AN EMPIRICAL ANALYSIS OF GENDER WAGE DIFFERENTIALS IN URBAN CHINA

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1 Kobe University Economic Review 54 (2008) 25 AN EMPIRICAL ANALYSIS OF GENDER WAGE DIFFERENTIALS IN URBAN CHINA By GUIFU CHEN AND SHIGEYUKI HAMORI On the basis of the Oaxaca and Reimers methods (Oaxaca, 1973; Reimers, 1983), using the China Health and Nutrition Survey (CHNS) questionnaire (2004 and 2006 pooling data) and employing the Heckman two-step procedure for sample selection bias, we provide new estimates for male-female hourly wage differentials in urban China. The results indicate that the hourly wage differentials and the unexplained part of the hourly wage differentials are smaller than the differentials obtained by ignoring the sample selection bias. 1. Introduction In China, women have lower average earnings than men, and the male-female average earnings differentials are widening. 1) According to the 2004 and 2006 pooling data of the China Health and Nutrition Survey (CHNS) 2) questionnaire, in 2005, the average male wage earner in urban China earned 7.21 yuan an hour, while the average female wage earner earned 6.68 yuan an hour, that is, 92.7 percent of male earnings. The lower wages for females can be attributed to factors such as individual characteristics, settlement area, location, occupation, type of work unit, and discrimination. This paper provides answers to the following questions. How much of the hourly wage differential is attributed to each of the abovementioned factors? In particular, how much impact does labor market discrimination have on female hourly wages? Several studies have analyzed gender wage differentials and discrimination in the Chinese labor market (Meng, 1998; Gustafsson and Li, 2000; Liu et al., 2000; Mason et al., 2000; Hughes et al., 2002). Others have focused on wage discrimination between urban residents and rural migrants in China (Meng and Zhang, 2001; Wang and Zuo, 1999; Zhao, 2000). Unlike previous studies, we take into account the possible selectivity bias in order to estimate the female hourly wage function. Thus, we hope to obtain a more accurate and up-to-date measure of labor market discrimination against females. Section 2 introduces the procedures used for our estimations; section 3 presents the data and the definitions of variables; section 4 presents the empirical results; and the final section offers concluding remarks. 2. Empirical Techniques The gross hourly wage differential between males and females can be expressed as 1) Women s Studies Institute of China (WSIC) (2006) indicated that in 1978, the average number of earning females in urban units was 83 percent of that of males; however, it fell to 81.9 percent in ) Source:

2 β mˆ β mˆ β mˆ 26 GUIFU CHEN AND SHIGEYUKI HAMORI W m D = W f W m = 1, (1) W f W f where W m and W f are male and female hourly wages. Using ordinary least squares (OLS) on the standard semilogarithmic hourly wage equation yields - ln W m = X' - ˆ m β m, (2) - ln W f = X' - ˆ f β f, (3) where W is the geometric mean of hourly wages, X' is a vector of the mean values of the regressors, and βˆ is the corresponding vector of coefficients. The gross hourly wage differential can be expressed as ln D = lnw - m ln W - f = X' - m X' - βm ˆ f ˆ β f. (4) Reimers (1983) has shown that this can be written as ln D = lnw - m lnw - f = (X' - m X' - f )[Ω + (I Ω) ˆ ] + [X' - m (I Ω) + X'f - Ω]( ˆ ), (5) β f β f where I is an identity matrix and Ω is a diagonal matrix of weights. Equation (5) decomposes the percentage difference between the geometric means of the observed hourly wage rates for the two groups into two parts: one is due to the differences in the average characteristics of the groups, and the other is due to differences in the parameters of the wage function caused by labor market discrimination and other omitted factors. Following Blinder (1973), who uses I = Ω, and using the male hourly wage structure as the nondiscriminatory norm, equation (5) can yield - ln D = X' ( f ) + (X - m X - βmˆ ˆ f )' β mˆ. (6) β f On the other hand, presenting this using Reimers s (1983) weighting system, which proposes Ω = 0.5I, equation (5) can yield - - ln D = (0.5) (X m + X f )'( - - ) + (0.5) (X m X f )'( + ˆ ). (7) ˆ β f β mˆ β f However, if participation in the wage and salary sector is not random, given one s observed characteristics, the average observed wage is subject to selectivity bias, as are OLS

3 AN EMPIRICAL ANALYSIS OF GENDER WAGE DIFFERENTIALS IN URBAN CHINA 27 estimates of the coefficients of the wage equation. Arguably, working women may not be randomly sampled from the overall female population. If working women self-select, the results from regressions according to the uncorrected wage equation might be biased, as some of the factors that increase the likelihood that a woman will work may also be factors that contribute to making her wages high or low. Heckman (1979) proposed a two-step correction procedure. The first step of this procedure is to specify a participation equation for women in the form of a Probit function. Using this function, a selectivity correction factor, λ, is estimated and is included in the female wage equation as a regressor. This constitutes the second step and yields coefficients that are free from selectivity bias due to the endogenous participation decision. Then, following Reimers (1983), equation (5) can be rewritten as ˆ ˆ - ln D = ln W - m ln W f - = (X' X' - m f ) [Ω β mˆ + (I Ω) ] + [X' - m (I Ω) + X' - f Ω]( β mˆ ) c - ˆf λ. β f β f (8) This implies that the percentage difference between the geometric means of the observed hourly wage rates for the two groups also includes the third part (ĉ f λ ) due to differences in selectivity bias. 3) 3. Data and the Definitions of Variables This paper uses questionnaire data from the CHNS (2004 and 2006) 4) to estimate the male hourly wage function and female hourly wage function corrected for selectivity bias. The questionnaires for CHNS 2004 and 2006 were distributed in 9 provinces, namely, Heilongjiang, Jiangsu, Shandong, Guizhou, Guangxi, Hubei, Henan, Hunan, and Liaoning (18 cities with 216 neighborhoods, and 36 counties with 432 villages). The data used in this paper, on the other hand, are taken from urban household data only. To focus on wage determination in the labor market, we restrict our sample to civilian wages and salaried employees. In accordance with standard practice, we exclude the following from the analysis: employers, self-employed individuals, retirees, students, agricultural workers, members of the armed forces, the disabled, retired employees who were rehired, and male household workers. We also exclude all persons aged 15 or less (China s labor law sets the minimum employment age at 16 years) as well as respondents who provided incomplete information on wage, education, household composition, or others. Our empirical analysis of wages is restricted to the sample of individuals whose wages were positive at the time of the survey. After the exclusions, the sample comprised 2,373 working 3) The third part comprises differences between males selectivity bias and females selectivity bias (ĉ m λ m ĉ f λ f). The sample of males aged doing housework (nonworking men), however, is only 28. Thus, we assume that males selectivity bias is zero. 4) The data for 2003 and 2005 are pooled for our analysis. We also convert the income in 2003 into the income in 2005, taking into account the price increase in each province.

4 28 GUIFU CHEN AND SHIGEYUKI HAMORI individuals (1,387 men and 986 women) between the ages of 16 (school-leaving age) and 55 (state retirement age for women) or 60 (state retirement age for men) all of whom earn wages from a main job and 534 females aged doing housework (nonworking women). In separate survey questions, the respondents are asked to indicate average daily working hours and average weekly working days. The hourly wage rate can be calculated from the annual wage and working hours. The dependent variable used in the wage equations is the log of hourly cash wages earned from the main job. Earnings from secondary jobs and nonmonetary benefits are excluded from the analysis. Main job wages exclude subsidies and bonuses. The survey includes eight categories of education, based on academic degrees. We include three education level dummies (DS1, DS2, and DS3) in hourly wage equations. The duration of job training is not observed in the present data; hence, we cannot control the variable of experience or tenure (years employed in the present job) with a direct measure. Instead, we use age, entered in both linear (AGE) and quadratic forms (Age2). Additional variables include a set of dummies representing the type of minority (FOLK), household registration (HUKOU), marital status (MARRIED), locality (METRO), settlement area (EAST), occupation (TECHN, MANAGER, CRAF, and SEVCL), and the type of work unit (GOVOWN and COLOWN). EAST is set as 1 for Jiangsu and Shandong and as 0 for elsewhere, as the average wages of on-post staff and workers in 2005 were higher in Jiangsu and Shandong (20,957 yuan and 16,614 yuan, respectively) than in the other seven provinces. 5) HUKUO is set as 1 for rural household registrations and as 0 for urban household registrations. However, people who belong to households registered as rural are not rural migrants, but live in the suburban villages of the city. The definitions, means, and standard deviations of these TABLE 1. Results of the Probit analysis on female participation Working women (n = 986) Variable Coefficient t-value dp / dx CONSTANT DS *** DS *** DS *** FOLK ** HUKOU *** AGE *** AGE *** MARRIED *** METRO EAST *** RATE ** Log likelihood Observed number 1520 Note: ***, **, and * indicate statistical significance at the 1, 5, and 10 percent levels, respectively. 5) Source: China Labour Statistical Yearbook 2006

5 AN EMPIRICAL ANALYSIS OF GENDER WAGE DIFFERENTIALS IN URBAN CHINA 29 variables are presented in TABLE A in the Appendix. 4. Empirical Results TABLE 1 presents the results of the Probit analysis. The effects of all education levels are positive and significant, indicating that women with higher education levels are more likely to participate in the labor force. As illustrated in TABLE 1, ethnic minorities are more likely to participate in the labor market. People from households registered as rural and living in outlying suburbs of the cities are less likely to participate in the labor market than people from households registered as urban. As women become older, the probability of participation increases, albeit at a decreasing rate. As expected, the effect of marital status for married women, as compared with single women, has a negative effect on the decision to work. The share of household members younger than 7 years or older than 65 years reduces the probability of participation. This variable is included in the female participation equation, but not in the female hourly wage equation. The results of the hourly wage functions with and without corrections for selectivity bias are presented in TABLE 2. The selectivity bias is negative and significant. This indicates TABLE 2. Results of hourly wage regressions for males and females Males Females, with correction for Females, without selectivity bias correction for selectivity bias Variable Coefficient t-value Coefficient t-value Coefficient t-value CONSTANT *** DS *** ** *** DS *** *** DS *** * FOLK ** * HUKOU ** AGE * AGE MARRIED *** METRO *** *** *** EAST TECHN *** *** *** MANAGER *** *** *** CRAFT *** *** *** SEVCL *** GOVOWN *** *** *** COLOWN ** λ * R N Note: * indicates that variables are significant at the 10 percent level. ** indicates that variables are significant at the 5 percent level. *** indicates that variables are significant at the 1 percent level.

6 30 GUIFU CHEN AND SHIGEYUKI HAMORI that women who have high-wage opportunities, given their observed characteristics, have even better opportunities outside the wage and salary sector and are hence less likely to be included in our wage samples (Reimers, 1983). Since the selectivity bias term is significant, OLS estimates as well as a wage-differential decomposition based on OLS results would be biased. The coefficients of the three education level dummies are positive and significant in the male hourly wage equation; however, only DS1 is positive and significant in the female hourly wage equation corrected for selectivity bias. There are also significant and positive effects on locality (METRO) and occupation (TECHN, MANAGER, and CRAFT). Being a government employee is associated with higher hourly wages, particularly for women. On the other hand, being a male worker in a collective enterprise or a service worker is associated with lower hourly wages, but the coefficient is not significant in the female hourly wage equation. TABLE 3 presents the observed male-female hourly wage differential that can be attributed to the difference in characteristics and that cannot be explained by differences in observed characteristics. The latter, due to differences in the parameters of the wage function, can be attributed to labor market discrimination on the basis of gender and other omitted variables. We show this measure of discrimination using two different sets of weights for adding up the differences in parameters, the average of the characteristics of the female group (Ω = I ) and the average of the two (Ω = 0.5I), and applying them to the corrected and non-corrected hourly wage equations. The results obtained without correcting the sample selection bias indicate that discrimination accounts for a much higher percentage of the gender hourly wage differential than do differences in the characteristics between men and women. Conversely, the latter is higher than the former for the corrected sample selection bias. The degree of discrimination varies according to the method of decomposition and between corrected and non-corrected samples. When the corrected regressions are not considered, the discrimination accounts for and percent. The results are similar with previous studies of gender wage differentials in China (Liu et al., 2000; Cai et al., 2005). However, when the corrected regressions are considered, the degrees of discrimination against women become only percent using TABLE 3. Results of the decompositions With correction for sample selection bias: Oaxaca method With correction for sample selection bias: Reimers method Without correction: Oaxaca method Without correction: Reimers method Total estimated differential Endowment differences Wage discrimination Percentage due to endowments Percentage due to discrimination Note: 1. lnw m lnw f + cˆ f λ 2. lnw m lnw f 3. (X m X f )' βˆ m 4. (0.5) (X m X f )'( βˆ m + βˆ f ) 5. X f '( βˆ m βˆ f ) 6. (0.5) (X m + X f )'( βˆ m βˆ f )

7 AN EMPIRICAL ANALYSIS OF GENDER WAGE DIFFERENTIALS IN URBAN CHINA 31 TABLE 4. Sources of hourly wage differentials due to characteristics and discrimination in the corrected sample Oaxaca explained Oaxaca unexplained Reimers explained Reimers unexplained Hourly wage differential Of which: Due to: DS DS DS FOLK HUKOU AGE AGE MARRIED METRO EAST TECHN MANAGER CRAFT SEVCL GOVOWN COLOWN the Oaxaca method and percent using Reimers method. This suggests that the results of previous studies overestimated the degree of discrimination because of biased coefficients arising from the selection of a nonrandom sample. The impact of the independent variables on the explained and unexplained parts of the hourly wage functions is presented in Table 4. The results obtained using the Oaxaca method indicate that occupation (TECHN, MANAGER, and SEVCL) accounts for a large share of the difference in characteristics between men and women, that is, approximately On the other hand, the type of work unit (GOVOWN) is also higher at approximately With respect to the unexplained part, the signs are positive for all education levels, and the total is This is interpreted in the sense that education contributes to discrimination in China; this result is converse with other transition countries (Arabsheibani, 1999). The share of working women in professional schools or higher; technical or vocational degrees; and upper middle school degrees are 34.48, 18.05, and percent, respectively and are higher than those of working men (28.7, 15.07, and percent, respectively). However, the share of working women is and 7.4 percent in high-wage occupations, namely, TECHN and MANAGER, respectively, and percent in low-wage occupations, namely, SEVCL. On the other hand, the share of working men is 34.82, 12.55, and 8.29 percent in TECHN, MANAGER, and SEVCL, respectively. Moreover, the share of working women in high-wage work units (GOVOWN) (50.2 percent) is lower than that of men (63.45 percent). Cai et al. (2005) indicate the following results. The share of working women in the high-wage sector increases by approximately 10 percent when female workers are treated on par with male workers. Conversely, the share of working men in the high-wage sector decreases

8 32 GUIFU CHEN AND SHIGEYUKI HAMORI by approximately 10 percent when male workers receive the same treatment as female workers. Discrimination against females entering the high-wage sector contributes to gender wage differentials. In this paper, the results indicate that gender wage differentials will be narrowed down if the share of working women increases in high-wage occupations and high-wage work units and decreases in low-wage occupations. 5. Some Concluding Remarks This paper provides answers to the following questions. How much of the hourly wage differentials is attributed to individual, regional and industry specific factors? In particular, how much impact does labor market discrimination have on female hourly wages? Based on the Oaxaca and Reimers methods (Oaxaca, 1973; Reimers, 1983), using the CHNS questionnaire (2004 and 2006 pooling data) and employing the Heckman two-step procedure for sample selection bias, we provide new estimates for male-female hourly wage differentials in urban China. First, the results indicate that when the corrected regressions are considered, the gender hourly wage differential (0.0686) is smaller than the differential obtained by neglecting the sample selection bias (0.1629). Second, the results indicate that when the corrected regressions are not considered, discrimination accounts for using the Oaxaca method and percent using the Reimers method. However, when the corrected regressions are considered, the degree of discrimination against women becomes only percent using the Oaxaca method and percent using the Reimers method. This suggests that the results of previous studies overestimated the degree of discrimination because of biased coefficients caused by the selection of a nonrandom sample. Finally, the results indicate that the gender wage differentials will be narrowed down if discrimination against females entering the high-wage sector is eliminated by means of increasing the share of working women in high-wage occupations and high-wage work units and decreasing their share in low-wage occupations.

9 AN EMPIRICAL ANALYSIS OF GENDER WAGE DIFFERENTIALS IN URBAN CHINA 33 Appendix TABLE A. Definitions and mean of variables Variable Definition Male Female LogWage Natural logarithm of hourly wages (0.6426) (0.7533) DS1 1 for professional school (three-year college) or higher, 0 for others (0.4525) (0.4756) DS2 1 for technical or vocational degree, 0 for others (0.3579) (0.3848) DS3 1 for upper middle school degree, 0 for others (0.3845) (0.3873) FOLK 1 for Han, 0 for others (0.2576) (0.2335) HUKOU 1 for households registered as rural, 0 for households registered as urban ( ) ( ) AGE Age in years (9.9592) (8.8212) AGE2 Age squared ( ) ( ) MARRIED 1 for married, 0 for others (0.3322) (0.3340) METRO 1 for metropolitan, 0 for others (0.4786) (0.4869) EAST 1 for Jiangsu and Shandong, 0 for others (0.2965) (0.3199) TECHN 1 for technicians, 0 for others (0.4766) (0.4581) MANAGER 1 for managers, 0 for others (0.3314) (0.2620) CRAFT 1 for craft workers, 0 for others (0.3455) (0.3753) SEVCL 1 for service workers, 0 for others (0.2759) (0.4324) GOVOWN 1 for workers in government-owned enterprises or organizations, 0 for others (0.4818) (0.5003) COLOWN 1 for workers in collective enterprises, 0 for others (0.2926) (0.3137) RATE The share of household members who are younger than 7 years or older than 65 years (0.1561) λ Inverse of Mill s ratio, predicted from Probit equation using all observations of females (0.3757) No. of (including nonworking women) observations (excluding nonworking women) 986 Note: Numbers in parentheses are standard deviations.

10 34 GUIFU CHEN AND SHIGEYUKI HAMORI Research Associate, Graduate School of Economics, Kobe University Professor, Graduate School of Economics, Kobe University REFERENCES Arabsheibani, G. R. and Lau, L. (1999), Mind the gap: an analysis of gender wage differentials in Russia, Labour, 13, pp Blinder, A. S. (1973), Wage discrimination: reduced form and structural estimates, Journal of Human Resources, 8(4), pp Cai, F., Du, Y. and Wang, M. (2005), How Close is China to a Labor Market? The Commercial Press (in Chinese). Gustafsson, B. and Li, S. (2000), Economic transformation and the gender earnings gap in urban China, Journal of Population Economics, 13, pp Heckman, J. J. (1979), Sample selection bias as a specification error, Econometrica, 47, pp Hughes, J. and Maurer-Fazio, M. (2002), Effects of marriage, education, and occupation on the female/male wage gap in China, Pacific Economic Review, 7, pp Liu, W., Meng, X. and Zhang, J. (2000), Sectoral gender wage differential and discrimination in the transitional Chinese economy, Journal of Population Economics, 13, pp Mason, A., Scott, R. and Zhang, L. (2000), Gender wage gaps in post-reform rural China, CCAP s Working Paper Series, No.WP-00-E25, Chinese Academy of Sciences. Meng, X. (1998), Male-female wage determination and gender wage discrimination in China s rural industrial sector, Labour Economics, 29, pp Meng, X. and Zhang, J. (2001), The two-tier labour market in urban China: occupational segregation and wage differential between urban residents and rural migrants in Shanghai, Journal of Comparative Economics, 5, pp National Bureau of Statistics of China (2004, 2005 and 2006), China Statistical Yearbook, China Statistics Press. Oaxaca, R. L. (1973), Male-female wage differentials in urban labour markets, International Economic Review, 14, pp Reimers, C. W. (1983), Labor market discrimination against hispanic and black men, The Review of Economics and Statistics, 65(4), pp Wang, F. and Zuo, X. (1999), History s largest labour flow: understanding China s rural migration inside China s cities: institutional barriers and opportunities for urban migrants, American Economic Review, Papers and Proceedings, 89, pp Women s Studies Institute of China (WSIC) (2006), Report on Gender Equality and Women Development in China: 1995~2005, Social Sciences Academic Press (in Chinese). Zhao, Y. (2000), Rural-to-urban labour migration in China: the past and the present, Rural Labour Flows in China, Berkeley, University of California Press, pp

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