reduction is considerable even in the top decile. A reduction in the tax allowance for high-income earners will have a negative effect on wealthy individuals’ income, but this will be more than compensated by the above positive effect of the change in the tax rate. Hence, the net effect on the incomes in the top deciles is estimated to be positive.
Finally, Table 2 summarizes the effect of the reform on the income distribution, measured by the Gini coefficient on equivalized disposable income. On the whole, the reform is estimated to slightly reduce income inequality – in 2020, the Gini coefficient is expected to be 0.6 points lower than it would have been in the absence of the reform. This reduction is mainly driven by the changes in the non-taxable allowance, while the three PIT rates are estimated to have an increasing impact on income inequality.
Table 2. Gini coefficient on equivalized disposable income in the reform and “no-reform” scenario
Source: authors’ calculations using EUROMOD-LV model
The 2018 tax reform was a long-awaited step from the Latvian authorities on the way to a more progressive tax system. The planned changes in the basic tax allowance are estimated to help reducing the tax wedge on low wages and help addressing the problem of high income-inequality.
At the same time, the second major aspect of the reform, the introduction of a progressive PIT rate, raises more questions than answers. The progressive rate, the way it is designed, will effectively lead to an across-the-board reduction of the tax burden on labor and will hardly help to reach the proclaimed objective of taxing incomes progressively. Given the limited budgetary resources and given that taxes on low wages will remain high compared to other countries even after the reform, a more targeted reduction of the taxes on low-income earners would have been a more preferred option.
- OECD, 2017. “OECD Economic Surveys: Latvia 2017”, OECD Publishing, Paris. http://dx.doi.org/10.1787/eco_surveys-lva-2017-en
- Sutherland, H. and Figari, F., 2013. “EUROMOD: the European Union tax-benefit microsimulation model”, International Journal of Microsimulation, 1(6), 4-26.
- World Bank, 2016. “Latvia Tax Review”, available at http://fm.gov.lv/files/nodoklupolitika/Latvia%20Tax%20Review%20Draft%20231216%20D.pdf
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.
The 2016 budget includes measures aimed at increasing the progressivity of the Latvian income tax system. In this brief we report some exercise on the impact of these measures using the Latvian EUROMOD tax-benefit microsimulation model. We show that by their design, the reforms are aimed at a reduction in income inequality and an increase in the progressivity of the tax system. However, there are risks that the behavioural response of the tax payers will subvert the intended impact of the reforms.
Ever since it was introduced in 1994 the Latvian personal income tax has been applied at a flat rate, albeit varying over time, mitigated only by a small untaxed personal allowance. Partly as a result of this, the Latvian tax-benefit system redistributes less original income than most other EU countries. Is this all about to change? The 2016 budget currently being debated in the Parliament contains two proposals aimed at introducing more progressivity in the personal income tax. These are the introduction of a “solidarity tax” aimed at high earners and the introduction of an earnings differentiated non-taxable allowance. The stated aims of these measures are to reduce inequality and help low wage-earners.
Description of the Reforms
The solidarity tax foresees that income above 48,600 EUR per year will be taxed at a rate of 10.5% (employee’s part), plus 23.59% (employer’s part). The new tax will affect a very small share of wage earners. According to Finance ministry’s estimate, this tax will affect 4.7 thousand persons, whose income in 2015 exceeded this threshold, or 0.59% of all employed individuals (Finance Ministry, 2015).
Differentiated Non-Taxable Personal Allowance
The differentiated non-taxable personal allowance will be introduced gradually between 2016 and 2020. The basic idea is to make the allowance dependent on income: individuals receiving income below a certain threshold are eligible for the maximum possible allowance, then the allowance gradually declines with income until it is zero. The system will be introduced gradually in the sense that the minimum allowance will not reach zero until 2020 – it will be gradually reduced from 85 EUR in 2016 to 0 EUR in 2020.
The way the system will be implemented foresees that during a fiscal year, all individuals will be taxed applying the minimum non-taxable allowance (e.g., 85 EUR in 2016). At the beginning of the next year, people eligible for a higher tax allowance will have the opportunity to apply for a tax refund, by making an income declaration, and to get the overpaid tax back.
Simulations of Reforms: Inequality
Below we present simulation results from EUROMOD, which is an EU-wide tax-benefit microsimulation model (for more details see Jara and Leventi, 2014). The results show the first-round effect of the simulated policies, i.e., they show the pure effect of the proposed reforms abstracting from any behavioural responses that these reforms might induce. We simulate the effect of five reform scenarios: two scenarios of differentiated non-taxable allowance (one scenario reflects the system that is planned to be introduced in 2016, the second scenario represents the system that is planned to be introduced in 2020), one scenario that simulates introduction of the solidarity tax, and two scenarios that combine the solidarity tax with the new non-taxable allowances. We compare these reforms with the baseline system, which describes the tax-benefit rules that are in place in 2015.
It is important to note that we assume in the simulations that everyone who is eligible for a tax refund under the new non-taxable allowance rules does in fact apply for the refund, which means that we estimate the maximum possible effect from the introduction of the higher tax allowances.
Table 1 summarizes the effect of the proposed reforms on income inequality as measured by the Gini coefficient. All the proposed reforms reduce income inequality, but the solidarity tax achieves higher equality by reducing incomes in the top decile. The non-taxable allowance mainly affects people in the middle of the income distribution, as the bottom deciles contain proportionally fewer employed individuals, while in the top deciles the allowance, which is set in absolute terms, makes a smaller share of the income – hence, a weaker effect. Pensioners, who mainly belong to the lower deciles of the income distribution, do not gain from a higher allowance, because of a special taxation regime for pensions that already provides for a higher personal allowance. All major benefits (unemployment benefit, social assistance, child-related benefits) are not subject to personal income tax, hence benefit recipients also do not gain from the proposed changes (see Figure 1).
Table 1. Gini Coefficient Associated with the Reforms
|Baseline||ST*||2016 allowance||2020 allowance||ST + 2016 allowance||ST + 2020 allowance|
Source: authors’ calculations using EUROMOD
Note: ST – solidarity tax
Figure 1. Deviation of Equivalised Disposable Income from the Baseline Scenario, %
Figure 1 also shows that the losers from the solidarity tax are in the highest decile, though it should be borne in mind that enterprises are also losers because they now have to pay part of the solidarity tax. The solidarity tax generates no direct gainers.
Impact on Progressivity
The progressivity of a tax or system is typically measured by the Kakwani index. The Kakwani index (Kakwani, 1977) can vary between −1 and 1 and the larger the index, the more progressive is the tax. A positive index indicates that the tax is progressive and a negative index indicates it is regressive. Table 2 shows the calculated Kakwani index for all major direct taxes (which include personal income tax, social contributions and the newly introduced solidarity tax) and separately for personal income tax (PIT) for each of the postulated scenarios. The results suggest that all of the proposed reforms increase the progressivity of the tax system.
Table 2. The Kakwani Index for the Six Scenarios
|Baseline||ST*||2016 allowance||2020 allowance||ST + 2016 allowance||ST + 2020 allowance|
|All income taxes*||0.034||0.040||0.048||0.058||0.054||0.064|
Source: authors’ calculations using EUROMOD
Note: ST – solidarity tax; income taxes include personal income tax, social contributions and the newly introduced solidarity tax
Qualifications and Risks
The above results capture the so-called first round impact of the tax changes. In practice people will react to the changed incentives by changing behaviour and thereby changing the impacts. For example, the higher net reward for working in low wage jobs may increase the supply of workers willing to work in such jobs thereby possibly having a bigger positive effect on the incomes of low income households than implied by the simulations.
Perhaps more significant is the potential effect of the solidarity tax on the behaviour of high earners and of the enterprises that employ them. This effect is captured by the concept of the elasticity of taxable income – defined as the change in taxable income in response to a change in the marginal tax rate. The taxable income elasticity concept takes into account all the behavioural aspects of the taxpayer in response to a change in the tax rate. As well as labour supply responses it includes other responses e.g. switching the form in which income is received as well as simple tax evasion (Saez et al., 2012). It is the switching of the form in which income is received, away from wage income towards other less-taxed forms of income that can be expected here. Thus according to an internal Latvian Employers Confederation employer survey, if the solidarity tax is implemented one third of employers will consider using legal tax optimization tools such as dividends or the microenterprise tax to avoid paying the tax. Here, employers are important as well as employees, because employers will pay the larger share of the tax. If this happens on a significant scale (high elasticity of taxable income) then the intention of the solidarity tax will be subverted.
There are also risks with the differentiated personal allowance. If the burden of annual reporting of income is too high then many may simply not do it and suffer the loss of income or find a way of recouping through shadow earnings.
The Latvian authorities should be applauded for grasping the nettle of progressive taxation but perhaps only with one hand for the way they have chosen to do it. Thus, the solidarity tax creates an incentive for both employers and employees to find ways of avoiding it and find they surely will. A tax accountant once said of the 80% supertax applied to high earnings in pre-Thatcher UK that it was a ‘voluntary tax’. This is also the likely fate of Latvia’s solidarity tax.
The differentiated personal allowance will clearly benefit low earners, if they claim it. In fact it will also benefit people earning well over the average wage. But will the low earners claim? Very few people in Latvia have ever filed an income declaration and we fear that many low earners will not do so now.
Thus at the top end progressivity is likely to be largely avoided and at the bottom end may not be fully claimed.
- Finance Ministry (2015). “Solidaritātes nodokli maksās tikai personas ar algu virs 48 600 eiro gadā,” available at http://www.fm.gov.lv/lv/aktualitates/jaunumi/nodokli/51253-solidaritates-nodokli-maksas-tikai-personas-ar-algu-virs-48-600-eiro-gada
- Kakwani, Nanak C. (1977). “Measurement of Tax Progressivity: An International Comparison”. Economic Journal 87 (345): 71–80
- Jara, X. and Leventi, C. (2014). “Baseline results from the EU27 EUROMOD (2009-2013),” EUROMOD Working Papers EM18/14, EUROMOD at the Institute for Social and Economic Research.
- Saez, E., J. Slemrod, and S. H. Giertz, (2012). “The Elasticity of Taxable Income with Respect to Marginal Tax Rates: A Critical Review.” Journal of Economic Literature, 50(1): 3-50