The Gender Earnings Gap Revisited: A Comparative...

1

The Gender Earnings Gap Revisited: A Comparative Study for Serbia and Five Countries in Eastern Europe and Central Asia

Niels-Hugo Blunch Washington and Lee University & IZA

Department of Economics Lexington, VA 24450, USA Email: [email protected]

This Version: November 22, 2010 JEL Classifications: J16, J31, J7 Keywords: Gender, earnings gap, Oaxaca-Blinder decomposition, Serbia, Eastern Europe and Central Asia.

Abstract Adding to the emerging evidence of the gender earnings gap in the former Socialist regimes of Eastern Europe and Central Asia, this paper examines the incidence and determinants of the gender earnings gap in Kazakhstan, Macedonia, Moldova, Serbia, Tajikistan and Ukraine using recent household data based on an identical survey instrument across all six countries. This paper establishes three main results that hold across all six countries: (1) the presence of a substantively large gender earnings gap (favoring males); (2) endowments diminish the earnings gaps, while the returns to characteristics increase the gapsindicating that women are concentrated in better paying sectors, have more education, and so on, while males have higher returns to characteristics overall; (3) while observed individual characteristics explain part of the gaps, a substantial part of the earnings gap is left unexplained. In sum, these results are consistent with the presence of earnings discrimination towards females but at the same time also point towards the importance of continued attention towards, among other factors, the education system as a potentially important vehicle for decreasing the gender earnings gap in these former formally gender neutral economies. This is a background paper commissioned by the World Banks Poverty Reduction and Economic Management Unit, Europe and Central Asia Region Department. I thank Victor Sulla for helpful comments and suggestions and Victor Sulla and Caterina Ruggeri Laderchi for managerial support. Remaining errors and omissions are my own. The data were kindly provided by the United Nations Development Programme (UNDP). Assistance from Susanne Milcher, UNDP, helped understand the data better and is greatly appreciated. The findings and interpretations are those of the author and should not be attributed to the World Bank, the United Nations Development Programme, or any affiliated institutions.

2

1. Introduction Despite a decline in recent years, the gender gap in earnings undoubtedly is one of the most persistent regularities in the labor market. Most of the available evidence, however, is for Western economies, especially the US (Albrecht, Bjrklund, and Vroman, 2003; Altonji and Blank, 1999; Blau, 1998; Blau and Kahn, 1992, 1996, 1997, 2000, 2003), though evidence for the former Socialist regimes of Eastern Europe and Central Asia is starting to emerge (Brainerd, 2000; Grajek, 2003; Hunt, 2002, Orazem, 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 makingincluding 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.

This paper extends the analysis in Blunch and Sulla (2010), which focused on labor market transitions and wages in Serbia and also established a gender earnings gap (though that was not the main focus of the study), to examine explicitly the gender earnings gapby means of a thorough examination of the incidence and nature of the gender earnings gap in Serbia and five other former socialist regimes from Eastern Europe and Central Asia. The data originate from a recent UNDP/UNICEF survey which was conducted using identical questionnaires for all six countries. The analysis starts out by establishing the prevalence of a substantively large gender earnings gap (favoring males) in all six countries, then goes on to estimate Mincer-type earnings regressions, and finally decomposes this gap using several alternative two-fold and three fold decompositions to test the robustness of resultsfor both aggregate and detailed gender earnings gap decompositions, where the latter decomposes the origins of the gender earnings gap into its component part in terms of (groups of) specific explanatory variables.

The remainder of this paper is structured as follows. First, the next section reviews recent developments in the six countries examined here, focusing at Serbia (though, given their common history in recent years, most of the issues reviewed for the other countries, as well). Section three presents the data, discusses the construction of the dependent and explanatory variables, and estimates the raw gender earnings gaps. This is followed, in section four, by a discussion of the estimation strategy and related issues. Section five presents the main results while, finally, section six concludes, discusses policy implications, and provides directions for further research.

3

2. Recent Developments in Serbia and Five Other Countries in Eastern Europe and Central Asia This section first gives a brief historical background and motivation for studying gender and labor market issues in Serbia (and in the former Socialist countries of Eastern Europe and Central Asia more generally, given their common history in the past 70 years or so) and then goes on to present recent economic trends in Serbia and the other five former Socialist countries examined in this paper. Gender and the Labor Market in Serbia1 To better understand the importance of gender in Serbian society (and, again, in the former Socialist countries of Eastern Europe and Central Asia more generally)including the labor marketit is important to first to realize that the background (before the transition) is one of formal gender equality, as in other former communist countries. With the collapse of the Berlin Wall in 1989 and the wars followed the transition towards market economythough, according to Babovi (2008: 13) During the last decade of the twentieth century, Serbian society was characterized by a state of blocked transformation that included the obstruction of essential changes in market economy and political democracy by the ruling elite. A profound economic crisis, a deterioration of social institutions, wars with grave economic, social and humanitarian consequences, the impoverishment of a large portion of the population, the expansion of the informal economy and the hampering of the development of civil society, were the main characteristics of Serbian society in this period.

Thus, it is not really until the beginning of the new millennium that the transformation towards a market economy has begun to really take off. Indeed, during the 19990s, Serbia experienced trends of re-traditionalization, which then led to the deterioration of the position of women in the economy overall (Babovi, 2008: 13-14). Reform endeavors since the year 2000 have tried to promote gender equality but as of yet has not been successful, as seen by a number of different indicators (Babovi (2008: 14-15):

(1) The participation of women in government and political life more generally is still quite lowfor example, following elections in 2000 and 2003, women comprise only 10.8 percent of the Members of Parliament.

(2) Female labor market participation has deteriorated severely in recent years, from about 70 percent in the socialist times to around 58 percent in recent years; additionally, females are hit harder than males in terms of long-term unemployment and poor entrepreneurship trends.

(3) Female education has improved relative to that of males but at the very highest levelsMasters and PhDfemales are still lagging behind, accounting for only about 30 to 32 percent.

(4) Due to increased marginalization socially, financially and otherwise, a number of female categories are particularly under pressure in recent years: single mothers (especially with small and/or special needs children), housewives, elderly, sick and/or disabled women, rural women (especially those without property), displaced and refugee women, uneducated and/or unemployed women.

(5) The private sphere is characterized by a patriarchal division of gender roles,

1 This section draws heavily upon Babovi (2008).

4

only limited access to financial resources, and a high level of violence.

Altogether, while some key laws related to the above have been established, otherssuch as the Anti-Discrimination Law and Gender Equality Lawhave not (Babovi (2008: 15). Females therefore still appear to be at a strong disadvantage in many of the dimensions of Serbian societynot least the labor market. There therefore seem to be ample reason to explore in more detail the nature and correlates of this disadvantage, so as to try to accommodate these potential inequalities. Before moving on to the more analytical analysis pertaining to thisfocusing at the female earnings disadvantagerecent economic trends in Serbia and the other five former Socialist countries examined in this paper are briefly reviewed so as to set the stage for the subsequent empirical analysis. Economic Trends in Six Former Socialist Economies While all the six countries examined in the analysis in this paper are all former Socialist economies they differ widely among themselves in terms of key economic indicators (Table 1). First, their GNI per capita (in 2008) are very different, ranging from 600 US$ for Tajikistan, so that that country is not even among the lower-middle income countries, to 6,170 US$ for Kazakhstan, thus brining that country well along the way towards upper-middle income status. Population growth also varies widely, ranging from Moldova, which declined at 1.4 percent annually over the period 2002-08, to Kazakhstan, which grew at almost one percent annually over the same period. The growth of the labor force also differed widely across countries over the period 2002-08, ranging from, again, a negative growth in Moldova, at 2.4 percent, to almost 5 percent annual growth of the labor force in Tajikistan. Table 1. GNI per cap (US$) in 2008 and Average Annual Population and Labor Force Growth, 2002-08in comparison with Lower and Upper-middle Income Countries

Lower-middle income:

Upper-middle income:

Kazakhstan:

Macedonia:

Moldova:

Serbia:

Tajikistan:

Ukraine:

GNI per cap (US$)

2,078 7,878 6,170 4,130 1,500 5,590 600 3,210

Population (percent)

1.2 0.8 0.9 0.1 -1.4 -0.3 1.3 -0.7

Labor Force (percent)

1.6 1.7 1.3 0.8 -2.4 NA 4.9 0.1

Source: World Bank (2010) The change in the sectoral composition of the six economies (in terms of the sectoral share of GDP) in recent years roughly corresponds to the similar change in Western economies, though with some variation across countries (Table 2). In particular, the agricultural sector declined in relative terms in all six economies (with the caveat that data are not available for the first period for Serbia), whereas the service sector mostly increasealso in line with the developments in Western economies in recent years. The

5

evidence for industry (and manufacturing) is more mixed, especially for Moldova, where the massive decline of the agricultural sector with about two-thirds leaves room for both a massive decline of the industrial sector and an impressive increase in the Services sector. Given the similarities with the developments in the Western economies it would, perhaps, therefore also not be surprising to discover the presence of a substantial gender earnings gap for these six countriesa finding which they would then also share with most (if not all) Western economies. Table 2. Change in the Sectoral Composition of the Six Economies, 1998-2008 (Percent of GDP) Kazakhstan: Macedonia: Moldova: Serbia: Tajikistan: Ukraine: 1998 2008 1998 2008 1998 2008 1998 2007 1998 2008 1998 2008 Agriculture 9.1 5.7 13.2 10.9 31.8 10.9 NA 13.0 27.2 18.0 14.2 8.3 Industry 31.2 43.3 33.8 34.0 24.5 14.7 NA 28.4 27.0 22.9 36.1 36.9 Manufacturing 12.8 12.7 20.9 21.7 17.2 14.1 NA NA 20.9 16.5 29.8 23.3 Services 59.7 51.0 52.9 55.1 43.8 74.5 NA 58.6 45.8 59.0 49.6 54.8 Source: World Bank (2010) 3. Data and Descriptive Analysis 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. The surveys used a multi-stage clustered sampling design, where the main respondent within the randomly selected household was selected using the next birthday principle. Basic household information (age, gender, educational attainment) was then recorded for all household members 15 years and olderand additional information, including labor market information such as employment status, earnings, and job characteristics (if working).

Interviews were conducted November-December 2009. 2700 individuals were interviewed in each country (except for Serbia, where 300 Roma persons in the so-called Roma booster part of the surveyas of the time of this analysiswere not released as part of the main dataset, leading to an initial sample for Serbia of 2,401 individuals).

Since the dependent variable is earnings, the sample was first conditioned on individuals who answered yes to having worked for payment in cash or kind for at least one day during the past month. Some of these later answer do not have any income when asked about their own total net monthly income and therefore must be excluded, leading to an initial sample of 6,902 individuals. Some individuals are either temporarily on leave from their main job and/or have missing information on earnings or on one or more explanatory variables and are therefore dropped from the estimation sample, leading to a final total estimation sample of 6,254 individuals, distributed across the six

6

countries (and across gender) as follows: Kazakhstan: 1220 (498 females, 722 males); Macedonia: 984 (460 females, 524 males); Moldova: 1061 (597 females, 464 males); Serbia: 1049 (473 females, 576 males); Tajikistan: 805 (332 females, 473 males); Ukraine: 1135 (536 females, 599 males). The means for the final estimation samples by country and gender are reported in Table A1, Appendix A. The dependent variable is based on the responses to the question What is your OWN total net monthly income? Since the sample is restricted to individuals who worked during the past month, the major part of this income is plausibly labor earnings. One issue, however, is that the responses are reported in intervals2 (five total)the upper and lower bound of each of which were determined by the respective country team3rather than the actual incomes themselves. A continuous variable was therefore created using the interval midpoints to impute actual earnings. While this is clearly less than ideal, it appears to be the only feasible way to proceed with an analysis of aggregate earnings data such as thisand thereby utilize this otherwise very desirable dataset. The explanatory variables are specified based on standard human capital theory ((Becker, 1964; Mincer, 1974; and, for a more recent exposition, Heckman, Lochner, and Todd, 2008) and include several potentially important individual and job characteristics, as well as geographical locationall of which have been shown to be important in previous studies of earnings determinants. First, a measure of years of schooling based on the question How many years in total have you spent in education, including all educational degrees? To capture potential labor market (and other) experience, age and age squared4 are included.

Other potentially important explanatory variables include ownership/sector, which is created as a set of five dummy variables (public; private; mixed; cooperative, NGO, and other; and not specified5) based on responses to the question What is the property of the organization in which you practice your main job? Dummy variables for full-time (the reference category) and part-time status are created based on the responses to the question What is your work status at your current main job? The responses include two full-time categories (Full time, but working short hours and Full time), which are collapsed into one full-time category (the reference category), and a part-time category. Dummy variables for contract status are constructed based on information on the responses to the question What is your formal status at your current main job? Based on the answer categories, which include unlimited permanent contract, fixed term contract (less than 12 months), fixed term contract (12 months or more), temporary employment agency contract, apprenticeship or other training scheme, without a written

2 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). 3 Based on personal correspondence with Susanne Milcher, UNDP. 4 Divided by 100, for scale consistency with the other explanatory variables. 5 Not specified was specified as a separate category in the questionnaire and is therefore also treated as a separate group here.

7

contract /informal, and other, two main dummy variables are constructed: one for no written contract /informal and one for written contract (the reference category, which is constructed by combining all other than the without a written contract /informal category). Social insurance coverage is based on the responses to the question Are you covered with social insurance in your main job? where dummy variables are constructed for the responses Yes (the reference category) and No. The last explanatory variable6 is geographical location, where a set of two dummy variables are constructed based on information on the type of settlement, where the categories are village, small town, regional/economic center, and capital. The first two categories are combined into one dummy variable, rural (the reference category), and the last two categories are combined into another dummy variable, urban. An interesting feature of this dataset is that for several of the questions used for constructing the explanatory variables used in this analysis Dont know and Refuse were given as additional categories, rather than as simply being missing per sewhich is how most other surveys treat these categories. Since these categories are potentially informative, especially with more sensitive information such as full-time/part-time and contract status, receipt of social security contributions from the employer, etc, where responding Dont know and/or Refuse could be interpreted as the person being in the least favorable category. Importantly, adding a separate dummy variable of Dont know/Refuse for these individualswhich otherwise would be excluded help retain these individuals in the estimation sample. This is therefore also the approach followed here. Turning to the descriptive analysis, the monthly earnings of females are far lower than those of males for all six countrieswith the estimated gender gaps ranging from 12.4 percent in Serbia, to 17.5 percent in Macedonia, 18 percent in Tajikistan, 19.4 percent in Kazakhstan, 24.7 percent in Moldova, and, at the top, 27.2 percent in Ukraine (Table 3). This supports earlier findings (Brainerd, 2000; Grajek, 2003; Hunt, 2002, Orazem, Vodopivec, 2000) of a substantial gender earnings gap in the former Socialist economies, much like what has been found in the Western economies. At the same time, there seems to have been a narrowing of the gapalso much like in Western economies. Staneva et al (2010) which examines 2003 data for two of the countries examined here, namely Serbia and Kazakhstan (as well as Bulgaria and Russia) establishes a male-female gap of 16.1 for Serbia and 47.8 percent for Kazakhstan. The difference in methodology notwithstanding (Staneva et al (2010) examines hourly wages, while the data examined here only allows examining monthly earnings) it nevertheless seems that the gender earnings gaps has narrowed over the 6-7 year period between the two datasets.

While the existence of substantively large gender earnings gaps have now been established across all six countries, the objective of the main analysis of this paper is to

6 The dataset also includes information on occupation (14 categories) and industry (18 categories) but inclusion of these as explanatory 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 analysis.

8

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 and, on the other hand, observable and unobservable characteristics. While the empirical strategy underlying this approach is widely used, it still seems fruitful to review the main components in some detailwhich, therefore, is the objective of the next section.

Table 3. Raw Gender Earnings Gap in Six Eastern European and Central Asian Countries

Kazakhstan: Macedonia: Moldova: Serbia: Tajikistan: Ukraine: Males: 10.451*** 9.583*** 7.378*** 10.229*** 6.021*** 7.361***

[0.034] [0.025] [0.043] [0.029] [0.033] [0.024] Females: 10.257*** 9.408*** 7.131*** 10.105*** 5.841*** 7.089***

[0.036] [0.032] [0.030] [0.032] [0.024] [0.026] Difference: 0.194*** 0.175*** 0.247*** 0.124*** 0.180*** 0.272***

[0.044] [0.038] [0.048] [0.036] [0.040] [0.031] Notes: Values in brackets are robust Huber-White (Huber, 1967; White, 1980) standard errors, further adjusted for within-community correlation/clustering (Froot, 1989; Williams, 2000). *: statistically significant at 10 percent; **: statistically significant at 5 percent; ***: statistically significant at 1 percent. Source: UNDP/UNICEF Social Exclusion Dataset 2010 (collected November-December 2009). 4. Estimation Strategy and Related Issues The starting point of the Blinder-Oaxaca approach to decomposing earnings (or other) differentials is an OLS regression of the outcome in question, estimated separately across the two relevant groups (Blinder, 1973; Oaxaca, 1973). As such, these regressions areat least in this contextmerely inputs into calculating the decompositions. However, it is potentially fruitful to consider these regressions in and of themselves as separate and integral parts of the overall analysis, also. Both because the results from these regressions can be interesting in an by itself in terms of directly indicating the different returns to characteristics across gender but also because their specification in terms of explanatory variables, functional form, etc, will affect the subsequent decomposition results.

Human capital theory suggests that education and potential experience directly affect earnings through the impact on individuals productivity in the labor market and also suggest additional factors that are potentially important determinants of earnings such as sector of employment, part-time status, type of contract, social security contributions, and location of residence.

The first part of the multivariate analysis will examine these relationships, using ordinary least squares. One potentially important econometric issue here is that educational attainment may be endogenous. The main concern here is possible omitted

9

variables bias. Preferences and ability, for example, are unobserved and at the same time also, at least to some extent, determine both educational attainment and labor market earnings. However, as there are not have available in this dataset any variables that may potentially act as instruments, it does not appear feasible to try to address this problem using instrumental variables methods. The effect of any omitted variables will therefore be captured by the error term, possibly causing omitted variables bias. As a result, we must interpret any subsequent results with caution and hence not give them a causal interpretation but rather as merely reflecting associations with labor market earnings. Further, so as to allow for arbitrary heteroskedasticity, the estimations will be carried out using Huber-White standard errors (Huber, 1967; White, 1980). Additionally, so as to allow for the possibility that observations are correlated within communities the standard errors are also adjusted for within-cluster correlation (Froot, 1989; Williams, 2000).

Again, these earnings regressions formally are merely inputs into the decomposition analysis. Specifically, the decomposition analysis amounts to examining to which extent the observed earnings gaps across gender are attributable to changes in the observable characteristics, to changes in the returns to those characteristics, and to the interaction of the two (three-fold division)7 and, relatedly, to which extent the observed earnings gaps are due to observable and unobservable characteristics (two-fold division).8 This analysis will comprise the second part of the multivariate empirical analysis and will be pursued as an Oaxaca-Blinder type decomposition, using several different specifications for the baseline (i.e., absence of discrimination) model. The standard errors of the individual components are computed according to the method detailed in Jann (2008), which extends the earlier method developed in Oaxaca and Ransom (1998) to deal with stochastic regressors.

In addition to examining the overall composition of the established earnings gaps, it would seem instructive to perform detailed decompositions, as well, whereby it would be possible to see which explanatory variables contribute the most to the three- and/or two-fold overall decompositions. An issue here is that while the overall decompositions are always identified, the results for categorical variables in detailed decompositions depend on the choice of the reference category (Oaxaca and Ransom 1999). A possible solution to this problem is to apply the deviation contrast transformation to the estimates before conducting the decomposition (Yun 2005); this is also the approach pursued here. Similar to the OLS regressions, the decomposition estimations also all allow for arbitrary heteroskedasticity (Huber, 1967; White, 1980) and, additionally, so as to allow for the possibility that observations are correlated within communities the standard errors are also adjusted for within-cluster correlation (Froot, 1989; Williams, 2000).

7 See Winsborough and Dickinson (1971). 8 See Oaxaca (1973), Blinder (1973), Cotton (1988), Reimers (1983), Neumark (1988), and Jann (2008) for different approaches.

10

5. Results This section reviews the main results. This is done in three main parts: (i) OLS Mincer earnings regressions, (ii) overall earnings decompositions, and (iii) detailed earnings 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). (A) Mincer Earnings 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 B1). 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 8.3 percent for females but only 5.3 percent for maleswhich is consistent with previous evidence (Blunch and Sulla, 2010; Staneva et al, 2010). The evidence on returns to ownership is mixed across countries, though frequently there is not much of an association. For Serbia, for example, there is essentially no statistical difference across ownership status. Perhaps not surprisingly, part-time status is associated with lower earnings across both genders and for most countries (relative to full-time status, the reference category). Interestingly, Serbian males who answer Dont know/Refuse to full-time/part-time status have a substantially large and strongly statistically significant and negative return, which is consistent with this group being part-timethey just refuse to reveal their status, perhaps out of stigma associated with part-time status. Having no written contract (reference: written contract) is associated with a negative wage premium, though not always statistically significantly so. The Dont know/Refuse category again experience a negative return in several casesand 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 large earnings premium in several casesand for Serbia for both females and males, both also statistically significant. Workers from urban areas tend to receive a positive earnings premium, which again accords well with their living expenses being larger, also. (B) Overall Earnings Decompositions: A couple of results stand out particularly strongly from the results of the three-fold decompositions (Table 4). First, the endowments decrease the female earnings gap overall (except for Moldova and Tajikistan, where the effect is small, both in substantive and statistical terms), indicating that women are concentrated in better paying sectors, have more education, and so on. Second, the returns to characteristics increase the gaps in both substantive and statistical terms, and for all countries, indicating that males have higher returns to characteristics overall. Notably, these results are fairly robust to

11

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). Table 4. Overall Earnings Decompositions: Three-fold

Kazakhstan: Macedonia: Moldova: Serbia: Tajikistan: Ukraine:

(i) Decomposition from females' viewpoint i.e., using male endowments and returns:

Endowments -0.056** -0.067** 0.002 -0.078*** 0.022 -0.018 [0.026] [0.030] [0.023] [0.024] [0.017] [0.019]

Coefficients 0.278*** 0.218*** 0.238*** 0.190*** 0.143*** 0.294*** [0.040] [0.035] [0.047] [0.031] [0.041] [0.031]

Interaction -0.029 0.024 0.008 0.013 0.015 -0.004 [0.023] [0.022] [0.022] [0.017] [0.023] [0.019]

(ii) Decomposition from males' viewpoint i.e., using female endowments and returns:

Endowments -0.084*** -0.043** 0.009 -0.066*** 0.037* -0.022 [0.028] [0.021] [0.028] [0.022] [0.020] [0.016]

Coefficients 0.250*** 0.242*** 0.246*** 0.202*** 0.158*** 0.290*** [0.042] [0.035] [0.046] [0.033] [0.043] [0.031]

Interaction 0.029 -0.024 -0.008 -0.013 -0.015 0.004 [0.023] [0.022] [0.022] [0.017] [0.023] [0.019]

Notes: Values in brackets are robust Huber-White (Huber, 1967; White, 1980) standard errors, further adjusted for within-community correlation/clustering (Froot, 1989; Williams, 2000). Source: UNDP/UNICEF Social Exclusion Dataset 2010 (collected November-December 2009). Moving to the two-fold decompositions, females on average have better employment-related characteristics as indicated by the negative sign in the explained partwhich in turn serves to narrow the overall earnings gapwhereas 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 earnings differential (Table 5). In turn, this is indicative of discrimination against females in the labor markets of all six countries. Table 5. Overall Earnings Decompositions: Two-fold

Weight given to males relative to females / regression model used in determining the reference coefficients for decompositions: 0 (Oaxaca, 1973):

1 (Oaxaca, 1973; Blinder, 1973):

0.5 (Reimers, 1983):

Share of Males (Cotton, 1988):

Pooled, excl. female dummy (Neumark, 1988):

Pooled, incl. female dummy (Jann, 2008):

Kazakhstan: explained -0.056** -0.084*** -0.070*** -0.072*** -0.050** -0.073***

12

[0.026] [0.028] [0.025] [0.025] [0.024] [0.025] unexplained 0.250*** 0.278*** 0.264*** 0.267*** 0.244*** 0.268***

[0.042] [0.040] [0.039] [0.039] [0.036] [0.039] Macedonia: explained -0.067** -0.043** -0.055** -0.053** -0.041* -0.052**

[0.030] [0.021] [0.023] [0.023] [0.021] [0.022] unexplained 0.242*** 0.218*** 0.230*** 0.228*** 0.216*** 0.227***

[0.035] [0.035] [0.033] [0.033] [0.031] [0.033] Moldova: explained 0.002 0.009 0.006 0.006 0.021 0.005

[0.023] [0.028] [0.023] [0.024] [0.023] [0.023] unexplained 0.246*** 0.238*** 0.242*** 0.241*** 0.226*** 0.242***

[0.046] [0.047] [0.045] [0.045] [0.042] [0.045] Serbia: explained -0.078*** -0.066*** -0.072*** -0.071*** -0.068*** -0.076***

[0.024] [0.022] [0.021] [0.021] [0.021] [0.022] unexplained 0.202*** 0.190*** 0.196*** 0.195*** 0.192*** 0.200***

[0.033] [0.031] [0.031] [0.031] [0.029] [0.030] Tajikistan: explained 0.022 0.037* 0.030** 0.031** 0.044*** 0.031**

[0.017] [0.020] [0.014] [0.014] [0.015] [0.014] unexplained 0.158*** 0.143*** 0.150*** 0.149*** 0.137*** 0.149***

[0.043] [0.041] [0.040] [0.040] [0.037] [0.040] Ukraine: explained -0.018 -0.022 -0.02 -0.02 -0.004 -0.019

[0.019] [0.016] [0.015] [0.015] [0.014] [0.014] unexplained 0.290*** 0.294*** 0.292*** 0.293*** 0.276*** 0.292***

[0.031] [0.031] [0.030] [0.030] [0.028] [0.030] Notes: Values in brackets are robust Huber-White (Huber, 1967; White, 1980) standard errors, further adjusted for within-community correlation/clustering (Froot, 1989; Williams, 2000). Source: UNDP/UNICEF Social Exclusion Dataset 2010 (collected November-December 2009). (C) Detailed Earnings Decompositions: The detailed earnings decompositions allow further decomposing the overall gaps just established into the individual explanatory variables from the Mincer earnings 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 three-fold decompositions (Tables C1-C6, Appendix C) reveal that the single most important contributor to the narrowing of the gender earnings gapboth substantively and statisticallyin terms of individual characteristics, is education. For Moldova and Tajikistan, however, the effect is practically nilboth 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; for example, for Serbia (Blunch and Sulla, 2010; Staneva et al, 2010). Other observable characteristics and returns work to widen the gender gap, however. For Serbia and Moldova, for example, the returns to contract status work to widen the gap, as do full-time/part-time status in Ukraine. With a few exceptions, most of the remaining

13

estimated effects are not statistically significant. The results from the detailed two-fold decompositions are mostly consistent with the results for the detailed three-fold decompositions (Tables D1-D6, Appendix D), so that education again is the most consistently important contributor to narrowing the gender earnings gap across all six countries; except for Moldova and Tajikistan, where the effect again is practically nilboth in substantive and statistical terms. . Altogether, the detailed decomposition results hint at the importance of continued emphasis on the education system as an integral part of a continued decline of the gender earnings gap in these former socialist economies. 6. Conclusion This paper examines the gender earnings 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 add to the emerging literature on the gender earnings gap for the former Socialist economies. Estimation of raw gender earnings gaps and overall and detailed earnings decompositions leads to three main results that hold across all six countries: (1) the presence of a substantively large gender earnings gap (favoring males); (2) endowments diminish the earnings gaps, while the returns to characteristics increase the gapsindicating that women are concentrated in better paying sectors, have more education, and so on, while males have higher returns to characteristics overall; (3) while observed individual characteristics explain part of the gaps, a substantial part of the earnings gap is left unexplained.

These results have strong policy implications, consistent as they are with the presence of earnings discrimination towards females in the labor market, but at the same time also point towards the importance of continued attention towards, among other factors, the education system as a potentially important vehicle for decreasing the gender earnings gap in these former formally gender neutral economies. In particular, the continued presence of a gender earnings gap is likely to keep out females from the labor force who would otherwise be part of this and add to the economy. While increased economic activity has been important during the transition from a planned to a market economy, with the current Financial Crisis such efforts are perhaps more important than everthus highlighting the importance of both employment generation but also improvements of the regulatory environment, since the former may be severely dampened with the continued presence of a substantively larger gender earnings gap.

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 earnings 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 earnings gap in terms of causal pathways. Crucial for these efforts, however, is the availabilityand therefore collectionof more and better data.

14

The data examined here is a case in point. While it is certainly commendableand very usefulto collect data using identical questionnaires for several countries simultaneously it a shame that such an important variable as labor earnings (income) is reported (if not collected) in 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.

15

References Albrecht James, Bjrklund Anders, Vroman Susan (2003) Is There a Glass Ceiling in Sweden? Journal of Labor Economics 21(1):145177. Altonji Joseph G., and Rebecca M. Blank (1999) Race and Gender in the Labor

Market, In: Orley Ashenfelter and David Card (eds) Handbook of Labor Economics, vol 3. Elsevier, Amsterdam.

Babovi, Marija (2008) The Position of Women on the Labour Market in Serbia, Belgrade: Gender Equality Council, Government of the Republic of Serbia and United Nations Development Programme, Serbia.

Becker, Gary S. (1964) Human Capital, Chicago: University of Chicago Press. Blau F. D. (1998) The Gender Pay Gap, In: Persson I, Jonung C (eds) Womens Work and Wages, Routledge, London. Blau F. D., Kahn L. M. (1992) The Gender Earnings Gap: Learning from International

Comparison, American Economic Review (Papers and Proceedings) 82(2): 533538.

Blau F. D., Kahn L. M. (1996) Wage Structure and Gender Earnings Differential: An International Comparison, Economica Suppl.: S29S62. Blau, F. D. and Kahn, L. M. (1997) Swimming Upstream: Trends in the Gender Wage Differential in the 1980s, Journal of Labor Economics 15(1): 142. Blau, F. D. and Kahn, L. M. (2000) Gender Differences in Pay, Journal of Economic

Perspectives 14(4): 7599. Blau F. D., Kahn L. M. (2003) Understanding International Differences in the Gender

Pay Gap, Journal of Labor Economics 21(1):106144. Blinder, A.S. (1973) Wage Discrimination: Reduced Form and Structural Estimates,

Journal of Human Resources 8: 436-455. Blunch, Niels-Hugo and Victor Sulla (2010) The Financial Crisis, Labor Market

Transitions and Earnings Growth: A Gendered Panel Data Analysis for Serbia, Background Paper, Poverty Reduction and Economic Management Unit, Europe and Central Asia Region Department, Washington, DC: World Bank.

Brainerd, Elizabeth (2000) Women in Transition: Changes in Gender Wage Differentials in Eastern Europe and the Former Soviet Union, Industrial and Labor Relations Review 54(1): 138-162.

Cotton, J. (1988) On the Decomposition of Wage Differentials, Review of Economics and Statistics 70: 236-243.

Froot, K. A. (1989) Consistent Covariance Matrix Estimation with Cross-sectional Dependence and Heteroskedasticity in Financial Data, Journal of Financial and Quantitative Analysis 24:333355.

Grajek, Michal (2003) Gender Pay Gap in Poland, Economics of Planning 36: 2344. Heckman, James J., Lance J. Lochner, and Petra E. Todd (2008) Earnings Functions and

Rates of Return, Journal of Human Capital 2(1):1-31. Huber, P. J. (1967) The Behavior of Maximum Likelihood Estimates under Nonstandard

Conditions, in: Lucien M. Le Cam and Jerzy Neyman (Eds.) Proceedings of the

16

Fifth Berkeley Symposium on Mathematical Statistics and Probability Vol. 1, Berkeley, CA: University of California Press.

Hunt, J. (2002) The transition in East Germany: when is a ten point-fall in the gender gap bad news? Journal of Labor Economics 20(1), 148169. Jann, Ben (2008) The Blinder-Oaxaca decomposition for linear regression models, The

Stata Journal 8(4): 453-479. Mincer, Jacob (1974) Schooling, Experience and Earnings, New York: National Bureau of Economic Research. Neumark, D. (1988) Employers Discriminatory Behavior and the Estimation of Wage Discrimination, Journal of Human Resources 23: 279-295. Oaxaca, R. (1973) Male-Female Wage Differentials in Urban Labor Markets, International Economic Review 14: 693-709. Oaxaca, R.L., Ransom, M.R. (1998) Calculation of approximate variances for wage

Decomposition differentials, Journal of Economic and Social Measurement 24: 55-61.

Oaxaca, R.L. and M.R. Ransom (1999) Identification in Detailed Wage Decompositions, Review of Economics and Statistics 81: 154-157.

Orazem, P. F. and Vodopivec, M. (2000) Malefemale differences in labor market outcomes during the early transition to market: the cases of Estonia and Slovenia, Journal of Population Economics 13(2), 283303.

Reimers, C.W. (1983) Labor Market Discrimination against Hispanic and Black Men, Review of Economics and Statistics 65: 570-579.

White, H. (1980) A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity, Econometrica 48(4):817830. Williams, R. L. (2000) A Note on Robust Variance Estimation for Cluster-correlated

Data, Biometrics 56:645646. World Bank (2010) Country Data ProfileAt a Glance, www.worldbank.org. Accessed

Nov 18, 2010. Yun, Myeong-Su (2005) A Simple Solution to the Identification Problem in Detailed

Wage Decompositions, Economic Inquiry 43: 766-772.

17

APPENDIX A: Descriptive Statistics for Estimation Samples Table A1. Means of Monthly Earnings and Explanatory Variables by Gender

Kazakhstan: Macedonia: Moldova: Serbia: Tajikistan: Ukraine: Males: Females: Males: Females: Males: Females: Males: Females: Males: Females: Males: Females:

ln income 10.451 10.257 9.583 9.408 7.378 7.131 10.229 10.105 6.021 5.841 7.361 7.089 Age 38.463 39.129 41.158 39.026 41.950 40.657 39.398 38.097 37.727 37.497 38.922 40.976 Age2/100 16.314 16.560 18.283 16.389 19.300 18.025 16.725 15.513 15.645 15.571 16.604 18.225 Years of schooling 12.652 13.494 13.229 13.600 12.381 12.531 12.210 12.923 12.376 11.798 13.154 13.524 Private 0.601 0.414 0.630 0.587 0.524 0.347 0.615 0.584 0.452 0.355 0.553 0.388 Public 0.263 0.470 0.305 0.361 0.381 0.590 0.319 0.374 0.370 0.509 0.356 0.543 Mixed 0.058 0.064 0.010 0.020 0.013 0.010 0.023 0.021 0.053 0.075 0.048 0.047 Coop, NGO, etc 0.028 0.042 0.011 0.024 0.002 0.000 0.026 0.015 0.057 0.027 0.017 0.011 Sector not specified 0.050 0.010 0.044 0.009 0.080 0.054 0.017 0.006 0.068 0.033 0.027 0.011 Full-time 0.906 0.914 0.935 0.952 0.759 0.851 0.932 0.956 0.780 0.738 0.896 0.925 Part-time 0.084 0.082 0.032 0.037 0.198 0.116 0.019 0.015 0.175 0.241 0.078 0.056 FT/PT: Dont know/refuse 0.010 0.004 0.032 0.011 0.043 0.034 0.049 0.030 0.044 0.021 0.025 0.019 Written contract 0.709 0.785 0.824 0.911 0.716 0.817 0.894 0.905 0.526 0.611 0.771 0.838 No written contract 0.262 0.179 0.132 0.067 0.226 0.134 0.069 0.042 0.414 0.346 0.182 0.121 Contract: Dont know/refuse 0.029 0.036 0.044 0.022 0.058 0.049 0.036 0.053 0.059 0.042 0.047 0.041 Soc security on job 0.195 0.201 0.817 0.878 0.616 0.670 0.837 0.860 0.171 0.114 0.316 0.304 No soc security on job 0.769 0.759 0.151 0.100 0.362 0.295 0.148 0.101 0.810 0.828 0.648 0.632 Social security: Dont know/refuse 0.036 0.040 0.032 0.022 0.022 0.035 0.016 0.038 0.019 0.057 0.037 0.063 Rural 0.665 0.614 0.513 0.511 0.575 0.633 0.674 0.672 0.850 0.813 0.659 0.629 Urban 0.335 0.386 0.487 0.489 0.425 0.367 0.326 0.328 0.150 0.187 0.341 0.371 N 722 498 524 460 464 597 576 473 473 332 599 536

Source: UNDP/UNICEF Social Exclusion Dataset 2010 (collected November-December 2009).

18

APPENDIX B: Mincer Earnings Regressions across Gender

Table B1. Mincer Earnings Regressions across Gender (OLS) Kazakhstan: Macedonia: Moldova: Serbia: Tajikistan: Ukraine:

Males: Females: Males: Females: Males: Females: Males: Females: Males: Females: Males: Females: Age 0.033*** 0.027* 0.040** 0.031* 0.026* 0.011 0.025* 0.019 0.022** 0.005 0.008 0.009

[0.012] [0.016] [0.019] [0.018] [0.016] [0.012] [0.013] [0.016] [0.011] [0.010] [0.010] [0.011] Age2 / 100 -0.036** -0.031 -0.044* -0.027 -0.039** -0.018 -0.028* -0.017 -0.029** -0.007 -0.011 -0.004

[0.014] [0.020] [0.023] [0.022] [0.018] [0.015] [0.016] [0.020] [0.013] [0.011] [0.013] [0.014] Years of schooling 0.052*** 0.061*** 0.049*** 0.071*** 0.090*** 0.063*** 0.053*** 0.083*** 0.027** 0.006 0.023*** 0.071***

[0.011] [0.013] [0.008] [0.010] [0.014] [0.011] [0.010] [0.008] [0.013] [0.010] [0.007] [0.009] Public -0.09 -0.139* 0.085** 0.129** -0.278*** -0.199*** 0.041 0.02 -0.144** -0.1 -0.048 -0.262***

[0.060] [0.075] [0.043] [0.054] [0.078] [0.062] [0.051] [0.051] [0.069] [0.072] [0.046] [0.044] Mixed 0.068 -0.138 -0.201 0.241 -0.501** 0.262 -0.061 -0.06 -0.105 -0.095 0.013 -0.004

[0.101] [0.126] [0.223] [0.189] [0.238] [0.278] [0.099] [0.084] [0.104] [0.076] [0.069] [0.110] Coop, NGO, etc -0.013 -0.13 -0.640** 0.152 -0.339** NA -0.088 -0.079 0.035 0.216 -0.313** -0.123

[0.165] [0.127] [0.280] [0.154] [0.159] [0.225] [0.217] [0.141] [0.191] [0.149] [0.269] Sector not specified -0.02 0.03 -0.332* -0.745*** -0.163 -0.274* 0.293* -0.046 -0.02 -0.072 0.092 -0.447

[0.129] [0.168] [0.177] [0.263] [0.136] [0.143] [0.157] [0.135] [0.116] [0.125] [0.152] [0.283] Part-time -0.289** -0.297** -0.384* -0.725*** -0.019 -0.233** 0.112 -0.211** -0.021 -0.081* -0.316*** -0.123

[0.116] [0.115] [0.217] [0.150] [0.103] [0.099] [0.184] [0.099] [0.081] [0.046] [0.092] [0.093] FT/PT: Dont know/refuse -0.634*** -0.175 -0.13 -0.741*** -0.124 -0.283 -0.558*** -0.145 -0.013 -0.022 -0.654*** -0.335*

[0.142] [0.666] [0.119] [0.265] [0.207] [0.204] [0.128] [0.180] [0.136] [0.096] [0.176] [0.177] No written contract -0.186*** -0.039 -0.07 0.073 -0.186 -0.105 -0.357*** 0.036 -0.011 -0.024 -0.105* -0.154*

[0.069] [0.090] [0.094] [0.103] [0.126] [0.116] [0.111] [0.151] [0.067] [0.052] [0.061] [0.080] Contract: Dont know/refuse 0.056 0.063 -0.13 0.01 -0.247* 0.118 -0.257* -0.111 -0.092 0.04 -0.113 0.073

[0.169] [0.178] [0.139] [0.177] [0.140] [0.111] [0.135] [0.104] [0.112] [0.142] [0.095] [0.082] No soc security on job -0.213*** -0.019 -0.166** -0.192 -0.063 -0.017 -0.181* -0.403*** -0.175** -0.056 -0.078 -0.037

[0.059] [0.080] [0.084] [0.122] [0.095] [0.078] [0.093] [0.145] [0.087] [0.087] [0.048] [0.046] Soc Sec: Dont know/refuse -0.102 0.071 -0.285** 0.011 0.048 -0.205 0.074 0.044 -0.005 0.101 -0.237** 0.071

[0.160] [0.182] [0.121] [0.222] [0.252] [0.149] [0.271] [0.116] [0.194] [0.183] [0.094] [0.067] Urban 0.469*** 0.407*** 0.024 0.089 0.241*** 0.228*** 0.321*** 0.340*** 0.163* 0.082 0.289*** 0.185***

[0.057] [0.071] [0.043] [0.056] [0.083] [0.056] [0.054] [0.062] [0.095] [0.079] [0.043] [0.045] Constant 9.225*** 8.841*** 8.139*** 7.650*** 6.017*** 6.305*** 9.044*** 8.506*** 5.481*** 5.808*** 6.954*** 5.975***

[0.266] [0.375] [0.390] [0.393] [0.372] [0.301] [0.295] [0.303] [0.292] [0.217] [0.233] [0.248]

R2 0.244 0.195 0.278 0.299 0.217 0.197 0.306 0.372 0.062 0.049 0.188 0.222 N 722 498 524 460 464 597 576 473 473 332 599 536

Notes: Values in brackets are robust Huber-White (Huber, 1967; White, 1980) standard errors, further adjusted for within-community correlation/clustering (Froot, 1989; Williams, 2000), computed according to Jann (2008). *: statistically significant at 10 percent; **: statistically significant at 5 percent; ***: statistically significant at 1 percent. Source: UNDP/UNICEF Social Exclusion Dataset 2010 (collected November-December 2009).

19

APPENDIX C: Detailed Three-fold Earnings Decompositions

Table C1. Detailed Three-fold Earnings Decompositions: Kazakhstan

Using male endowments and returns: Using female endowments and returns: Endowments Coefficients Interaction Endowments Coefficients Interaction

Education -0.052*** -0.127 0.008 -0.044*** -0.119 -0.008 [0.014] [0.224] [0.014] [0.012] [0.210] [0.014]

Potential exp -0.01 0.14 -0.003 -0.013** 0.137 0.003 [0.007] [0.365] [0.007] [0.006] [0.358] [0.007]

Sector 0.033* -0.024 -0.015 0.018 -0.039 0.015 [0.017] [0.055] [0.021] [0.014] [0.051] [0.021]

FT/PT status -0.002 0.149 -0.003 -0.004 0.147 0.003 [0.006] [0.226] [0.004] [0.006] [0.222] [0.004]

Contract status -0.004 0.025 -0.012 -0.016** 0.013 0.012 [0.007] [0.076] [0.009] [0.007] [0.075] [0.009]

Social security 0 -0.032 -0.001 -0.002 -0.033 0.001 [0.001] [0.073] [0.005] [0.005] [0.074] [0.005]

Location -0.021 -0.007 -0.003 -0.024 -0.01 0.003 [0.015] [0.009] [0.005] [0.017] [0.013] [0.005]

Constant 0.155 0.155 [0.502] [0.502]


20

Table C2. Detailed Three-fold Earnings Decompositions: Macedonia Using male endowments and returns: Using female endowments and returns: Endowments Coefficients Interaction Endowments Coefficients Interaction

Education -0.026** -0.289* 0.008 -0.018* -0.281* -0.008 [0.013] [0.162] [0.006] [0.009] [0.157] [0.006]

Potential exp 0.014 0.087 -0.013 0.002 0.074 0.013 [0.009] [0.497] [0.009] [0.007] [0.495] [0.009]

Sector -0.038*** 0.133 0.031** -0.006 0.164* -0.031** [0.014] [0.096] [0.015] [0.011] [0.094] [0.015]

FT/PT status -0.013 -0.298** 0.012 -0.001 -0.287** -0.012 [0.013] [0.125] [0.009] [0.006] [0.121] [0.009]

Contract status 0.005 0.082 -0.012 -0.007 0.069 0.012 [0.008] [0.081] [0.011] [0.007] [0.072] [0.011]

Social security -0.01 0.086 -0.002 -0.011* 0.085 0.002 [0.008] [0.085] [0.008] [0.006] [0.080] [0.008]

Location 0 0.001 0 0 0.001 0 [0.003] [0.002] [0.002] [0.001] [0.002] [0.002]

Constant 0.417 0.417 [0.543] [0.543]


21

Table C3. Detailed Three-fold Earnings Decompositions: Moldova Using male endowments and returns: Using female endowments and returns: Endowments Coefficients Interaction Endowments Coefficients Interaction

Education -0.009 0.342 -0.004 -0.013 0.338 0.004 [0.011] [0.209] [0.005] [0.016] [0.206] [0.005]

Potential exp -0.008 0.231 -0.007 -0.015** 0.224 0.007 [0.005] [0.402] [0.007] [0.008] [0.399] [0.007]

Sector 0.035** 0.166** 0.016 0.051*** 0.182** -0.016 [0.014] [0.082] [0.020] [0.018] [0.078] [0.020]

FT/PT status -0.022** -0.094 0.019 -0.003 -0.075 -0.019 [0.011] [0.097] [0.013] [0.009] [0.089] [0.013]

Contract status -0.009 0.120** -0.011 -0.02 0.109** 0.011 [0.011] [0.061] [0.016] [0.013] [0.051] [0.016]

Social security 0.002 -0.074 -0.007 -0.005 -0.08 0.007 [0.005] [0.088] [0.009] [0.007] [0.090] [0.009]

Location 0.013* -0.002 0.001 0.014 -0.001 -0.001 [0.008] [0.012] [0.005] [0.009] [0.007] [0.005]

Constant -0.451 -0.451 [0.470] [0.470]


22

Table C4. Detailed Three-fold Earnings Decompositions: Serbia Using male endowments and returns: Using female endowments and returns: Endowments Coefficients Interaction Endowments Coefficients Interaction

Education -0.059*** -0.391** 0.022** -0.038*** -0.369** -0.022** [0.016] [0.156] [0.010] [0.012] [0.148] [0.010]

Potential exp 0.004 0.047 -0.006 -0.002 0.041 0.006 [0.006] [0.435] [0.006] [0.004] [0.432] [0.006]

Sector -0.003 -0.06 0.003 0 -0.058 -0.003 [0.004] [0.072] [0.006] [0.005] [0.068] [0.006]

FT/PT status -0.004 0.023 -0.006 -0.01 0.016 0.006 [0.004] [0.093] [0.007] [0.007] [0.089] [0.007]

Contract status 0.003 0.155** -0.008 -0.005 0.147** 0.008 [0.004] [0.075] [0.008] [0.006] [0.073] [0.008]

Social security -0.020* -0.06 0.01 -0.01 -0.051 -0.01 [0.011] [0.091] [0.010] [0.008] [0.094] [0.010]

Location 0 0.003 0 0 0.003 0 [0.008] [0.011] [0.000] [0.008] [0.012] [0.000]

Constant 0.473 0.473 [0.451] [0.451]


23

Table C5. Detailed Three-fold Earnings Decompositions: Tajikistan Using male endowments and returns: Using female endowments and returns: Endowments Coefficients Interaction Endowments Coefficients Interaction

Education 0.004 0.243 0.012 0.015 0.255 -0.012 [0.006] [0.192] [0.010] [0.010] [0.202] [0.010]

Potential exp 0.001 0.329 0.002 0.003 0.331 -0.002 [0.002] [0.287] [0.004] [0.004] [0.289] [0.004]

Sector 0.020* 0.01 0.003 0.023* 0.013 -0.003 [0.012] [0.054] [0.015] [0.012] [0.045] [0.015]

FT/PT status 0.005 -0.008 -0.004 0.001 -0.012 0.004 [0.004] [0.052] [0.007] [0.006] [0.050] [0.007]

Contract status -0.001 0.039 -0.001 -0.002 0.037 0.001 [0.005] [0.051] [0.007] [0.005] [0.047] [0.007]

Social security -0.003 -0.03 0.006 0.003 -0.023 -0.006 [0.008] [0.070] [0.011] [0.009] [0.076] [0.011]

Location -0.003 -0.025 -0.003 -0.006 -0.028 0.003 [0.004] [0.038] [0.005] [0.006] [0.042] [0.005]

Constant -0.415 -0.415 [0.365] [0.365]


24

Table C6. Detailed Three-fold Earnings Decompositions: Ukraine Using male endowments and returns: Using female endowments and returns: Endowments Coefficients Interaction Endowments Coefficients Interaction

Education -0.026** -0.654*** 0.018** -0.009* -0.636*** -0.018** [0.011] [0.153] [0.008] [0.004] [0.149] [0.008]

Potential exp -0.011** -0.127 0.011* 0 -0.115 -0.011* [0.005] [0.292] [0.006] [0.004] [0.290] [0.006]

Sector 0.041*** 0.005 -0.033** 0.009 -0.028 0.033** [0.012] [0.087] [0.014] [0.009] [0.082] [0.014]

FT/PT status -0.005 0.154* -0.006 -0.011 0.148* 0.006 [0.004] [0.087] [0.005] [0.007] [0.085] [0.005]

Contract status -0.009 0.044 0.002 -0.007 0.046 -0.002 [0.006] [0.046] [0.006] [0.005] [0.042] [0.006]

Social security -0.002 0.071** 0.008 0.005 0.078** -0.008 [0.002] [0.028] [0.005] [0.004] [0.031] [0.005]

Location -0.006 -0.013* -0.003 -0.009 -0.017* 0.003 [0.005] [0.008] [0.003] [0.008] [0.009] [0.003]

Constant 0.814** 0.814** [0.332] [0.332]


25

APPENDIX D: Detailed Two-fold Earnings Decompositions

Table D1. Detailed Two-fold Earnings Decompositions: Kazakhstan



0.5 (Reimers, 1983):




Expl. Unexpl. Expl. Unexpl. Expl. Unexpl. Expl. Unexpl. Expl. Unexpl. Expl. Unexpl. Education -0.052*** -0.119 -0.044*** -0.127 -0.048*** -0.123 -0.047*** -0.124 -0.041*** -0.13 -0.046*** -0.125

[0.014] [0.210] [0.012] [0.224] [0.011] [0.217] [0.011] [0.219] [0.010] [0.219] [0.011] [0.219] Potential exp -0.01 0.137 -0.013** 0.14 -0.012** 0.139 -0.012** 0.139 -0.010* 0.136 -0.012** 0.139

[0.007] [0.358] [0.006] [0.365] [0.005] [0.362] [0.005] [0.362] [0.005] [0.363] [0.005] [0.363] Sector 0.033* -0.039 0.018 -0.024 0.025** -0.031 0.024** -0.03 0.039*** -0.045 0.024** -0.03

[0.017] [0.051] [0.014] [0.055] [0.012] [0.052] [0.011] [0.053] [0.012] [0.054] [0.012] [0.054] FT/PT status -0.002 0.147 -0.004 0.149 -0.003 0.148 -0.003 0.148 -0.004 0.149 -0.004 0.149

[0.006] [0.222] [0.006] [0.226] [0.006] [0.224] [0.006] [0.224] [0.006] [0.225] [0.006] [0.225] Contract status -0.004 0.013 -0.016** 0.025 -0.010* 0.019 -0.011* 0.02 -0.012* 0.021 -0.012** 0.021

[0.007] [0.075] [0.007] [0.076] [0.006] [0.075] [0.006] [0.075] [0.006] [0.076] [0.006] [0.076] Social security 0 -0.033 -0.002 -0.032 -0.001 -0.033 -0.001 -0.033 -0.001 -0.032 -0.001 -0.033

[0.001] [0.074] [0.005] [0.073] [0.003] [0.073] [0.004] [0.073] [0.004] [0.073] [0.004] [0.073] Location -0.021 -0.01 -0.024 -0.007 -0.022 -0.009 -0.022 -0.008 -0.022 -0.009 -0.022 -0.008

[0.015] [0.013] [0.017] [0.009] [0.016] [0.011] [0.016] [0.011] [0.015] [0.011] [0.016] [0.011] Constant 0.155 0.155 0.155 0.155 0.155 0.155

[0.502] [0.502] [0.502] [0.502] [0.502] [0.502] Notes: Values in brackets are robust Huber-White (Huber, 1967; White, 1980) standard errors, further adjusted for within-community correlation/clustering (Froot, 1989; Williams, 2000), computed according to Jann (2008). *: statistically significant at 10 percent; **: statistically significant at 5 percent; ***: statistically significant at 1 percent. Source: UNDP/UNICEF Social Exclusion Dataset 2010 (collected November-December 2009).

26

Table D2. Detailed Two-fold Earnings Decompositions: Macedonia



0.5 (Reimers, 1983):




Expl. Unexpl. Expl. Unexpl. Expl. Unexpl. Expl. Unexpl. Expl. Unexpl. Expl. Unexpl. Education -0.026** -0.281* -0.018* -0.289* -0.022** -0.285* -0.022** -0.285* -0.022** -0.285* -0.022** -0.285*

[0.013] [0.157] [0.009] [0.162] [0.011] [0.159] [0.011] [0.160] [0.011] [0.160] [0.011] [0.160] Potential exp 0.014 0.074 0.002 0.087 0.008 0.081 0.008 0.081 0.009 0.08 0.005 0.083

[0.009] [0.495] [0.007] [0.497] [0.007] [0.496] [0.007] [0.496] [0.007] [0.497] [0.007] [0.497] Sector -0.038*** 0.164* -0.006 0.133 -0.022** 0.149 -0.021** 0.148 -0.014* 0.141 -0.018** 0.145

[0.014] [0.094] [0.011] [0.096] [0.010] [0.095] [0.010] [0.095] [0.008] [0.097] [0.009] [0.097] FT/PT status -0.013 -0.287** -0.001 -0.298** -0.007 -0.293** -0.007 -0.293** -0.004 -0.296** -0.004 -0.295**

[0.013] [0.121] [0.006] [0.125] [0.009] [0.123] [0.008] [0.123] [0.008] [0.124] [0.008] [0.124] Contract status 0.005 0.069 -0.007 0.082 -0.001 0.075 -0.002 0.076 0.001 0.073 -0.001 0.076

[0.008] [0.072] [0.007] [0.081] [0.005] [0.077] [0.005] [0.077] [0.006] [0.078] [0.005] [0.078] Social security -0.01 0.085 -0.011* 0.086 -0.011* 0.086 -0.011* 0.086 -0.010* 0.085 -0.011* 0.086

[0.008] [0.080] [0.006] [0.085] [0.006] [0.082] [0.006] [0.083] [0.006] [0.083] [0.006] [0.083] Location 0 0.001 0 0.001 0 0.001 0 0.001 0 0.001 0 0.001

[0.003] [0.002] [0.001] [0.002] [0.002] [0.002] [0.002] [0.002] [0.002] [0.002] [0.002] [0.002] Constant 0.417 0.417 0.417 0.417 0.417 0.417


27

Table D3. Detailed Two-fold Earnings Decompositions: Moldova



0.5 (Reimers, 1983):




Expl. Unexpl. Expl. Unexpl. Expl. Unexpl. Expl. Unexpl. Expl. Unexpl. Expl. Unexpl. Education -0.009 0.338 -0.013 0.342 -0.011 0.34 -0.011 0.34 -0.011 0.34 -0.011 0.34

[0.011] [0.206] [0.016] [0.209] [0.014] [0.207] [0.013] [0.207] [0.013] [0.207] [0.013] [0.207] Potential exp -0.008 0.224 -0.015** 0.231 -0.012** 0.227 -0.011** 0.227 -0.010** 0.226 -0.012** 0.227

[0.005] [0.399] [0.008] [0.402] [0.006] [0.400] [0.005] [0.400] [0.005] [0.400] [0.006] [0.400] Sector 0.035** 0.182** 0.051*** 0.166** 0.043*** 0.174** 0.042*** 0.175** 0.052*** 0.165** 0.042*** 0.175**

[0.014] [0.078] [0.018] [0.082] [0.012] [0.079] [0.012] [0.079] [0.013] [0.079] [0.012] [0.079] FT/PT status -0.022** -0.075 -0.003 -0.094 -0.012 -0.085 -0.014* -0.083 -0.01 -0.087 -0.012 -0.085

[0.011] [0.089] [0.009] [0.097] [0.008] [0.093] [0.008] [0.093] [0.008] [0.093] [0.008] [0.093] Contract status -0.009 0.109** -0.02 0.120** -0.014 0.114** -0.013 0.114** -0.013 0.113** -0.015 0.115**

[0.011] [0.051] [0.013] [0.061] [0.009] [0.056] [0.009] [0.055] [0.009] [0.056] [0.009] [0.056] Social security 0.002 -0.08 -0.005 -0.074 -0.002 -0.077 -0.001 -0.077 -0.001 -0.078 -0.001 -0.078

[0.005] [0.090] [0.007] [0.088] [0.004] [0.089] [0.004] [0.089] [0.005] [0.089] [0.004] [0.089] Location 0.013* -0.001 0.014 -0.002 0.014* -0.001 0.013* -0.001 0.014* -0.002 0.013* -0.001

[0.008] [0.007] [0.009] [0.012] [0.008] [0.010] [0.008] [0.009] [0.008] [0.009] [0.008] [0.009] Constant -0.451 -0.451 -0.451 -0.451 -0.451 -0.451


28

Table D4. Detailed Two-fold Earnings Decompositions: Serbia



0.5 (Reimers, 1983):




Expl. Unexpl. Expl. Unexpl. Expl. Unexpl. Expl. Unexpl. Expl. Unexpl. Expl. Unexpl. Education -0.059*** -0.369** -0.038*** -0.391** -0.048*** -0.380** -0.047*** -0.381** -0.044*** -0.384** -0.047*** -0.381**

[0.016] [0.148] [0.012] [0.156] [0.013] [0.152] [0.013] [0.152] [0.012] [0.152] [0.013] [0.152] Potential exp 0.004 0.041 -0.002 0.047 0.001 0.044 0.001 0.044 0.002 0.043 0.001 0.044

[0.006] [0.432] [0.004] [0.435] [0.004] [0.433] [0.004] [0.434] [0.004] [0.434] [0.004] [0.434] Sector -0.003 -0.058 0 -0.06 -0.001 -0.059 -0.001 -0.059 -0.001 -0.06 -0.002 -0.059

[0.004] [0.068] [0.005] [0.072] [0.003] [0.070] [0.003] [0.070] [0.004] [0.070] [0.004] [0.070] FT/PT status -0.004 0.016 -0.01 0.023 -0.007 0.02 -0.007 0.02 -0.008 0.021 -0.009 0.021

[0.004] [0.089] [0.007] [0.093] [0.005] [0.091] [0.005] [0.091] [0.005] [0.091] [0.006] [0.091] Contract status 0.003 0.147** -0.005 0.155** -0.001 0.151** -0.002 0.151** -0.002 0.152** -0.003 0.153**

[0.004] [0.073] [0.006] [0.075] [0.004] [0.074] [0.004] [0.074] [0.004] [0.074] [0.004] [0.074] Social security -0.020* -0.051 -0.01 -0.06 -0.015* -0.056 -0.014* -0.056 -0.015** -0.056 -0.016** -0.054

[0.011] [0.094] [0.008] [0.091] [0.008] [0.092] [0.008] [0.092] [0.007] [0.093] [0.008] [0.093] Location 0 0.003 0 0.003 0 0.003 0 0.003 0 0.003 0 0.003

[0.008] [0.012] [0.008] [0.011] [0.008] [0.012] [0.008] [0.011] [0.008] [0.011] [0.008] [0.011] Constant 0.473 0.473 0.473 0.473 0.473 0.473


29

Table D5. Detailed Two-fold Earnings Decompositions: Tajikistan



0.5 (Reimers, 1983):




Expl. Unexpl. Expl. Unexpl. Expl. Unexpl. Expl. Unexpl. Expl. Unexpl. Expl. Unexpl. Education 0.004 0.255 0.015 0.243 0.009 0.249 0.011* 0.248 0.013* 0.246 0.01 0.249

[0.006] [0.202] [0.010] [0.192] [0.006] [0.197] [0.006] [0.196] [0.007] [0.196] [0.006] [0.196] Potential exp 0.001 0.331 0.003 0.329 0.002 0.33 0.002 0.33 0.002 0.33 0.002 0.33

[0.002] [0.289] [0.004] [0.287] [0.003] [0.288] [0.003] [0.288] [0.003] [0.288] [0.003] [0.288] Sector 0.020* 0.013 0.023* 0.01 0.021** 0.012 0.022** 0.011 0.026*** 0.007 0.021** 0.012

[0.012] [0.045] [0.012] [0.054] [0.009] [0.049] [0.009] [0.050] [0.010] [0.051] [0.009] [0.051] FT/PT status 0.005 -0.012 0.001 -0.008 0.003 -0.01 0.003 -0.01 0.004 -0.012 0.003 -0.01

[0.004] [0.050] [0.006] [0.052] [0.004] [0.051] [0.004] [0.051] [0.004] [0.052] [0.004] [0.052] Contract status -0.001 0.037 -0.002 0.039 -0.002 0.038 -0.002 0.038 -0.001 0.037 -0.002 0.038

[0.005] [0.047] [0.005] [0.051] [0.004] [0.049] [0.004] [0.049] [0.004] [0.050] [0.004] [0.050] Social security -0.003 -0.023 0.003 -0.03 0 -0.027 0.001 -0.027 0.003 -0.029 0.001 -0.027

[0.008] [0.076] [0.009] [0.070] [0.007] [0.073] [0.007] [0.072] [0.007] [0.074] [0.007] [0.074] Location -0.003 -0.028 -0.006 -0.025 -0.004 -0.027 -0.005 -0.027 -0.004 -0.027 -0.004 -0.027

[0.004] [0.042] [0.006] [0.038] [0.005] [0.040] [0.005] [0.039] [0.004] [0.039] [0.005] [0.039] Constant -0.415 -0.415 -0.415 -0.415 -0.415 -0.415


30

Table D6. Detailed Two-fold Earnings Decompositions: Ukraine



0.5 (Reimers, 1983):




Expl. Unexpl. Expl. Unexpl. Expl. Unexpl. Expl. Unexpl. Expl. Unexpl. Expl. Unexpl. Education -0.026** -0.636*** -0.009* -0.654*** -0.017** -0.645*** -0.017** -0.645*** -0.015** -0.648*** -0.016** -0.646***

[0.011] [0.149] [0.004] [0.153] [0.007] [0.151] [0.007] [0.151] [0.006] [0.151] [0.007] [0.151] Potential exp -0.011** -0.115 0 -0.127 -0.006* -0.121 -0.005* -0.121 -0.003 -0.123 -0.005 -0.122

[0.005] [0.290] [0.004] [0.292] [0.003] [0.291] [0.003] [0.291] [0.003] [0.291] [0.003] [0.291] Sector 0.041*** -0.028 0.009 0.005 0.025*** -0.011 0.024*** -0.01 0.035*** -0.021 0.025*** -0.011

[0.012] [0.082] [0.009] [0.087] [0.008] [0.084] [0.008] [0.084] [0.009] [0.085] [0.008] [0.084] FT/PT status -0.005 0.148* -0.011 0.154* -0.008 0.151* -0.008 0.151* -0.007 0.150* -0.008 0.151*

[0.004] [0.085] [0.007] [0.087] [0.005] [0.086] [0.005] [0.086] [0.005] [0.086] [0.005] [0.086] Contract status -0.009 0.046 -0.007 0.044 -0.008* 0.045 -0.008* 0.045 -0.008* 0.045 -0.008** 0.045

[0.006] [0.042] [0.005] [0.046] [0.004] [0.044] [0.004] [0.044] [0.004] [0.044] [0.004] [0.044] Social security -0.002 0.078** 0.005 0.071** 0.001 0.075** 0.002 0.074** 0.001 0.074** 0.001 0.075**

[0.002] [0.031] [0.004] [0.028] [0.002] [0.029] [0.002] [0.029] [0.003] [0.030] [0.002] [0.030] Location -0.006 -0.017* -0.009 -0.013* -0.007 -0.015* -0.007 -0.015* -0.007 -0.015* -0.007 -0.015*

[0.005] [0.009] [0.008] [0.008] [0.006] [0.008] [0.006] [0.008] [0.006] [0.008] [0.006] [0.008] Constant 0.814** 0.814** 0.814** 0.814** 0.814** 0.814**


The Gender Earnings Gap Revisited: A Comparative...

Documents

Transcript of The Gender Earnings Gap Revisited: A Comparative...