- Original article
- Open Access
Just like a woman? New comparative evidence on the gender income gap across Eastern Europe and Central Asia
© The Author(s). 2018
- Received: 13 April 2017
- Accepted: 27 December 2017
- Published: 14 June 2018
I examine the incidence and determinants of the gender income gap in Kazakhstan, Macedonia, Moldova, Serbia, Tajikistan, and Ukraine using recent household data based on an identical survey instrument across countries. Four main results are established, using a range of estimators, including OLS, interval regression, and quantile regression: (1) the presence of a substantively large gender income gap (favoring males) in all six countries; (2) some evidence of a gender-related glass ceiling in some of these countries; (3) some evidence that endowments diminish the income gaps, while the returns to characteristics increase the gaps; and (4) while observed individual characteristics explain a part of the gaps, a substantial part of the income gap is left unexplained. In sum, these results are consistent with the presence of income discrimination towards females but at the same time also point towards the importance of continued attention towards institutions and economic policy for decreasing the gender income gap in these former formally gender neutral economies—notably through attention towards the maternity and paternity leave system, as well as public provision of child care.
JEL Classification: J16, J31, J7
- Income gap
- Oaxaca-blinder decomposition
- Detailed decomposition
- Maternity/paternity leave policies
- Eastern Europe and Central Asia
Despite a decline in recent years, the gender gap in income (or earnings or wages) undoubtedly is one of the most persistent regularities in the labor market. Most of the available evidence, however, is for Western economies, especially the USA (Albrecht et al. 2003; Altonji and Blank 1999; Blau 1998; Blau and Kahn 1992, 1996, 1997, 2000, 2003; Cho and Cho 2011), though evidence for the former socialist regimes of Eastern Europe and Central Asia is starting to emerge (Brainerd 2000; Grajek 2003; Hunt 2002; Orazem and Vodopivec 2000).
This decline notwithstanding the inequality of women in the labor market is important for several reasons. Most notably, the lack of gender equality in the labor market likely is associated with economic dependence of women more generally, leading to lack of influence in decision making—including investments in health and education for the household, including children, and greater susceptibility to violence in the home. Could the position of women in the labor market instead be improved, these outcomes will likely be reversed, also.
In light of these considerations, this paper provides a thorough examination of the incidence and nature of the gender income gap across six former socialist countries from Eastern Europe and Central Asia: Kazakhstan, Macedonia, Moldova, Serbia, Tajikistan, and Ukraine. Again, while evidence on the gender gap in transition countries in general is starting to emerge, it seems fair to say that it is still the case that little or no systematic data collection and reporting has been taking place, so far—thus mostly resulting in only fragmented data analysis for individual countries, at best. Contrary to this, the data examined here originate from a recent UNDP/UNICEF survey which was conducted using identical questionnaires for all six countries, thus greatly facilitating such comparative analysis as is pursued here. Indeed, examining the gender income gap for a collection of transition countries using comparable survey instruments is likely to increase our understanding of the gender income gap in transition countries in general. This includes the extent to which income-based gender discrimination seems to be present, as well as the extent to which the drivers of a possible gender income gap differs across countries—thus ultimately also serving as inputs for policy makers to help better address such gender-based discrimination by implementing appropriate gender-targeted policies.
The analysis starts out by establishing the prevalence of a substantively large gender income gap (favoring males) in all six countries, then goes on to estimate Mincer-type income regressions, and finally decomposes this gap using several alternative twofold and threefold decompositions to test the robustness of results—for both aggregate and detailed gender income gap decompositions, where the latter decomposes the origins of the gender income gap into its component part in terms of (groups of) specific explanatory variables such as education and sector of occupation.
The remainder of this paper is structured as follows. First, the next section reviews recent developments in the six countries examined here to provide a foundation for the subsequent analysis, including a context in which to both perform the analysis and interpret the subsequent results. Section 3 presents the data, discusses the construction of the dependent and explanatory variables, and estimates the raw gender income gaps. This is followed, in Section 4, by a discussion of the estimation strategy and related issues. Section 5 presents the main results while, finally, Section 6 concludes, discusses policy implications, and provides directions for further research.
The UNDP Social Exclusion Survey is a comprehensive nationally representative household survey aimed at evaluating living conditions and the level of social exclusion to help better plan future social and economic programs in a country. The survey was carried out for Kazakhstan, Macedonia, Moldova, Serbia, Tajikistan, and Ukraine using an identical survey instrument across all six countries of the adult population (15 years and above). The surveys used a multi-stage clustered and stratified sampling design involving multiple stages for each country including the region, rural-urban location, and cities/administrative division of an individual country, where the main respondent within the randomly selected household was selected either using the “next birthday” principle4 or the Kish Grid,5 both of which help ensure that the respondent is chosen randomly among all the eligible respondents in the selected household.6 Basic household information (age, gender, educational attainment) was then recorded for all household members 15 years and older—and additional information, including labor market information such as employment status, income, and job characteristics (if working).
Interviews were conducted November–December 2009. Two thousand seven hundred individuals were interviewed in each country (except for Serbia, where 300 Roma persons in the so-called Roma booster part of the survey—as of the time of this analysis—were not released as part of the main dataset, leading to an initial sample for Serbia of 2401 individuals).
Since the dependent variable is income, the sample was first conditioned on individuals who answered “yes” to having worked for payment in cash or kind for at least 1 day during the past month (7044 observations). The kind of employment which women answering “yes” to this question have, however, is likely to differ significantly across countries, so that the wage gap in one country is then potentially estimated using many more part-time or informal workers than the estimate in another country. So as to base the wage gap estimates on a similarly employed group of women (and men) in each country, the sample is therefore further restricted to full-time workers, only 6032 observations. Some workers answer “do not have any income” when asked about their own total net monthly income later in the questionnaire and therefore must be excluded, leading to an initial sample of 5971 individuals. Some individuals are either temporarily on leave from their main job and/or have missing information on income or on one or more explanatory variables and are therefore dropped from the estimation sample, as well, leading to a final total estimation sample of 5533 individuals, distributed across the six countries (and across gender) as follows: Kazakhstan 1109 (455 females, 654 males), Macedonia 928 (438 females, 490 males), Moldova 860 (508 females, 352 males), Serbia 989 (452 females, 537 males), Tajikistan 614 (245 females, 369 males), and Ukraine 1033 (496 females, 537 males). The means and standard deviations for the final estimation samples by country and gender are reported in Table 7 in Appendix 1.
Total monthly income data: original intervals and percentage of workers in each interval
100 (N = 1109)
100 (N = 860)
100 (N = 928)
100 (N = 989)
100 (N = 614)
100 (N = 1033)
The explanatory variables are specified based on standard human capital theory (Becker 1964; Mincer 1974; and, for a more recent exposition, Heckman et al. 2008) and include several potentially important individual and job characteristics, as well as geographical location—all of which have been shown to be important in previous studies of income (or earnings or wage) determinants: years of schooling, age, and age squared13 (to capture potential labor market (and other) experience), ownership/sector (created as a set of five dummy variables (public; private; mixed; cooperative, NGO, and other; and not specified14)), contract status (dummy variable for no written contract/informal), social insurance coverage (dummy variable for no coverage), and geographical location (dummy for urban location).15 Lastly, it should be noted that for several of the questions used for constructing the explanatory variables used in this analysis “Don’t know” and “Refuse” were given as additional categories, rather than as simply being missing per se—which is how most other surveys treat these categories. Adding a separate dummy variable of “Don’t know/Refuse” for these individuals—which otherwise would be excluded—help retain these individuals in the estimation sample, and is therefore also the approach followed here.
Raw gender income gap in six Eastern European and Central Asian countries
While the existence of substantively large gender income gaps have now been established across all six countries, the objective of the main analysis of this paper is to try to explain these gaps in terms of, on the one hand, characteristics/endowments such as educational attainment and job characteristics and returns to these characteristics (threefold division) and, on the other hand, observable and unobservable characteristics (twofold division). While the empirical strategy underlying this approach is widely used, it still seems fruitful to review the main components in some detail—which, therefore, is the objective of the next section.
This section reviews the main results. This is done in three main parts: (i) Mincer income regressions, (ii) overall income decompositions, and (iii) detailed income decompositions. It should be noted that since some of the tables are rather large, they have been placed in the appendices (but are referred to, and discussed, in the body text below).
5.1 Mincer income regressions
Starting with the results that are most consistent across all six countries, in line with previous research, the results from the Mincer regressions reveal substantial returns to education (Table 8 in Appendix 2). Frequently, the return to an additional year of schooling is larger for females than for males. For Serbia, for example, the return to an additional year of education is 8% for females but only 5.5% for males—which is consistent with previous evidence (Blunch and Sulla 2010; Staneva et al. 2010).21 The evidence on returns to ownership is mixed across countries, though frequently there is not much of an association. For Kazakhstan and Serbia, for example, there is no statistical difference across ownership status. Having no written contract (reference: written contract) is associated with an income penalty, though not always statistically significantly so. The “Don’t know”/“Refuse” category again experiences a negative return in several cases—and both substantively and statistically significantly so for the cases of Serbian and Moldovan males. Not being covered by social security on the main job (reference category: covered) is associated with a negative and frequently substantively large income premium in several cases—and for Serbia for both females and males, both also statistically significant. Workers from urban areas tend to receive a positive income premium, which again accords well with their living expenses being larger, also.
Is there a glass ceiling related to gender in one or more of these former socialist economies? This is a testable hypothesis and I examine this using the approach laid out in Albrecht et al. (2003) for the case of Sweden, by estimating quantile regressions for the pooled (by gender) Mincer regressions—with clustered standard errors, following Parente and Santos Silva (2016). From these results, there is some evidence of a glass ceiling for Moldova and Ukraine, where the gender gaps are stronger at the higher end of the income distribution, whereas the evidence for the other countries is more mixed (Table 11 in Appendix 3).
5.2 Sensitivity analysis
Estimated gender coefficients from gender dummy, only. Estimations: OLS and interval regression
OLS (using mid-points)
From Table 5, the results for the raw gender gaps are virtually identical to the OLS results, except for Tajikistan. Further estimating the full Mincer regressions using interval regression (Table 9 in Appendix 2), while there are some differences, the results are fairly robust, overall. With such relatively minor differences between the OLS and interval regression results, it seems prudent to continue with OLS for the remainder of the analysis.
The Mincer regressions estimated so far are purposely sparse in terms of the amount of explanatory variables. This is both to keep the analysis simple and because the inclusion of certain explanatory variables is debatable. In particular, some explanatory variables may themselves reflect the impact of discrimination, whereby their inclusion leads to understating the “unexplained gender wage/income gap” (the presence of which is taken by many researchers to measure the amount of discrimination, though it is really only consistent with the presence of discrimination) (Altonji and Blank 1999: 3191). Hence, one view here is that such variables may better be left out when estimating Mincer regressions, especially when the focus is on possible (gender, racial or other) discrimination. This is the case especially for industry and occupation, which is why (in addition to the inclusion of these variables leading to quite thin cells for many of these groups) I have left these variables out of the analysis, so far. On the other hand, it would still seem useful to at least explore the consequences of adding industry and occupation as a robustness check—as well as to potentially gain insights into possible gender-based sorting into occupations and/or industry, so that including these may provide additional information on segregation.
From the results from these augmented Mincer (OLS) regressions, it can be seen that the estimated coefficients are indeed frequently statistically insignificant due to the frequently quite small cell sizes (Table 10 in Appendix 2). Additionally, however, the results also reveal that men receive an income premium in traditional male-dominated industries such as mining, manufacturing, and construction—whereas there does not seem to be any such patterns for women. For occupation, the dummies are largely statistically insignificant. These results thus provides some, though arguably limited, evidence on selection and sorting into industry—if not occupation—in these countries. Given the small cell sizes and therefore frequently statistically insignificant results, it again seems prudent to continue with the more parsimonious specification estimated previously for the remainder of this analysis.
5.3 Overall income decompositions
Overall income decompositions: three- and twofold
Moving to the twofold decompositions, females on average have better employment-related characteristics (such as educational attainment and sector of employment) as indicated by the negative sign in the explained part—which in turn serves to narrow the overall income gap—whereas the unexplained part (capturing all the factors that cannot be attributed to differences in observed worker characteristics) accounts for an even larger share of the gender income differential (Table 6, bottom panel).
Notably—as can be seen from the results from the sensitivity analysis shown in Appendices 4 and 5 (Tables 12 and 13 respectively)—these results are quite robust to whether the decomposition is performed from females’ viewpoint (i.e., using male endowments and returns) or whether the decomposition is performed from males’ viewpoint (i.e., using female endowments and returns) for the threefold decompositions or from any of the many different possibilities of specifying the “absence of discrimination” group in the twofold decompositions.
Overall, these results are consistent with earlier findings for the region (Newell and Reilly 2001; Reva 2010; Babović 2008; Staneva et al., 2010) —and, thus, are indicative of substantial income discrimination against females in the labor markets of all six countries. But how are the overall gaps—both two- and threefold—explained by the endowment of and returns to the separate individual characteristics (or groups of characteristics), rather than by the endowment of and returns to individual characteristics overall? This is the object of the final empirical analysis, following next.
5.4 Detailed income decompositions
The detailed income decompositions allow further decomposing the overall gaps just established into the individual explanatory variables from the Mincer income regressions, discussed earlier. To help better facilitate interpretation, however, results are reported in groups of individual variables (e.g., aggregating up the contribution from all the ownership variables).
The results from the detailed threefold decompositions (Tables 14–19 in Appendix 6) reveal that in several cases, one of the most important contributors to the narrowing of the gender income gap—both substantively and statistically—in terms of individual characteristics, is education. For both Kazakhstan and Serbia, for example, education accounts for almost all of the explained gap—and at about 3%-points, education also accounts for a substantial part of the Ukrainian income gap. For Macedonia, although substantively large (at 2.2%-points), the effect is not statistically significant. For Moldova and Tajikistan, however, the effect is practically nil—both in substantive and statistical terms. In several cases, education also works to improve the gender gaps through the part attributable to characteristics, again consistent with earlier studies (Babović 2008; Blunch and Sulla 2010; Staneva et al. 2010).22 Other observable characteristics and returns widen the gender gap, however. For Serbia and Moldova, for example, the returns to contract status widen the gap, as do social security in Ukraine. With a few exceptions, most of the remaining estimated effects are not statistically significant.
The results from the detailed twofold decompositions are mostly consistent with the results for the detailed threefold decompositions (Tables 20–25 in Appendix 7), so that education again is the most consistently important contributor to narrowing the gender income gap across all six countries, except for Moldova and Tajikistan, where the effect again is practically nil—both in substantive and statistical terms.
This paper examines the gender income gap in terms of its prevalence, magnitude, and determinants using a recent data set collected using identical survey instruments for six countries from Eastern Europe and Central Asia and thereby adds to the emerging, somewhat fragmented (partly because of using many different, not always comparable data sources) literature on the gender income gap for the former socialist economies.
Using a range of estimators, including OLS, interval regression, quantile regression, and overall and detailed income decompositions, four main results are established: (1) the presence of a substantively large gender income gap (favoring males) in all six countries; (2) some evidence of a gender-related glass ceiling in some of these countries; (3) some evidence that endowments diminish the income gaps, while the returns to characteristics increase the gaps—indicating that in some countries, women are concentrated in better paying sectors, have more education, and so on, while males have higher returns to characteristics overall; and (4) while observed individual characteristics explain a part of the gaps, a substantial part of the income gap is left unexplained.
These results have strong policy implications, consistent as they are with the presence of income discrimination towards females in the labor market. In particular, the continued presence of a gender income gap is likely to keep out females from the labor force that would otherwise be active participants and add to the economy. While increased economic activity has been important during the transition from a planned to a market economy, with the recent financial crisis, such efforts are perhaps more important than ever—thus highlighting not only the importance of both employment generation but also the improvements of the regulatory environment, since the former may be severely dampened with the continued presence of a substantively larger gender income gap.
But what are some of the potential mechanisms driving the gender income gap observed here—and does economic policy have a possible role to play? It was noted in the review of these countries’ historical and economic background how, after initially abandoning programs specifically supporting the role of women in the labor market, most countries have gone back to instituting such programs anew.
Among such programs are paid maternity leave, where many countries have programs providing extensive programs—frequently of a duration longer even than in many advanced Western economies. As has also been noted elsewhere (Kuddo 2009: 78-79), these extensive programs may adversely affect women’s labor market participation, as well as lead to actual or perceived erosion of skills, and, perhaps even more importantly, act to create reluctance on the part of employers to hire women of childbearing age, to avoid the associated indirect costs such as replacement workers. And the longer the leave, the greater the perceived disincentive from the employers’ point of view. As also noted earlier, the parental leaves prevalent in some transition countries—since they frequently can be expected to be taken by the mother—may effectively act as an additional maternity leave. To counter this cycle, therefore, one possibility is to bring the leave durations more in line with those in Western economies—though, as also suggested by Kuddo (2009: 78), so as to continually help support women’s access to the labor market, this should be combined with better access to child care facilities. Alternatively, extension of paternal leaves may be an option. In many transition countries, these are either absent or of an extremely short duration, sometimes only 1 week (Kuddo 2009: Table A10). Introducing (or extending) paternal leaves of a much longer duration would help level the playing field for men and women more in the labor market, since employers now would have to expect a potential leave of any employee of childbearing age (or for the males, with a wife of childbearing age), regardless of gender. As a possible side effect, such institutionalized gender equality in terms of child-birth related leaves may also help bring about more tolerance and openness to childbearing as a reason for detaching from the labor market for a shorter or longer period, regardless of the gender of the worker.
In terms of future research, even with the evidence emerging in recent years, we are only beginning to start to get a grasp of the prevalence and the nature of the gender income gap in the former socialist economies in Eastern Europe and Central Asia. Even more research is needed, especially if we want to go into the “black box” of what determines the gender income gap in terms of causal pathways. Crucial for these efforts, however, is the availability—and therefore collection—of more and better data.
The data examined here is a case in point. While it is certainly commendable—and very useful—to collect data using identical questionnaires for several countries simultaneously, it is a shame that such an important variable as labor income (income) is reported (if not collected) in such a way that the variation and therefore the informational content of this key variable is heavily diminished. An additional limitation of this dataset was the somewhat small survey sample sizes (certainly if conditioning on currently working adults), among other things limiting the amount of explanatory variables to relatively few individual and job characteristics, so as to avoid too small cell sizes. In turn, these comments may well serve as a warning to national and international agencies in charge of future data collection.
Among the six countries studied here, only Macedonia has paternity leave—and with a duration of only up to 7 days (Kuddo 2009: Table A10).
In Tajikistan, there is a possibility for an additional unpaid leave until the child reached 3 years of age.
Which refers to choosing, among all the eligible respondents (here, individuals 15 years and above), in a given chosen household the person with the next birthday as the respondent of the household.
Different countries may have different taxation methods, but there is unfortunately not much to do about this in practice, apart from providing a cautionary remark.
Some might prefer instead to examine wage rates, but unfortunately, hours worked are not available in the current dataset. It may be argued, however, that if one is interested in total worker welfare per se, one should indeed be examining total labor earnings (a proxy of which is available here) rather than the wage rate.
The questionnaire refers to them as “local currency (20 quintile) UNDP intervals,” “local currency (40 quintile) UNDP intervals,” etc., but they are not quintiles in the usual meaning of the word since they do not each contain 20 of the sample (neither among the total sample or the subsample that was working within the past month).
Based on personal correspondence with Susanne Milcher, Social Inclusion and Poverty Reduction Specialist, UNDP
Specifically, if belonging in the four lowest income brackets (see Table 3 below), I assume that peoples’ income is the midpoint of the respective income bracket, and if belonging in the top bracket, I assume that peoples’ income is the sum of the upper class point of the bottom bracket and the lower class point of the top bracket. The latter seems to help provide a conservative estimate of the degree of overall earnings inequality and therefore, to the extent that females probably are underrepresented in the top earnings bracket, also a conservative estimate of the female-male earnings gap.
Additionally, however, I also conduct a sensitivity analysis where I instead estimate interval regressions to examine the robustness of the OLS results using the interval mid-points.
Divided by 100, for scale consistency with the other explanatory variables
“Not specified” was specified as a separate category in the questionnaire and is therefore also treated as a separate group here.
The dataset also includes information on occupation (14 categories) and industry (18 categories), but inclusion of these as explanatory variables is debatable since if they themselves reflect the impact of discrimination, they will understate the “unexplained gender wage/income gap” (the presence of which is taken by many researchers to measure the amount of discrimination, though it is really only consistent with the presence of discrimination) (Altonji and Blank 1999: 3191). Additionally, including these variables frequently leads to some very small cell sizes and therefore also very imprecisely measured results for these variables; these variables are therefore not included in the main analysis (I do, however, include them in a sensitivity analysis).
I also allow for clustered standard errors in the quantile regressions, following Parente and Santos Silva (2016).
Alternatively, however, this equation could also be represented based on the prevailing earnings structure of female workers; this will be explored further in the sensitivity analysis.
See Oaxaca (1973), Blinder (1973), Cotton (1988), Reimers (1983), Neumark (1988), and Jann (2008) for different approaches—basically, these differ in the relative weights they attribute to the two groups in the decomposition.
In contrast, somewhat surprisingly, Reva 2010 finds that male returns are higher than female returns for all levels of education in Serbia.
Again, detailed gender earnings decomposition are only available for very few countries in the region.
This manuscript is based on a background paper commissioned by the World Bank’s Poverty Reduction and Economic Management Unit, Europe and Central Asia Region Department. I thank Mihail Arandarenko, David Ribar, Victor Sulla, and the participants at the first GRAPE Gender Gaps Conference for their helpful comments and suggestions and Victor Sulla and Caterina Ruggeri Laderchi for their managerial support. Remaining errors and omissions are my own. The data were kindly provided by the United Nations Development Program (UNDP). Assistance from Susanne Milcher, UNDP, helped me understand the data better and is greatly appreciated. I would like to thank the anonymous referee and the editor for the useful remarks. The findings and interpretations are those of the author and should not be attributed to the World Bank, the United Nations Development Program, or any affiliated institutions.
Responsible editor: Hartmut F. Lehmann.
This manuscript is based on a background paper commissioned and funded by the World Bank’s Poverty Reduction and Economic Management Unit, Europe and Central Asia Region Department. The World Bank ECA Department, however, was not involved in the design of the study, nor in the collection, analysis, and interpretation of data, or in writing the manuscript.
Availability of data and materials
The data examined here is proprietary to the UNDP but was obtained and should, according to a UNDP Report, still be obtainable upon request via the following Website: http://europeandcis.undp.org/poverty/socialinclusion (when I last checked, this page seemed to have been removed, however). For more details, the UNDP Report can be downloaded from (as of January 14, 2018): http://www.eurasia.undp.org/content/rbec/en/home/library/poverty/Regional_Human_Development_Report_on_social_inclusion.html?download.
The author is an Associate Professor at the Economics Department at Washington and Lee University in Lexington, Virginia, USA.
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