This policy brief introduces two related papers examining two types of gender gaps in Estonia. First, it presents the work of Vahter and Masso (2019), who study the wage gender gaps in foreign-owned firms and compare this gap with the situation in domestic ones. Then it summarizes a paper of Meriküll, Kukk, and Rõõm (2019), who focus on the wealth gender gaps and highlight the role of entrepreneurship in this gap.
Gender inequality is not only a moral issue. An extensive literature has highlighted the cost of gender inequality in terms of economic (in)efficiency. Most of the academic work has, however, focused on either the US and Western Europe or developing countries. Research focusing on systematic gender disparities in Eastern Europe is rather scarce. Yet, there is much to be learned from this region. The purpose of the FROGEE (Forum for Research on Gender in Eastern Europe) project is to study several issues related to gender inequality in former socialist countries.
This policy brief summarizes two papers presented at the 2nd Baltic Economic Conference at the Stockholm School of Economics in Riga, on June 10-11, where a special session on gender economics was held with the support of the FROGEE project. The event, organized by the Baltic Economic Association (see balticecon.org), gathered more than 85 researchers from the Baltics and all over the world. These two papers focus on Estonia, one of the most successful economies among the transition countries, where however the gender wage gap is among the largest in the European Union.
Firm ownership and gender wage gap
An important source of wage inequality originates in firm-specific pay schemes (see for instance Card et al. 2016). Understanding the characteristics of firms associated with a gender pay gap is thus a necessary step to design relevant policy responses. In a paper entitled “The contribution of multinationals to wage inequality: foreign ownership and the gender pay gap”, Jaan Masso and Priit Vahter, both at the University of Tartu, compare the situation in foreign-owned firms with domestic ones. The fact that foreign-owned firms provide on average higher wages to their employees is well documented. However, the question of whether this premium differs between men and women remains largely overlooked.
A potential channel linking firm ownership and gender wage gap is the transfer of management practices from the home country of the investor to the affiliate. The great majority of FDI in Estonia originates from Finland and Sweden, two countries that regularly top international rankings on gender equality and that have set the fight against gender inequality as a top priority. Observing a lower level of gender wage gap in firms owned by Swedish and Finnish capital would suggest the existence of such a mechanism, even if there is evidence that Scandinavian countries do not stand out in a positive way when it comes to women in the top of the distribution (see for instance Boschini et al., 2018, and Bobilev et al., 2019).
On the other hand, Goldin (2014) has shown that a large part of the gender wage gap in the US can be explained by differences in job “commitment”: firms disproportionately reward workers willing to be available 24/7, more flexible regarding business trips, spending longer hours in the office, etc. Such workers happen to be more often men than women. Multinational firms may require such commitment and flexibility to a larger extent than domestic firms, due for instance to their higher exposure to international competition. This would imply a larger gender pay gap in foreign-owned firms compared to local firms.
To investigate this issue, Masso and Vahter (2019) rely on Estonian administrative data, providing information on the whole universe of workers and of firms in the country between 2006 and 2014. This matched employer-employee dataset allows to track the wage of individuals over the years, but also to compare wages both across and within firms. It thus becomes possible to estimate the gender wage gap at the firm level (controlling for relevant individual-level factors affecting wages, such as age and experience), and then to check whether this measure systematically differs between domestic and foreign-owned firms.
However, simply comparing the gender pay gap between these two types of firms could lead to spurious conclusions. Foreign-owned firms have on average different characteristics than domestic ones: they do not operate in the same sectors, they do not have the same size nor the same productivity. To overcome this issue, the authors rely on a matching method: for each foreign-owned firms, they match a domestic firm with similar (observable) characteristics.
They find that in domestic firms, women are on average paid 19% less than men, even after accounting for many other factors associated with wage. In foreign-owned companies, both men and women are better paid. However, both genders do not benefit from the same premium: men are paid roughly 15% more in foreign-owned firms, whereas the premium for women is only 5.4%. This difference implies an even larger gender wage gap in multinational firms. To illustrate the economic significance of these results, for a man and a woman earning a monthly wage of 1146 euros (the average gross wage in Estonia in 2016), the premium for switching from a domestic to a foreign-owned firm is respectively 171 and 62 euros. Further, they provide some evidence that lower “commitment” is associated with a stronger wage penalty in foreign-owned firms. All in all, these results suggest that there is not necessarily a relationship between a multinational wage policy (especially in its gender wage-gap dimension) and the gender norms prevailing in its country of incorporation.
Gender and wealth gap
The vast majority of academic papers studying gender inequality focuses on the wage gap. But gender inequality can affect other types of economic outcomes, such as labor force participation, unemployment duration, or wealth. The latter is of particular interest since wealth can greatly contribute to empowerment. Merike Kukk, Jaanika Meriküll and Tairi Rõõm, all at the Bank of Estonia, extend the literature with a paper entitled “What explains the Gender Gap in Wealth? Evidence from Administrative Data”. This paper is one of the first to study the gender wealth gap in a post-transition country. The literature on the gender wealth gap is rather scarce because of a lack of suitable data: wealth measures are often computed at the household level, while individual-level data is necessary for such a study.
The main aim of this paper is to depict a precise portrait of this phenomenon in Estonia. In particular, the authors do not simply estimate the overall wealth gap but investigate the magnitude of the gap across the wealth distribution. In other words, is there a difference between the poorest men and the poorest women? Or on the other side of the distribution, are the richest men more wealthy than the richest women?
For this purpose, Kukk, Meriküll and Rõõm combine administrative individual-level data on wealth with survey results. The administrative data are generally considered of much better quality than the other, but they do not provide a lot of additional information on individuals. On the other hand, survey data provide a wealth of information about individual characteristics. Merging allows getting the best of both worlds. Regarding the methodology, the authors use unconditional quantile regression to track gender differences at different deciles of the wealth distribution. They further decompose this “raw” gender gap into two components: the “explained” part, i.e., the part of the gap resulting in differences in characteristics between men and women (demographics, education, etc.), and the “unexplained” part.
This study estimates the raw, unconditional gender wealth gap in Estonia to be 45%, which is of similar magnitude as in Germany. Interestingly, this difference is essentially driven by differences in the top of the distribution: there is a large gap between the richest men and the richest women. This “raw” difference is however explained by a single variable: self-employment, as men are much more likely to have business assets than women. Once controlling for the entrepreneurship status, the wealth difference between the richest Estonians becomes insignificant. This suggests the need to support policies encouraging female entrepreneurship and to remove barriers particularly affecting women. For instance, the literature has previously pointed out that women have less access to external sources of capital than men (e.g., Aidis et al., 2007). Such distortions can ultimately result in a wealth gap at the top of the distribution, as documented by this paper.
In addition, the literature has proposed several mechanisms that could result in gender-specific patterns of wealth accumulation. The simplest channel is through the wage gap, as it can be seen as the accumulation of the wage gap over time (e.g. Blau and Kahn, 2000). The authors thus compare the gender gaps in wealth and income. They uncover a strong wage gap, with men earning significantly more than women starting at the 6th decile: the higher we go in the income distribution, the larger the wage gap. How to reconcile this finding with the absence of a wealth gap conditional on entrepreneurship status? A possible explanation suggested by the authors is that women simply accumulate wealth better than men do.
These two papers illustrate two different mechanisms explaining gender-specific economic outcomes. The larger wage gap observed in multinational companies can be explained by a stronger commitment penalty for women, mostly because of childcare. This asks for two potential policy interventions. First, the development of childcare could facilitate the reduction in the “commitment gap” that disrupts women’s careers. Second, institutions could support a more flexible repartition of childcare responsibilities. Note however that Estonia already has the longest duration of leave at full pay (85 weeks), and that this leave can be freely split between parents. As for the wealth gap at the top of the wealth distribution, it can to a large extent be explained by the entrepreneurship status. This difference could partly be explained by differences in preferences and risk-aversion, which would require long-run policies to be mitigated. But in the short run, there is room for specific policies supporting female entrepreneurship and removing barriers particularly affecting women, such as a tighter credit constraint.
- Aidis, R., Welter, F., Smallbone, D., & Isakova, N. (2007). Female entrepreneurship in transition economies: the case of Lithuania and Ukraine. Feminist Economics, 13(2), 157-183.
- Blau, F. D., & Kahn, L. M. (2000). Gender differences in pay. Journal of Economic perspectives, 14(4), 75-99.
- Bobilev, R., Boschini, A., & Roine, J. (2019). Women in the Top of the Income Distribution: What Can We Learn From LIS-Data?. Italian Economic Journal, 1-45.
- Boschini, A., Gunnarsson, K., & Roine, J. (2018). Women in Top Incomes: Evidence from Sweden 1974-2013. IZA Discussion Paper No. 10979 .
- Card, D., Cardoso, A. R., & Kline, P. (2015). Bargaining, sorting, and the gender wage gap: Quantifying the impact of firms on the relative pay of women. The Quarterly Journal of Economics, 131(2), 633-686.
- Goldin, C. (2014). A grand gender convergence: Its last chapter. American Economic Review, 104(4), 1091-1119.
- Meriküll, J., Kukk, M., & Rõõm, T. (2019). What explains the gender gap in wealth? Evidence from administrative data. Bank of Estonia WP No. 2019-04.
- Vahter, P., & Masso, J. (2019). The contribution of multinationals to wage inequality: foreign ownership and the gender pay gap. Review of World Economics, 155(1), 105-148.
This brief is based on research that studies gender difference in wages in Belarus using survey data from 2017. According to the results, the unconditional gender wage differential equals 22.6%. The size of the wage gap is higher in the state sector than in the private sector. Additionally, it increases in the state sector throughout the wage distribution and accelerates at the top percentiles, indicating the presence of a strong glass ceiling effect.
The causes and consequences of the gender wage gap in the labor market, that is the difference between the wages earned by women and men, continue to attract increasing attention in empirical studies worldwide.
Belarus’ labor market is not an exception and faces the problem of wage inequality like other neighboring and transition countries. According to the National Statistical Committee of the Republic of Belarus (Belstat), the average gender wage gap in terms of monthly wages was 19% in 2000, it increased up to 23.8% in 2015, and reached 25.4% in 2017.
In this regard, this brief updates the estimates of the gender wage gap in Belarus. And it summarizes the results of the study on what the role of the state and private sectors are in the distribution of gender wage differences in Belarus (Akulava and Mazol, 2018).
Data and methodology
The data used in the research is from the Generations and Gender Survey (GGS) conducted in Belarus in 2017. This survey is a nationally representative dataset that is based on interviews of about 10,000 permanent residents of Belarus, aged 18–79, covering the whole country disaggregated by regions. The GGS contains information on a range of individual (age, gender, marital status, educational attainment, employment status, hours worked, wages earned etc.) and household-level characteristics (household size and composition, land holding, location, asset ownership etc.).
The analysis is based on the typical Mincer model of earnings that estimates individual wage income as a function of various influencing factors using the OLS approach (Mincer, 1974). Specifically, the Mincerian wage equation is defined where the log of the hourly wage rate is regressed on a set of male and female workers’ personal and job characteristics (educational level, working experience, occupational type, organization type, family characteristics, and region).
Next, we use the Oaxaca-Blinder (OB) methodology (Oaxaca, 1973; Blinder, 1973) to identify and quantify the contribution of personal characteristics and the unexplained component (which is referred to as differences in returns) to the wage difference between males and females.
Finally, we apply the Machado-Mata (MM) technique (Machado and Mata, 2005) to look into the nature of the wage gap at various points of the income distribution and also to test the difference for individuals employed in the state or private sectors. For the Machado-Mata procedure, we estimate our specifications at the 10th, 25th, median, 75th and 90th percentiles of the wage distribution.
The analysis shows that women’s wages are lower than men’s wages all over the wage distribution. The average raw gender wage gap equals 22.6% and it increased substantially compared with 9.0% in 1996 and 17.8% in 2006, the numbers obtained in the study conducted by Pastore and Verashchagina (2011).
Figure 1. Gender differential by quantile of the wage distribution
Source: Authors’ estimates based on GGS.
The level of female earnings is lower than the male regardless of the occupational type, educational background, work experience and organizational type. Moreover, the underpayment of women is lower for low earning workers, but increases up to the end of the wage distribution (see Figure 1).
The OB decomposition shows that female educational attainment and job-related experience help to decrease the level of the wage gap slightly (see Table 1).
Table 1. Oaxaca-Blinder decomposition results
Source: Authors’ estimates based on GGS.
However, the occupational choice is leading to an expansion of the difference in earnings. However, its effect is also small, indicating that occupational segregation plays a minor role in explaining the gender wage gap. The major share of the gender wage gap is formed by the unexplained part, which is likely to be attributed to discrimination.
Next, the level of remuneration is higher among private companies. However, contrary to other countries in transition, the average gender wage gap in Belarus in the private sector is lower than in the public sector.
Moreover, the MM decomposition estimates presented in Table 2 demonstrate that the gender wage gap in the state sector shows evidence of the glass ceiling effect (the size of the total wage gap expands at the top of the wage distribution), while no evidence of either glass ceiling or sticky floor (the size of the total wage gap increases at the bottom of the wage distribution) in the private sector.
The negative coefficient near the characteristics part in the private sector shows that female endowments outweighs their male counterparts. Thus, controlling for personal characteristics, if the labor market rewards males and females equally, the wages of females in the private sector should be substantially higher (see Table 2).
Table 2. Machado-Mata decomposition of the observed gender wage gap by organization type
Source: Authors’ estimates based on GGS.
Finally, the results also suggest that female workers are better off being in the private sector at the lowest and the highest percentiles (i.e. the size of the gender wage gap is lower there compared to the 25th and 50th percentile).
A possible explanation for all the above is that institutional differences seem to play a crucial role here. First, Belarusian private firms work under stronger regulation than in other transition economies which makes it harder for them to set low wages. Second, they also operate under stronger competition (compared to state companies), which force them to identify individual productivity more correctly, narrowing the gender difference in pay. In contrast, the paternalistic attitude to women left as a legacy from the Soviet Union further increases the gender wage gap in the public sector.
In this brief, we present new evidence on the existence of a gender wage gap in the Belarusian labor market and analyze the differences in its distribution between the state and private sectors.
Our results show that the unconditional gender wage gap in terms of hourly wages equals 22.6%. Thus, jointly with a previous study (see Pastore and Verashchagina, 2011) and recent official indicators, all these indicate that the pace towards gender equality in Belarus seems to be sluggish. For the moment, all institutional changes accomplished by the Belarusian government to reduce gender discrimination are not enough and require additional efforts to cope with that problem.
However, the gender wage gap is shown to be much wider in the public sector than in the private sector. At the same time the private sector appears to be more attractive than the public sector in the country in terms of the level of remuneration. Therefore, additional structural shifts of the economy accompanied by the growth of competition are needed to induce a further reduction of the gender wage gap.
- Akulava, M. and A. Mazol. (2018). What Forms Gender Wage Gap in Belarus? BEROC Working Paper Series, WP no. 55.
- Blinder, A. (1973). Wage Discrimination: Reduced Form and Structural Estimates. Journal of Human Resources, 8, 436-455.
- Machado, J., and J. Mata. (2005). Counterfactual Decomposition of Changes in Wage Distributions Using Quantile Regression. Journal of Applied Econometrics, 20(4), 445‑465.
- Mincer, J. (1974). Schooling, Experience, and Earnings. New York: Columbia University.
- Oaxaca, R. (1973). Male-Female Wage Differentials in Urban Labor Markets. International Economic Review, 14(3), 693-709.
- Pastore, F., and A. Verashchagina. (2011). When Does Transition Increase the Gender Wage Gap? An application to Belarus. The Economics of Transition, 19(2), 333-369.
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