MONITORING THE RELATIVE POSITION OF WOMEN ON THE CZECH LABOR MARKET

Transcription

1 MONITORING THE RELATIVE POSITION OF WOMEN ON THE CZECH LABOR MARKET Štěpán JURAJDA and Daniel MÜNICH Discussion Paper No March 2006 P.O. Box 882, Politických vězňů 7, Praha 1, Czech Republic

2 Monitoring the relative position of women on the Czech labor market * Štěpán Jurajda a Daniel Münich CERGE-EI ** March 2006 Abstract In this survey, we provide a discussion of the various dimensions of the relative position of women on labor markets and the techniques that have been used in the empirical economics literature to understand the sources of the observed gender differences. We also give a brief overview of the available data and analyses in the Czech Republic and present new gender wage gap decompositions for the Czech enterprise sector in * This research is part of the EU Equal project "Fifty - fifty: equal opportunities for women and men" and is co-financed by the European Social Fund of the EU and the state budget of the Czech Republic. ** CERGE-EI is a joint workplace of the Center for Economic Research and Graduate Education, Charles University, and the Economics Institute of the Academy of Sciences of the Czech Republic. 1

3 1. Introduction What does the relative position of women on the labor market consist of? First, we may ask about pre-market productive and other characteristics such as the level of education and the field of study (technical fields versus humanities), health status, access to health and child care, occupational and career preferences (inherited versus acquired through gender stereotyping). Second, we can ask about how women are treated on the labor market relative to men and how their career choices differ from those of men. Here, we measure the relative male/female employment gaps and the gender pay gap. We also ask about the differential propensity of men and women to be employed in specific industries, firms or occupations, coined as gender segregation. Much of the economics research on the relative position of women focuses on understanding the sources of the observed gender pay gap, in particular in connection to segregation. 1 Hence, this survey will also focus primarily on gender segregation and the gender wage gap. 2 It is important to consider gender equality as equality of opportunities, not necessarily of outcomes. A fundamental problem in much of the analysis we discuss and propose here is the inability to effectively separate gender differences in choices from discriminatory barriers that women may face on the labor market. We mostly only discuss measuring departures from equality of outcomes on the labor market and only in certain circumstances are we able to get closer to differentiating choice from discrimination. 3 The survey proceeds as follows. In Section 2 we start by discussing measures of gender employment inequality including occupational segregation. Section 3 then presents the methodology typically used for understanding the sources of the gender pay gap together with various recent extensions. In both of these methodological sections we link the estimation techniques with the underlying theories of determinants of gender-specific 1 Although there are now several papers looking at the effects of field of study, gender-specific preferences, or behavior in competitive environments. 2 We do not discuss directly measuring either the extent of gender stereotyping that occurs before entering labor markets or the gender differences in life expectancy and returns to social-security contributions, etc. 3 There are two main sources of discrimination: that based on preferences (i.e. employers preferring to hire men over more qualified women, possibly only in some occupations) and that based on estimating the productivity of a given individual from the average productivity of a group (statistical discrimination). 2

4 labor market outcomes. Section 4 then briefly discusses the existing literature in postcommunist economies and focuses on the case of the Czech Republic. Here we also present new gender wage gap decompositions for the Czech enterprise sector in A Caveat One of the key parameters of the relative standing of women on the labor market is the average pay gap between men and women. However, to approximate more closely what we have in mind when we can ask about relative gender-specific wages, one should compare comparable male and female workers, i.e. compare wages of workers employed in the same industry and firm and with the same level of experience and education, etc. Such measure of the gender pay gap would correspond more closely to the object of the Equal pay act. 4 A fundamental problem with finding comparable men and women is the ability to control for skills that are not observed (i.e. abilities and qualifications that are not captured by simple age and education indicators and therefore not recorded in most data sets). A closely related fundamental problem is that in most countries a large fraction of women does not work and the selection of which women end up being employed is often related to their skill level (observed and unobserved). This issue is at the core of how we interpret the typical measures of gender equality. The result of most empirical exercises focusing on the relative position of women on a given labor market is some quantification of the extent of gender segregation and of the gender pay gap. To interpret the size of these measurements, one typically relies on international comparisons. For example, we ask whether a given country has a better gender wage gap than other economies. Similarly, we often compare the gap over time within a country. However, such comparisons represent a potentially misleading indicator of differences or changes in discrimination against women if the differences in the relative gender outcomes are driven to a large extent by the variation in the skill structure of female employment participation. 4 Note that the remaining part of the overall gender pay gap would then be due to employed men and women having a different level of skills or working in different types of positions, i.e. gender segregation. 3

5 Why should this be the case? If most low-skill (low-educated) women in a given country are not employed, but both low-skill and high-skill men are working, then the gender pay gap will be very small even if there is a substantial degree of discrimination. OECD (2002), a cross-country study based largely on the European Community Household Panel, suggests that cross-country differences in female employment rates are driven mainly by the degree of integration of less-educated, lower-paid women into employment and that such compositional effects are important for understanding international differences in the gender pay gap as well as in the extent of segregation. 5 Countries with a higher degree of participation of less educated women in employment would therefore be expected to feature a relatively high level of gender segregation and gender wage gap. Similarly for time changes in the gender pay gap: Hunt (2002) and Kazakova (2005) suggest that large changes in the observed gender wage gap (even after we attempt to compare comparable male and female workers) can be linked to major changes in the skill structure of female employment in East Germany and Russia, respectively. The differences in the level and structure of female labor-market participation can then be either related to discrimination (if women are discriminated against, they may be less likely to participate in the labor market), labor-market institutions (such as high wage floors, which may prevent low-productivity workers from being employed) or be driven by country-specific culture or history Gender Employment Equality As argued above, it is useful to put into international perspective the aggregate genderspecific age and education structure as well as the overall level of female employment before one interprets specific relative outcomes of women on a given labor market. Providing such perspective is the topic of Section 2.1. In Section 2.2, we then introduce 5 Olivetti and Petrongolo (2005) reach the same conclusion regarding the gender pay gap. Along similar lines, Dolado et al. (2002) suggest that the reason why among the EU-15 countries gender segregation is highest in the Nordic economies is that they feature unusually high female share on employment in femaledominated occupations such as education, health care, and some social services which, at the same time support the high labor market participation of women in these countries. 6 See Algan and Cahuc (2005) for related evidence on cultural determinants of cross-country differences in the employment rates of women. 4

6 measures of gender equality focused on the gender patterns of employment with respect to occupations, industries or firm types, coined gender segregation. 2.1 Gender-Specific Employment Level and Structure Before comparing the female employment rates across countries, it is necessary to take into account the fact that highly educated workers typically have higher participation rates. Hence, an exercise focused on the relative position of women in a country may well start by comparing the gender composition of the population and employment to those of the EU-15 and the US, which are easily available. Based on this comparison, one can evaluate the contribution of various labor-force sub-groups to the difference in the aggregate employment level between EU-15 and a given country and ultimately judge whether the female employment rate in a given country is relatively high or low, which helps in the interpretation of the other labor-market gender outcomes. Specifically, one can use the Labor Force Survey and divide population into groups defined by gender, age and education and compare the population shares and employment rates of these groups in a given country to those estimated off similar data for the EU and US by Dolado et al. (2002). To quantify the role of these gender patterns for the aggregate employment differential across countries, one can follow Dolado et al. (2002) and decompose the employment rate differential between the Czech Republic and the EU in the following way: where CZ e and EU e CZ EU CZ CZ EU ( si si ) + si ( ei ei ) CZ EU EU e e = e, i i i are the aggregate employment rates in the Czech Republic and in the EU, respectively. The decomposition sums employment rates over the demographic groups of Table 1, indexed by i, and weights the group-specific employment rates ei with the group-specific population share s i. The decomposition allows us to distinguish a population composition effect, which is driven by differences in population weights (given the EU employment rates), and an employment incidence effect, which reflects differences in employment rates (given the Czech population weights). 5

7 Next, one can use a similar decomposition to shed light on the changes in female and male employment level over time. An important caveat in this type of analysis is that one should carefully consider the role of part-time jobs. Female employment rates in postcommunist countries are typically high in comparison to the EU-15, but they are even higher when calculated in terms of full-time equivalents, which is driven by the low incidence of part-time employment in post-communist economies and the high share of women in the many part-time positions in the most developed European economies Occupational Gender Segregation One of the most clearly established labor-market gender facts is that women and men tend to concentrate in different occupations and industries. This is an important concern because those occupations and industries staffed mainly with female workers typically pay lower wages to both men and women compared to predominantly male occupations and industries. The observed persistent concentration of women in low-paid groups of workers, coined gender segregation, is therefore a key explanation for the existence of the gender wage gap. Below, in Section 3, we return to the issue of measuring the size of this wage penalty to highly female jobs. Here, we are concerned with measuring the extent of dissimilarity in gender-specific employment patterns. The differential concentration of women in certain types of employment may be a matter of gender-specific preferences and choice. It could also be the result of gender stereotyping or discrimination. To the extent that the observed unbalanced gender employment pattern is the consequence of unequal access to certain types of occupations, anti-discrimination legislation is aiming at reducing it. Standard equal employment opportunity clauses aim to reduce all forms of segregation resulting from potentially discriminatory hiring, firing, and promotion practices. 7 While most of the literature focuses on gender differences in employment rates (allocation of home responsibilities, etc.), a recent paper studies gender gaps in unemployment rates, i.e. asks what makes women more likely to be unemployed, given that they decide to participate in the labor force. Specifically, Azmat, Guell and Manning (in press) suggest that gender gaps in unemployment seem to correlate with attitudes on whether men are more deserving of work than women. 6

8 Below, we discuss measures of occupational segregation that do not allow one to differentiate between the discrimination-related and choice-driven explanations of segregation. However, descriptive evidence on segregation is an important first step towards such understanding and for guiding anti-discrimination policies. Measuring occupational segregation clearly provides us with an important indicator of women s economic status in the labor market. A first descriptive tool that is available to a researcher is to present the extent of genderspecific concentration of employment in specific occupations. While female workers are typically more likely to be in clerical or sales occupations, male workers are usually more likely to be employed in manual or craft occupations and also in managerial occupations. Such descriptive analysis should also differentiate workers by age as the experience of younger cohorts may be different from those who have started their careers long time ago. While such simple descriptive comparisons are interesting, they do not allow us to make general quantified statements about the degree to which men and women are concentrated in different occupations. Obviously, we need to use a composite summarizing index of segregation that could be used to compare the extent of segregation at the aggregate level over time as well as across countries. A Segregation Index There are many alternative indexes of segregation in the literature, used in, e.g., gender occupational segregation or ethnic residential segregation research. The index most often used in the studies focused on gender issues is the Duncan segregation index, also referred to as the Duncan index of dissimilarity, introduced in Duncan and Duncan (1955). For a given year t, the index is defined as follows: M i, t Fi, t M t Ft D t = 100 *, 2 i 7

9 where M i denotes the number of males in an employment category i, F i is the corresponding number of females in group i, and where M, F represent the total number of males and females, respectively. The index consists of the sum of the group-specific absolute differences in the fraction of each gender employed in a given group, taken over all employment groups, such as for example all occupations. The index can be interpreted as reflecting the percentage share of the total workforce that would have to reallocate (change occupations) in order to equalize the gender composition across groups (occupations); it ranges between 100 (complete segregation) and 0 (complete integration). 8 Such index can be calculated using representative worker-level data that reports occupations (i.e. ISCO codes). The index can be compared across demographic groups as well as over time. Over time, both changes in the occupational mix of employment and changes in the gender composition of each occupation affect the overall degree of segregation captured by the index. In order to improve our understanding of the observed changes in the segregation index, we need to decompose the change in the segregation index between two years into a gender-composition effect within occupations, holding constant the size of occupations, and an occupation-mix effect (structural shift effect) due to changes in the occupational mix of the economy, holding sex composition within occupations constant. 9 Theoretical Sources of Gender Segregation and their Testing How can we interpret the observed measures of gender occupational segregation? There are two fundamentally different potential explanations for why women concentrate in 8 More discussion on the properties and interpretation of the Duncan index can be found in Tzannatos (1990). 9 In order to distinguish between these two types of effects we compute a standardized index of dissimilarity (Das Gupta, 1987), which controls for the changing size of occupations. 8

10 certain occupations that simultaneously explain why these predominantly female occupations pay less: 10 (i) discriminating employers may prevent women from working in highwage occupations, 11 (ii) female occupations may offer costly non-wage characteristics preferred by women such as for example flexible working hours. There is relatively little research directly exploring the second explanation. Filer (1986) uses U.S. individual-level data capturing personal preferences and tastes as well as occupational choice and finds a strong link between the two, which goes a long way towards explaining the observed gender differences in occupational choice. Unfortunately, there are several problems with his data and taste measures as well as with his rather crude occupational division. Sloane et al. (2005) survey and extend the line of research linking the higher measured risk aversion of women to their lower probability of becoming self-employed and choosing occupations with higher accident risks. 12 There is more indirect research trying to differentiate between the two explanations by estimating models of wage determination. Wage structure research on U.S. and Canadian data has established the existence of a penalty to working in female occupations and has also shown that the size of the penalty decreases significantly after controlling for occupational attributes and/or unmeasured worker preferences and quality (Macpherson and Hirsh, 1995; Baker and Fortin, 2001). This would suggest that occupational gender segregation in the U.S. is to a large extent driven by preferences, not discrimination For a general discussion of many alternative theories explaining the existence of segregation see Anker (1997) or Miller at al. (2004). 11 An explanation for why the wage penalty to working in predominantly female occupations applies to both men and women suggests that workers of both genders employed in female occupations may have lower labor quality. If women are discouraged from entering high-wage occupations by discriminatory barriers, then only highly productive women will enter the typically male occupations. The fraction of female workforce then becomes an index of labor quality and only low-quality men will join female occupations. 12 In the U.S., DeLeire and Levy (2004) show that especially single women with children are less willing to trade lower on-the-job safety for higher wages; this explains a quarter of the occupational segregation. 13 However, preferences themselves may be the result of unfair gender stereotyping and there are other potentially negative consequences of gender segregation on top of the wage penalty. To the extent that segregation is discrimination-related, it results in lower overall economic efficiency (Tzannatos, 1998), and it also has negative social consequences (Blackburn et al., 2000). 9

11 Where does occupational segregation come from? One possibility is that it is related to the field (not level) of study that student choose in school. Indeed, Machin and Puhani (2003) show that a major part of the gender wage gap among recent school graduates in Germany and the U.K. can be explained by gender differences in the field of study. Again, one can measure the extent of gender segregation across fields of study (technical versus humanities), but it is difficult to determine the extent to which such segregation is the result of differences in inherited preferences or in gender stereotyping. We return to the issue of separating discriminatory gender segregation below when we discuss gender wage gap studies. Descriptive Studies Documenting the Extent and Structure of Gender Segregation There are now several studies documenting the extent, structure and evolution of gender segregation in many developed and some developing economies on the background of increasing female labor-market participation. 14 Most of the evidence comes from the U.S., however, where Blau et al. (1998) finds a decreasing degree of gender differences in employment patters during the 1980s and early 1990s. The main source of this decline in segregation is the changing representation of women within occupations, not a diminishing importance of typically male occupations. See also Baunach (2002) for related evidence. Dolado et al. (2002) offer the first comprehensive comparison of the U.S. segregation patterns to those present in the EU-15 countries. Using 1999 data, they compare segregation indexes for specific demographic groups and they also establish basic segregation trends. They find that segregation in Europe is decreasing among workers with higher education while it is stable for the less educated. Looking across the Atlantic, segregation appears to be higher in Europe than in the U.S. for highly educated labor force. 14 See Fortin and Huberman (2002) for evidence on Canada, Oliveira (2001) for Brazil, or Lewis (1985) for Australia. 10

12 Managers One can also study segregation by focusing on specific occupations, such as the highly visible group of managers. The representation of women among top-level managers and their relative wage position are of high academic as well as general public interest. The increasing share of female executives and the narrowing managerial gender pay gap in the U.S. (Bertrand and Hallock, 2001; Bell, 2005) may represent a cracking of the discriminatory glass ceiling precluding women from achieving high-paying positions. The higher incidence of women-led firms may improve the relative standing of lowerranking female employees. The study of gender compensation gaps among homogenous groups of highly motivated and educated workers, such as top-level managers, may also be ideal from the perspective of controlling for unobserved labor quality differences and may therefore credibly isolate the discriminatory component of gender pay gaps. There are now two detailed studies of the gender gap in top corporate jobs in the U.S., both relying primarily on the Standard and Poor's ExecuComp data, which contain information on five highest-paid executives in large publicly traded U.S. firms. Bertrand and Hallock (2001) study the data from 1992 to 1997 and find that women represent about 2.5% of the sample and earn about 45% less than men. Bell (2005) covers the period and reports a much smaller average gap in gross compensation of 25%, suggesting dramatic reductions in the gap after Both studies show that women gradually increase their participation in top executive ranks (to over 6% after 2001) as well as their relative compensation. They both also suggest that about 50 to 75% of the raw wage gap can be explained by women being less likely to manage large companies and to be CEO, Chair or company President (we explain what we mean by explained below in Section 3). 15 Outside of the U.S., there is so far apparently only one study of the 15 Both of these studies argue that male and female top-ranking managers are similarly educated and motivated, which makes it unlikely that their observed gender wage gap can be explained by unmeasured differences in human capital or motivation. Hence, if there is a significant unexplained component of the gender wage gap, it is likely to be due to employer gender discrimination. (A similar argument has been put forward by other studies focusing on the highly paid. For example, Wood et al. (1993) explore the gender wage gap among U.S. lawyers. Even though their analysis controls for rich human-capital and job-related characteristics, about one-third of the earnings gap remains unexplained.) However, this exclusive interpretation of the unexplained gap has been recently challenged by research focusing on female-specific 11

13 gender pay differences among executives. Gregg and Machin (1993) study British senior managers and find that women are less likely to be promoted and are paid 30% less. 3. Gender Wage Equality As was argued in the introduction, the simple overall average wage gap between men and women is not a useful tool for detecting the way women are treated on the labor market. To approximate more closely what we have in mind when we can ask about relative gender-specific wages and to provide a measure of wage differences that relates closely to the Equal pay act, one should compare wages of only comparable male and female workers, i.e. those with similar qualifications, performing similar tasks. A simple descriptive way of doing so is made possible by the recent availability of matched employer-employee data, i.e. data where there are several (all) workers available from a given company (employer). Using such data we can compare wage rates of workers working on the same job, i.e. employed in the same firm in the same detailed occupation. 16 (We can also constrain this exercise to only compare workers of the same age group within jobs.) One can take the average wage gap within each such job cell and then average over all job cells in the data. This generates a simple and more useful descriptive gender wage gap measures. Below, we present several alternative methodologies that are aiming at the same goal: to summarize the gender wage gap among comparable workers. This wage gap is often referred to as the pure, conditional, unexplained gender wage gap. A fundamental problem with finding comparable men and women will be the ability to control for skills that are not recorded in most data sets (i.e. abilities and qualifications that are not performance in managerial tasks. Gneezy, et al. (2003) suggest that women may be less effective than men in competitive environments, even if they can perform similarly well in non-competitive environments. In a related line of work, Babcock and Laschever (2003) report that women may not negotiate as toughly as men on salary issues. 16 It is ideal to use hourly wage rates for the calculation of the gender wage gap because hours worked differ by gender. One also needs to be careful about overtime wage rates, which are raising male wages more than female wages due to the typically higher overtime hours recorded by male workers. Also, if women are more likely to work in part-time jobs and part-time jobs pay lower hourly wage rates, this could also be a source of the overall gender wage gap. See, e.g., Wolf (2002) for an analysis of gender wage gaps that separately considers part-time and full-time jobs. 12

14 captured by simple age and education indicators). To the extent that some productive factors remain uncontrolled for, the remaining pure gender wage gap will provide only an upper bound on the possible extent of discriminatory gender wage differences, i.e. those wage differences that cannot be explained by observed factors. There is another important interpretation problem with the pure gender wage gap: to the extent that some of the characteristics that we control for are themselves the outcome of discriminatory practices, the unexplained gap may under-estimate the overall degree of discrimination. For example, if we condition on occupational choice and if occupational segregation is in part affected by discriminatory hiring, firing or promotion practices, then we will miss an important source of gender discrimination in our measurement (Blau and Ferber, 1987). Similarly for firm-type controls. Below, we present techniques that allow one to decompose the gender wage gap into parts attributable to gender differences in (observed) productive characteristics, gender segregation, and to potential violations of the equal pay act. 3.1 Oaxaca-Blinder Decomposition A traditional way of controlling for differences in factors affecting wages between two groups of workers consists of the estimation of log-wage regression functions (often called Mincerian regressions after Jacob Mincer) that simultaneously quantify the relationship between wages and a set of explanatory factors, such as age and education. 17 Many studies attempting to measure the extent of wage discrimination follow Oaxaca (1973) in decomposing the overall mean wage difference between the advantaged, i.e., men, and disadvantaged, i.e., women, into two parts. The first part reflects the difference in average productive endowments of individuals in each group and the second part is due to the differences in the coefficients. First, logarithmic wage regressions are estimated separately for each gender, controlling for explanatory variables. The 17 A regression function is essentially a conditional expectation function. Here, we only consider linear regression functions. See Heckman et al. (2003) for a review of the Mincerian literature; the authors actually reject the functional form of the Mincerian wage regressions in favor of a more flexible approach. 13

15 decomposition technique relies on the fact that the fitted regressions pass through the sample means as follows: ln w g ˆ ' = β X, g { f, m}, (1) g g where f denotes females and m denotes males, ln wg is the gender-specific mean of the natural logarithm of hourly wage, and X g represents the respective vectors of mean values of explanatory variables for men and women. Finally, βˆ m and βˆ f are the corresponding vectors of estimated coefficients. A general form of the mean wage decomposition is: ' ~ ' ~ ' ~ ln w ln ( ) ( ( ˆ ) ( ˆ m w f = X m X f β + X m β m β + X f β β f )), (2) where ~ β represents a counterfactual non-discriminatory wage structure. The first term on the right hand side of equation (2) represents that part of the total logarithmic wage difference that stems from the difference in average productive characteristics across gender. The second term originates in the differences in gender-specific coefficients from the non-discriminatory wage structure and is often interpreted as wage discrimination. 18 Several variants of this method, often referred to as Oaxaca-Blinder decomposition, arise depending on how the non-discriminatory wage structure ~ β is simulated. Neumark (1988) and Oaxaca and Ransom (1994) suggest the use of regression coefficients based on pooled data including both men and women. 19 A similar approximation, based on weighting the male and female coefficients with sample proportions of each gender, is used in Macpherson and Hirsh (1995) Objections can be made against this decomposition approach. First, by focusing on the mean gap, it ignores meaningful differences in gender-specific wage distributions. Second, if characteristics that might differ between males and females are omitted in the vector of regressors, the contribution of these characteristics will be captured by the constant term and appear erroneously in the measure of discrimination. 19 Neumark (1988) provides a theoretical justification for this approach using a model of discrimination with many types of labor in which employers care about the proportion of women they employ. 20 There is a number of extensions of this approach available in the literature. See, e.g., Albrecht et al. (2003) for an application of quantile regressions to gender wage gaps. Quantile regressions allow one to fit regression lines not through the mean of wages, conditional on X, but through a chosen quantile, for example the median. Machado and Mata (2005) and Autor et al. (2005) offer decomposition techniques extending the Oaxaca Blinder method to quantile regressions. The latter paper also bridges the decomposition approach with those extensions of Oaxaca-Blinder taking the decomposition idea beyond first moments and into decomposing whole distributions (DiNardo, Fortin, and Lemiuex, 1996). 14

16 Following Groshen (1991) and Bayard et al. (2003), one may also use for initial analysis pooled-data regressions in which the unexplained part of equation (2) is presented using a female dummy coefficient as follows: ( ) ' ~ ln w ln w = X X β + αf. m (3) f m This approach corresponds to the traditional Oaxaca-Blinder decomposition to the extent that the introduction of the female dummy does not affect the slope parameters of the pooled regression. In most situations, the standard approach is preferable. f The conditioning set of variables X typically includes observed worker- and firm-specific characteristics, denoted Z. Next, one may also attempt to measure the effect of gender segregation on wages by conditioning on segregation-related measures, such as the percent of females, denoted P, in a given group of employees. I.e., the elements of the P vector are the fraction of female employment in the worker's occupation, firm, and job cell. Therefore, one can estimate wage regressions of the following form: ' ' ln wij = α Fij + Z ijδ + Pijγ + ε ij, with i = 1,..., N j, and j = 1,..., J, (4) where w ij denotes the hourly wage of the i-th worker in the j-th firm, and where F ij equals 1 if the worker is female and 0 otherwise (this variable is dropped, of course, when we use the traditional form of the Oaxaca-Blinder decomposition). P ij is the vector of the femaleness measures for the given worker, J denotes the number of firms in the sample, and N is the number of workers in the j-th firm. j Whenever one uses matched employer-employee data, statistical inference (the calculation of standard errors) should allow for any form of unconditional heteroscedasticity as well as for interdependence of error terms within firms. This is important because person-specific error terms will not be independent within firms. To capture this firm-level clustering, one can use a panel data version of the Huber/White estimator given by: ( ˆ ' 1 J ' ' ' ) = ( X X ) X ˆ ε ˆ ε X ( X X ), 1 ˆ j j j j j= 1 V β (5) where ˆ ε = ln w X ˆ β is the column vector of estimated error terms for employees of j the j-th establishment. j j 15

17 Sample Selection into Employment It was argued in the introduction that non-random selection of women into work across countries may explain part of variation in the gender pay gap across countries. This notion is supported by the observed variation in employment gaps, from 10% in the US, UK and Scandinavian countries, to 15-25% in northern and central EU, up to 30-40% in southern EU and Ireland (Olivetti and Petrongolo, 2005). If women who are employed tend to have relatively high-wage characteristics (both observed and unobserved), low female employment rates may become consistent with low gender wage gaps simply because low-wage women would not feature in the observed wage distribution. How can we control for unobserved worker quality (skills)? This issue is closely tied with the estimation of the gender-specific wage regressions. A substantial portion of women does not work. If those who work have unobservable characteristics that are different from those of women that are jobless, and if these unobservables are correlated with the explanatory characteristics used in these regressions, then the estimated parameters will be biased and so will be the Oaxaca- Blinder decomposition. A traditional way of controlling for this so-called sample-selection bias is to estimate the Heckman s sample-selection correction. This is achieved by first estimating a binary model of female participation in employment (i.e. using both employed and nonemployed women) in which we need to have at least one strong explanatory variable that we believe does not directly affect wages (i.e. the presence of very young children). Based on this participation regression and a distributional assumption, one then forms the expected value of unobservables for those women that do work and enters this value as another explanatory variable in the wage regression. 21 For a recent application see, for example, Beblo et al. (2003). We note that the estimation of the sample selection models is sensitive to the particular technique used and to the choice of variables that are excluded from the wage regression and only used in the participation model. 21 There are now also semi-parametric methods of controlling for the sample selection bias that do not require a parametric distributional assumption. 16

18 A simple alternative to estimating sample selection models is to assume that those women who do not work, in case they did work, would have wages that would be below the median of those women with similar characteristics who actually do work. This assumption allows one to consider wages of all workers, employed or not, and measure the median wage gap across the two genders. While the mean wage gap would be affected by the value of the assumed wage for those who do not work, median wages are not affected by the particular value, as long as wages of those not working would indeed be below the median wage, were they to work. See Olivetti and Petrongolo (2005) for such empirical analysis. Their estimates deliver higher median wage gaps on imputed rather than actual wage distributions for most countries in their sample. This is consistent with women being on average more strongly positively selected into work based on productivity than men. However, this difference is only sizeable in countries in which gender employment gaps are high. 22 Studies Linking Wage Gap to Segregation The empirical research on the relationship between gender segregation and pay gap is based mainly on U.S. data. In particular, Killingsworth (1990) and Macpherson and Hirsh (1995) find that not only female, but also male wages are lower in predominantly female occupations. Johnson and Solon (1986) suggest that employer segregation in the U.S. may be more important than occupational segregation. Further, Blau (1977) and Bielby and Baron (1984) point to the presence of significant job-cell segregation. Recently, Groshen (1991) and Bayard et al. (2003) use U.S. matched employer-employee data-sets to simultaneously estimate the effect of all of these types of segregation on the gender wage gap. The latter study is based on a large data set covering all industries and 22 In a related line of work, For example, Blau and Kahn (2003) use data for 22 countries over 10 years and find that more compressed male wage structures and lower female level of employment are associated with a lower gender pay gap. See also Hunt (2002) and OECD (2002). Even though traditional Oaxaca-Blinder decompositions attempt to control for productive characteristics, it is highly likely that unobserved productive characteristics are important and positively correlated with observed ones. Hence, the unexplained gap will be larger in countries with high low-skill female employment rates in part because we are not able to fully control for skills in estimated wage regressions. This idea is consistent with the above surveyed cross-country studies and as Jurajda and Harmgart (2004) suggest, it may even explain the size of the wage penalty to female occupations. 17

19 occupations; the results suggest that both the effect of an individual's gender within a job cell and various forms of gender segregation are important in accounting for the total U.S. gender pay gap. These cross-sectional studies do not control for occupation- or firm-specific taste differences across gender, which is important for the interpretation of the estimated segregation effects. Employer discrimination in hiring and promotion is only one of the potential causes of gender segregation. Segregation may also arise as a result of human capital differences or from differences in school inputs. Alternatively, women may concentrate in certain occupations because of social norms or differences in job tastes. 23 Macpherson and Hirsh (1995) explore the causes of occupational segregation using longitudinal U.S. data. They account for much of the wage effect of occupational gender segregation by controlling for skill-related occupational characteristics and unmeasured skill or taste differences of workers. Hence, these authors imply that the relative proportion of female employment in an occupation largely reflects these characteristics and taste differences and may not be of major policy concern in the U.S. However, in order to be able to use within-worker time variation in femaleness of occupations (to estimate worker-fixed-effect regressions and so to control for unobservable worker heterogeneity), their approach relies on the assumption that workers switch occupations for reasons unrelated to discrimination, which is a potentially questionable assumption. Other Wage-Gap Studies There are hundreds of cross-section single-country Oaxaca-Blinder decompositions. 24 There are also other types of studies. For example one can follow workers over time since the entry into the labor market, in order to discover the sources of the gender wage gap England (1992) or Anker (1997) provide a discussion of the potential channels that relate segregation and gender wage gap. 24 A recent survey of the gender-related economic literature is provided in Altonji and Blank (1999). 25 Kunze (2003) focuses on occupational gender segregation and looks at the early careers of workers entering the West German labor market during 1975 to 1990 with apprenticeship degrees. She shows that occupational gender segregation explains a large and persistent part of the gender pay gap for these workers. 18

20 One can also link firm outcomes (e.g. profitability) with the gender pay gap, arguing that preference-based discrimination should be connected to lower profitability (Becker, 1985). See for example Hellerstein and Neumark (1999). One can also relate firms monopsony power to the gender wage gap (Ransom and Oaxaca, 2005). 3.2 Matching as Opposed to Regressions The vast majority of the existing gender wage gap studies employ the Oaxaca-Blinder decomposition to separate the part of the observed raw gap attributable to differences in average worker characteristics and the part due to differences in the rewards of these characteristics (i.e. the unexplained part). This approach, however, relies on restrictive parametric assumptions about the functional form of the wage conditional expectation function, which could lead to misleading results, especially when there are important differences in the supports of the empirical distributions of male and female individual characteristics (i.e. in the range of the observed characteristics of men and women). For example, consider the likely case of women not being represented in certain types of firms or occupations. In this situation, the regression-based traditional framework will attempt to compare male and female wages conditional on a given set of characteristics by projecting the conditional wage distribution of women onto regions of the male wage distribution in which females may be virtually nonexistent. Recent work reviewed below suggests that employing the regression-based techniques and thus ignoring this comparability issue, often referred to as the common support problem, may lead to misleading results. The alternative non-parametric (decomposition) technique, advocated recently in the program evaluation literature (see, e.g., Heckman et al., 1998), relies on comparing only wages of matched men and women -- those with a very similar set of observed characteristics. The matching approach then remains silent about the extent of wage differences between men and women in those types of firms or occupations from which women are generally absent. Non-parametric matching, which contrasts wages of matched male and female workers with similar observable characteristics, is an intuitive 19

21 alternative to regression-based methods. We ask how much a woman with a specific set of observables would earn if she were treated like a man. The answer is given by the wage of men with the same set of observable characteristics. We again rely on logarithmic wages and we sort both males and females into data cells, indexed by k, based on their age, education, industry, firm size and ownership. This allows us to write the overall average wage of females (males)as an average of the k- group-specific wages, weighted by the share of females (males) in each group on the total gender-specific employment. This then allows us to write the overall wage gap as: Here p k is the share of women in a group k and q k is the corresponding weight for men. This equation is of course reminiscent of the Oaxaca-Blinder decomposition. The first term in the equation describes how much less women would earn if they shared equal observable characteristics with men. The group-specific average-wage gender wage gap is then averaged across groups with respect to the distribution of the treated (p k ), resulting in an estimate of the average treatment on the treated (ATT) in the terminology of the program evaluation literature or the unexplained gap in the jargon of the regression-based decompositions. 26 The equation makes clear that a basic requirement for the implementation of the matching approach is a sufficiently large overlap between the distribution of the observable characteristics of the treated and untreated individuals. This is known as the common support condition. In our application, it asks that for every female worker a sufficient number of similar males be available. By the same token it stipulates that we only use similar males for comparison with every female worker in our data and drop from the calculation of the ATT those males who work in firm types where no women are employed. The common support condition is ignored in the parametric decomposition techniques, which use functional form assumptions to project the conditional wage 26 Note that using β m or β f for β in the Oaxaca-Blinder approach corresponds to estimating the average treatment effect on the untreated (ATU) or ATT, respectively, when being a female is the treatment. 20

22 distribution of women onto regions of the male wage distribution in which females are virtually nonexistent. Matching thus eliminates two of the three selection-bias sources distinguished by Heckman et al. (1998): the bias resulting from having different ranges of X (characteristics) for the treated and controls (comparing non-comparable individuals) and the bias resulting from having different distributions of X across their common support (weighting comparable individuals incomparably). The absence of the third source of the bias, namely differences in unobservables across groups, is a condition for the correct identification of the ATT; it also the condition underlying the interpretation of the unexplained gap as corresponding to discrimination. There are available several algorithms for matching comparable workers. Matching exactly on the whole vector of observed covariates leads to the curse of dimensionality as the number of data cells containing a given combination of X increases with the number of covariates and the size of the available data within each cell dwindles. Fortunately, Rosenbaum and Rubin (1985) demonstrate that matching can be done on the conditional probability of treatment (in our context the conditional probability of being a female) known as the propensity score, thus reducing the dimensionality problem considerably. Next, a similarity distance must be chosen to guide the selection of controls (males) that are to be matched to a given treated individual (female). For example, to each woman we can match all men who have their propensity score within a given distance from that of the woman. Alternatively, we can fix the number of nearest men (in terms of the propensity score) to be matched to each woman. Kernel matching, an often applied technique, is based on the estimated propensity scores and takes local averages of the untreated observations near each treated observation, where the weights are based on the distance of the untreated observation from the treated one in terms of the estimated propensity score An assessment of the matching quality consists of checking whether the matching procedure is able to balance the distribution of the relevant variables across the control and the treatment group. The basic idea of all approaches is to compare the situation before and after matching and check if there remain important differences after conditioning on the propensity score. To this effect one can for example perform twosample t-tests suggested by Rosenbaum and Rubin (1985). 21

23 There are now first few applications available of the matching approach to gender wage gap decompositions. Nopo (2004) matches men and women with an exactly identical combination of observed characteristics. In his analysis of Peruvian workers, he finds that 23 and 30% of males and females, respectively, fall outside the common support -- that is have no comparable workers of opposite gender -- and that about one quarter of the raw gender wage gap is due to this difference in support. Black et al. (2004) also use this so-called exact matching approach to estimate the unexplained gender wage gap among highly educated white, black, Hispanic, and Asian workers in the U.S. Both studies compare results based on matching with those of the traditional parametric approach and suggest that the explained portion of the overall raw gap is typically significantly higher when matching is used. 4. Applications in Post-Communist Countries 4.1 Existing Literature Wage Gap The existing literature investigating the wage position of women in transition studies the impact of early pro-market reforms on relative female wages. See, e.g., Orazem and Vodopivec (1995) for Slovenia, Hunt (2002) for East Germany, and Newell and Reilly (1996), Brainerd (1998), Ogloblin (1999) for Russia, Jolliffe (2002) for Bulgaria, Jolliffe and Campos (2005) for Hungary, or Brainerd (2000) for a set of post-communist countries. A typical finding from these studies is that gender differences in observed worker characteristics contribute little towards the significant raw wage gaps between men and women. While the gender wage gap has been quite stable in many transition countries (Newell and Reilly, 2000), it has dropped by about 10 percentage points in East Germany. Hunt (2002) attributes much of this decrease to low-earning women selectively dropping out of the labor force The 1990 monetary union between East and West Germany led to a large increase in East German wage level while western trade unions took over the East German wage bargaining system. Drastic restructuring 22