Gender differences in earnings and labor supply in early career: Evidence from Kosovo's school-to-work transition survey

Pastore, Francesco; Sattar, Sarosh; Tiongson, Erwin R. Article Gender differences in earnings and labor supply in early career: Evidence from Kosovo's school-to-work transition survey IZA Journal of Labor & Development Provided in Cooperation with: IZA – Institute of Labor Economics Suggested Citation: Pastore, Francesco; Sattar, Sarosh; Tiongson, Erwin R. (2013) : Gender differences in earnings and labor supply in early career: Evidence from Kosovo's school-to-work transition survey, IZA Journal of Labor & Development, ISSN 2193-9020, Springer, Heidelberg, Vol. 2, pp. 1-34, https://doi.org/10.1186/2193-9020-2-5 This Version is available at: https://hdl.handle.net/10419/92290 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/2.0/ ORIGINAL ARTICLE Open Access Gender differences in earnings and labor supply in early career: evidence from Kosovo’s school-to-work transition survey Francesco Pastore 1* , Sarosh Sattar 2 and Erwin R Tiongson 2 * Correspondence: [email protected] 1 Seconda Università di Napoli, Italy Full list of author information is available at the end of the article Abstract Very little is known about gender wage disparities in Kosovo and, to date, nothing is known about how such wage disparities evolve over time, particularly during the first few years spent by young workers in the labor market. More generally, not much is known about gender wage gaps in early career worldwide, a period which is perceived to be an important determinant of the overall gender wage disparity. This paper analyzes data from the School-to-Work Transition (SWT) survey, an unusual survey conducted by the ILO between 2004 and 2006 in eight countries, including Kosovo, that documents the labor market experiences of the youngest age segment in the labor force (age 15–25 years). The results of the analysis suggest that, on average, women have lower education attainment than men but this educational disparity is masked among the sample of employed men and women who tend to be well-educated. The consequences of this dramatic segmentation of labor market participation are striking. On average, there is little or no gender wage gap. The results of the Juhn et al. (J Polit Econ 101:410–442, 1993) decomposition analysis reveals that gender wage differences are almost entirely driven by differences in characteristics (rather than either the returns to those characteristics or the residual). The greater average educational attainment of employed women, among other characteristics, tends to fully offset the gender wage gap. Not surprisingly, the returns to women’s education among employed women are low because there is little variation in educational attainment among the sample of well-educated employed women. When the analysis controls for sample selection bias and heterogeneity, the returns to women’s education rise, confirming the lower productivity-related characteristics of non-employed women compared to employed women. The relatively small sample constrains a fuller analysis of the emergence of the gender wage gap, which, according to a small but growing international literature, typically materializes during childbearing years. JEL codes: I21, J13, J15, J16, J24, J31, J7, P30 Keywords: Gender wage gap and dynamics, Early labor market outcomes, School-to-work transitions, Earnings equations, Decomposition analysis, Balkans area, Kosovo © 2013 Pastore et al.; licensee Springer. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Pastore et al. IZA Journal of Labor & Development 2013, 2:5 http://www.izajold.com/content/2/1/5 1. Introduction In the Western Balkans region, Kosovo has been hit hard by various shocks over the last two decades. As a result, in addition to numerous consequences of previous conflict and crises, the labor market is presently characterized by substantial imbalances along several dimensions, with gender disparities possibly being the most dramatic. As reported in a number of policy documents (e.g., UNFPA 2007), women are often the object of discrimination and violence; they also represent the weakest segment of the working population. Existing studies 1 of gender differences in Kosovo have noted that in contrast to gender disparities in other developing countries, the women in Kosovo are at a disadvantage compared to men both in terms of their educational attainment and their employment prospects. However, it is not clear how this affects gender differences in earnings, as available databases to date typically do not provide information on wages in Kosovo. As a result, little is known about the gender wage gap in Kosovo. In addition, in Kosovo and in many other countries more generally, little is known about the evolution of gender differences in wages (as well as in labor supply) over the life cycle. An understanding of the first few years in the labor market is particularly important because it is likely that the wage growth during this period is “the primary cause of the overall gender pay gap,”as has been argued recently 2 . Data collected by the ILO on School-to-Work Transitions (SWT) in 8 countries, including Kosovo, provide an opportunity to analyze the earnings differentials between men and women, particularly in their early labor market experience. ILO collected the data between 2004 and 2006 covering young workers aged 15–29, and recently made the micro data available to researchers. As the name of the survey suggests, the ILO collected information on the experiences of young workers at the beginning of their career, as they left school and entered the labor market. The ILO conducted the SWT survey in September and October 2004 in 5 out of 7 regions in Kosovo (Pristina, Mitrovica, Gijlan, Gjakova, and Prizren) 3 . In Kosovo’s case, available SWT data allow an analysis of early careers only up to age 25, because the final sample includes only individuals aged 15–25 (as opposed to 15–29 in other countries). Although the SWT data are by now a few years old, the general lack of publicly available data on earnings or wages in Kosovo means that the availability of SWT data still provides a rare opportunity to look at gender wage differentials in Kosovo. The results of the analysis reveal that women tend to have lower education attainment not only in the adult population, as noted in a recent (World Bank 2012) report, but also among those in the youngest age group. In general, young women tend to be disproportionately represented among those with low educational attainment. However, this educational disadvantage disappears when one considers only the employed men and women. This provides compelling evidence of the existence of a notable segmentation between employed and non-employed young women in Kosovo. The former are generally much better educated, at the same level or more than their contemporary men, and have higher than average productivity characteristics. The latter, instead, have a much lower educational level and productivity characteristics than their male counterparts. The consequences of this segmentation on labor market participation and wages in early career are striking. Among employed men and women in their late teens (age 15–19), there is no evidence of a gender wage gap in favor of men. This indicates that in the period prior Pastore et al. IZA Journal of Labor & Development 2013, 2:5 Page 2 of 34 http://www.izajold.com/content/2/1/5 to the childbearing years, women tend to fare as well as their male counterparts. The results of Juhn et al. (1993) decomposition analysis reveal that differences in productivity characteristics, rather than in prices or in residual inequality, drive the gender gap. The greater mean educational attainment of employed women tends to compensate for the higher average number of working hours of their male counterparts. The last part of the analysis attempts to assess the impact of observed and unobserved differences between employed and non-employed women on the returns to education and the gender wage gap. Interestingly, when we control for selection bias, the gender wage gap for the SWT sample as a whole and for the component of the young adults is still not statistically significant, despite the lower productivity characteristics of the non-employed women compared to employed women. This may be due to the relatively small sample size. At the same time, the returns to women’s education rise when selection bias is taken into account, confirming the lower productivity-related characteristics of non-employed women compared to employed women. The rest of the paper is outlined as follows. Section 2 reviews the existing literature on gender disparities in Kosovo’s labor market. Section 3 presents the methodology adopted in the econometric analysis. Section 4 discusses the main advantages and shortcomings of the Kosovo SWT survey database. Section 5 reports the main empirical findings. The final section provides some concluding remarks. 2. Literature review: gender issues in Kosovo’s labor market 2.1 Educational attainment Women’s educational attainment compares unfavorably with men in Kosovo, according to a recent report (World Bank 2012). The report finds that there are distinct gender differences in literacy and educational attainment in favor of men. In particular, 2.2% of adult men are illiterate compared to 7.2% of women; 37.3% of men have only basic education compared to 61.7% of women; and 11.9% of men possess a university degree compared to only 5.5% among women. More generally, the educational level of Kosovo’s population is relatively low by international standards. Half of the population has only 9 years of schooling, equivalent to basic education. Although educational attainment has been improving slightly in recent years, the share of university graduates remains higher among the adult population compared to the youth population, because the educational system was interrupted by the war and because of the high emigration rate of some of the most educated members of the younger generation. Figure 1 reproduced from (Pastore 2011) provides a graphical representation of gender gaps in educational attainment among young people across eight countries for which SWT data are available, namely Azerbaijan, China, Egypt, Iran, Kosovo, Mongolia, Nepal and Syria. The panels are focused on the following levels of education: panel (a) on primary education or below; panel (b) on low secondary education; panel (c) on high secondary education; panel (d) on tertiary and higher education. The female share is measured along the vertical axis while the male share is measured along the vertical axis. The diagonal line represents the case of exact gender equality. Points above the diagonal line represent countries that offer an advantage to women and those below the diagonal line are those that offer an advantage to men. An advantage at lower education levels of course implies a disadvantage at higher educational attainment levels. China, Syria and Mongolia can be found below the diagonal line in panel (a), which Pastore et al. IZA Journal of Labor & Development 2013, 2:5 Page 3 of 34 http://www.izajold.com/content/2/1/5 suggests that the shares of women holding primary education or below is lower than average in these countries; in turn, women enjoy some advantage at higher educational levels. In fact, women’advantage in these countries is substantial, starting from low secondary education, but especially in terms of high secondary and tertiary education or above. In the case of Kosovo, the educational disadvantage of women is quickly disappearing among the youngest generation, as the country can be found very close to the diagonal line. The figures also confirm the low average educational level of the youth population. Kosovo children tend to be found on the upper right side of the panels (a), (b) and (c), and at the bottom left side of panel (d). 2.2 Employment In addition to gender disparities in educational attainment, the report suggests that women in Kosovo are less likely to be employed 4 . More generally, labor market outcomes in Kosovo compare unfavorably with those in Europe and women are particularly at a disadvantage. The report indicates that 34.1% of men are employed compared to only 11.5% of women. In addition, women represent less than a tenth of all entrepreneurs and are under-represented among top management (0.3%). Meanwhile, Kosovo’s unemployment rate at 45.4% is at the highest end of the distribution of unemployment rates in the region. Women, young workers, and the least educated have the highest probability of being unemployed. Unemployment rates for women gradually fell in the years leading to the global financial crisis but mostly due to decreasing activity rates rather than due to rising job opportunities. Most of the unemployed have never worked previously. Furthermore, about half have been looking for work for four years or more. Unemployment is very high among young people aged 15–24 which as a group represents close to a fifth of Kosovo’s population. Azerbaijan Kosovo Azerbaijan China Mongolia Nepal Azerbaijan Egypt Syria China Egypt Iran Mongolia Nepal Syria 0 5 10 15 20 25 30 35 40 45 0 5 10 15 20 25 30 35 40 45 Women Men Egypt Iran Kosovo Syria 0 5 10 15 20 25 30 35 40 45 50 0 102030405060 Women Men Azerbaijan China Egypt Iran Kosovo Mongolia Nepal Syria 0 10 20 30 40 50 60 70 0 10203040506070 Women Men China Iran Kosovo Mongolia Nepal 0 5 10 15 20 25 0 5 10 15 20 25 30 Women Men Figure 1 Educational levels by gender. Source: own elaboration on SWT surveys. Pastore et al. IZA Journal of Labor & Development 2013, 2:5 Page 4 of 34 http://www.izajold.com/content/2/1/5 Several studies cited by the report indicate that workers in the private sector typically do not receive a basic benefits package (e.g., maternity leave, paid annual leave, paid sick leave and unpaid leave). This effectively weakens the productivity of all workers and, in particular, women, because they are more likely to benefit from leave options. According to the report, although a new Labor Law was enacted in 2010 that mandates the provision of basic benefits, some worry that the new law is not sufficiently enforced. 2.3 Earnings In this difficult environment, very little is known about gender wage disparities or even about earnings in general. We know of only a handful of studies that examine wages systematically in Kosovo 6 . These include a labor market study conducted by the World Bank in 2003 based on 2002 Labor Force Survey data; a global study of gender wage disparities (Oostendorp 2009) in 63 countries, including Kosovo, based on 2001 LSMS data; a study of earnings among emigrants, based on a survey conducted by the Riinvest Institute along Kosovo’s borders (Havolli 2011); and a very recent paper on returns to education based on the 2002 Riinvest Household and Labor Force Survey (Hoti 2011). The results of the few studies of Kosovo that exist suggest the following: The overall gender wage differential is either small or insignificant in Kosovo. In the private sector, it is equivalent to 8 percent and in the public sector it is insignificant (World Bank 2003). Although the sample of emigrants in the Riinvest Institute survey is likely very different from the general population of domestic workers, the results of the analysis of emigrant earnings are nonetheless potentially instructive, and they suggest that there are no statistically significant differences in earnings by gender, though there are gender differences in the likelihood of sending home remittances (Havolli 2011). The global study of gender wage gaps also suggests that Kosovo is at the lower end of the distribution (Oostendorp 2009). Although the general lack of a gender wage gap may be interpreted by some as evidence of gender equality, one possibility –given the differences in educational attainment and employment –is that the empirical study of earnings largely ignores issues related to sample selection biases. In other words, the women who are typically included in empirical analyses of earnings may be systematically different from unemployed women who are, by construction, left out of the sample. The recent study of returns to education cited earlier (Hoti 2011) in fact, provides evidence that workers, particularly women, are not randomly selected into employment. The study used a Heckman procedure to account for this selection bias, but the study does not at all explore gender wage differentials. Taken all together, this suggests that a new systematic study of gender wage differentials would be a valuable contribution to the literature. Such a study would have to carefully account for likely selection biases. In addition, a study of the first few years spent by young workers in the labor market—as they transition from schools to the labor market—may be warranted given the importance of the relationship between education and employment. A small but growing international literature in fact suggests that wage developments in this “early career”period are critical drivers of the overall gender wage gap. For example, one very recent cross-sectional study of male and Pastore et al. IZA Journal of Labor & Development 2013, 2:5 Page 5 of 34 http://www.izajold.com/content/2/1/5 female professionals in the U.S. financial sector suggests that although wages are essentially equal at the beginning of their careers, the women’s earnings soon lag behind those of their male counterparts 7 . It finds that the divergence in wages is due in part to differences between men and women in the type of training received prior to the completion of an MBA (and rising returns to this training), differences in career interruptions (and large penalties associated with such interruptions), and differences in the number of hours worked, typically around the childbearing years. However, another recent strand of the literature suggests that there is a large, measurable gender wage gap, already present at entry into the labor market—largely driven by occupational segregation by gender—which then persists throughout people’s careers 8 . In any case, a study of this early career period is potentially illuminating. 3. Methodology 9 To shed light on the stylized facts discussed above, this paper will first estimate extended and augmented wage equations to assess the impact of different determinants of wages on the gender wage gap and quantify returns to education by gender. Second, we implement several decomposition techniques to assess the role of –both quantity and price –determinants of wages at the mean and at different quantiles of the distribution. Third, as noted in the previous sections the participation rates of both men and women are very low and in order to assess the possible impact of the ensuing sample selection bias on their relative wages, we estimate (Heckman’s 1979) corrected earnings equations and then assess the importance of selection bias on wages by way of the Neumann and Oaxaca (2004) decomposition. 3.1 Baseline approach: Mincerian wage equations The Mincerian approach to estimating the returns to human capital and the gender wage gap is used as baseline framework. As is well known, the basic Mincerian earnings equation amended to include also control for gender differences is as follows: lnwi¼αþγWiþrSiþδEXPiþuið1Þ where w i are monthly labor earnings for an individual i,W i represents the gender dummy variable, S i represents a measure of the years of schooling, EXP i is a measure of work experience and u i is a disturbance term representing other variables that are not explicitly measured and assumed to be independent of the other regressors. Work experience is typically included also as a quadratic term to account for the concavity of the earnings profile, which would be consistent with decreasing returns to human capital. However, because this sample covers younger workers alone, the quadratic term is omitted here. The parameters of interest are clearly γand r, namely the coefficient of the gender dummy and the private financial return to schooling or, equivalently, the proportionate effect on wages of incremental increases in S. The above earnings function is in fact a log-linear transformation of an exponential function and can be estimated by OLS, with the coefficients requiring a semi-elasticity interpretation. That is, as is well known, they measure the percentage change in the dependent variable for any unit change in any independent variable, holding other things constant 10 . Pastore et al. IZA Journal of Labor & Development 2013, 2:5 Page 6 of 34 http://www.izajold.com/content/2/1/5 The paper also provides estimates of the augmented version of the Mincerian specification of the earnings equation: lnwi¼αþγWiþrSiþδEXPiþX n j¼1 βXij þμið2Þ where X n j¼1 βXij is a set of other variables assumed to affect earnings. Among such variables, we include mainly pre-work individual characteristics, such as civil status, having children, ethnic differences, type of job search adopted, time spent waiting before finding a job, aspirations in life. In the final specification, we also include such job characteristics as whether the young person has a work contract, the type of contract (whether written or oral), the duration of the contract, the ownership, size and industry of the firm where the worker is employed, and the type of on-the-job training received if any. 3.2 The Juhn, Murphy and Pierce decomposition This paper uses the Juhn et al. (1993) methodology to disentangle the components of the wage gap 11 . It differs from the classical Oaxaca and Ransom decomposition because it decomposes the wage gap not only at the mean, but along the entire wage distribution, thereby accounting for role of the residual unexplained component, measured by the residual wage distribution. In this study, these properties of the Juhn et al. (1993) decomposition may be useful, considering the absence of a gender wage gap at the mean of the distribution and toned to ascertain whether this holds along different quantiles of the distribution. Without discussing all the analytical details, it is sufficient to mention that in order to capture gender differences at different quintiles of the wage distribution, two different equations will be estimated as a first step, namely the male and the female wage equations, both of the type described in equation [2], but without the gender dummy. Intuitively the decomposition allows disentangling the following components: Δlnwm¼lnwm−lnww¼ ^ βm  Xq m−  Xq f  þ  Xq m ^ βm− ^ βf  þ^ μm−^ μf  ð3Þ where the hat denotes estimated coefficient, the horizontal bar signifies the mean characteristics and q indicates the corresponding quintile of the wage distribution. The row wage gap (Δlnw m ) is decomposed into the following: a) an endowment effect, measured by differences in observed characteristics weighted by the estimated coefficients of the male earnings equation; b) a remuneration effect, measured by the estimated differences in prices of the observed characteristics; c) an unobservable effect, measured by the difference in the estimated residuals. These are usually called, respectively, the quantity effect(Q),thepriceeffect(P)andtheresidual wage distribution, which is unobserved. The male distribution is used as a reference category. Findings are presented at the mean and at different percentiles of the wage distribution, namely 25, 50 and 75. Pastore et al. IZA Journal of Labor & Development 2013, 2:5 Page 7 of 34 http://www.izajold.com/content/2/1/5 3.3 The Machado and Mata decomposition A shortcoming of the Juhn et al. (1993) decomposition is that it does not allow an assessment of the size of the gender wage gap along the entire wage distribution. As such, we also implement the (Machado and Mata 2005) decomposition analysis. It allows the definition of different counterfactual distributions on the basis of conditional wage distributions at different percentiles. We attempted to implement the van Albrecht et al. (2009) version of the (Machado and Mata 2005) decomposition. This version controls for sample selection bias along the entire wage distribution. We expect that this might be the case considering how gender differences vary along the distribution of educational attainment. Nonetheless, likely due to the small number of observations, we failed to obtain any useful results and therefore we turn now to the more traditional analysis of sample selection bias using the Heckit methodology, which looks only at the mean value of the wage distribution. 3.4 Sample selection correction The last part of the analysis aims to correct for sample selection bias the baseline estimates drawn from the Mincerian earnings equation. Toward this end, the standard (Heckman 1979) methodology is implemented. In brief, the basic intuition of this model is the following: the baseline earnings equation is missing some variables that may explain women’s (labor force) participation. If one finds factors that help explain female participation but are not statistically related to their wages, it makes it possible to estimate a specific equation, the so-called participation or selection equation. This equation has, as a dependent variable, a dummy variable taking a value of one if an individual is employed and zero otherwise and, thus, yields a measure of the probability of participating in the labor market (the inverse Mills ratio). Adding this variable to the main equation—in this case, the earnings equation—wouldprovideatestoftheexistenceofsampleselectionbiasandcorrectthe coefficients in case the added variable proves to be statistically significant. Analytically, model [2] consists now of two equations, whereas the main equation is the usual earnings equation, which can be estimated by OLS, but there is a selection equation of the following type that can be estimated by Probit: LS ¼1if X m j¼1 γjZjþv≥0ð4Þ where LS represents labor supply. Sample selection arises when there is correlation between uand v. In this case, and assuming that u is independent of zγ, namely the covariates of the selection equation, the expected value of lnw i ,will be: Elnwi ðj∑Zj;vÞ¼αþrSiþδEXPiþ∑n j¼1βXij þρλ ∑m j¼1γjZj  ð5Þ where ρis the correlation between the error terms of the main and of the participation equation and λis the inverse Mills ratio evaluated at the mean of the covariates (zγ). This equation shows that when there is sample selection bias, one should include the term λ(zγ) as an additional regressor in order to obtain unbiased estimates of the parameters of interest. If ρ=0, λ(zγ) does not appear in the main Pastore et al. IZA Journal of Labor & Development 2013, 2:5 Page 8 of 34 http://www.izajold.com/content/2/1/5 all together, this suggests a strong correlation between the decision to enter the labor market and the decision to start a family. Women who work tend to postpone marriage for work-related reasons, more so than men. Considering the age at marriage of all the workers in Kosovo, it is worth noting that the decision to marry is taken not much later than the age at marriage of this sample of young men and women. According to the Statistical Office of Kosovo (SOK), the average age at marriage in Kosovo is 27 for women and 30 for men. The highest number of marriages is Table 1 The gender wage gap by age group All Teenagers Young adults Monthly wages Monthly wages, unconditional 0.025 0.0624 0.0064 Monthly wages, lhours 0.0323 0.0657 0.0106 Monthly wages, lhours, education −0.0068 0.0502 −0.0187 Monthly wages, lhours, education, human capital −0.0113 0.0205 −0.0261 Monthly wages, lhours, education, human capital, demographics −0.0211 −0.031 −0.0215 Monthly wages, lhours, all controls 1 −0.0066 0.4002 −0.043 Number of observations 315 58 257 Hourly wages Hourly wages, unconditional 0.0624 0.0769 0.055 Hourly wages, lhours, education 0.0226 0.1006 0.0125 Hourly wages, lhours, education, human capital 0.014 0.1294 −0.0066 Hourly wages, lhours, education, human capital, demographics −0.0153 0.1143 −0.0373 Hourly wages, lhours, all controls 1 −0.0025 0.5201** −0.0706 Number of observations 315 58 257 Note: * p<.1; ** p<.05; *** p<.01. 1 The controls included in the last row of each group of estimates are the same as those included in the augmented wage equations in Table 4. Source: own elaboration on the ILO School-to-work transition survey of Kosovo. -1.5 -1 -0.5 0 0.5 1 1.5 15-16 17 18 19 20 21 22 23 24 25 Gender wage gap, unconditional Gender wage gap, hours Gender wage gap, hours, hc, demographics and ethnics Gender wa g e g ap, hours, all vars Figure 3 The gender wage gap at different years of early career. Source: own elaboration on the ILO School-to-work transition survey of Kosovo. Pastore et al. IZA Journal of Labor & Development 2013, 2:5 Page 15 of 34 http://www.izajold.com/content/2/1/5 for women in the age group 20–29 and for men in the age group 25–34. According to statistics for 2005, the average age for marriage recently increased by 11 months for men and by 14 months for women. Nonetheless, in Kosovo the practice of marrying prior to reaching the majority age of 18 is still relatively common, especially in rural areas. 5.3 Mincerian earnings regressions: returns to education Table 2 presents the first set of estimates of the returns to years of education by age group and gender. Each row represents a different set of estimates, with an increasing number of controls. The first row refers to the basic earning equations, the second row to the extended version and the following rows to different versions of the augmented earnings equations. The Table reports only the returns to years of schooling. It shows that the returns to education are quite low in Kosovo, at about 5% per year of schooling. Similar to other countries, the annual rate of return is higher for womenthanformenintheentiresampleandineachoftheagegroupsconsidered. However, the returns to education might be far from linear and returns might differ by type of degree. Table 3 reports the results of the augmented Mincerian earnings equations 17 .The coefficient of determination is higher for women this time. The coefficient of the gender dummy does not seem to be affected by inclusion of all observed characteristics of men and women, remaining statistically insignificant. But this does not necessarily imply the absence of a gender gap; as noted previously, employed women have a higher educational level than men and, therefore, are expected to have higher wages. (a): entire sample 0.1 .2 .3 15 16 17 18 19 20 21 22 23 24 25 15 16 17 18 19 20 21 22 23 24 25 Men Women Density At what age were you first married? (B8) Graphs by women (b): the employed 0.2 .4 15 16 17 18 19 20 21 22 23 24 25 15 16 17 18 19 20 21 22 23 24 25 Men Women Density At what age were you first married? (B8) Graphs by women (c): the non-employed 0.1 .2 .3 15 16 17 18 19 20 21 22 23 24 25 15 16 17 18 19 20 21 22 23 24 25 Men Women Density kdensity age_m normal age_m Density kdensity age_m normal age_m Density kdensity age_m normal age_m Density At what age were you first married? (B8) Graphs by women Figure 4 Age at marriage by labor market status and gender. Source: own elaboration on the ILO School-to-work transition survey of Kosovo. Pastore et al. IZA Journal of Labor & Development 2013, 2:5 Page 16 of 34 http://www.izajold.com/content/2/1/5 Table 2 Returns to years of education by age group All Women Men Teenagers Teenagers (women) Teenagers (men) Young adults Young adults (women) Young adults (men) Monthly wages, lhours, years of education 0.0489*** 0.0655*** 0.0364*** 0.0137 0.0471 −0.0038 0.0516*** 0.0656*** 0.0407*** Monthly wages, lhours, years of education, human capital 0.0458*** 0.0575*** 0.0394*** 0.0043 0.0301 −0.0076 0.0498*** 0.0616*** 0.0451*** Monthly wages, lhours, years of education, human capital, demographics 0.0460*** 0.0591*** 0.0386*** 0.0171 0.0647* −0.0101 0.0487*** 0.0610*** 0.0433*** Monthly wages, lhours, years of education, all controls 0.0358*** 0.0275 0.0353** −0.0208 −0.2983 0.1363 0.0309*** 0.0271 0.0271 Note: * p<.1; ** p<.05; *** p<.01. The controls included in the last row of each group of estimates are the same as those included in the augmented wage equations in Table 4. Source: own elaboration on the ILO School-to-work transition survey of Kosovo. Pastore et al. IZA Journal of Labor & Development 2013, 2:5 Page 17 of 34 http://www.izajold.com/content/2/1/5 Table 3 Augmented Mincerian earnings equations (aged 15–25 years) All Men Women Teenagers Young adults Women −0.0006 (omitted) (omitted) 0.2898 −0.0355 Natural log of weekly hours 0.2146*** 0.3200*** 0.0945 0.2893 0.2270** Education (baseline: elementary or below) Vocational 0.1095 0.0982 −0.0884 0.9722 0.0866 Highsec 0.2001*** 0.1824 0.0942 0.493 0.1716** University or above 0.3666*** 0.6241** 0.0881 (omitted) 0.2955** Overeducation −0.085 −0.0973 −0.096 −0.1951 −0.0914 Overskilling 0.0758 0.1812* −0.0361 0.3605 0.0829 Job tenure −0.0157 0.0078 −0.0406* −0.0025 −0.0082 Civil status (baseline: single with no children) Women with children −0.0461 (omitted) −0.0535 (omitted) −0.0219 Men with children −0.038 −0.1014 (omitted) (omitted) −0.0504 Women engaged 0.0941 (omitted) 0.0685 2.7038** 0.0953 Men engaged 0.0986 0.0087 (omitted) (omitted) 0.1212 Women married, separated, divorced 0.1093 (omitted) 0.1379 (omitted) 0.0835 Men married, separated, divorced 0.0154 0.0121 (omitted) −0.6939 −0.0104 Ethnic group (baseline: albanian) Serbian −0.5123*** −0.2827* −0.6319*** −0.822 −0.4077*** Roma 0.0902 0.0644 0.5021* −0.3615 0.2258* Turkish −0.0681 −0.0923 0.0079 0.3328 −0.0285 Bosnian 0.0758 (omitted) 0.1312 −0.8624 (omitted) Registered at the Public employment Services when unemployed 0.0133 0.1056 −0.0228 −0.4964 −0.0373 Type of job search used to find the current job (baseline: through own’network of relatives and friends) Educational institutions −0.1028 −0.0712 0.0115 1.2673 −0.0231 Direct contact with employer −0.0693 −0.0472 −0.0164 −0.4706 −0.0748 Search through the PES −0.0325 (omitted) −0.0766 1.063 0.0085 Labor contractor −0.1084 −0.3537 0.0735 0.3732 0.0638 Other search method −0.1611 0.0146 −1.1659*** 0.6252 (omitted) Job advertisement −0.0146 0.0997 −0.0437 −1.1517 0.0089 Type of working contract (baseline: no contract) Written contract 0.0891 0.0177 −0.0684 0.1381 0.0049 Oral contract 0.0486 −0.0035 −0.0823 −0.4332 −0.0044 Length of contract (baseline: no contract) Unlimited 0.0844 0.1804 0.1689 0.0317 0.1341* 1 year or less 0.0823 0.1651 0.2345 0.3315 0.1019 From 1 to 3 years 0.0919 0.1472 0.2238 0.1663 0.0948 Firm’s ownership (baseline: private company) Family business 0.008 0.1596 −0.0463 0.2726 −0.0621 Government / public sector −0.2194*** 0.0837 −0.3152*** −2.175 −0.1652* Multinational corporation 0.5323** (omitted) 0.4287* −1.7853 0.6153** Joint venture 0.0327 −0.0135 0.1385 1.6849 0.302 Pastore et al. IZA Journal of Labor & Development 2013, 2:5 Page 18 of 34 http://www.izajold.com/content/2/1/5 Interestingly, the hours worked weekly affect wages in a statistically significant way only in the case of men, not women. This is likely driven in part by the higher share of state sector jobs among women compared to men and the tendency of state sector jobs to pay a fixed salary independent of the hours worked. Table 3 Augmented Mincerian earnings equations (aged 15–25 years) (Continued) Nonprofit organization 0.1493 0.2569 0.0211 −0.4166 0.3106** Other 0.2184 0.2457 0.1746 (omitted) 0.1321 Size of firm (baseline: less than 5 workers) From 5 to 9 workers 0.2094*** 0.2988*** 0.1662* 0.3255 0.2596*** From 10 to 19 workers 0.1159 0.1436 0.1810* 0.0128 0.1595** More than 20 workers 0.1865*** 0.1645 0.2921*** 1.1227* 0.1620** Does not know the size of the firm 0.1495 −0.0285 0.2955** 0.492 0.1449 Sector of activity (baseline: trade fairs) Finance 0.4182*** 0.7041*** 0.2201 (omitted) 0.3760** Public administration −0.0105 −0.0981 −0.0104 (omitted) −0.0326 Manufacturing 0.0883 0.1935 −0.0011 0.4889 0.0755 Trade, hotels and restaurants 0.0519 0.0738 0.1108 0.4532 −0.0235 Real estate 0.1611 0.1448 (omitted) (omitted) 0.044 Transportation and telecommunications 0.2294** 0.3692** 0.1032 0.0259 0.1809* Constructions −0.0231 −0.1436 0.0943 3.0073** −0.0376 Social services −0.0798 0.0009 −0.1504* 0.6861** −0.1259* Electricity 0.0647 0.1288 (omitted) (omitted) 0.0269 Moonlighting 0.1984* 0.2965 0.1202 (omitted) 0.145 Job satisfaction (baseline: mainly satisfied) Partly satisfied −0.1663*** −0.1414 −0.1767*** −0.3472 −0.1414*** Not satisfied −0.2897*** −0.3642* −0.2343* −0.7205* −0.2048** Waiting time before finding the current job (baseline: less than a week) 1 through 4 weeks −0.0222 −0.0799 0.0337 −0.8459** −0.0239 From 1 to 2 months −0.07 −0.0547 −0.0201 0.5689* −0.1912** From 3 to 6 months 0.0019 −0.0321 −0.0262 0.7232 −0.0556 From 6 to 12 months −0.1017 −0.1858 −0.0388 0.4599 −0.1953** More than 13 months −0.0844 −0.1092 −0.0792 0.4616 −0.1682* On-the-job training (baseline: no training) Apprenticeship type 0.0453 0.0541 0.1271 0.8838 0.1072 Non apprenticeship type −0.0501 −0.1088 0.0538 −1.0425 −0.0248 Non job related training −0.025 −0.1755 −0.0167 0.6032 −0.0606 Business development 0.0375 −0.0901 0.052 (omitted) 0.058 Accounting 0.173 (omitted) 0.326 (omitted) 0.1082 Union membership 0.0459 0.0909 0.1196 2.4004 0.0485 Constant 4.1311*** 3.5603*** 4.8066*** 2.7633* 4.2217*** Nobs 315 154 161 58 257 R 2 0.507 0.519 0.676 0.942 0.549 Note: * p<.1; ** p<.05; *** p<.01. Source: own elaboration on the ILO School-to-work transition survey of Kosovo. Pastore et al. IZA Journal of Labor & Development 2013, 2:5 Page 19 of 34 http://www.izajold.com/content/2/1/5 For the sample of women, the educational variables are not statistically significant. This suggests that education provides access to jobs with better characteristics and, once we control for such characteristics, the relative return to education shrinks. More generally, the new controls for individual and job characteristics previously excluded are able to account for at least part of the impact of education on earnings. Again, for reasons that have already been discussed previously, job tenure does not seem to matter in terms of wages. Overeducation generates a wage penalty of between 8% and 10%, similar to those found in other EU countries using the REFLEX data (McGuinness and Sloane 2010) though in the case of Kosovo it is not statistically significant. Meanwhile, job tenure does not seem to affect wages in a statistically significant manner. One possibility is that average job tenure may be too short to have any impact on wages. Recall from the previous section that it equals on average 1.7 years. Interestingly, using the same type of data for Mongolia, Pastore (2011, p. 248) finds that declared work experience bears a wage premium only when it is long 5 years or more, which is rarely the case in the sample under consideration. Civil status and children do not yield statistically significant wage effects, neither for women nor for men, although coefficients have the expected signs. Wages are, ceteris paribus, much lower for Serbians, by about a half, a substantial amount considering the relatively low earnings of young people in Kosovo. This wage penalty is highly significant from a statistical point of view for men, at 65%, but less so for women. Other ethnic groups do not seem to suffer statistically significant wage differences, except for Roma adult women who declare on average higher than average wages. The search method used to find a job does not seem to affect wages either. The baseline is having found their own job through the network of friends and relatives. Differences are small also for those who hold a contract versus their counterparts with no contract, although contracts of unlimited length and written contracts seem to correlate with higher than average wages. Working in the governmental sector implies a wage penalty of about 20% for the entire sample. The effect is mainly due to women who bear a wage penalty of over 30%. This might depend on the type of job women tend to choose in the state sector, jobs that trade off lower pay for greater job security and legal commitment to maternity leave arrangements and maternity benefits in general. Working in a multinational corporation increases wages by about 50%. It is essentially women who work in multinational corporations when they are above 20 years old. Bigger firms pay higher wages, in general, although there are some fluctuations across firm groups. The finance and transportation and telecommunication industries pay higher-than-average wages. One possibility is the presence of more foreign firms in these industries and foreign firms are documented to pay higher wages. In both cases the wage premium is essentially a male effect; the impact on female wages is not statistically significant. Being dissatisfied with one’s own job is correlated with a strong wage penalty, though some endogeneity might exist here. On-the-job training does not seem to affect wages in any measurable way. 5.4 Decomposition analysis Table 4 reports the results of the Juhn et al. (1993) decomposition of the gender wage gap at the mean and at different percentiles of the wage distribution, using Pastore et al. IZA Journal of Labor & Development 2013, 2:5 Page 20 of 34 http://www.izajold.com/content/2/1/5 the estimated male distribution as the one providing the baseline values of the estimated coefficients. Only wage employees are included in the estimates. Interestingly, the average effect, which is close to zero, is, in fact, the algebraic sum of gaps of different signs along the wage distribution. Although small, the gender gap is positive (in favor of women) at low wage levels, when the educational level of women is lower than that of men and becomes negative (in favor of men) and wider at higher wage levels, when the educational level of women is higher than that of men. When looking at the mean gender wage gap, all of the effect seems to come from differences in characteristics, while differences in prices and the impact of the residual wage distribution seem to be irrelevant. The impact of the quantity effect is higher at all percentiles, but especially at the highest percentile considered, the 75 th , where it is greater than 10% in the direction of a reduction of the gap. This effect is quite high considering the low incomes of young people in Kosovo. Assuming a monotonic relationship between education and earnings it is no surprise that at low wage levels, differences in individual characteristics are in favor of men: in fact, as documented above, men tend to have higher educational levels at low income (and therefore educational) levels and women at high income (and therefore educational) levels. One can disentangle these quantity effects, as illustrated by Table 5 and Figure 5. The Figure represents the factors that tend to either increase or reduce the gap at the mean and at different percentiles of the wage distribution. Factors associated with a rightflagging bar tend to widen the gender gap, while factors associated with a left-flagging bar tend to narrow the gender gap. Among the factors that tend to increase the gap, there are training experiences (at high levels of the wage distribution), moonlighting, the sector of activity, the firm’s size, the ethnic group and the weekly hours worked, although the impact is not very strong, but in few cases. In other words, if these were the only characteristics able to affect wages, the gender gap should increase overall by about 7% in favor of men at the mean of the wage distribution. In fact, men tend to work more hours, are more likely to moonlighting, include a smaller number of Serbian workers, and work in sectors that tend to provide wage premiums, such as private sector jobs in the finance’and the transportation and communication industry. On the other hand, young women tend to be associated with factors and characteristics that likely decrease the gender wage gap. Among the factors that tend to reduce the gender gap include the educational qualifications of employed women, the greater degree of job satisfaction, the type of job search adopted to find a job, and the waiting time to find a job. Overall, these factors offset the gender wage gap by about 12.9 per cent. Table 4 Juhn et al. (1993) decomposition TQPU Mean −0.0250 −0.0342 −0.0040 −0.0051 25 th percentile .0408 .0530 −.0063 −.0059 50 th percentile .0124 −.0541 .0876 −.0211 75 th percentile −.0488 −.1184 .0378 .0319 Note: T = total gender wage gap; Q = impact on the wage differential of individual characteristics; P = impact on the wage differential of prices to individual characteristics; U = impact on the wage differential of the residual wage distribution. The male distribution is used as a reference category. Source: own elaboration on the ILO School-to-work transition survey of Kosovo. Pastore et al. IZA Journal of Labor & Development 2013, 2:5 Page 21 of 34 http://www.izajold.com/content/2/1/5 Table 5 Juhn et al. (1993) quantity effects Hours Experience Education Demog Ethnic Search Contract Own Size Sectors Moonlighting Satisfaction Union Waiting Training p25 −0.00118 −0.00082 −0.03779 0 0.009334 −0.0056 0.003418 −0.05999 0.054583 0.020446 0.006041 −0.05772 −0.00061 −0.04793 0.06642 p50 0.004138 −0.00225 −0.02595 −0.00966 0.001394 −0.0494 0.02765 −0.0089 −0.02883 0.053167 0.008843 −0.00727 0.010051 −0.00503 0.000748 p75 0.006075 0.00109 −0.01521 0 0.007633 −0.02982 −0.0426 −0.01356 0.036464 0.043174 −0.02212 −0.01228 −0.0287 −0.02281 −0.04787 Source: own elaboration on the ILO School-to-work transition survey of Kosovo. Pastore et al. IZA Journal of Labor & Development 2013, 2:5 Page 22 of 34 http://www.izajold.com/content/2/1/5 Results of the Juhn, Murphy and Pierce decomposition at different percentiles of the wage distribution suggest that the gender gap, which is not statistically significant at the mean, might be so at different percentiles of the wage distribution. A way to assess this is by estimating counterfactual distributions as based on Machado and Mata (2005). Figure 6 presents the results. It shows that while in fact the OLS measure of the gender gap is close to the horizontal axis, instead, the distribution of coefficients at different percentiles of the distribution tend to be higher than zero at the bottom and upper part of the distribution and to be negative between the 40 th and 50 th percentiles. In other words, women tend to earn more than men at both the lower and upper tail of the distribution. The higher wages of women in the upper tail of the distribution are due both to the better characteristics of women and partly to their better remuneration in that part of the distribution. In fact, the row gap is strongly in favor of women in that upper partofthewagedistribution. 5.5 Sample selection procedure The last step of the analysis is an attempt to assess whether some of the findings relative to employed young people apply also to the non-employed. In other words, given the differences in characteristics between employed and non-employed young people that we noted above, the returns to education, for example, may be different once we control for the heterogeneity of the non-employed. Figures 7 and 8 confirm striking differences that exist between the employed and non-employed women and men. Figure 7 shows that the probability of falling into unemployment or inactivity is very high among those women who have low educational attainment, especially primary or below, but also vocational. The -0.08 -0.06 -0.04 -0.02 0 0.02 0.04 0.06 0.08 Hours experience education demog ethnic search contract own size sectors moonlighting satisfaction union waiting training p75 p50 p25 mean Figure 5 Quantity effects on the gender pay gap at different quantiles based on JMP (1993). Source: Own elaboration on the ILO School-to-work transition survey of Kosovo. Pastore et al. IZA Journal of Labor & Development 2013, 2:5 Page 23 of 34 http://www.izajold.com/content/2/1/5 oppositeholdstrueforwomenholdingauniversitydegree.Thisisdueonlyin part to the fact that some of the women who hold the lowest educational attainment are current students. Not surprisingly, education is also positively associated with employment among men, but the effect is much less striking. Figure 8 shows that female labor supply is much higher among young adults compared to teenagers. The share of employed men is higher for every age group. As a first step, we study the impact of different factors on labor supply decisions by gender by means of the (Fairlie 2005) decomposition analysis. For the sake of brevity, we do not discuss fully the methodology here 18 . It is sufficient to say that the method aims to assess the contribution of each determinant of labor supply on the gender gap. Since the probability of labor supply is estimated by a nonlinear model (LOGIT), the Blinder and Oaxaca decomposition provides unsatisfactory results. In contrast, the Fairlie decomposition was especially designed for nonlinear estimates. This preliminary step of the analysis will provide information useful also for the implementation of the ensuing sample selection correction estimates of the gender wage gap. Table 6 reports the results of the (Fairlie 2005) decomposition. The probability of participating in the labor market among men is on average about 5.5% higher than women. The variables included in the model represent educational attainment (vocational education and high secondary education or above, the baseline being represented by education attainment below vocational education), ethnic group (Serbians, Roma, Turkish, Bosniac, the baseline being Albanians) and demographic factors (being engaged to be married, married, separated, divorced or widow, the baseline being the condition of single) seem to explain 100% of the gap. Demographic factors account for a sizeable share (about 85%) of the gender -.1 0 .1 .2 Gap 0.1 .2 .3 .4 .5 .6 .7 .8 .9 Quantile Coefficients Coef 95% confidence intervals OLS Raw gap Characteristics Predicted gap Figure 6 Machado and Mata (2005) decomposition of the gender wage gap. Source: Own elaboration on the ILO School-to-work transition survey of Kosovo. Pastore et al. IZA Journal of Labor & Development 2013, 2:5 Page 24 of 34 http://www.izajold.com/content/2/1/5 entirely driven by differences in characteristics rather than either the returns to those characteristics or the residual. The greater mean educational attainment of employed women, among other characteristics, tends to fully offset the gender wage gap. In particular, the greater mean educational attainment of employed women tends to compensate for the higher average number of working hours of their male counterparts. The Machado and Mata (2005) decomposition of the gap along the entire wage distribution shows that in fact women located in the upper part of the wage distribution should receive a higher wage than their male colleagues, considering their higher educational level and, therefore, productivity characteristics. When the analysis accounts for selection bias and the heterogeneity of nonemployed women, the gender wage gap turns negative (though insignificant) while the returns to education become higher for women than for men, confirming the lower productivity-related characteristics of non-employed versus employed women. Despite the general absence of a gender wage gap among Kosovo’s employed menandwomeninearlycareer,someevidencesuggeststhatagenderwagegap does begin to emerge among the young adults (age 20–25) compared to teenage workers (age 15 to 19), consistent with a small but growing literature on gender wage gaps during the first few years in the labor market. However, the relatively small sample constrains a fuller analysis of this phenomenon. The policy implications of the paper are clear. The general absence of a gender wage gap among the young sample of under-25 provides indirect confirmation of the importance of providing childrearing facilities and arrangements to prevent the emergence of the gap at a later age, documented in previous studies (World Bank 2003, 2010, 2012). More directly, the Fairlie analysis shows the primary of demographic factors among the drivers of gender differences in labor supply. The small number of women who marry and give birth at the age covered by the SWT survey may help explain why the gender gap in labor supply is relatively small, while suggesting that it might dramatically increase if more women had children at a young age. Endnotes 1 See, in particular, a recent comprehensive report (World Bank 2012). 2 See Manning and Swaffield (2008). Table 8 Maximum likelihood estimate of the selectivity corrected earnings equation (Heckman procedure) (Continued) Statistics N1336 657 679 472 864 414 450 Rho −0.18 −0.27 0.85 −0.14 −0.24 −0.39 0.94 Sigma 0.53 0.52 0.73 0.57 0.51 0.52 0.76 Lambda −0.92 −0.14 0.62 −0.08 −0.12 −0.2 0.72 Likelihood ratio test of independent equations 1.14 4.84 24.3 0.06 2.51 10.34 75.6 Probability > χ 2 0.29 0.03 0.00 0.81 0.11 0.00 0.00 Note: * p<.1; ** p<.05; *** p<.01. Source: own elaboration on the ILO School-to-work transition survey of Kosovo. Pastore et al. IZA Journal of Labor & Development 2013, 2:5 Page 31 of 34 http://www.izajold.com/content/2/1/5 3 For further information on the SWT survey, see the following website: http://www. ilo.org/employment/areas/WCMS_159352/lang–en/index.htm. 4 Women seem to receive higher returns to education than men in terms of employment probability. In other words, men with high educational attainment tend to have higher employment rates than men with low educational attainment, but this education-employment relationship is much stronger for women. 5 World Bank 2010. 6 In fact, there is no discussion of wages or earnings in the World Bank (2012) report. 7 See Bertrand et al. (2010). 8 See for example (Kunze 2005) based on data from West Germany. Manning and Swaffield (2008) use UK data to look at gender wage disparities in “early career”, seeking to explain how the gender gap rises from zero to 25 log points, after the first 10 years in the labor market. They find that a number of theories can explain the gap, but up to a third is still left unexplained. 9 The discussion in this section follows closely the lead author’s previous exposition (Pastore 2009, 2010, 2011, and Pastore and Verashchagina 2011). 10 When the regressor is a continuous variable, such as years of work experience, the elasticity at the mean of the covariates, namely the percentage change in the regressand for a percentage change in the regressor, can be computed multiplying the coefficient by the mean of the regressor: β  X.Inthecaseofindependent dummy variables, such as for example the levels of educational attainment, the semi-elasticity interpretation is flawed and, following Halvorsen and Palmquist (1980), it should be computed as: (e β −1) ∗100. This formula measures the percentage change in the median wage which is less affected by outliers. Nonetheless, many authors interpret the estimated coefficients of dummy variables directly as semi-elasticity as well. This is acceptable when the estimated coefficient is sufficiently close to zero. 11 See also Blau and Kahn (1996) for an application of this methodology and the STATA help file for an introductory presentation of the methodology (command jmpierce). 12 We thank Indhira Santos for pointing out the caveats related to the use of the instruments actually available in the survey at our disposal. 13 In fact, the survey includes not only a questionnaire administered to young people, but also one administered to a sample of firms. The questionnaire of the Employer and Managers module survey contains detailed information on the ownership and industrial sector of the firm, union membership, number and skill requirements of employees, vacancies, recruitment methods, criteria of selection of the personnel, training practices in the firm and so on. Unfortunately, the two questionnaires cannot be linked with each other because there is no connection between the individuals and the firms. 14 Together with that of Syria, the SWT survey of Kosovo does not include the oldest age segment, aged 25–29 years, which is instead available in the other countries. 15 The question asked (G42) is: “On average what is your total income from work per month?”It is not clear from the wording of the questionnaire whether Pastore et al. IZA Journal of Labor & Development 2013, 2:5 Page 32 of 34 http://www.izajold.com/content/2/1/5 this is the gross wage or the wage net of taxes. In the other surveys, it was the net monthly wage. In addition, it is not clear whether the declared earnings are coming from the main job or from any working activity. Based on personal communications, the ILO staff in charge of the survey does not have any additional information on the exact definition of wages used in the survey. 16 The wage variable has only few missing observations. 17 No estimates are provided by gender for different age groups, due to the small number of observations available in the sample for the level of detail that is requested in the augmented version of the estimates. 18 For a presentation in Stata of this type of decomposition, see Jann (2006). 19 For shortness sake, the results of the logit equation of the probability of labor supply are omitted and available on request from the authors. The same applies also to the labor supply equation estimate by gender, which is discussed next. 20 Other results, including the two-step version and the maximum likelihood with different instruments, are available on request from the authors. Additional file Additional file 1: Table S1. Descriptive statistics: the employed. Table S2. Descriptive statistics: the non-employed. Competing interests The IZA Journal of Labor & Development is committed to the IZA Guiding Principles of Research Integrity. The authors declare that they have observed these principles. Acknowledgements This paper has been written as an outcome of the research project on: “Kosovo Gender Wage Gap Dynamics”within the context of the Western Balkans Gender Programmatic Work. We are grateful to Indhira Santos, Sailesh Tiwari, an anonymous referee and one of the Editors of the IZA Journal of Labor & Development, Hartmut Lehmann, for very valuable comments. The usual disclaimer applies. Responsible editor: Hartmut Lehmann Author details 1 Seconda Università di Napoli, Italy. 2 World Bank, Washington, DC, USA. Received: 15 August 2012 Accepted: 20 November 2012 Published: 28 June 2013 References Bertrand M, Goldin C, Katz LF (2010) Dynamics of the gender gap for young professionals in the corporate and financial sectors. 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The World Bank, Washington doi:10.1186/2193-9020-2-5 Cite this article as: Pastore et al.:Gender differences in earnings and labor supply in early career: evidence from Kosovo’s school-to-work transition survey. IZA Journal of Labor & Development 2013 2:5. Submit your manuscript to a journal and benefi t from: 7 Convenient online submission 7 Rigorous peer review 7 Immediate publication on acceptance 7 Open access: articles freely available online 7 High visibility within the fi eld 7 Retaining the copyright to your article Submit your next manuscript at 7 springeropen.com Pastore et al. IZA Journal of Labor & Development 2013, 2:5 Page 34 of 34 http://www.izajold.com/content/2/1/5