Gender Wage Gaps in Greece 1 - an examination among University Graduates

Gender Wage Gaps in Greece 1 - an examination among University Graduates George Agiomirgianakis 2*, Georgios Bertsatos 3, Nicholas Tsounis 4 Abstract Though the issue of gender wage gaps is of increasing importance worldwide both in terms of economic and social literature, as well as in terms of required policy measures, economic research in Greece of this issue is rather limited and mainly focused on the gender differences of the rates of returns rather than on wage gender gaps. In this paper, we examine this issue by using a large sample from three Greek universities for 2013. JEL classification: I26; J16; J24; J31 Keywords: Gender Differences in the Labor market; Gender wage gaps; Economic policy; Returns to investment in Education; Mincer Equation; Time asymmetries 1 This research was carried out as a research project co-financed by the European Union (European Social Fund ESF) and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF). Financial support is gratefully acknowledged. 2 Professor of Economic Analysis, Hellenic Open University, Parodos Aristotelous 18, Perivola, 26335 Patras, Greece. E-mail address: gmagios@eap.gr * Corresponding author Tel: +30-2610-367451, fax +30-2610-367117. 3 Ph.D. Candidate, Athens University of Economics and Business, Department of Economics, Patission 76, Athens, 104 34, Greece. E-mail address: bertsatosg@aueb.gr 4 Professor of Economics, Technological Institute of Western Macedonia, Department of International Trade, Kastoria, Greece. Adjunct Faculty, Hellenic Open University. E-mail address: tsounis@kastoria.teikoz.gr

1. Introduction and literature review The gender wage gap has been investigated since the early 70s but it still remains a subject of research. It refers to the differences between the wages earned by women and men. The wage difference is checked after accounting for both for individual characteristics related to their productivity and generally their human capital; the unexplained wage difference part consists of the discrimination between the two genders. The first wage discrimination studies between men and women that used a formal econometric technique was made by Oaxaca (1973) and Blinder 1973). The decomposition approach that they developed (also known as the Oaxaca-Blinder (OB) method) establishes the existence of discrimination in wages between men and women, taking into account the person s productive characteristics (such as level of education and years of work experience) that can be used as marginal productivity approximations. Individual characteristics affecting the productive capability and therefore, the wages earned are accounted for. The OB method checks for differences in characteristics between men and women and the gap in the average earnings is broken down in two parts: the first is the endowment effect that represents the difference in observable human capital of men and women and the second is the wage discrimination effect that represents the unexplained component. The discrimination effect however, includes the wage difference due to unobservable factors that affect productivity. In the early study, Oaxaca (1973) obtained separate estimates using both the male and the female weighting procedure to establish a range of possible values. All methods of decomposition of the wage gap must deal with the problem of the choice of weighting. The differences in characteristics are weighted by the average male returns, and the differences in returns are weighted by the average female characteristics. In our paper we used a modified to the original OB method, proposed again by Oaxaca and Ransom (1994) (OR) that involves the construction of nondiscriminatory returns on individual characteristics. The wage gap therefore, is expressed as the sum of an advantage for men and a disadvantage for women, and the difference between characteristics valued at the nondiscriminatory returns. The problem with the OR method is that the wage equation cannot include all relevant variables measuring skills and individual productivity. Therefore, seemingly equivalent may not be truly equivalent. This is called the omitted variable problem and leads to an overestimation of the degree of discrimination. An alternative method has been proposed by Juhn, Murphy and Pierce (1991) (JMP) to overcome this problem. The JMP explains wage differentials in terms of differences in 2

characteristics (predicted gap) and in terms of differences in residuals (residual gap). The residual gap is further specified in terms of the standard deviation of the residuals and standardized residuals. The standard deviation of the residuals of the wage equation is considered as both within group wage inequality and the price of unobserved skills 5. An additional, more advanced, technique has been developed in the literature that of quintile regression (QR). QR models are more general than simple linear regression models allowing for heteroskedastic errors, since they allow for more general dependence of the distribution of the dependent variable on the independent variables instead of just the mean and the variance of the conditional mean alone. When the QR method is applied to study gender wage pay gaps instead of examining effects of gender and other covariates on the conditional mean of the log wage distribution, it examines the effects of gender and other covariates on different quantiles of the log wage distribution. The QR method allows the characteristics to have different returns at different quantiles therefore, at each point of the distribution it can control for differences between men's and women's wages that are attributable to their characteristics. In our paper, we use also the QR method in combination to the JMP, to calculate the gender gap at the ith quantile i.e. the difference in pay that women would have faced at the ith quantile if their labor market characteristics had been rewarded as men's were. The results obtained by the QR and the JMP methods are compared with these obtained by the JMP using the QR method, described above. Regarding the gender wage gap findings, Arulampalam et al 2007, use harmonised data for the years 1995-2001 from the European Community Household Panel to analyse the gap by sector in eleven countries. They take into account the effects of individual characteristics at different points of the wage distribution and they calculate the part of the gap attributable to differing returns between men and women. Their findings show that the magnitude of the gender pay gap varied substantially across countries and across the public and private sector wage distributions. The gap widened toward the top of the wage distribution ( glass ceiling effect), and in a few cases it also widened at the bottom ( sticky floor effect). The authors suggest that differences in childcare provision and wage setting institutions across EU countries may partly account for the variation in patterns by country and sector. 5 The caveats of the JMP method are discussed in detail by Yun 2007). 3

Chang et al (2014) find that the gender wage gap in Australia has increased over time, with the largest contributory factor identified as gender discrimination. They conclude that the policy responses supporting women in the workplace appear are ineffective in closing gender wage gaps. Hirsch (2016) argues that a substantial part of the unexplained gender wage gap stems from imperfect competition in the labor market. Monopsonistic wage discrimination arises when employers exploit their more pronounced wage-setting power over female workers, whose labor supply to the single employer is less sensitive to wages. Since wage discrimination raises employers profits, it is fostered by market forces and thus likely to persist in the long term. He concludes that fighting monopsonistic discrimination involves introducing policy measures to raise women s wage sensitivity as well as introducing equal pay legislation. Christofides et al (2013) study the gender wage gap across 26 European countries, using 2007 data from the European Union Statistics on Income and Living Conditions. Their findings show that the size of the gender wage gap varies considerably across countries. Using the QR method show that, in a number of countries, the wage gap is wider at the top ( glass ceilings ) and/or at the bottom of the wage distribution ( sticky floors ). Glass ceilings prevail in full-time full-year employees, suggesting more female disadvantage in better jobs. Their findings also show that a large part of the unexplained gender wage gaps is due to country policies and institutions. Generally, it is recorded in the literature that that men earn higher wages than women (see, among others, Nicodemo 2009 and the references therein). The existence of the gender pay gap is the result of discrimination against women. The average gender pay gap appears to be different across countries ranging from 15% in Finland and Norway to 52% in Yemen for 2015 (The Global Gender Gap Report 2015). The purpose of this paper is to provide evidence for the gender wage gap in Greece among secondary school and University graduates. The data used was extracted from 2,112 questionnaires that were collected with field research in 2014 6. Wage data refer to 2013, the fourth year after graduation, from three Greek Universities and include both first degree and master s graduates. According to research of the European Commission (2014), the gender pay gap in Greece in 2010 was 15%. In EU, this is the unadjusted gender pay gap and it does not take 6 Please see the Data description section bellow for details. 4

into account factors that may have impact on the gender pay gap. This gender pay gap is shown as a percentage of men s earnings and it shows how much women earn per hour less than men. In our dataset, which contains graduates of high school, graduate and postgraduate students of Hellenic Open University (HOU), University of Macedonia (UOM) and University of Crete (UOC) in 2013, the deciles of the wage distribution of male and female workers are presented in Figure 1. Figure 1: Wages by decile 30,000.00 27,500.00 25,000.00 22,500.00 20,000.00 17,500.00 15,000.00 12,500.00 10,000.00 7,500.00 5,000.00 2,500.00-1st 2nd 3rd 4th 5th 6th 7th 8th 9th Decile male female difference Notes: These are the wages of male and female workers by decile in euros. Also, the difference between male and female workers is depicted. Wages are deflated. Source: Authors calculations Moreover, the average wage of male workers is 16,373.98 and that of female workers is 13,766.38. The respective standard deviations are 11,537.02 and 9,104.55. The coefficient of variation for the male group is 70.46% and for the female group is 66.14%. Therefore, male wages are more volatile than female wages. Obviously, the two means and 5

standard deviations are not equal. 7 Finally, the number of male workers is 767 and the number of female workers is 797. Figure 2: Gender pay gap 15.31% 13.55% 12.23% 12.58% 10.00% Unadjusted BOD JMPD JMPD* JMPD** Notes: The first chart column shows the gender pay gap according to the European Commission methodology, i.e. on gross earnings. The second column show gender pay gaps according to OR method while the last three, according to the JMP decomposition. The values for JMPD, JMPD* and JMPD** correspond to the median values of the nine deciles of the wage distribution. Wages are deflated. Source: Authors calculations In brief, we find that the adjusted gender wage gaps (OR and JMP methods) are ranging from 10% to 13.55% and they are lower than the unadjusted gender wage gap of our sample. We should also notice that the unadjusted gender pay gap of our sample is very close to that reported in the European Commission s (2014) report (15%). Finally, the OR decomposition provides the minimum value, while the JMP decomposition delivers the highest value of the gender pay gap in our sample. The remainder of the paper is organized as follows: Section 2, presents the specification of variables used, provides a description of the data, presents the estimating methodology for measuring gender wage gaps and presents the estimation results. Finally, Section 3, contains concluding remarks and states the policy implications of our findings. 2. Methodology, data description and results 7 Performing equality tests for the mean (assuming equal or unequal variances) and test that the ratio of the two standard deviations is unity, we find strong evidence against both null hypotheses. 6

To examine the wage differentials between male and female workers in our sample, we use the standard methodology of OB decomposition in its generalized version as developed by Oaxaca and Ransom (OR). Moreover, we use the JMP decomposition which can be seen as an extension of the OR and Melly s (2005) decomposition 8. For the JMP-oriented decompositions, we examine the nine deciles of the wage distribution. The data was extracted from the questionnaires collected for a Hellenic Open University (HOU) research project co-financed by the European Union (European Social Fund ESF) and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF). The survey period was from June to October 2015. The total number of questionnaires collected by the survey was 2,112. Each individual was asked to report remuneration and employment data for 2013. For all respondents this year was their fourth after graduation. According to the OB method, wage gaps between two groups can be decomposed into two parts: the explained and the unexplained. The explained part represents the endowment or characteristics or quantity effect, while the unexplained part measures the discrimination or coefficients effect. lnw lnw X X X (1a); M F M F M F M F where, in the left-hand-side (LHS) there is the difference of average wages (in logarithms) between male and female workers, and in the right-hand-side (RHS), the first term is the explained part and then second term is the unexplained part. The explained part measures the difference in the average characteristics between male and female workers weighted by the coefficients of the male equation, while the unexplained part quantifies the difference in the coefficients between male and female equation weighted by the female workers average characteristics. Alternatively, equation (1a) is formulated from the point of view of the male workers. If we express the wage differential from the point of view of the female workers, then we get: lnw lnw X X X (1b) M F M F F M M F 8 The decompositions we present can be generalized for groups A and B. However, since our interest in this paper is wage gaps between male (M) and female (F) workers, we use the notation of M and F groups. 7

In the first case (equation 1a), discrimination is directed against female workers and there is no positive discrimination of male workers, while in the second case (equation 1b) discrimination is directed against male workers and there is no positive discrimination of female workers. One may see that these two relations constitute extreme cases, where the weighting procedure is done by either the male or the female predictors, i.e. estimated coefficients. Oaxaca and Ransom (1994) propose an alternative decomposition, which allows for different weighting schemes, including equations (1a and b) as special cases. Specifically, they attribute (possible) wage gaps between male and female workers into three parts. The explained part and the unexplained part, which is further decomposed into to subparts: the male advantage and the female advantage. The OR decomposition is given by: * * * lnw lnw X X X X (2). M F M F M M F F The first term of the RHS is the explained part, while the last two terms constitute the unexplained part. Specifically, the second part is the male advantage and the third part the female advantage. β * is the reference coefficient or the weighting scheme. If the reference coefficient is that of the male equation, then we get equation (1a). On the other hand, if it is that of the female equation, then we get equation (1b). Various suggestions have been made in the literature (e.g. Reimers, 1983, with the average coefficients, Cotton, 1988, with the weighted coefficient by group size, Neumark, 1988, with the pooled coefficient). In our analysis, we examine four cases regarding the weighting procedure: (1) with the male coefficients, (2) with the female coefficients, (3) with the coefficients from the pooled regression augmented with the sex indicator in the RHS as Ben (2008) suggests, and (4) with the OR coefficients (see equation 13 in Oaxaca and Ransom, 1994). From equation (2) one can easily understand why in the case when the reference coefficients come from the male (female) equation, there is no positive discrimination of male (female) workers, and why this case constitutes an extreme weighting scheme. In Figures 3a and 3b, we present the results of the OR method according to the four aforementioned cases: 8

Figure 3a: Oaxaca-Ransom decompositions 12% 10% 8% 6% 4% 2% 0% b_male b_female b_pooled b_or gap explained unexplained Notes: The gap is equal to the explained plus the unexplained part. b_male is the OR when we use the male predictors as the reference coefficients, b_female with the female predictors, b_pooled when we use the coefficients from the pooled regression augmented with the group indicator variable, i.e. a dummy variable for sex, and b_or when we use the weighted coefficients according to Oaxacan and Ransom (1994, see equation 13). Figure 3b: Oaxaca-Ransom decompositions 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% b_male b_female b_pooled b_or explained unexplained Notes: The explained and unexplained parts are in percentages of the total wage gap. b_male is the OR when we use the male predictors as the reference coefficients, b_female is the OR with the female predictors, b_pooled is the OR when we use the coefficients from the pooled regression augmented with the group indicator variable, i.e. a dummy variable for sex, and b_or is the OR when we use the weighted coefficients according to Oaxaca and Ransom (1994, see equation 13).. We use OLS to estimate the cross-sectional regression and the bootstrap method to calculate the standard errors (10,000 replications). 9

4 4 7 4 2 t, i 0 1 i 2 i e e, i j j, i 3 i n n, i e 1 j 1 n 1 lnw b b X b X DOM f SLM b M r MATCH b School hhou u i i i (3) We use an augmented Mincer (1974) wage equation (3) with the following explanatory variables (by order of appearance): years of working experience, squared years of working experience, dummy variables for family status, dummy variables for the current state of the respondent in the labor market, the degree mark, dummy variables for the relevance of the job to the degree (mismatching), years of schooling and a dummy variable for HOU graduates. The latter has been included because their average age is about 35 years and therefore, their characteristics (that have not been included in (3)) may be different that those of the traditional university graduates. We find that the gender pay gap (of 10%) is statistically significant at 5% or higher, significance level. The explained parts (4.2%, 4.9%, 4.5% and 4.8% in the four aforementioned cases) are also statistically significant at 5% or higher, significance level. However, the unexplained parts are statistically insignificant at any convenient significance level. Moreover, there is robustness in the explained part with the alternative weights of the OR. Finally, when we used a robust variance-covariance matrix, the results are quite similar. Next, we use the JMP method, as it was first implemented in Juhn et al. (1993), i.e. with a linear location-shift model. Then, we apply two modified JMPs (JMPD* and JMPD**) as described in Melly (2005) and Chernozukov et al. (2008). JMPD* uses a linear locationscale model while JMPD** uses a linear quantile estimator for the conditional distribution function. The JMP method not only accounts for the characteristics (or observed quantities) and coefficients (or observed prices) effects, but for the residuals (or unobserved prices and quantities) effects as well. 9 In the OR, the residuals effects are zero because each average predicted residual is zero due to OLS first-order conditions. In the JMP-oriented decompositions, two steps are required: In step 1 we need to approximate the conditional distribution (this involves many repetitions of the chosen estimator) and then in step 2, bootstrap step 1 to calculate the standard errors. In JMPD and JMPD*, the conditional mean is estimated with OLS, while in the JMPD** a quantile regression in the spirit of Koenker and Basset (1978) is used to obtain the conditional distribution function. To estimate the conditional model we run 10,000 OLS 9 The terms in parentheses are used in Juhn et al. (1993). 10

regressions for the JMPD and JMPD*, and 100 quantile regressions for the JMPD** 10. Because the bootstrap part of these methods, required for the standard errors, is computationally intensive and time consuming, especially for the case of quantile regressions, we limit our analysis to 100 replications for JMPD and JMPD*, and to 1,000 replications for JMPD**. 11 In Figures 4, 5 and 6 we present the results of the JMPD, JMPD* and JMPD**. The quantile effect represents the total wage gap in that quantile (decile in our case). It is further decomposed into the characteristic effect, the coefficients effect and the residuals effect. The characteristics and coefficients effects can be interpreted as in the case of OR, but here they are calculated for the specific quantile and not for the mean of the distribution. In addition, the residuals effects captures the effects of changes in the distribution of the residuals. Once again, we find robustness in the statistical significance of the three aforementioned decompositions, i.e. the quantile effects are statistically significant at 5%, or higher, significance level. The total and statistically significant (at 5%, or higher, significance level) gender pay gap, as it is measured by the quantile effect, ranges from 13.5% to 15.8%, from 12.2% to 18.6% and from 11% to 15.8% in the JMPD, JMPD* and JMPD**, respectively. 10 Chernozhukov et al. (2008) point out that 100 regressions tend to be enough for quantile models, but low for the rest estimators. 11 Analyzing the results from JMPD and JMPD* with 100 replications, the statistical significance of the wage differentials and their decompositions do not change. 11

Figure 4a: Juhn-Murphy-Pierce decompositions 20% 15% 10% 5% 0% -5% -10% -15% -20% -25% -30% -35% -40% Quantile effect Characteristics effect Coefficients effect Residuals effect 1st decile 2nd decile 3rd decile 4th decile 5th decile 6th decile 7th decile 8th decile 9th decile Notes: The quantile effect is the sum of the characteristics, coefficients and residuals effects. In each effect, the first column corresponds to the first decile, the second column to the second decile etc. Wages are deflated. 100% 80% 60% 40% 20% Figure 4b: Juhn-Murphy-Piece decompositions 0% -20% 1 2 3 4 5 6 7 8 9-40% -60% -80% -100% Deciles Characteristics effect Coefficients effect Residuals effect Notes: In this figure, we present each effect as percentage of the quantile effect. The quantile effect is the sum of the characteristics, coefficients and residuals effects. 12

Figure 5a: Juhn-Murphy-Pierce* decompositions 20% 15% 10% 5% 0% -5% Quantile effect Characteristics effect Coefficients effect Residuals effect -10% -15% -20% -25% 1st decile 2nd decile 3rd decile 4th decile 5th decile 6th decile 7th decile 8th decile 9th decile Notes: The quantile effect is the sum of the characteristics, coefficients and residuals effects. In each effect, the first column corresponds to the first decile, the second column to the second decile etc. Wages are deflated. 100% 80% 60% 40% 20% Figure 5b: Juhn-Murphy-Pierce* decompositions 0% -20% 1 2 3 4 5 6 7 8 9-40% -60% -80% Deciles Characteristics effect Coefficients effect Residuals effect Notes: In this figure, we present each effect as percentage of the quantile effect. The quantile effect is the sum of the characteristics, coefficients and residuals effects. 13

Figure 6a: Juhn-Murphy-Pierce** decompositions 20% 15% 10% 5% 0% -5% Quantile effect Characteristics effect Coefficients effect Residuals effect -10% -15% -20% -25% 1st decile 2nd decile 3rd decile 4th decile 5th decile 6th decile 7th decile 8th decile 9th decile Notes: The quantile effect is the sum of the characteristics, coefficients and residuals effects. In each effect, the first column corresponds to the first decile, the second column to the second decile etc. 100% 80% 60% 40% 20% Figure 6b: Juhn-Murphy-Pierce** decompositions 0% -20% 1 2 3 4 5 6 7 8 9-40% -60% -80% Deciles Characteristics effect Coefficients effect Residuals effect Notes: In this figure, we present each effect as percentage of the quantile effect. The quantile effect is the sum of the characteristics, coefficients and residuals effects. 14

3. Conclusions and policy implication According to our finding suggest that in our survey sample, a gender pay gap of 10%, statistically significant at 5% significance level exist using the OR method. Further, when the JMP method with QR was used the highest value of the discrimination (coefficient) effect is found to be of 18.8% in the second decile, followed by 11.5% in the third and 10.3% in the fourth. Discrimination is consistently decreasing as wage deciles increase down to 6.7% in the 9 th decile. 15

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