Managerial Gender Earnings Gap in China. Lin Xiu. April, 2009

Transcription

1 Managerial Gender Earnings Gap in China Lin Xiu April, 2009

2 Section I: Introduction China has a long tradition of Confucianism, which emphasizes the subordinate roles of females in the society, as illustrated by the famous saying lack of talent is a virtue of women (nuzi wucai bianshi de). Although such beliefs have been diminishing during the planned economy ( ) when the Chinese central government implemented a system of national wage scales based on the socialist egalitarianism principle wage dispersion due to human capital characteristics was suppressed, the portion of females in top organization jobs still remains low and gender earning differential still exists. Several studies have investigated the gender earnings differential in recent years and shown an increasing gap between males and females (e.g. Zhang et al., 2008) in the last two decades. It is also shown that the gap is particularly large at the lower end of the earning distribution; however the gap widened greatly at the upper end in most recent years ( ), This paper aims at analyzing gender earnings differentials among top managers in China. Among a few studies that have investigated the female-male earnings differentials using Chinese data (e.g. Wang & Cai, 2008; Zhang, et al., 2008; Ng, 2007, Meng, 1998), none of them looked at how the gender pay differential varies by occupation groups, especially among those high paid. On the other hand, studies on executive compensation in China (e.g. Firth, Fung and Rui, 2007; Kato and Long, 2006; Zhu, 2007) largely use the data of listed firms which contains limited information pertaining to executives individual characteristics such as tenure, marital status and training. This prohibits researchers from investigating the underlying rationale why female managers are paid lower, e.g. how much of the gender CEO compensation gap relates to CEO s individual characteristics. 1

3 Our motivation to conduct this study is threefold. First, the question of whether and how females are treated financially differently from their male counterparts in contemporary Chinese organizations is of great interest to the three parties in the employment relationship: the government policy makers, organizations, and employees. If pay differentials do exist and are largely due to workplace discrimination, then policy makers may need to address the employment equity issue through policies such as affirmative action regulation. On the other hand, if the gender pay differential is largely due to the lower education or training received by female managers, then discrimination policies regarding education and manager development/training would be more relevant than pay equity issue because this will address why male and females end up with different human capital. From the organization s perspective, if gender pay differential are largely due to institutional barriers that disadvantage women, or other discriminatory factors at the workplace, firm performance will suffer because organizations are not maximizing the likelihood that pay and hiring go to the most productive managers. From the employees side, it would be interesting to know how these highest paid women are paid after they cross the glass ceiling. Second, the unobserved differences between men and women are minimized as we are focusing on a specific occupation group where men and women are more likely to share some common unobserved characteristics such as career ambition. This is crucial for identifying the factors that lead to gender pay differential since the unexplained part of male female wage differential could be attributable to labour market discrimination, but could also be due to differences between men and women that are unobservable, such as relative lack of career commitment or job motivation (Bertrand and Hallock, 2001). 2

4 Third, we use data from a survey of executives and firms in 2006 from Liuzhou, Guangxi, China. An important advantage of using this survey data is that it contains information from both executives and the firms that they worked for. The inclusion of workplace characteristics into analysis will help to identify the factors that underlying the managerial gender pay differential. For example, Drolet (2002) analyzing Canadian Workplace and Employee Survey found that when workplace and industry measures were included, the explained component of gender pay differential was increased substantially. Also, compared to earlier studies on managerial pay, we have a relatively larger portion of women in our sample (21.56%), which allows us to make nonmisleading estimation. This paper will draw on two major literatures: studies on gender pay differential in China, and CEO compensation studies. Section 2 will briefly discussed the relevant findings from these two literatures, and our hypotheses based on these findings. Section 3 will introduce the data and methodology. Analysis results will be presented in Section 4. Policy indications and further discussion will be discussed in section 5. Section II: Literature Review on Gender Pay Differentials and CEO Compensation in China 2.1 Gender Pay Differentials in China There is a growing literature on gender earnings inequality in China since the middle 1990s. A wide range of the differentials is found in different studies, from 50% to 90%. Most studies have shown that the income differential has been enlarged in the past two decades. For example, a recent study, Zhang et al. (2008) analyzing changes in the gender income gap in urban China over the period using urban household survey data, found that female-male earnings 3

5 gap increased from 15.8% in 1988 to 24.3% in Similar trend was revealed in Maurer-Fazio, Rawski and Zhang (1999). With the official data presented in labour year book, they found that the gender earnings gap increased in urban industry between 1988 and 1994, from 15.5% to 17.5%. Other studies also provided evidence of gender earnings gap using various data sources. For instance, Khan (1996) noted that women s income were about 80 percent of men s overall during the late 1980s; Maurer-Fazio and Hughes (2002) show that the overall gender wage gap was 14% in In terms of the earnings of high paid, earlier research shows that the gap widened much more at the lower end of the earnings distribution than at the upper end from 1988 to 2004; however, in more recent years, the gender gap of highly paid workers is widening greatly (Zhang et al., 2008) Two components explained portion and unexplained portion of the overall gender earning gap have been analyzed in order to identify the sources of gender wage inequality. Hughes and Maurer-Fazio (2002), for example, use the total monthly income data collected in 1992 Chinese Labour Market Research Project as dependent variable to compare the gender gap across different groups, and found an overall urban wage ratio of 86 percent. After controlling for education, age, job tenure and work experience, 40% remains unexplained. The results also show that there is larger wage gap for married women than single women, and the unexplained portion is also larger for married than single women; unexplained wage differentials decrease with educational attainment; and the gap does not vary much in size across occupational groupings. Using the same data set, Maurer-Fazio and Hughes (2002) found that the degree of wage dispersion plays an important role in explaining the larger wage gaps in the joint-venture and collective sectors relative to the state-owned sector. Qian (1996) applied human capital type models to two sets of cross sectional data collected from Beijing and Guangdong Province in 4

6 1993 to analyze China's current urban gender wage differentials. He found that there is a significant 8% gender wage gap in urban China after controlling for education, experience, occupation, industry, ownership, and region. He attributed this gender gap to the Chinese women's continuous labor force participation as well as China's over-four-decade practice of Equal Pay for Equal Job policy. Liu, Meng and Zhang (2000) employed two Chinese data sets from Tianjin and Jinan and found that overall gender wage differential declines substantially across ownership sectors from the state to the collective to the private sector. Gustafsson and Li (2000), utilizing two large scale surveys covering 10 provinces for the years 1988 and 1995, analyzed the gender wage gap. They argued that from an international perspective, the gender wage gap in urban China appears to be relatively small. Decompositions based on estimated regression-models show that somewhat less than half of the average gender wage gap can be attributed to differences in variables. In terms of the trend, Bishop, Luo and Wang (2005) using the data of the Chinese Household Income Project (CHIP) found that while the gender earning gap increased slightly from 19% in 1998 to 20% in 1994, but the relative share of unexplained portion declined from 71 percent to 61 percent during the study period. In other words, the overall gender gap was enlarging, but more of the gender gap could be explained by the observable factors such as education, occupation and so on. Similar trend has been found in Shu and Bian (2003), Rozelle et al. (2002) and Liu et al. (2000). They argued that the privatization and marketization of the economy leads to larger wage differentials as human capital characteristics are more appropriately rewarded. On the other hand, opposite findings are shown in Gustafsson and Li (2000) who analyzed the urban household income survey in 1989 and 1991, and show that the share of wage discrimination in the total wage gap increased from 1988 to

7 As for the identified factors that leads to gender pay inequality, Liu et al (2000) found widening gender wage gap across ownership sectors from state to collective, and then to private; however, they also observed that the relative share of discrimination in the overall gender wage differential declines across the ownership sectors. They argued that since private/collective firms exhibit larger wage discrimination because they have more autonomy than state firms. On the other hand, competition between and within sectors may reduce discrimination. Maurer-Fazio et al (1999) found that ownership sector most subject to market forces has the largest wage gap. Maurer- Fazio and Hughes (2002) found that both the gender wage gap and the portion of unexplained are largest in the most competitive sector. In terms of geographical areas, Gustafsson and Li (2000) found larger wage gaps and a higher unexplained residual in eastern provinces. Married Chinese women experience much larger absolute gender wage gaps their their unmarried counterparts. The portion of the "unexplained" portion is also higher for married women than single women. Gender wage gaps are smaller for more educated women although education for women appears not to pay earnings dividends until college. Unexplained wage differentials decrease with education attainment (Hughes and Maurer-Fazio, 2002) Researchers have found smaller wage gaps for more narrowly defined industry and job categories (Robinson, 1998). Hughes and Maurer-Fazio (2002), on the other hand, did not find the wage gaps increase when separating the data by occupational groupings. They argued that occupational segregation by gender is not an important factor in China; rather, industrial segregation is more important than occupational segregation in explaining the gender gap in China s urban labour markets. In terms of occupational segregation, Maurer-Fazio, Rawski and Zhang (1999) noticed that women s share in blue-collar, manual work declined, while their share in all types of white collar work increased. 6

8 An exception in the literature is Lam and Dreher (2004) s article, where the authors argued that much of the observed gender pay differential is due to extra-firm mobility rather than intra-firm gender discrimination. They using data from 739 U.S. managers and professional and 593 Hong Kong Chinese managers and professionals to examine the effect of gender on the relationship between changing employers and compensation attainments, found that large pay differences favouring men were only observed among those who had followed an external labour market strategy. Literature on CEO Compensation in China Contrast to the large number of CEO compensation studies in Western countries, only a few studies have been done in this area in the context of China and most of them are in the past decade. To develop a better understanding of the main determinants of Chinese executive compensation, we review the research in this area. Proquest (academic papers) and JSTOR databases were used for search the multidiscipline peer-reviewed scholarly publications in English. The period chosen was , as this represents the time when the economic restructuring began. Four search terms were used: executive compensation China, executive pay China CEO compensation China CEO pay China. After further examination, 10 articles were identified. One article was published in 1995, and the other nine articles were published between 2000 and There were two types of data that were used for analysis, one using the listed firm data (6 articles) and the other using survey data (4 articles), which mainly focused on SOEs. CEO compensation under analysis in these papers usually refers to cash compensation. Share ownership by executives is very low in China (Xu, 2004). An important reason is that the 7

9 government decided not allowing listed firms to offer stock options to executives after some debate because there is no source from which to give shares to the executives and treasury stock usually is not allowed (Firth, Fung and Rui, 2007). So studies using listed firm data used CEO s total cash compensation including base salary, bonuses, and commissions is the first year that listed companies are required to disclose top management compensation, so the studies employing listed firms data are increasing after These studies have shown consistent evidence of pay-for-performance relation: firm performance has found to be correlated with the compensation of top managers. For example, Buck, Liu and Skiovoroda (2008) showed that executive pay and firm performance mutually affect each other through reward and motivation. Firth et al. (2007) find a positive pay-performance relation in China when performance is measured as return on assets although the relationship is not significant when performance is measured by stock returns for the period Kato and Long (2006) extend the data range to 2002, and obtained a higher and significantly positive results of pay-for-performance relations. In addition, Groves et al (1995) and Mengistae and Xu (2004) showed that top management pay in state-owned-enterprises (SOEs) depends on performance. The consistency of the results across various studies differs from CEO compensation literature in North American where some empirical research has resulted in mixed findings on the pay-for-performance relation (e.g. Devers et al., 2007; Conyon and Murphy, 2000; Core et al., 1999) Various factors have been shown having impact on CEO compensation. For example, Firth et al. (2007) showed that the compensation level is higher in foreign owned companies and lower in 8

10 state-owned companies. Firms with joint CEO/Chairman positions are less likely to use performance-based pay. Ding, Akhtar and Ge (2006) analyzed a firm level data from three major cities, Shanghai, Nanjing and Guangzhou, and showed that ownership, firm size, firm age, location and industrial sector, have significant impacts on the variances in the Chinese managers compensation. Section III: Data This paper used survey data from enterprises and entrepreneurs from Liuzhou, Guangxi, China. Questionnaires were delivered and reclaimed anonymously by the Federation of Industry and Commerce of Liuzhou, Guangxi. The entrepreneur questionnaire contains information on entrepreneurs and their enterprises, including gender, age, political status (CPP member or not), education, marriage status, source of employment earnings, job tenure, received training. The enterprise questionnaire contains information on industry, registered capital, corporate life, and number of employees questionnaires were distributed, 1017 returned the questionnaire and 831 provided usable observations, among which 582 answered both questions on gender and pay. We employed the regression missing value imputation method to deal with the missing values present in variable capital (36 missing values), firm history (years) (23 missing values), marital status (1 missing value), age (6 missing values), job tenure (12 missing values), and number of employees (51 missing values). The descriptive statistics before and after imputation are close. For example, the average job tenure for male is 9.33 years for 450 observations (before 9

11 manipulation), and 9.34 years for 456 observations (after manipulation). After missing value imputation, the sample size is 582. There were 126 females, accounting for 21.65% of the total sample. This relatively larger percentage of females than earlier studies on CEO or top organization executives (e.g. 2.4% in Bertrand and Hallock (2001) with US data, 4% in Kato and Long (2006b) with Chinese listed firm data from the year of ) might be due to the fact that most firms in this data source are small and medium size firms while the above studies employed data from listed firms, usually larger in size. As illustrated in table 1, the total compensation was, on average, 34.1% lower for females and for males. On average, women earned RMB162,400 (2006 Yuen) in total compensation, compared to RMB246,400 for the average male leaders. The compensation comprised base wage, bonus, stock options, and profit sharing. Table1 shows that women in top managerial positions work for smaller firms. Female executives firms were 10% smaller when firm size was measured as the registered capital, and 66% smaller in terms of number of employees. The average number of employees per firm for male and female executives was 90 and 31, respectively. We computed the fraction of women by deciles of firm registered capital. Women constituted about 26% of top management employment in the bottom three deciles and only 16% in the top decile. Earlier studies on executive compensation shows that CEOs tend to be paid more in larger firms (e.g. Murphy, 1998). It would be interesting to see how much of the gender gap can be attributed to the under-representation of women in larger firms. 10

12 Women in the sample were about 4 years younger than the men, on average (40.0 versus 44.1 years old), and had 2 fewer years of seniority in their company (7.1 versus 9.3 years). As the respondents were CEOs/Chairs of their company or other top managers, we created a variable called president/chair, indicating whether the respondent was the very top manager of the company. 68% of men and 55% of women reported they were CEOs or Chairs. Due to the small size of the sample, we categorized the industry into two categories service industry and non-service industry. 29% of men and 40% of women worked in service industry. Due to the same reason, the education categories 1 were combined and respondents were grouped into three categories: less than high school, high school, college/university or higher. Women and men had roughly the same education level. On the other hand, 57% male CEOs and 44% female CEOs had taken business training. 93% males and 82% females are married. 33% males and 24% females are CPP members, which is an indicator of how closely they are with the local government. Section IV: Results In this section, we investigate how various characteristics of CEOs and the enterprises that they worked for might account for the gender pay gap. We first examine how the gender coefficient changes as more variables are added into the model, compare the OLS estimates for males and females, and then use Oaxaca decomposition methods to further look into how much of the gender gap could be explained and how much remains unexplained. OLS regression with the pooled sample 1 The survey contains information on 8 education categories: less than elementary school, elementary school, junior middle school, high school, two years college, university, graduate (master level), and graduate (PhD level). 11

13 The dependent variable is the logarithmic form of pay, as shown in data section, comprises base wage, bonus, stock options, and profit sharing, and others. We had two sets of independent variables enterprises characteristics and individual characteristics. Enterprises characteristics included registered capital, years of history, industry, and executive rank in the firm. Individual characteristics included age, job tenure, job tenure square, marital status, party membership, education, business training, and whether profit was counted as part of the pay. Table 2 shows the results of the pay regressions. The unconditional gender gap is about 32.5% (column 1). Age and job tenure hardly explain any of the gap (column 2), and neither do marital status and CPP membership (column 3). Education level variables are statistically significant in the model, but do not contribute to explaining the gender gap as the gender coefficient does not decrease when education variables are added into the model (column 4). The gender pay differential reduces to 26.6% when business training variables was controlled for (column 5). In total, individual characteristics explained 18.2% of the total pay gap. Then, we examine the effect of firm characteristics on gender pay gap. When firm size, as measured by logarithmic form of firm capital, was controlled for, the gender pay differential fell to 22.0% (column 6). Adding further industry and company history variables does not contribute to reducing the remaining gender pay gap (column 7). Adding the executives rank (column 8) reduces the gender gap by 2.9 percentage point compared to column 7. Finally, column 9 examines the effect of compensation payment method, whether company profit was part of the executives reward. When it was controlled for, the gender pay gap reduces another 3.9 percentage point. Compared column 9 and column 5, we can see that firm characteristics explain 10.7 percentage points or 32.9% of the total gender pay gap. 12

14 As firm characteristics might be a mechanism through which the gender pay differential between male and female executives happens, we regard in the following analysis model 5 as the simple model, and model 9 as the expanded model percent pay advantage of male executives continues to persist, unexplained by any of the above factors. The results above indicate that if female executives had the same human capital characteristics as males, their pay would be 75.6% percent of males pay. Further, if they were managing the same enterprises as males, they would be earning 84.1% percent of what male executives earn. OLS regression with males and females separately We examine male and female executives pay separately with the same variables that were used in the total sample. The expanded wage regressions are shown in table 3. Age, after controlling for gender, is negatively associated with both male and female executives pay. The effect of age on female pay is not significant, but the magnitude is larger than for females. The quadratic form of age was included originally, but the values were nearly zero for both male and female regressions and statistically insignificant on quadratic term, which means that the effect of age is roughly linear. Also, the inflection points were beyond the age range, so we decided to remove the quadratic age term. The return to job tenure is higher for women than for men. The marginal effects of job tenure are and respectively, which shows that one additional year of job tenure leads to male pay increases of 4.8% while female pay increase of 7.8% on average. This may indicate the influence of interrupted work careers of women. The inflection points for men and women are 13

15 21.3 years versus 16.8 years respectively. Pay increases with job tenure up to these points and then decrease afterwards. The coefficient of marital status is insignificant (t=1.59 for male, for female). However, it is interesting to note that married men earned more than single while married women earned less than single after controlling for other individual characteristics and firm variables. This difference could be due to the fact that men are more likely to gain family support for their work after married, while women tend to assume more family responsibilities after married. A striking difference in coefficients is with respect to the CPP membership variable. Male CPP members earned less than non-cpp members while female CPP members earned more. The return to education is higher for women than for men in general. In particular, men who completed high school enjoy a 32.5% increase in pay as compared with their counterpart whose education level were less than high school. However, women with high school completed earned 47.1% than those who did not finish high school, the coefficient is not significant. On the other side, college and university education does not make difference for males earnings while have a significant large effect on females earnings. Women with college, university or higher education enjoyed an 79.5 percent increase in pay than those who did not finish high school. As distinct from most studies on income return topic, our data set allows us to add business training among regressors. We found that the return to business training is significant men (25.7%) and insignificant for women (34.9%). Firm size has a significant effect on both and females and males pay. This indicates that not only females concentrated in smaller firms, and firm size is important in accounting for the gender gap between male and female executive, firm size is also important in explaining the in- 14

16 group variance for female executives. 10% increase in firm registered capital leads to 1.58% increase in females pay. For male executives, firm size is also a significant indicator of pay. In particular, 10% increase in firm registered capital leads to 2.21% change in pay. Whether the firms are in service industry does not have a significant effect on executive pay for both males and females. The firm tenure does not have an effect either. Further, it is interesting to notice that female executives who held president/chair positions earned 46.3% higher pay than non-top leader while such difference was not shown for male executives. To better discern whether profit is counted part of the reported pay, we include profit in the regression. Those who had profit as part of their received pay earned much more than those did not. In particular, the difference is 50.0% for males and 72.3% for females. In summary, these above factors explain 14.4% variance in male s pay and 29.8% variance in female s pay. Decomposition Following Oaxaca (1973) and Newmark (1988), we use three different specifications to analyze the composition of gender pay differential by splitting the total gender pay differential into two components: the differential attributable to gender difference in observable characteristics, and the residual gap which might be due to discrimination or gender differences in unobserved productive characteristics. The functions work as following: lny M lny F X M X F β M β M β X F F (1) 15

17 lny M lny F X M X F β F β M β X F M (2) lny M lny F X M X F β P β M β X P M β F β X P F (3) The first term on the right hand side of equation (1)-(3) represents the explained portion. The residual or unexplained includes differences in the returns to worker and workplace characteristics, calculated in the second term for equation (1) and (2), and the last two terms for equation (3). Equation (1) is male base decomposition, using male pay structure as baseline. In particular, the first term in equation (1) is that portion of the pay differential attributable to differences in the average productive and other characteristics is multiplied by the estimated coefficients form the men s regression. These coefficients are interpreted as the men s pay structure. The second term on the right hand side is that portion of the pay differential attributable to the differences in the male and female regression coefficients, which is the unexplained residuals, or called discrimination effect by some researchers who believe that discrimination exists (Weichselbaumer & Winter-Ebmer, 2006). In Equation (2), the differences in the mean characteristics between men and women are valued according to the female return, which assumes that the female pay structure would prevail in the absence of discrimination. Equation (3) represents a pooled decomposition model, which regards the non-discriminatory pay structure that was estimated using the regression coefficients from a pooled male-female pay regression as baseline. As shown in table 4, three decomposition methods were employed to decompose the gender pay gap into a portion explained by the model variables and a portion that remains unexplained by the model variables. Several findings are worth noting. 16

18 First, when firm characteristics are excluded (simple model), three decomposition methods consistently show that gender differences in the coefficients tend to dominate gender differences in characteristics. For instance, the male base yields an estimate of about 78.8 percent and the pooled method estimate is 81.8 percent. The female base method yields the largest unexplained component compared to the male base and pooled method (96.0 percent). As a result, three methods produce the adjusted gender pay ratio ranging from 69 to 74 percent. This result indicates that if men and women held the same individual characteristics, whether they were paid according to male pay structure, female pay structure or the general (pooled) pay structure, women would be paid about 3/4 of men s pay. Second, when firm characteristics are included, two of the three decomposition methods (male base model and pooled model) produce similar results. In particular, 78 to 82 percent of the gender pay differential remains unexplained, and the adjusted gender pay ratio is around 75 to 77 percent. In contrast, female base model yields a higher unexplained portion (95 percent) and lower adjusted differential (72 percent). Third, it is interesting to note that when female base method is used to decompose the gender pay gap, the unexplained portion decreased from 96.0 percent to 37.5 percent as firm characteristics variables are included. This indicates that if female base model is regarded as the nondiscriminatory pay structure, a substantially additional portion (58.5%) of the gender pay differential could be explained if men were paid through female pay structure after controlling for the firm characteristics, showing that the women are paid less probably because they are sorted to firms that have less payment capacity. 17

19 Next, we focus on the results from the male based-decomposition since it provided the most useful comparison to the existing literature on the China gender pay differential, and also we believe that in Chinese labour markets the male pay structure is more likely to be prevailing and non-discriminatory than the female structure (Hughes and Maurer-Fazio, 2002). We separate our estimation interpretation into two parts: characteristics effects and coefficient effects. Earlier studies have shown that Oaxaca decomposition of wage differentials is not invariant to the choice of reference group when dummy variables are used. In particular, although the sum of the contributions of the single indicator variables, that is, the total contribution of the categorical variables is invariant to the choice of reference group, the detailed coefficients effects attributed to dummy variables are not invariant to the choice of the omitted group (Oaxaca and Ransom, 1999). Changing the reference category not only alters the results for the singly dummy variables but also changes the sum of the coefficients effect of the categorical variables. Several solutions have been proposed to deal with this identification problem, such as Nielsen (2000), Gardeazabal and Ugidos (2005) and Yun (2005). Nielsen s method is not suitable for this particular analysis as it cannot distinguish the constant term from dummy variables and become cumbersome if there are several sets of dummy variables. GU method and Yun s method bear the same idea that is to restrict the coefficients for the single categories to sum to zero, that is, to express effects as deviations from the grand mean. In GU method, the dummy variables are transformed by implementing restricted least squares estimation before model estimation (Gardeazabal and Ugidos, 2005). More conveniently, in Yun s method, standard dummy coding is used for model estimation, and then one can transform the coefficients vectors so that deviations from the grand mean are expressed and the coefficient for the base category is added (Yun, 2005). When these methods are applied to such transformed estimates, the results of the decomposition are 18

20 unaffected by the choice of the reference category. In the following analysis, we use Yun(2005) to apply the transformation of dummy variables sets and report the contribution of a categorical predictor to the unexplained part of the decomposition. The results are shown in table 5. The first panel of table 5 describes the breakdown of the proportions explained by each set of explanatory variables. The characteristics of firm account for more of gender pay differential than the individual characteristics. When both individual and firm characteristics are included, gender differences in individual characteristics account for 9.5 percent and differences in the firm characteristics account for 33.0 percent of the difference in the pay men and women receive. This finding suggests that firm characteristics account for a substantial part of gender pay differential. Further, the concentration of female executives in smaller firms tends to be the driving force behind the explained component of the gender pay gap. About 27.6 percent of the gender pay gap is explained by the firm size, as measured by the registered capital. Other variables that contribute substantially to the gender pay gap are marital status, which explains about 10.7 percent of the gender pay differentials, and business training, which accounts for 12.0 percent. The second panel illustrated the coefficient effects. About half (54.4%) of the total wage differential is due to unobservable differences between men and women or discrimination. In particular, workplace characteristics account for 64.9%, group membership accounts for 55.9%, while the negative sign of individual characteristics seems to indicate that men were actually discriminated on their individual characteristics as a whole. However, further examination shows that although men were treated unfavorably than women with same job tenure and education, women did received discrimination on all other individual characteristics. For example, a large portion (19.8%) of the gender pay differential comes from the different treatment of men and 19

21 women with the same CPP membership status. The discrimination effects on firm characteristics are more obvious. The fact that female leaders managing firms of the same size as males received less pay than male leaders explains 78.3% of the gender pay differential. 20

22 Table 1: Summary Statistics Total Male Female t Value Variable Mean Std. Dev. Mean Std. Dev. Mean Std. Dev. Pay (Unit: 10,000 Yuan) Registered Capital (Unit: 10,000 Yuan) Number of employees/ Age Job tenure President Service Sector Educatoin (less than high school) High School College, University or higher Business Training Married CPP Member Firm Tenure (years since established) Profit as Part of Compensation Obs Note: 21

23 Table 2 Regression of ln(pay) on enterprise characteristics and CEO individual characteristics (1) (2) (3) (4) (5) (6) (7) (8) (9) VARIABLES lnpay lnpay lnpay lnpay lnpay lnpay lnpay lnpay lnpay female 0.325** 0.316** 0.311** 0.304** 0.266** 0.220* 0.227* (0.132) (0.133) (0.134) (0.133) (0.132) (0.128) (0.129) (0.129) (0.129) age * 0.011* 0.012* 0.011* (0.007) (0.007) (0.007) (0.007) (0.007) (0.007) (0.007) (0.007) jobtenure 0.102*** 0.101*** 0.105*** 0.104*** 0.107*** 0.110*** 0.105*** 0.096*** (0.024) (0.024) (0.024) (0.023) (0.023) (0.024) (0.024) (0.024) jobtenure *** 0.003*** 0.003*** 0.003*** 0.003*** 0.003*** 0.003*** 0.003*** (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) married (0.188) (0.186) (0.184) (0.179) (0.180) (0.180) (0.178) party * 0.219* 0.225* 0.218* 0.219* (0.119) (0.121) (0.121) (0.118) (0.119) (0.118) (0.117) high school 0.447*** 0.377** 0.318** 0.313** 0.313** 0.338** (0.163) (0.162) (0.158) (0.159) (0.158) (0.157) two years college 0.444*** 0.293** * (0.144) (0.147) (0.145) (0.145) (0.145) (0.145) business training 0.448*** 0.272** 0.271** 0.261** 0.292*** (0.113) (0.114) (0.114) (0.114) (0.113) lncapital 0.197*** 0.201*** 0.204*** 0.208*** (0.034) (0.035) (0.035) (0.034) service (0.113) (0.113) (0.112) years (0.008) (0.008) (0.008) president 0.218** 0.205* (0.109) (0.108) profit as part of the p 0.546*** (0.151) Constant 2.003*** 1.852*** 1.785*** 1.275*** 1.257*** 0.802** 0.744** (0.061) (0.302) (0.325) (0.363) (0.358) (0.357) (0.364) (0.371) (0.370) Observations R squared Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 22

24 Table 3 Regression of ln(pay) for males and females seperately lnpay lnpay VARIABLES (male) (female) age (0.007) (0.016) jobtenure 0.085*** 0.134** (0.027) (0.067) jobtenure *** (0.001) (0.003) married (0.230) (0.292) party 0.270** (0.130) (0.290) high school 0.325* (0.179) (0.329) college, university or higher ** (0.167) (0.307) business training 0.257** (0.128) (0.244) lncapital 0.221*** 0.158** (0.040) (0.070) service (0.131) (0.223) years (0.009) (0.023) president ** (0.126) (0.223) profit 0.500*** 0.723** (0.167) (0.363) Constant (0.424) (0.739) Observations R squared Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 23

25 Table 4 Decomposition gender wage differential Individual Characteristics Model Raw Differntial "unexplained" Percentage Adjusted Differential Male Pay Structure as Baseline % 74.4% Female Pay Structure as Baseline % 68.8% Pooled % 73.4% Expanded Model Raw Differntial "unexplained" Percentage Adjusted Differential Male Pay Structure as Baseline % 83.4% Female Pay Structure as Baseline % 87.8% Pooled % 84.1% 24

26 Table 5 Gender Pay Gap Attributable to Specific Characteristics Ind. Characteristics Model Expanded Model % of the % of the Explained Pay gap pay gap Pay gap pay gap Individual Characteristics Age Job Tenure Married CPP Membership Education less than high school high school college, university or higher Business Training Firm Characteristics Industry (service) Firm History (years) President Profit as part of the Pay Firm Size Total Explained by individual characteristics by workplace characteristics Unexplained Individual Characteristics Age Job Tenure Married CPP Membership Education less than high school high school college, university or higher Business Training Firm Characteristics Industry (service) Firm History (years) President Profit as part of the Pay Firm Size Total Unexplained by individual characteristics by workplace characteristics constant /group membership

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