Georgia’s gender pay gap has started to attract the attention of the population and policymakers alike. The gap persists despite working women generally reporting better labor-market skills and personal characteristics. It has been argued that this could be the result of systematic gender-based workplace wage discrimination, resulting in unequal pay for equal work. The discussion that ensued highlights how the fight to guarantee equal pay for equal work could benefit from establishing an Equal Pay Review and Reporting Mechanism. In response, the ISET-PI team – after reviewing the best international practices – devised and tested an excel based tool that could help companies and governmental agencies identify, monitor, and fight gender discrimination in Georgia. The main quantitative result of the exercise identified that, should reporting be made mandatory, extending the obligation to companies that employ up to 50 people would make the administrative costs for companies and public administration up to twenty times higher; thus, the usefulness of the tool was found to be substantially limited when applied to smaller companies. Finally, the exercise emphasized the reluctance of companies to provide the data required, leading to the conclusion that the successful implementation of such an initiative would require the enforcing agency to have the legal authority to sanction failures to provide the necessary data.
One of the key gender inequality indicators is the gender pay gap – or gender wage gap – calculated as the average difference between the remuneration for men and women in the labor market. Its evolution is monitored worldwide, and closing this gap is considered a key step towards more inclusive and prosperous economies and societies. According to the World Economic Forum, as of 2020, no country (including the top-ranked ones) had yet achieved gender parity in wages.
In Georgia, the unadjusted hourly gender pay gap amounts to 17.7 percent of the average male hourly wage (UN Women, 2020). Moreover, when controlling for personal characteristics of men and women, the adjusted hourly gender pay gap in Georgia is estimated to be 24.8 percent (UN Women, 2020). This implies that women, on average, have better observable labor-market characteristics but are still paid less than men.
These findings prompted a core discussion within the Georgian society on the presence of unequal pay for equal work in Georgia as one of the possible reasons for the gap and how to tackle the problem. The idea of equal pay for equal work entails that individuals in the same workplace are given equal pay if they perform the same type of work. Consequently, this potential source of the pay gap can only be verified at the individual employer level. This is accomplished by calculating the unexplained gender pay gap at the organizational/employer level and validating whether, and why, these differences exist.
Given the attention the topic holds in the national discourse, ISET Policy Institute created and tested an excel tool, built in line with the international best practices and adapted to the Georgian context, to help employers and government offices identify and measure the differences in wages between men and women performing equal work. During this process, the team learned several noteworthy lessons, as summarized in this policy brief.
There is growing consensus that transparency is critical when dealing with pay inequality and, therefore, gender pay reporting should become the norm. Since 2010, several (mostly developed) countries have introduced reporting schemes to monitor gender pay gaps, promote awareness about gender equality issues throughout society (particularly among employees), and increase organizations’ accountability to address gender inequalities (Equileap, 2021).
However, the gender pay gap is a key issue for which the disclosure of information remains particularly low. Equileap’s 2021 report revealed that 85 percent of organizations worldwide did not publish information on remuneration differences between female and male workers in 2020.
Three countries, according to Equileap, lead the way in gender pay gap reporting: Spain, the UK, and Italy (Figure 1). In each of these top three countries, reporting is mandatory.
Figure 1. Percentage of organizations publishing gender pay information, per country
However, even in these countries, and, more generally, in all countries scrutinized by Equileap but Iceland, firms with 50 or fewer employees are not required to report on gender pay gaps.
The Case of Georgia
Georgian legislation clearly establishes the principle of equal pay for equal work for all employees. The requirement applies to both public and private organizations. Nevertheless, enforcement of the law remains a significant challenge.
At present, Georgia has no reporting requirements regarding employee salaries for private organizations. It has not yet designed a reporting scheme for equal pay for equal work, nor has it assigned the task of collecting this information to any governmental body.
Moreover, Labour Inspectorate representatives state that few wage discrimination cases are currently being filed in the country. The main reason behind this is that norms regarding equal pay for equal work have never been properly specified. In addition, there are no explicit criteria defining the concept of ‘equal work’. Thus, employers and employees alike do not seem to fully understand the phrase – equal pay for equal work.
The Excel Tool
After a careful review of the three tools presently utilized to calculate gender pay inequality (the Swiss Logib, the German Logib-D, and the Diagnosis of Equal Remuneration (DER) tool developed by UN Women), ISET-PI built a Georgian model as a modified version of the DER tool that is adapted to the Georgian context and includes some variables from the Swiss tool.
The tool itself is an excel file with several worksheets. The two main facets are the inputted data sheet and the results sheet. Companies may input information on their employees in the data sheet, and the findings will then be demonstrated in the results sheet. The tool first identifies people performing the same work, and classifies jobs based on their official titles, alongside managerial responsibilities and skill requirements. After individuals are grouped by job, the tool calculates the average salary within each group separately for men and women. Thereafter, the pay gap is calculated based on the average salary for the two gender groups.
With the support of the Employers’ Association, several companies of all sizes were approached to test the tool. Unfortunately, only a few agreed to participate, and just two completed the trial: one small-sized enterprise (with 50 or fewer employees) and a large-sized enterprise (with 250 or more employees).
While low participation rates have significantly limited our analysis, we still obtained several important insights which are discussed in the next subsection.
Firstly, it is important to note that companies’ willingness to share anonymized salary data was very low, even among the companies that completed the test.
Secondly, the usefulness of the tool for obtaining a comprehensive view of equal pay for equal work in small companies (with 50 or fewer employees) appeared fairly limited as few people within the same firm perform the same job.
Thirdly, we performed a simple cost assessment exercise to evaluate the compliance costs – to both companies and the government – of collecting and reporting the gender pay gap. We found that extending the data collection requirement to small companies would increase the compliance costs by up to 20 times (high-cost scenario) compared to an example where small companies are exempt. This is because there are many more small companies in Georgia (146,802), than those classified as medium or large ones (2,752 and 609, respectively).
In addition, during the implementation of the exercise, we became aware of the following:
- Under the existing legal provisions, it would be extremely difficult to introduce the EPRR in a mandatory format – no governmental agency could sanction companies for failing to comply.
- Opting for the mandatory option and sanctioning the emergence of unequal pay in certain job categories could incentivize companies to manipulate the data input. In this case, therefore, it would be ill-advised to provide the full tool to companies, as they could more easily adjust data inputting to obtain more favorable indicators through successive iterations.
Setting up an EPRR system is one way to contribute to the implementation of the equal pay for equal work principle.
Designing the Georgian Model for the Implementation of an Equal Pay Review and Reporting Methodology generated several useful insights that might prove valuable for policymakers in Georgia and other developing countries:
1) The EPRR instrument can be utilized for the analysis of gender pay gaps within companies with more than 50 employees. Within smaller companies, evaluating the gender pay gap significantly increases the costs to society, while providing rather limited additional information.
2) The decisions about whether to provide the analytical part of the tool to companies, and whether reporting should be voluntary or mandatory should be taken jointly. If the goal is to provide an instrument to the agency enforcing the equal pay for equal work principle and to facilitate appeals from workers, the tool should be made mandatory. However, in this case, companies should only provide the input data, without having access to the part of the tool that assesses pay gaps at the job level. On the other hand, if the goal of the reform is to support willing companies in their efforts to eliminate unequal pay for equal work conditions, a non-mandatory form may be preferable. In this instance, companies should have access to the full version of the tool. This would allow them to better understand the dynamics that lead to unequal pay and thus put in place internal remedial actions.
3) If the goal is to provide a tool to the agency enforcing the equal pay for equal work principle, it is crucial that any gaps in the associated legislation are closed. As such, the enforcing agency should be capable of sanctioning failures to provide the required data, and prosecuting violations of the equal pay for equal work principle.
Finally, it is important to note that testing the application of the equal pay for equal work principle at the company level through an EPRR system, while useful for identifying potential causes of the gender pay gap and the existence of gender disparities within companies, is just a first step in a longer and more complex process. Once disparities are identified, both companies and enforcing agencies should follow up with additional research and analysis to determine whether these disparities are linked to discriminatory practices, and what type of remedial options could be adopted.
- Equileap. 2021. Gender equality global report & ranking. Equileap Research Paper. Available at: https://equileap.com/wp-content/uploads/2021/07/Equileap_Global_Report_2021.pdf
- Geostat. 2020. Business Sector in Georgia. Geostat. Available at: https://www.geostat.ge/media/35014/Krebuli-2020.pdf%20
- UN Women. 2020. Analysis of the Gender Pay Gap and Gender Inequality in the Labor Market in Georgia. Tbilisi: UN Women. Available at: https://georgia.unwomen.org/en/digital-library/publications/2020/03/analysis-of-the-gender-pay-gap-and-gender-inequality-in-the-labor-market-in-georgia
- UN Women. 2021. Assessment of the models for the implementation of the models for the implementation of the Equal Pay Review and Reporting (EPRR) methodology in Georgia. Tbilisi: UN Women. Available at: https://iset-pi.ge/en/publications/research-reports/3020-assessment-of-the-models-for-the-implementation-of-eprr-methodology-in-georgia
- WGEA. 2019. International Gender Equality Reporting Schemes. Workplace Gender Equality Agency Annual Report. Available at: https://www.wgea.gov.au/publications/international-gender-equality-reporting-schemes
- WEF. 2021. Global Gender Gap Report 2021. The World Economic Forum. Geneva, Switzerland. Available at: https://www.weforum.org/reports/ab6795a1-960c-42b2-b3d5-587eccda6023
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.
This policy brief reviews recent research on women at the top of the income distribution. The overall trend across a number of countries is that, while women are still a minority (and more so the closer to the top one moves), their share in top income groups has steadily increased since the 1970s. Detailed data from Sweden suggests that most of this rise is due to women increasingly earning high labor incomes (rather than capital becoming more important). It also shows that there are important differences between top income men and women, especially with respect to family circumstances. Comparing preliminary results from former Soviet and Eastern European countries indicates that there are, on average, more women at the top of the income distribution in these countries. On the other hand, the average time trend indicates that the share of women in top groups is falling. The preliminary results also indicate considerable heterogeneity across countries. These preliminary results require more detailed study, as does the question to which extent the relatively strong representation of women at the top of the income distribution reflects the “economic power” of women in the region.
The Gender Aspect of Rising Top Shares
Rising inequality has received a lot of attention in the policy debate as well as in the academic literature over the past decade. A particular feature of this discussion has been the increased concentration of both wealth and income in top groups. The summary of the World Inequality Report 2018 starts by stating that “The top 1% has captured twice as much of global income growth as the bottom 50% since 1980”. Such facts have, in turn, brought a lot of attention to the characteristics of top groups. What is driving their income growth? What is their income composition? Why have top shares increased so much in recent decades? (see, e.g., Roine and Waldenström, 2015, for an extensive overview, or Roine, 2016, for a brief summary).
However, one aspect which has received relatively little attention is that of gender. This may seem a little surprising. In a time when gender dimensions are often acknowledged as being important, one would expect that questions about the gender composition of top groups would also be of interest. If we know that top income shares are increasing, what is the gender composition of these groups? How has this changed over time?
This brief outlines some recent results on these questions and also points to some preliminary findings about a potential contrast between Western countries and (former) transition countries.
Evidence from Sweden, 1971-2017
Sweden is one of the few countries having had independent taxation of all taxpayers for a long period of time, allowing for a thorough analysis of the gender composition of top income groups. After having had joint taxation for married couples for most of the 20th century, and a short period of the option to be taxed independently even if married, Sweden switched to fully independent taxation in 1971. In a recent paper Boschini et al. (2020) study developments of men and women in top income groups in Sweden using detailed registry data on the full population for the almost 50-year period since.
The study finds a number of interesting results. First, it is evident that the share of women in top income groups has increased significantly, yet women remain clearly underrepresented, and more so the higher up in the distribution one moves. Figure 1 below shows the basic development over time for three top groups: the top 10 (P90-100), the top 1 (P99-100), and the top 0.1 group (P99.9-100) in the total income distribution and the labor income distribution respectively.
Figure 1. Share women in top groups in Sweden.
Besides showing the general development comparing the two panels also reveals a subtler point: especially in the earlier decades and in the very top group (the top 0.1 group), there were substantially more women at the top of the total income distribution than at the top of the labor earnings distribution. In the 1970s and 1980s, the share of women in the top 0.1 group of the total income distribution is about two to three times as large as in the labor earnings distribution. Put differently, this means that in the past, to the extent that there were any women at the very top, they were mainly there thanks to capital incomes. Over time this changes and detailed analysis in the paper shows that the growth of the share of women in top groups is driven by an increasing share of high-income women in the labor income distribution.
While it seems that top income men and women have converged in terms of income composition and observable individual characteristics, the one area that still stands out as being markedly different is partner income. Figure 2 shows that top income women are much more likely to have partners who are also in the top of the income distribution. Even if the trend indicates convergence, large differences remain. Out of the top 1 women who are married, 70% have a partner who is at least in the top 10 (and about 30% are also in the top 1). For married top 1 men, only 30% have a partner who is in the top 10, and only a couple of percentage points are in the top 1. Part of this is, of course, a reflection of there being fewer women in top groups, but this is far from explaining all the difference (See Boschini et al., 2020 for more details).
Figure 2. Share of top income partners in Sweden.
This is of course far from conclusive, but it points in the direction of family circumstances being a potential factor for explaining the relative absence of women in top income groups. Having a partner with a top (income) career is likely to be more demanding (for both parties) and such couples are much more common among top income women than men.
Several strands of research connect to this: for example, Fisman et al. (2006) find, among other things, that men are significantly “less likely to accept a woman who is more ambitious than he”. Also, work by Bertrand et al. (2015), on the impact of gender identity suggest that there is a social norm prescribing that men should earn more than women, which creates a discontinuity in the distribution of women’s contribution to total household income at 50 % (although Hederos Eriksson and Stenberg (2015) and Zinovyeva and Tverdostup (2018) find alternative explanations for this observation). Folke and Rickne (2020) find that women who are elected to high political office in Sweden face a higher probability of divorce (while this is not the case for men). Furthermore, according to the World Values Survey, close to 40% of Americans as well as Europeans agree with the statement “(i)f a woman earns more money than her husband, it’s almost certain to cause problems”. Taken together, findings like these suggest that, even in relatively progressive countries, social norms may contribute to women shying away from entering career paths leading to top incomes.
What About Other Countries?
Even though the Swedish data is unusually detailed, it is certainly not the only country where individual tax data exist. Atkinson et al. (2018) calculate the share of women in top groups for eight countries over time periods when individual tax data exist. Figure 3 puts their results next to those from Sweden. The resulting picture shows a remarkably similar development across countries and over time. The share of women in the top 10 has approximately tripled since the 1970s, from around 10% to around 30%. For the top 1 group, the level is slightly lower, but the relative increase is similarly large, from slightly above 5% to around 20%.
Figure 3. International comparison.
Bobilev et al. (2019) explore the extent to which Luxemburg Income Study (LIS) data can be used to shed light on the presence of women at the top of the income distribution. Their findings point to a similar trend across a broader set of countries. Even though the main analysis has to be limited to the share of women at the top of the labor income distribution (since the possibilities to separate out individual capital incomes is limited), the picture in terms of the share of women in top groups is surprisingly similar across the 28 countries for which sufficient data exists from around 1980 until today. The overall finding is that the share of women in the top 10 group increases from about 10% around 1980 to just below 30% today.
To the extent that LIS data allows us to look at partners and family circumstances, the data shows a consistent pattern of asymmetries between top income men and women similar to that in Sweden found by Boschini et al. (2020). Having a partner and having children are positively associated with being in top income groups for men, but negatively associated for women (even though these differences have decreased over time). Also, top income men are likely to have partners who are not in the top of the income distribution, while this is not the case for top income women. Understanding patterns like these and the underlying channels is likely to contribute to our comprehension of the remaining differences in top income shares between men and women.
Are There Differences Between “East and West”?
A particularly interesting pattern in the LIS data is the difference that emerges when contrasting transition countries to Western countries.
As has often been pointed out, the Soviet Union and many of the countries in Eastern and Central Europe were, at least in some dimensions, forerunners in terms of promoting gender equality (e.g., Brainerd, 2000; Pollert, 2003; Campa and Serafinelli, 2019). This was mainly due to the high participation of women in the labor market as well as the (officially) universal access to basic health care and education.
However, some scholars have suggested that not all aspects of gender equality were as advanced in the countries in the Soviet Union and in Central and Eastern Europe (Einhorn, 1993; Wolchik and Meyer, 1985). Even though women were highly integrated in the labor market, they were still expected to take care of child rearing and housework at the same time (UNICEF, 1999). The gender pay gap and gender segregation in the labor market was also similar to levels found in OECD countries. In addition, despite the high number of women in representative positions in communist party politics, women were rarely found in positions of real power in the political sphere (Pollert, 2003).
Looking just at average values (in the labor income distributions), there are clear differences between East and West in top groups. The share of women among the top earning groups was considerably higher in some former Soviet countries during and after transition. However, the shares of women in top income groups have been converging in East and West.
Figure 4. Share of women in the top 10 / top 1 income groups, East vs. West.
An analysis of the situation at the country level, provides a more complex picture. Figure 5 clearly indicates that the total representation of women in the top 10 income group has been higher in Eastern European countries than in the West (the pattern is similar for the top 1). However, while the share of women in top income groups has consistently increased in Western countries, the developments for women are much less homogenous in Eastern Europe (being below the diagonal indicates a higher share of women in the top 10 in 2005-2020 as compared to 1990-2005).
In Estonia, Slovakia and Poland, women are less likely to be part of the top income group in the period from 2005 to 2020 than they were in the years directly following transition. Considering that the most recent family policies in Poland have been shown to discourage female labor supply (Myck, Trzciński, 2019), this is maybe not so surprising.
Figure 5: Share of women in top 10 income group by country.
The share of women in the top 10 income group in Estonia declined from an astonishingly high 53% in 2000 to about 31% in 2013, which, admittedly, is still high compared to the corresponding average rate for Western countries (28%). Women in Russia, Hungary, Slovenia and the Czech Republic, by contrast, are more likely to be among the top earners in the period from 2005 to 2020 than they were between 1990 and 2005. Moreover, among all the countries in our sample, more recently, Slovenia is the country with the highest share of women in the top 10 of income earners (44% in 2007); Slovenian women seem to have gained grounds even after transition.
How come the representation of women in top income groups remains high (or even increases) in some transition countries but decreases in others? What is the role played by policy and regulation and what role can be attributed to social norms, family circumstances and institutions such as childcare? May economic growth have led to women dropping out of the labor force or never entering it to do care work, even when they had been or potentially could have been part of top income groups? What would be the impact of adding capital incomes to the picture?
Looking across a large number of countries, women seem to have increased their presence in top income groups since the 1970s. This has mostly been driven by women increasingly having high paying jobs. A preliminary look at LIS data indicates that former Soviet and Eastern European countries on average had higher shares of women in top groups around 1990, probably reflecting high labor market participation as well as relatively high education levels for women. But it also indicates that in some Eastern European countries, the share of women in top groups has dropped since the 1990s. As noted by Campa, Demirel, and Roine (2018) there seems to be an overall convergence in some dimensions of gender equality in transition countries, but there is also considerable variation across countries. More detailed studies of how men and women fare in terms of reaching top positions in incomes – but also in other areas like politics – are much needed and likely to be an interesting research area for years to come.
- Atkinson, Anthony B., Alessandra Casarico and Sarah Voitchovsky (2018). “Top incomes and the gender divide”, The Journal of Economic Inequality. 16 (2), 225–256.
- Azmat, Ghazala and Barbara Petrongolo, (2014). “Gender and the labour market: what have we learned from field and lab experiments?” Labour Economics. 30, 32–40.
- Blau, Francine D., Lawrence M. Kahn (2017). “The gender wage gap: Extent, trends, and explanations”, Journal of Economic Literature 55(3), 789-865.
- Bertrand, Marianne, Jessica Pan and Emir Kamenic (2015). “Gender identity and relative income within households”. The Quarterly Journal of Economics, 130 (2), 571–614.
- Bertrand, Marianne, (2018). “Coase Lecture – The Glass Ceiling”. Economica 85: 205–231.
- Bobilev, Roman, Anne Boschini, Jesper Roine (2019). Women in the Top of the Income Distribution –What Can we Learn from LIS-Data?, Forthcoming Italian Economic Journal.
- Boschini, Anne, Kristin Gunnarsson, Jesper Roine (2020). “Women in top incomes – Evidence from Sweden 1971–2017”. Journal of Public Economics, 181, January 2020.
- Brainerd, Elizabeth (2000). “Women in Transition: Changes in Gender Wage Differentials in Eastern Europe and the Former Soviet Union”, ILR Review, 54(1): 138-162.
- Campa, Pamela, Merve Demirel, Jesper Roine (2018). “How Should Policy-Makers Use Gender Equality Indexes?”. FREE Policy Paper, November 2018.
- Campa, Pamela and Michel Serafinell (2019). “Politico-Economic Regimes and Attitudes: Female Workers under State-Socialism.” The Review of Economics and Statistics, 101 (2). 233 – 248.
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- Folke, Olle and Johanna Rickne (2020). “All the Single Ladies: Job Promotions and the Durability of Marriage”. forthcoming American Economic Journal: Applied Economics.
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This policy brief summarizes the results of an analysis of wage inequality among the districts of Belarus over the period 2000-2015. The developments in wage inequality varied noticeably by sub-periods: wage disparity decreased in 2000-2005, stayed stable in 2006-2012, and increased again during the last three years. I find evidence for spatial dependency in wages between districts, and increasing separation within districts (between rural and urban population). A decomposition of wage inequality by different quantiles of districts shows that the real wage increase rate in the lower percentiles exceeds the real wage increase rate in the higher percentiles. From a theoretical point of view, my results reject the inverted U-shaped relationship between spatial inequality and economic development for Belarus, and support the hypothesis made by the French economist Thomas Piketty that slow growth rates lead to rise in inequality.
In Belarus, wages make up approximately 60% of household income and account for 46% of GDP. The equality of the wage distribution therefore affects the scale and degree of socio-economic disconnect in the country. On the one hand, too much inequality may dampen long-term growth. On the other hand, too much equality may reduce incentives for productivity improvements.
This policy brief outlines a study (Mazol, 2016), where I examine the wage inequality concern of Belarus using annual Belstat data on district average monthly nominal wages (excluding large cities) from year 2000 to 2015, corrected by the country’s CPI index (using 2000 as the base year).
Characteristics of district wages
According to the Belarusian statistical definitions by the end of 2015, Belarus has 118 districts with an overall population of 4.9 million (excluding large cities), which corresponds to approximately 50% of total population. Average district wages relative to the national mean has increased from 74% in 2000 to 82% in 2005, indicating a catching-up process in wage income between districts and large cities (see Figure 1).
Figure 1. Decomposition of district real wages at the regional level of Belarus
However, from 2013, the convergence of wages reverted to divergence (79% in 2015) suggesting that the relatively poor district population have become even poorer in recent years.
District wages differed by 2.8 times in 2000 and by 2.4 times in 2015. The largest number of districts with the lowest wages concentrate in the northern part of Belarus, represented by Vitebsk region with a mostly rural population, whereas districts with the highest wages are mostly in the Minsk and Gomel region, which are the central and most industrialized parts of Belarus (Minsk, Zhlobin, Mozyr and Soligorsk) (see Figure 2).
Figure 2. Map of Belarus’ districts by levels of real wages in 2015
However, the common feature in the allocation of different levels of district wages is that the higher/lower wage districts tend to concentrate with similar districts, indicating presence of spatial dependence in the wage distribution.
Spatial interdependencies of district wages
The spatial characteristics are tested using the Global Moran’s I statistic (Moran, 1950). A positive coefficient means that neighboring districts have similar wages and a higher value indicates an increase in the relationship.
The results show that the values of the Global Moran’s I statistic are positive and significant at the 5 percent level for the periods 2000-2008 and 2014-2015 (see Figure 3). This suggests that districts with similar high or low levels of wages tend to concentrate geographically.
Figure 3. Global Moran’s I statistic and GDP growth in Belarus
Additionally, starting from 2012, the substantial increase in positive spatial interdependencies in wages between districts coincides with a significant decrease in economic growth. This suggests that the districts of Belarus tend to cluster more closely with each other during economic recessions, indicating a more profound formation of rich and poor clusters of districts. Such a trend could be caused by a lack of public financial resources, which restricts administrative redistribution of financial support in favor of poor districts. As a result, such districts tend to become even poorer (for example, districts in Vitebsk region).
Wage inequality in the districts of Belarus
Overall, the level of wage inequality among the districts of Belarus remains low for the studied period. Moreover, the growth rates of wages in districts with low wages are higher than in the richer districts, indicating presence of a convergence process (see Figure 4). Yet, the differences between these two groups continue to be large. In 2015, the 10th and 90th percentiles of district wages were 4.6 and 6.1 million Belarusian rubles, respectively.
Figure 4. Indexed real wage
Regarding inequality dynamics, the country experienced a decline in wage disparity 2000-2005, but from 2013, the inequality in wages started to rise (see Figure 5) and this coincides with an economic slowdown and subsequent recession.
Figure 5. Measures of wage inequality
Thus, during 2000-2015, Belarus’ accelerating levels of economic growth first led to a decrease in district wage inequality. During a time of high and stable economic growth, the level of district wage inequality was constant. But, during the last years’ negative economic growth, the district wage inequality in Belarus has started to increase again. From a theoretical point of view, these results reject the hypothesis of an inverted-U-shaped relationship between spatial inequality and economic development stated by Kuznets (1955), and confirms the hypothesis stated by the French economist Thomas Piketty (2014) that declining growth rates increase inequality.
My results suggest that spatial wage inequality in Belarus is a persistent phenomenon that has increased in recent years. I found evidence for a spatial dependency in wages between districts and an increasing separation within districts (between rural and urban population). These may lead to a socio-economic instability, growth of shadow economy, and even an emergence of depressed regions (for example, Vitebsk region).
In order to decrease spatial wage inequality and increase overall economic efficiency in the districts of Belarus, the government needs to implement specific policies aimed at facilitating regional drivers of economic growth through the formation of new economic centers at the district level.
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