Gender wage gap across the distribution: What is the role of within- and between-firm effects?

GWG is one of the most salient issues that has been investigated extensively in the literature (see Blau and Kahn, 2017 for a recent review). Most research has focused on the mean of the wage distribution and used the conventional Blinder-Oaxaca (BO) decomposition method (Blinder, 1973; Oaxaca, 1973) to separate the raw GWG into one part that can be explained by the gender differences in observable characteristics and another part that is due to gender differences in returns to those characteristics. Recent studies on European economies, however, have suggested that the GWG is not constant across the wage distribution even after controlling for observable personal, work-related, and firm characteristics, so comparing the average man and woman provides an incomplete picture (see Albrecht et al., 2003 for Sweden; Arulampalam et al., 2007 for a comparison of 11 European countries and De la Rica et al., 2008 for Spain). Using an extension of the BO decomposition to the (conditional) quantile regression models, these studies have shown that the GWGs in several countries are larger at the upper end of the wage distribution, consistent with the presence of glass-ceiling effects.

Existing theories on human capital, discrimination, and labor-market segmentation, however, fail to adequately explain a widening GWG at high pay levels (Peetz and Murray, 2017). Although glass-ceiling phenomena are rarely theorized, Peetz and Murray (2017) argue that the interaction between labor-market segmentation and the concept of regulation distance, that is, a greater role for less transparent, more subjective wage-setting arrangements for highly paid workers, helps understand a widening GWG at the upper end of the wage distribution.

This paper is also related to the empirical literature on how within- and between-establishment effects are reflected in the GWG. The seminal studies in this field have emphasized the role of female segregation into lower-paying industries, occupations, establishments, and jobs within establishments (e.g., Sorensen, 1990; Groshen, 1991; Reilly and Wirjanto, 1999). However, the majority of this evidence is based on samples that are limited to a narrow range of occupations, industries, and regions. With the availability of large-scale linked employer–employee datasets, recent studies have explored the role of firms using nationally representative samples and by distinguishing the two dimensions: mechanisms that operate within- and between-firms (e.g., Bayard et al., 2003; Card et al., 2016; Jewell et al., 2020). These studies have consistently found that although the latter accounts for some fraction of the mean GWG, a sizeable portion of the GWG still persists from within-firm gender pay inequalities. Most of these studies, however, considered only the mean GWG, neglecting important insights offered by the wage distribution. The notable exceptions include Javdani (2015) and Cardoso et al. (2016), who found evidence of economy-wide glass-ceiling effects in Canada and Portugal, respectively, and Hara (2018), who found a larger GWG at the top and bottom ends of the wage distribution than in the middle, consistent with the presence of both glass ceiling and sticky floor effects in Japan. Javdani (2015) and Cardoso et al. (2016) showed that economy-wide glass-ceiling effects faced by women are mainly due to mechanisms that operate between firms rather than within firms, while Hara (2018) found that the economy-wide glass-ceiling and sticky-floor effects in Japan were mainly driven by mechanisms that operate within firms rather than between firms. Evidence from these studies indicates that the role of within- and between-establishment effects on GWG is country-specific and it is of substantial interest to learn about the patterns in other countries, including Turkey.

During the past two decades, Turkey has experienced a series of structural changes that have increased the relative labor cost of female employees (see Section 2.2). The change in the overall GWG driven by changing labor costs is theoretically ambiguous and becomes an empirical question, with implications relevant not only to Turkey but also to other countries that have similar policies in place. Most empirical studies on the GWG in Turkey, however, have been carried out using data from the early 2000s or before (e.g., Tansel, 2005; Ilkkaracan and Selim, 2007), and the gap in post-structural change is relatively poorly studied. The only exception is Akhmedjonov (2012), who applied BO decomposition to data from the Turkish Household Budget Survey for 2009 and found that >85% of the GWG in annual pay remains unexplained even after controlling for a wide range of characteristics. However, his analysis is limited to the mean and abstracts from firm effects and gender differences in work hours, both of which are important determinants of the GWG (Blau and Kahn, 2017).

Turkey became an official candidate for the European Union (EU) in 1999 and began the accession negotiations in 2005. As a part of this process, Turkey has adapted its legislation to EU standards, including the EU Directives on gender equality policies. The constitutional amendments of 2001 and 2004, the adoption of the new Civil Code (2001), the new Labor Law (2003), and the new Penal Code (2005) are products of this process (Dedeoglu, 2012; 2013) (see Table B1 in Appendix B for a brief description of these policy changes). Despite the legal basis of gender equality in the workplace, the female labor force participation in Turkey is the lowest among the Organization for Economic Cooperation and Development (OECD) countries. In 2017, only 37.6% of women were in the labor market, which was significantly behind the OECD average of 64% and the EU's Lisbon goals of 60%, while the male labor force participation rate (78.2%) was comparable to the OECD average of 80.2% (OECD, 2017).

Several authors have argued that low female labor force participation in Turkey is, at least to some extent, due to cultural and social norms, which reinforce the position of women as wives and mothers (Dayioglu and Kirdar, 2010; Ilkkaracan, 2012). Despite improvements in the legal framework, the protective approach of the current labor regulation reinforces these traditional gender roles (Bugra, 2012) and makes it more constrained and more expensive for employers to hire women (World Bank, 2009; Anderson, 2017). For instance, until recently, the Labour Law prohibited women from doing night-time work. The 2003 amendments lifted this ban but kept some others, such as prohibiting the employment of women (and young men) in mines and underground or underwater activities, such as cabling, canalization, and tunnel construction. The current Labor Law also grants women the right to terminate employment contracts within a year of marriage and acquire severance payments based on this reason, whereas men are not entitled to severance pay in the case of marriage; as such, the law encourages women to focus on their roles as wives rather than on their labor market careers (Dedeoglu, 2012).

The prime change in the Labor Law, the extension of maternity leave from 12 weeks to a compulsory 16 weeks (plus an additional 6 months of unpaid leave) is also longer than the 14-week leave period in the EU and is not transferrable to the spouse. Although the generous maternity leave in Turkey may encourage women to maintain their labor market attachments, the lack of provision of paternity leave reinforces women's role as mothers (Dayioglu and Kirdar, 2010; Ilkkaracan, 2012) and may discourage employers from hiring women (World Bank, 2006). Another primary change in the new Labor Law is the requirement for any workplace with 100–150 female employees to set up a nursing room that is separate from the workplace but within a distance of 250 m and for any workplace with at least 150 female workers to provide a childcare center for children below six and a separate facility or nursery close to the workplace for breastfeeding mothers. The dependence of childcare provision on the number of female workers not only implies childcare as a woman's responsibility but also increases the cost of female workers relative to male workers (Dayioglu and Kirdar, 2010) and may become an indirect form of discrimination against women, as the employers must bear this cost (Anker, 1997).

In terms of the institutional setting, Turkey has a statutory national minimum wage set by the government after nonbinding tripartite consultations, and the Minimum Wage Regulation (Law Number 25540) states that “no gender difference can be considered in setting minimum wage”. However, the minimum wage is binding for the formal sector and for low-skilled women in particular (World Bank, 2009). Thus, the increase in the minimum wage between 2005 and 2015 (approximately 10% each year on average, corresponding to a cumulative real increase of 24%) increased the cost of low-skilled workers in the formal sector, many of whom are youth and women, lowering the demand for this group and pushing them into the informal economy (World Bank, 2009). Indeed, despite the dramatic increase in the minimum wage in Turkey in the 2000s, the current minimum monthly gross wage is 2,943 Turkish Lira (TRY) (around 279.35 pounds), ranking the country among the lowest in Europe.

The 2008 Employment Package, on the other hand, introduced reforms that were designed to increase the formal labor demand for individuals in these groups by reducing their employment costs in the form of subsidizing employer social security contributions for newly hired youth and female workers in the first year, with reduced rates over a 5-year period. However, the evidence suggests that this reform did not have an overall statistically significant effect on women's formal employment in Turkey (Balkan et al., 2016), possibly due to low levels of labor demand as a result of the economic crisis during the period of implementation (World Bank, 2009). The labor market effects of the global economic crisis are also worth noting. Although, evidence suggests that Turkey recovered fast from the global financial crisis, the labor market effects of the crisis were not gender neutral (see Yucel, 2015 for a similar discussion). In particular, women's employment in Turkey increased substantially at the initial stages of the crisis, confirming a case for the added worker hypothesis that suggests women who did not previously participate in the labor market may enter into the labor force to compensate for the job loss of their husbands. Such increase in women's labor market participation, may lead to differential changes in selection patterns for men and women (see Dolado et al. 2020 for evidence from European countries). Indeed, it is widely recognized that non-random selection into employment may also create a negative correlation between female labor force participation and the GWG (Olivetti and Petrongolo, 2008). Once that is taken into account, in countries with high employment gaps, the GWG is usually larger, consistent with the positive selection into employment for women.

Given the low female labor force participation in Turkey, the non-random selection into employment is a concern. Unfortunately, SES does not provide information on the household context (e.g. marital status or children) or information on non-participations to account for non-random selection into employment using the conventional two-stage Heckman (1979) approach. However, we explore the sensitivity of our main results to the potential heterogeneity in the sample data using a matching estimate as an alternative to the BO decomposition (see Section 4).

The data used in this study come from the Turkish linked employer–employee SES dataset for 2006 and 2014. SES is carried out by the Turkish Statistical Institute, the official government agency commissioned with producing official statistics for Turkey, at 4 yearly intervals from 2006 until 2014. The main aim of the survey is to give detailed information on earnings from and hours of formal employment. As such, the SES includes detailed information provided by employers on the contractual weekly working hours, monthly paid hours, monthly average basic and gross pay, and information on the key characteristics of workers and their jobs and employers.

The SES adopts a two-stage stratified random sampling method. In the first stage, local units associated with firms with 10 or more employees are selected, and in the second stage, employees from the selected local units are sampled. Establishments with at least 10 employees in all sectors of the formal economy, except agriculture, public administration and defense, and compulsory social security, are covered by the survey, which forms about 70% of the paid employment in the formal sector in Turkey (see Appendix A).

The SES dataset has clear advantages over surveys based on household data, as the information on hours and earnings are based on employer records considered the most reliable (Cebeci, 2015). Moreover, as it is a linked employer–employee dataset, it enables comparisons between the hourly wages of men and women working at the same establishment through within-establishment analysis.

It is worth noting that the survey unit in the SES is the establishment, not the firm to which the establishment belongs. Hence, in our analysis, we control for establishment fixed effects rather than firm fixed effects. However, as all establishments within a firm adhere to the same rules determined at the firm level, controlling for establishment fixed effects is essentially the same as controlling for firm fixed effects (see Hara, 2018 for a similar approach).

Our dependent variable is (log) hourly wage, which is calculated as the simple division of monthly gross pay by monthly paid hours and deflated using the Consumer Price Index base year 2003. In our analyses, we control for personal characteristics, such as educational attainment and years of potential experience (and squared), and work-related characteristics, including years of tenure with current employer (and tenure squared), full-time employment (a binary indicator that takes a value of 1 if the job is full-time and 0 otherwise), permanent employment contract (a binary indicator that takes a value of 1 if the job is temporary and 0 otherwise), and overtime work (a binary indicator that takes a value of 1 if the employee worked overtime and 0 otherwise). We also have information on employees’ occupations; however, as occupation and wages are likely to be jointly determined, our benchmark estimates do not include occupation (see Albrecht et al., 2003 for a similar discussion). Nonetheless, we explore the sensitivity of our results to the inclusion of occupation dummies (see Section 4).

In terms of employer characteristics, SES includes the size band of the firm to which an establishment belongs (in terms of the number of employees), collective bargaining (a binary indicator that takes a value of 1 if a collective pay agreement exists in the establishment and 0 otherwise) and the industry of the establishment. Although no information exists on other employer characteristics that have been found to be important in the literature, such as the public/private sector (e.g., Chatterji et al., 2011) or type of ownership (e.g., Magda and Salach, 2019), this is less of a concern here, as SES data allow us to control for establishment fixed effects, which account for both observed characteristics (including firm size, industry, and collective agreement) and unobserved characteristics (such as market power and managerial ability) that are shared by all the workers employed at the same establishment. The region, another important variable in explaining the GWG (e.g., Hirsch et al., 2013), is available for 2014 but not for 2006. For comparative purposes, in our benchmark analysis, we do not include a control for region, but we also test the sensitivity of our results for 2014 to the inclusion of region (see Section 4). Table A1 in Appendix A provides a detailed description of all the variables and their construction.

In our analysis, we restrict our sample to individuals of working age (those aged 15–64) excluding paid stagers and apprentices.

Our age restriction is consistent with the OECD (2021) definition of working age, but our findings are robust to excluding the youngest cohorts from our sample (see Section 4).

In Table B2 in Appendix B, we present the sample means for establishments to illustrate how establishment characteristics in our sample have changed over time. These statistics show that compared with 2006, there were fewer but larger firms in 2014 with an increased fraction of establishments engaged in collective bargaining. The change in the firm-size composition of our sample is consistent with the findings of Atiyas et al. (2017), who studied the changes in firm and employment dynamics in Turkey between 2005 and 2014 and found that the distribution of employment moved from small firms to larger firms. Additionally, we observe changes in the industry composition of establishments from 2006 to 2014, with a relative shift from manufacturing to wholesale and retail trade, a trend that has been observed in Turkey since the 1980s and is shared by other emerging economies (Gezici et al., 2017).

In Tables B3 and B4 in Appendix B, we also present the sample means of explanatory variables for employees within and across all establishments. These conform to some well-established gender differences and display largely common patterns in both years. However, they also highlight important features of the Turkish labor market. Consistent with the increase in the female employment rates in Turkey over recent decades (OECD, 2019), from 2006 to 2014, there was an increase in the fraction of females within establishments. Female employees in our sample are increasingly more highly qualified than males, a feature commonly observed in other emerging economies (OECD, 2017). However, they have shorter average potential experience and firm tenure than males. Over time, there has been a decline in the average tenure of both males and females, consistent with the increase in the retail industry (see Table B2 in Appendix B), where average tenures are typically shorter (Auer and Cazes, 2000). Nonetheless, both within and across establishments, the gender differences in experience and tenure have narrowed over time. Confirming the well-established gender differences in overtime work, female employees worked relatively less overtime in both years, and they were also less likely to be covered by a collective pay agreement than men. However, there is no significant gender difference in firm size, full-time or permanent employment in our samples.

Turning our attention to our dependent variable, Figure 1 presents the kernel estimates of the wage densities for men and women in 2006 and 2014. By contrasting the male and female kernel density estimates in each year, we obtain a visual inspection of the GWG. In Figure 1, the distributions are hardly distinguishable in 2006, suggesting a closed GWG, particularly at the bottom and in the middle of the wage distribution. From 2006 to 2014, the average hourly real wage in our sample increased for both males (by 20%) and females (by 12%), and as a result, the male and female wage distributions shifted to the right. However, the unequal growth in the wages of men and women led to an increased average GWG, which may be attributed to increases in the gap throughout the wage distribution. In fact, this becomes apparent in Figure 2, where we present the observed GWG for each year, calculated as the difference in the (log) hourly wages of men and women at each percentile of the wage distribution. In 2006, there is a closed GWG at the bottom of the wage distribution, but the gap widens as we move up the wage distribution and then declines at the top. In 2014, however, there is an apparent W-shape distribution, with wider GWGs at the two ends of the wage distribution and in the middle. Moreover, the GWG in 2014 is wider than in 2006 at all points in the wage distribution except between the 13th and 26th percentiles, where there is no observed GWG. In the next section, we explore these descriptive patterns in more detail and provide evidence for their potential drivers.

Figure 1

Figure 2

To estimate the economy-wide GWG across all establishments, we use a simple ordinary least squares (OLS) wage equation that pools observations from both male and female workers in each year as: (1) ln wij=αgij+xijβ+uij. {ln}\,{w_{ij}} = \alpha {g_{ij}} + {\boldsymbol{x}_{\boldsymbol{ij}}}\boldsymbol{\beta } + {u_{ij}}. where ln w_ij is the log hourly wage of individual i (for i = 1,…, N) working in establishment j (for j = 1,…, M), g_ij is the binary indicator of gender (for males g = m and for females g = f) and x_ij is a vector of explanatory variables. In the first specification (Specification (1)), we regress the log hourly wage on a binary indicator of gender that takes a value of 1 for males and 0 for females and a constant term. The coefficient estimate of the male dummy in this specification provides a measure of the economy-wide raw GWG that does not consider any gender differences in terms of productivity-related characteristics. We then estimate four more specifications where we gradually add the personal characteristics (Specification (2a)), work-related characteristics (Specification (2b)), and observed firm and establishments characteristics (Specification (2c)) discussed above. The estimate of the male dummy coefficient estimate from each of these specifications provides information on the mean GWG adjusted for the covariates included in the model. In our final specification (Specification (2d)), in addition to personal and work-related characteristics, we control for establishment fixed effects, which account for observed (such as industry, firm size, and collective pay agreement) and unobserved (such as managerial ability and market power) firm characteristics that are shared by all the workers employed at the same establishment. Formally, to estimate the within-establishment GWG, we consider a version of Eq. (1): (2) ln wij=αgij+xijβ+fj+uij. {ln}\,{w_{ij}} = \alpha {g_{ij}} + {\boldsymbol{x}_{\boldsymbol{ij}}}\boldsymbol{\beta } + {f_j} + {u_{ij}}. where f_j is the full set of establishment dummies (for j = 1,…, M), i.e., establishment fixed effects.

As proposed by Mundlak (1978), analytically equivalent estimates of β can be obtained using the following model: lnwij=αgij+xijβ+x¯jγ+uij \ln {w_{ij}} = \alpha {g_{ij}} + {\boldsymbol{x}_{\boldsymbol{ij}}}\boldsymbol{\beta } + {\boldsymbol{\bar x}_\boldsymbol{j}}\boldsymbol{\gamma } + {u_{ij}} where x¯j {\boldsymbol{\bar x}_\boldsymbol{j}} are establishment-level averages of the covariates – so called Mundlak terms. Our benchmark estimates are based on equation (2), but we use Mundlak terms when we explore sensitivity of our results to using (conditional) quantile regressions which are computationally demanding to estimate with a large set of fixed effects.

Table 1 presents these results, where we focus on our key variable and present the coefficient estimates on the male indicator. The full set of coefficient estimates on all other explanatory variables is available upon request, but they generally conform to expectations with higher returns to higher levels of education, a positive but diminishing return to experience and tenure, a permanent contract premium, a firm size premium, a premium attached to any collective bargaining agreement, and differential returns to different industries. However, in contrast to most developed countries (see Fernández-Kranz and Rodríguez-Planas, 2011 for Spain and Mumford and Smith, 2009 for Britain) but consistent with previous evidence for Turkey (see Aldan and Yunculer, 2017), we find a significant part-time premium. Overtime work is also associated with a premium, although this turns out to be either insignificant or negative once we account for firm heterogeneity. This might reflect the fact that, in contrast to many European countries, in Turkey, low-skilled workers are more likely to work overtime (Anxo and Karlsson, 2019).

Turning our attention to the results presented in Table 1, the estimates from Specification (1) suggest a significant raw GWG in each year, but the gap increased from 0.024 log points (2.4%) in 2006 to 0.084 log points (8.8%) in 2014. When personal characteristics are controlled for (Specification (2a)), the GWG widens substantially in both years. This contrasts starkly with the evidence from European countries but is consistent with the descriptive statistics presented above, indicating that women are more highly qualified than men on average, particularly in terms of education. While the inclusion of work-related characteristics narrows the GWG slightly in each year (Specification (2b)), observed firm and establishment characteristics appear to have a limited influence (Specification (2c)). The coefficient estimates from Specification (2d), where we control for establishment fixed-effects, however, reveal a substantial within-establishment GWG (about 0.051 log points in 2006 and 0.095 log points in 2014), bringing into question whether women and men in Turkey are equally rewarded by employers.

A comparison of estimates from Specifications (2b) and (2d) suggests that the within-establishment GWG between comparable men and women is the main driver of the economy-wide adjusted GWG. For instance, the within-establishment adjusted GWG (Specification (2d)) is similar in magnitude to the economy-wide adjusted GWG (Specification (2b)), while in 2014, it forms >87% of the economy-wide gap. As discussed in Section 2.2, from 2006 to 2014, Turkey experienced a series of changes that increased the relative cost of female employees. On one hand, this change could move females out of low-productive, low-paying firms. On the other hand, it may increase the within-establishment GWG by depressing female wages within each firm. Consistent with the latter, our results indicate a 4.4 (log) percentage point increase in within-establishment adjusted GWG from 2006 to 2014 (from 0.051 log points to 0.095 log points). This is smaller than the 6 (log) percentage point increase in the economy-wide adjusted GWG (from 0.048 log points to 0.108 log points), also suggesting a widening between-establishment adjusted GWG. The increase in between-establishment adjusted GWG, however, contributes a modest proportion (less than one-third) to the 6 (log) percentage point rise in the economy-wide GWG, implying that increasing pay inequalities within establishments were the main drivers of the widening of the GWG.

The results presented in Table 1 are based on wage equations that assume that returns to characteristics are the same for men and women. To examine the extent to which the returns to characteristics differ by gender, a version of wage Eq. (3) can be estimated separately for males and females as follows: (3) ln wij,g=xij,gβg+uij,g. {ln}\,{w_{ij,g}} = {\boldsymbol{x}_{\boldsymbol{ij,g}}}{\boldsymbol{\beta }_\boldsymbol{g}} + {u_{ij,g}}.

The OLS estimates of the gender specific coefficient vectors β^g {\boldsymbol{\hat \beta }_\boldsymbol{g}} (for males g = m and for females g = f) from Eq. (3) can be then used in BO decomposition which has been widely used to analyze the GWG. Consistent with the literature (see Blau and Kahn, 2017 for a recent review), we decompose the raw GWG into its explained and unexplained components (Blinder, 1973; Oaxaca, 1973) as follows: (4) lnwm¯−lnwf¯=(x¯m−x¯f)β^m︸explained gap+x¯f(β^m−β^f)︸unexplained gap \overline {{ln}\,{w_m}} - \overline {{ln}\,{w_f}} = \underbrace {\left( {{{\boldsymbol{\bar x}}_m} - {{\boldsymbol{\bar x}}_f}} \right){{\boldsymbol{\hat \beta }}_\boldsymbol{m}}}_{{exp}lained\,gap} + \underbrace {{{\boldsymbol{\bar x}}_f}\left( {{{\boldsymbol{\hat \beta }}_\boldsymbol{m}} - {{\boldsymbol{\hat \beta }}_\boldsymbol{f}}} \right)}_{{unexp}lained\,gap} where the bar above a variable denotes the mean value.

To identify the contribution of each covariate to the explained component, we apply the normalized regression method proposed by Suits (1984) and Yun (2005), which transforms the coefficients vectors of the categorical variables so that the results of the BO decomposition are independent of the choice of the omitted category.

In Table 2, we present the BO decomposition results performed by estimating Specifications (2a)–(2d) separately for males and females for each year.

A full set of coefficient estimates is available upon request, but they show lower returns to higher education levels for women and a higher firm-size wage premium for men in both years. Although we find a higher return to experience for women in 2006, in 2014 this is reversed with men receiving a higher return to experience. We also find that males receive a higher return to tenure and have a pay advantage for working in establishments with collective agreement bargaining coverage in 2006, but these turn to a female advantage in 2014.

Before we move to the distributional analysis, it is worth noting that a valid concern surrounding our empirical strategy is the non-randomness of selection into work particularly given the low female labor force participation rates in Turkey. If the selection process into work is not exactly the same for men and women, this could lead to biased estimates of the unexplained component of the GWG. In order to address the potential heterogeneity in the sample data, we follow Meara et al. (2020) and use the propensity score matching (PSM) method as an alternative to the BO decomposition. Further details are reported in Table B8 in Appendix B, but these analyses produce an estimate of a statistically significant average treatment effect on the treated (ATT) (GWG) of about 0.020 log points for 2006 sample and of about 0.065 log points for the 2014 sample. Consistent with the presence of potential heterogeneity in the sample data and the findings of the previous literature (see Meara et al. 2020 for a discussion), these estimates are lower than the unexplained components obtained from the BO decomposition. Nevertheless, the main pattern remains consistent, with a widening of the GWG between the 2 years. In fact, the increase in the estimated gap between 2006 and 2014 from PSM is of comparable magnitude to the economy-wide increase in the unexplained GWG obtained from the BO decomposition (at around 4.5–6.3 (log) percentage points).

Next, we explore whether the GWG differs across the wage distribution and estimate the GWG at different points of the wage distribution using the unconditional quantile regression model proposed by Firpo et al. (2009) which is based on a recentered influence function (RIF). The method is similar to the standard OLS regression, except that the RIF of the statistic of interest is used as a dependent variable (Fortin et al., 2011).

In Figures B1 and B2 in Appendix, we explore the sensitivity of our findings to the decomposition method, by using the Machado and Mata (2005) decomposition. While the precise point estimates vary, the patterns remain robust to the decomposition method employed.

Parallel to the analysis at the mean, Table 3 presents RIF-OLS regression estimates of the male dummy for selected percentiles of the wage distribution using the five specifications presented above. For comparison, estimates for the mean are displayed in the last column. A comparison of the male coefficient estimates within each row of Table 3 enables us to identify where the GWG is more pronounced. The previous literature (e.g., Albrecht et al., 2003; Arulampalam et al., 2007) has defined the existence of a glass-ceiling effect if the adjusted (or unexplained) GWG at the 90th percentile exceeds the gap at other reference points (e.g., the 50th or 75th percentiles) of the wage distribution by at least two percentage points. Similarly, the existence of a sticky-floor effect is determined if the adjusted GWG at the 10th percentile of the wage distribution exceeds the gap at the 25th or 50th percentiles. For instance, if females are under-represented at the top of the wage distribution and their under-representation becomes more pronounced as we move up the distribution, then the GWG would be larger at the top than in the middle or bottom, which is consistent with the presence of a glass-ceiling effect. However, if women face larger wage differentials at the bottom of the wage distribution than at the upper percentiles or in the middle, this indicates the presence of a sticky-floor effect.

In Table 3, the first row of the upper panel (Specification (1)) shows an insignificant raw GWG in 2006 below the median of the wage distribution. At and above the median, however, women face a significant gap, at least until the 90th percentile. Once we control for personal characteristics (Specification (2a)), the GWG turns to be larger in magnitude at and above the median and becomes significant at the top of the distribution. This implies that, although women at and above the median of the wage distribution have superior productivity-related characteristics than their male counterparts, they face a larger GWG. After including work-related characteristics (Specification (2b)) and observed firm and establishment characteristics (Specification (2c)), the largest GWG remains among highly paid men and women, who are otherwise comparable. The results of Specification (2d) also reveal that females in Turkey also face a substantial within-establishment glass-ceiling effect, with a widening GWG as we move up the wage distribution. In fact, the within-establishment gap widens around 11.0 (log) percentage points between the 10th and 90th percentiles of the wage distribution, while the comparable figure is only around 7.1 (log) percentage points for the economy-wide GWG (Specification (2b)). This brings into question the mechanisms within establishments that give rise to the economy-wide glass-ceiling effect that women face.

The results presented in the lower panel of Table 3 display similar patterns, but they also show an increase in the GWG from 2006 to 2014 at all percentiles of the wage distribution, particularly at the bottom end of the distribution. This becomes more apparent in Figure 3, where we plot the adjusted GWG estimated from Specifications (2a)–(2d) for both years along with associated 95% confidence intervals (CI) for the mean and at selected percentiles of the wage distribution. The increase in the adjusted GWG is particularly substantial at the bottom end of the distribution and at and above the median. Although the increase at the bottom end turns to be insignificant once the establishment fixed effects are controlled for (Specification (2d)), the results from 2014 highlight the importance of the within-establishment glass-ceiling effect, which widens by around 16.4 (log) percentage points between the 10th and 90th percentiles of the wage distribution, while the economy-wide comparable figure (Specification (2b)) is only around 12.9 (log) percentage points (see Table 3). These results also reinforce the importance of distributional analysis where we observe a more pronounced GWG at the median and upper tail of the wage distribution, which is neglected when the analysis focuses exclusively on the mean.

Figure 3

We explore the sensitivity of these results to using alternative samples and model specifications by restricting the sample to 18–64 years, including additional controls such as occupation, region of establishment (only available in 2014), more detailed industry; excluding overtime dummy; and to using weighted data in the regressions. As an additional sensitivity check, we also explore the influence of the method by using the conditional quantile regression approach of Koenker and Bassett (1978). These results, which are presented in Tables B9 and B10 in Appendix B, show that our key findings are robust, with an increase in the economy-wide adjusted GWG across the distribution from 2006 to 2014 and a wider GWG at the median and upper tail of the wage distribution in both years, both of which are mainly driven by mechanisms that operate within establishments.

Parallel to our analysis at the mean, in this section, we decompose the raw GWG at different points of the wage distribution into its explained and unexplained components using the BO decomposition that relies on the RIF regression (RIF-BO decomposition) proposed by Firpo et al. (2009). In implementing the RIF-BO decomposition, we first run the RIF-OLS regression at each quantile and then perform the usual BO decomposition. This approach, as opposed to other decomposition methods that rely on conditional quantile regression (e.g., Machado and Mata, 2005 and Melly, 2005), is less computationally demanding and allows for a detailed decomposition to be carried out yielding an unconditional quantile decomposition (Fortin et al., 2011).

Figures 4 and 5 present these results for 2006 and 2014, respectively. Parallel to the analysis at the mean, in each figure, we present the decomposition results performed by estimating Specifications (2a)–(2d) separately for males and females for each year at selected points of the wage distribution.

All the point estimates and standard errors are available upon request.

Figure 4

Figure 5

The results presented in Figure 5 display similar patterns, although the unexplained GWG is greater in 2014 than in 2006 across the entire distribution.

The only exceptions are between the 26th and 33rd percentiles, where the unexplained gap in 2006 exceeds that in 2014 by around up to 6 (log) percentage points.

Figure 6