Gender earnings gaps in Hong Kong: Empirical evidence from across the earnings distribution in 2006

China Economic Review 22 (2011) 151–164

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China Economic Review

Gender earnings gaps in Hong Kong: Empirical evidence from across the earnings distribution in 2006☆ Yuhao GE a, Hongbin LI b, Junsen ZHANG c,⁎ a b c

School of Labor and Human Resources, Renmin University of China, Beijing, China Department of Economics, School of Economics and Management, Tsinghua University, Beijing, China Department of Economics, Chinese University of Hong Kong, Shatin, Hong Kong, China

a r t i c l e

i n f o

Article history: Received 7 August 2008 Received in revised form 25 October 2010 Accepted 26 October 2010 Available online 31 October 2010 JEL classification: J16 J31 J71 Keywords: Gender earnings gaps Glass ceiling effect Sticky floor effect Occupational segregation Quantile decomposition

a b s t r a c t This paper analyzes gender earnings gaps in Hong Kong using the 2006 by-census data. To explore the sources of gender earnings gaps, we decompose the gaps using the method proposed by Machado and Mata (2005). We have three major findings. First, gender earnings gaps are larger both in lower positions and in higher positions in the earnings distribution. Both the “glass ceiling effect” and the “sticky floor effect” exist in Hong Kong. Second, gender earnings gaps in higher positions are much explained by gender differentials in productivity-related characteristics; however, gender earnings gaps in lower positions are barely explained by these characteristics. Third, the effect of occupational segregation on gender earnings gaps depends on specific positions in the earnings distribution. In lower positions, occupational segregation is not a big problem and has little impact on gender earnings gaps; in higher positions, however, occupational segregation favors male workers and enlarges gender earnings gaps. © 2010 Elsevier Inc. All rights reserved.

1. Introduction Labor economists have conducted many studies on gender earnings gaps (e.g., Oaxaca, 1973; Blau and Kahn, 1997, 2000, 2006; Zhang et al., 2008). There is also surging interest in examining gender earnings gaps across an earnings distribution. This kind of analysis can provide more information that may be hidden in the mean-level analysis of gender earnings gaps. It can help us test whether gender earnings gaps are larger in higher positions or in lower positions—the “glass ceiling effect” and the “sticky floor effect,”1 respectively. More interesting results will be found if we explore gender earnings gaps from the perspective of earnings distribution. For example, Albrecht et al. (2003) found that gender wage gaps in Sweden in higher positions in the wage distribution are much larger than those in the US. However, at mean-level analysis, the gap is lower than in the US. Rica et al. (2008) found that gender wage gap patterns are totally different among high-level and low-level education groups. In the former group, gender wage gaps are larger in higher positions in the wage distribution; in the latter, gender wage gaps are larger in lower ☆ We wish to thank the editor and the two anonymous referees for their valuable comments and suggestions. Junsen Zhang would like to thank the Hong Kong Research Grants Council (CUHK 4006-PPR-2) for the financial support. The authors bear responsibility for any errors. Hongbin Li acknowledges supports from the National Natural Science Foundation of China (Project ID 71025004) and the China Data Center of Tsinghua University. ⁎ Corresponding author. E-mail address: [email protected] (J. Zhang). 1 The “glass ceiling effect” often refers to the barrier to further advancement once women have attained a certain level. In contrast, the “sticky floor effect” often refers to the situation where women face more severe working conditions when they first enter the labor market. Within the literature of gender earnings gaps, the “glass ceiling effect” means that earnings gaps are much larger among highly paid workers, while the “sticky floor effect” connotes that earnings gaps are much larger among lowly paid workers. 1043-951X/$ – see front matter © 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.chieco.2010.10.002

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Y. Ge et al. / China Economic Review 22 (2011) 151–164

positions. However, at mean-level analysis, gender wage gaps between these two groups show little differences. Thus, if we only focus on mean-level analysis, this information will remain hidden. These two studies indicate that, to understand gender earnings gaps thoroughly, extending the mean-level analysis to distributional-level analysis is necessary. Hong Kong provides a good environment for gender-related research. On one hand, Hong Kong is a developed economy [see Suen and Chan (1997) for an early but comprehensive review of Hong Kong's labor market]. Like other developed economies, there are many groups and organizations that call for the recognition of women's rights, and the government has also promulgated a number of regulations (e.g., the Sex Discrimination Ordinance) to prevent sex discrimination in Hong Kong. On the other hand, Hong Kong is greatly influenced by traditional Confucian thoughts. In Confucian philosophy, women are usually engaged in household affairs. Even if women can work outside the household, they are often discriminated against in the labor market in terms of occupations and wages. Thus, Hong Kong serves as a good example in illustrating how “modern” and “traditional” forces interact to affect gender earnings gaps. Overall, we expect gender earnings gaps in Hong Kong to be similar to those in developed economies. Several papers have examined gender earnings gaps in Hong Kong, including Lui and Suen (1993, 1994), Suen (1995), and Sung, Zhang, and Chan (2001). Most of these studies examined gender wage gaps at the mean.2 As we have discussed, although a mean-level analysis is useful, it is not appropriate for testing whether a glass ceiling effect or a sticky floor effect exists. Hong Kong's labor market also has its own features that require us to examine gender gaps across the earnings distribution. First, the transition of Hong Kong's economy from manufacturing to service may affect female workers in two diverging directions. Females with higher quality (also with higher earnings) may benefit from the transition, but females with lower quality may be hurt by the transition. According to Fan and Lui (2003), women have a comparative advantage in working in the service sectors because physical strength is less important in these sectors. Based on this, they predicted that gender earnings gaps would be smaller during the transition. However, such prediction may not be true for female workers with lower quality. They quit the manufacturing sector only to be forced to work in lowly-paying service sectors, such as in the provision of cleaning and domestic help services. Compared with their male counterparts, their earnings tend to be reduced. To assess separately the effect of economic transition on female workers with lower quality and those with higher quality, it is necessary to examine gender gaps across the earnings distribution. Second, immigration may have more adverse effects on females with lower quality than those with higher quality. Hong Kong is an immigrant society. As mentioned by Lam and Liu (2002), the present immigration policy emphasizes family reunions, and many wives are permitted to enter Hong Kong because of cross-boundary marriages.3 Generally, these wives have low skills and are usually employed in low-wage sectors, which may produce a “crowding effect” for local female workers working in these sectors. Thus, after considering immigration, gender gaps may be wider in lower positions in the earnings distribution. Third, earnings inequality is very high in Hong Kong, both for male and female workers. Based on the 2006 by-census data, the Gini coefficients of monthly earnings for male workers and female workers were 0.451 and 0.457, respectively; both were higher than the international yellow line of 0.40. Based on this high variability, it is reasonable to expect that both the extent and the driving forces of gender earnings gaps will vary with different positions in the earnings distribution. In this paper, we analyze gender earnings gaps across the earnings distribution using the 2006 by-census data. To do so, we first present the stylized facts on gender earnings gaps across the earnings distribution. Thereafter, we decompose the gaps using the method proposed by Machado and Mata (2005)4 (which we will call “MM method” hereafter). We have three primary findings. First, gender earnings gaps are larger both in lower positions and in higher positions in the earnings distribution. Thus, both the “glass ceiling effect” and the “sticky floor effect” exist in Hong Kong. Second, gender earnings gaps in lower positions cannot be explained by gender differences in productivity-related characteristics. However, gender earnings gaps in higher positions are much explained by gender differences in characteristics. This implies that females in lower positions in the earnings distribution may face more serious discrimination compared with females in higher positions. Thus, policies aimed at gender equality should pay more attention to the “sticky floor” rather than to the “glass ceiling.” Third, the effect of occupational segregation on gender earnings gaps may depend on specific positions in the earnings distribution. In lower positions, occupational segregation is not a big problem and has little impact on gender earnings gaps. At higher positions, on the other hand, occupational segregation greatly favors male workers and enlarges gender earnings gaps. If we focus solely on mean-level analysis, we may find a misleading result that occupational segregation explains little about gender earnings gaps. The rest of this paper is organized as follows: Section 2 describes the data used in this paper and presents some basic stylized facts on gender earnings gaps in Hong Kong; Section 3 provides a brief introduction on the methodology used; Section 4 presents the major empirical results of the paper; and Section 5 presents the conclusions. 2. Data, Variables, and Some Stylized Facts This paper uses Hong Kong's 2006 by-census data (5% sampling of the population). The following individual level variables are of particular interest: monthly earnings, education, age, industry, occupation, residence, and birthplace. There are four variables to describe monthly earnings, namely, monthly income from the main employment, monthly income from other employments, other cash income, and total personal income from all employments. The first variable is used in this paper. Due to the lack of information on working hours, we cannot obtain hourly wages from monthly earnings, and we cannot distinguish part-time workers from full-time 2 The study by Lui and Suen (1994) is an exception. Using 1986 by-census data, they presented evidence that gender earnings gaps are wider among highly paid workers. 3 Many local men in Hong Kong are married to women from Mainland China. According to Hong Kong's immigration policy, these women can apply to become permanent residents of Hong Kong. 4 Albrecht et al. (2009) proved the consistency and asymptotic properties of this method. There are other methods to conduct wage decomposition across different quantiles, such as Melly's Melly (2005).

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workers. Original information on education is a categorical variable of educational level. We transform it into years of education according to Hong Kong's educational system. There are many small items under the industry and occupation variables, and we integrate them into 13 industries and 9 occupations, respectively. Immigrant information is formed on the information of “birthplace” and “place of residence five years ago.” If a person's birthplace is Hong Kong, he/she is treated as a local resident. If a person's birthplace is Mainland China and his/her place of residence five years ago is also Mainland China, he/she is treated as a new immigrant. If a person's birthplace is Mainland China and his/her place of residence five years ago is Hong Kong, he/she is treated as an old immigrant. To ensure that all persons in the sample are economically active and to improve the robustness of the results, we choose our estimation sample as follows. First, we restrict the age span to 15–65 years. In Hong Kong, working in the labor market before the age of 15 is illegal. Second, we exclude self-employed persons. Their earnings cannot be defined consistently with employees' earnings. Third, we exclude persons who were born neither in Hong Kong nor in China Mainland. This treatment ensures that persons in the sample share the same Chinese traditional culture. Table 1 shows summary statistics by gender. The gender earnings gap at the mean is 0.233 (males' log earnings are 9.439 and those of females are 9.206), which is less than what was given by Suen (1995) for year 1986 (0.34) and 1991 (0.30). It is clear to see that gender earnings gaps have been declining over the years. Fig. 1 presents the gender earnings differentials in log points at different percentiles in the earnings distribution. For all the percentiles, the earnings of males are higher than those of females. The largest gender earnings gap is 0.442 log points at the 97th percentile, and the smallest gender earnings gap is 0.105 log points at the 45th percentile. As mentioned earlier, the mean gender earnings gap is 0.233 log points. Thus, it is clear that considerable variations will remain unnoticed if focus is given solely to mean-level gender earnings gaps. After further exploration, it can also be seen that gender earnings gaps are higher both in lower positions and in higher positions. According to the literature, larger gender earnings gaps in higher positions are evidence of the “glass ceiling effect,” while larger gender earnings gaps in lower positions are evidence of the “sticky floor effect.” Thus, it appears that both the “glass ceiling effect” and the “sticky floor effect” exist in Hong Kong's labor market. There are many studies in the literature discussing the “glass ceiling effect” and the “sticky floor effect.” Albrecht et al. (2003) and Rica et al. (2008) found the “glass ceiling effect” in Sweden and Spain. Kee (2006) found the same effect in the private sectors of Australia. Arulampalam et al. (2007) found the “glass ceiling effect” in most European countries and the “sticky floor effect” only in a few European countries. According to Pham and Reilly (2007), however, neither the “glass ceiling effect” nor the “sticky floor effect” is found in Vietnam. Interestingly, our findings for Hong Kong are different from all these previous studies, as both the “glass ceiling effect” and the “sticky floor effect” exist in Hong Kong. In the following sections, we will explore whether the “glass ceiling effect” and the “sticky floor effect” originate from similar causes. Table 1 Summary statistics of male and female samples. Year

Ln(Earnings) Education years Age Industry Agriculture, fishing Food, leather Plastic, wood, paper Metal, machinery Electricity, gas, water Construction Wholesale, retail Transportation, storage Communication Finance, insurance, real estate Government services Recreational cultural services Personal services Occupation Administrators, managers Professionals Associate professionals Clerks Service workers Sale and transport workers Craft and related workers Machine operators and assemblers Elementary occupations Immigrant New immigrant Old immigrant Number of observations Source: Computed from the 2006 Hong Kong By-Census Data.

2006 Male

Female

9.439 11.358 39.891

9.206 11.496 37.476

0.002 0.032 0.033 0.044 0.007 0.118 0.248 0.131 0.023 0.188 0.122 0.022 0.030

0.001 0.046 0.022 0.029 0.002 0.015 0.337 0.057 0.013 0.176 0.239 0.023 0.040

0.135 0.072 0.160 0.099 0.096 0.064 0.145 0.093 0.135

0.075 0.059 0.192 0.298 0.098 0.092 0.018 0.013 0.155

0.020 0.281 82064

0.032 0.267 65519

Y. Ge et al. / China Economic Review 22 (2011) 151–164

.4 .3 .2 .1

gender earnings gap

.5

154

0

20

40

60

80

100

quantile Fig. 1. Log gender earnings gaps across earnings distribution. Note: Gender earnings gaps are wider both in lower and in higher positions.

Table 1 also shows that the education of females is slightly higher than that of males. It may be because females with low-level education choose not to participate in the labor market. This suggests that education itself cannot be the driving force behind gender earnings gaps. Table 1 also shows that the average age of males is higher than that of females. In this study, we use potential experience (age-education years: –6) in the regression of earnings function. Higher age means higher potential experience. Moreover, female labor market activities are sometimes interrupted by childbirth. As such, the real working experience of females is smaller than that of males even if their ages are the same. Less experience (potential or real) for females may be one of the explanations behind gender earnings gaps. Based on Table 1, there is a large gender difference in employment in different industries in Hong Kong. For example, compared with males, females are more likely to work in “wholesale and retail” and “government services” but are less likely to work in “construction” and “transportation and storage.” Similarly, there is a large gender difference in occupations. For example, the proportion of females being “clerks” is much larger than that of males, and the proportions of “administrators and business managers” and “craft and related workers” are much lower. Gender differences in industries and occupations seem consistent with gender comparative advantages. The large differences between males and females in industries and occupations prompt us to explore whether these differences are likely explanations for gender earnings gaps. Finally, Table 1 shows that the ratio of females who are recent immigrants is larger than that of males, reflecting the immigration policy that emphasizes family reunions. As mentioned above, many female immigrants are wives from Mainland China. In the labor market, they usually compete with local female workers who have lower skills. 3. Methodology In examining gender earnings gap at the mean, the Oaxaca–Blinder decomposition method (Oaxaca, 1973; Blinder, 1973) is sufficient for the analysis. However, in examining gender earnings gaps across the whole earnings distribution, the Oaxaca–Blinder decomposition method does not apply, and thus we need a new method. In this paper, we use the method proposed by Machado and Mata (2005). Similar to the Oaxaca–Blinder type decomposition, the MM method also decomposes the gender earnings gap into two components: one is due to the differences in the labor market characteristics and the other is due to the differences in the returns to worker characteristics. The MM method is widely used in the literature (e.g., Albrecht et al., 2003; Ganguli and Terrell, 2005; Blau and Kahn, 2006; Kee, 2006; Bargain et al., 2009; Rica et al., 2008). To apply it, the following three steps are required: running quantile regression, obtaining marginal distribution, and conducting counterfactual analyses. 3.1. Running quantile regression The quantile regression model was formally introduced by Koenker and Bassett (1978). The general quantile regression model is given as Qθ ð yjxÞ = x′βθ : In this paper, y is the log of monthly earnings, and x includes education, potential experience (and its squares), industry dummies, occupation dummies, and immigrant dummies.5 5 Occupational selection bias is an important issue that one should seriously consider (see, e.g., Buchinsky, 1998). The major difficulty we have is the absence of a plausible instrumental variable or exclusion restriction for identification, especially given the monthly earnings that we are looking at. Any variable that could affect participation would also potentially affect monthly earnings. Like some other studies, we are thus forced to ignore the problem.

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The coefficient vector βθ (depending on θth quantile) can be obtained by minimizing: n

∑ jyi −xi′βθ j ½θI ðyi N xi′βθ Þ + ð1−θÞIðyi ≤xi′βθ Þ

i=1

where n is the size of the sample, and I(.) is the usual indicator function. Choosing a different θ will result in a different series of coefficients. Based on these coefficients, a conditional earnings distribution will be obtained. However, a marginal earnings distribution rather than a conditional earnings distribution is needed to conduct counterfactual analyses. 3.2. Obtaining marginal distribution There are several methods to obtain a marginal distribution from the conditional quantile regression coefficients. Machado and Mata (2005) performed the following (as illustrated using the female sample): a b c d

Draw random samples (size m) from the uniform distribution U[0,1], and denote them as u1,u2 ⋯ um. f ˆ ðui Þ,i = 1, 2 ⋯ m. Run quantile regressions based on u1,u2 ⋯ um using the female sample, and obtain m series quantile coefficients β Draw a random subsample of females with a size of m with replacement, and denote the new subsample as {xi*(f)}, i = 1, 2 ⋯ m. Combining the coefficients from step b and the subsample from step c, we obtain the desired marginal distribution, f ˆ ðui Þgm . y ð f Þ≡fx ð f Þ β i

i

i = 1

Generally, the precision of decomposition results depends on the size of m. A larger m produces better results, although the computational cost rises with the size of m. In our paper, we set m = 4000 (Machado and Mata (2005) set m = 4500). 3.3. Conducting counterfactual analyses In this paper, two kinds of counterfactual analyses are performed. One is based on returns to worker characteristics, such as what gender earnings gaps would be if females were paid like males; the other is based on characteristics, such as what gender earnings gaps would be if the characteristics of females were distributed like those of males. The former is easier, as the only thing required is to replace the quantile regression coefficients of females with the quantile regression coefficients of males in the aforementioned steps. The method used in dealing with the counterfactual analysis of characteristics in this paper is similar to the MM method. The distribution of a specific characteristic of the male subsample (size of m) is used to replace the corresponding distribution of the female subsample (size of m). For example, if we want to separate the effect of gender differences in the occupational distribution, we first draw a random subsample (size of m) from the male population, and then substitute the occupation information of males for the occupation information of females. When the required counterfactual distributions are ready, the next step is to decompose the gender earnings differential, which is described as6  
m m m m f f f f fm β1 ; β2 ; X1 ; X2 −ff β1 ; β2 ; X1 ; X2

ð1Þ

where fj(.)(j = m, f)denotes the earnings distribution of males and females, respectively; βj1, βj2(j = m, f)denote the returns to characteristics; and Xj1, Xj2(j = m, f)denote productivity-related characteristics.7 Eq. (1) can be decomposed as        

m m m m m m f f m m f f f f f f m m m f f f f fm β m + ff β1 ; β2 ; X1 ; X2 −ff β1 ; β2 ; X1 ; X2 1 ; β2 ; X1 ; X2 −ff β1 ; β2 ; X1 ; X2 = fm β1 ; β2 ; X1 ; X2 −ff β1 ; β2 ; X1 ; X2 |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl} |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl} ð1Þ

ð2Þ

ð2Þ The first part denotes the gender earnings gap caused by gender differences in characteristics, while the second part denotes the gender earnings gap caused by gender differences in returns to characteristics. Similar to the Oaxaca–Blinder decomposition, the second part is often regarded as the discrimination effect, although it can also reflect unmeasured productivity differences between males and females. To isolate the effect of specific characteristics and coefficients, we can further decompose the foregoing equation as follows:        

m m m m m m f m m m f m m m f f m m m m m f f fm β m + ff β1 ; β2 ; X1 ; X2 −ff β1 ; β2 ; X1 ; X2 1 ; β2 ; X1 ; X2 −ff β1 ; β2 ; X1 ; X2 = fm β1 ; β2 ; X1 ; X2 −ff β1 ; β2 ; X1 ; X2 |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl} |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl} ð3Þ

ð4Þ

ð3Þ 6 7

Here we use the predicted gender earnings differential at different quantiles. If we use the raw earnings differential, there would be a residual term. For simplicity, only two characteristics are considered for illustration.

156

Male Education Experience Experience2 Agriculture, fishing Food, leather Plastic, wood, paper Metal, machinery Electricity, gas, water Construction Wholesale, retail Transportation, storage Communication Finance, insurance, real estate

10 0.0452*** (0.002) 0.0553*** (0.001) −0.00092*** (0.000) −0.088 (0.089) 0.204*** (0.030) 0.183*** (0.029) 0.193*** (0.027) 0.503*** (0.048) 0.109*** (0.023) 0.0703*** (0.023) 0.213*** (0.024) 0.213*** (0.032) 0.235*** (0.024)

25 0.0470*** (0.001) 0.0505*** (0.001) −0.00080*** (0.000) −0.029 (0.058) 0.184*** (0.019) 0.162*** (0.019) 0.164*** (0.018) 0.448*** (0.031) 0.118*** (0.015) 0.0768*** (0.015) 0.205*** (0.016) 0.182*** (0.021) 0.202*** (0.015)

50 0.0506*** (0.001) 0.0518*** (0.001) −0.00077*** (0.000) 0.0954** (0.045) 0.105*** (0.015) 0.108*** (0.014) 0.121*** (0.014) 0.361*** (0.024) 0.111*** (0.012) 0.0456*** (0.011) 0.183*** (0.012) 0.169*** (0.016) 0.151*** (0.012)

75 0.0544*** (0.001) 0.0555*** (0.001) −0.00078*** (0.000) 0.060 (0.049) 0.0847*** (0.016) 0.0816*** (0.016) 0.0911*** (0.015) 0.236*** (0.026) 0.0813*** (0.013) 0.0311** (0.012) 0.158*** (0.013) 0.160*** (0.017) 0.131*** (0.013)

Female 90 0.0531*** (0.001) 0.0587*** (0.001) −0.00078*** (0.000) 0.039 (0.072) 0.0854*** (0.024) 0.0823*** (0.023) 0.0833*** (0.022) 0.169*** (0.038) 0.0459** (0.019) 0.006 (0.018) 0.121*** (0.020) 0.110*** (0.025) 0.109*** (0.018)

mean 0.0528*** (0.001) 0.0556*** (0.001) −0.00082*** (0.000) 0.041 (0.048) 0.127*** (0.016) 0.130*** (0.015) 0.125*** (0.015) 0.346*** (0.025) 0.1000*** (0.013) 0.0473*** (0.012) 0.187*** (0.013) 0.174*** (0.017) 0.189*** (0.012)

10 0.0610*** (0.002) 0.0456*** (0.001) −0.00079*** (0.000) 0.357** (0.149) 0.261*** (0.032) 0.215*** (0.039) 0.236*** (0.036) 0.369*** (0.106) 0.239*** (0.044) 0.111*** (0.025) 0.278*** (0.030) 0.158*** (0.047) 0.284*** (0.027)

25 0.0593*** (0.001) 0.0407*** (0.001) −0.00067*** (0.000) 0.341*** (0.089) 0.221*** (0.019) 0.199*** (0.022) 0.215*** (0.021) 0.386*** (0.062) 0.216*** (0.025) 0.152*** (0.014) 0.268*** (0.017) 0.263*** (0.027) 0.295*** (0.015)

50 0.0629*** (0.001) 0.0426*** (0.001) −0.00064*** (0.000) 0.273*** (0.072) 0.119*** (0.015) 0.0938*** (0.018) 0.0921*** (0.017) 0.284*** (0.051) 0.159*** (0.021) 0.0733*** (0.012) 0.158*** (0.014) 0.190*** (0.022) 0.185*** (0.012)

75 0.0667*** (0.001) 0.0470*** (0.001) −0.00064*** (0.000) 0.265*** (0.081) 0.0734*** (0.017) 0.0592*** (0.020) 0.024 (0.019) 0.261*** (0.056) 0.133*** (0.023) 0.0302** (0.013) 0.140*** (0.016) 0.148*** (0.024) 0.161*** (0.014)

90 0.0652*** (0.001) 0.0514*** (0.001) −0.00067*** (0.000) 0.230** (0.100) 0.0917*** (0.021) 0.0606** (0.025) (0.005) (0.023) 0.219*** (0.068) 0.141*** (0.028) 0.0331** (0.016) 0.171*** (0.020) 0.159*** (0.030) 0.200*** (0.017)

Mean 0.0628*** (0.001) 0.0446*** (0.001) −0.00065*** (0.000) 0.299*** (0.073) 0.146*** (0.015) 0.134*** (0.018) 0.113*** (0.017) 0.292*** (0.051) 0.194*** (0.020) 0.0741*** (0.012) 0.210*** (0.014) 0.200*** (0.022) 0.234*** (0.012)

Y. Ge et al. / China Economic Review 22 (2011) 151–164

Table 2 Quantile log earnings regression results for males and females.

Government services Recreational cultural services Administrators, managers Professionals Associate professionals Clerks Service workers Sale and transport workers Craft and related workers

New immigrant Old immigrant Constant

0.313*** (0.016) 0.029 (0.021) 1.059*** (0.011) 0.994*** (0.013) 0.666*** (0.010) 0.378*** (0.010) 0.510*** (0.011) 0.398*** (0.012) 0.399*** (0.009) 0.341***

0.411*** (0.012) 0.0479*** (0.016) 1.193*** (0.008) 1.051*** (0.010) 0.709*** (0.007) 0.366*** (0.008) 0.540*** (0.008) 0.438*** (0.009) 0.382*** (0.007) 0.315***

0.377*** (0.013) 0.0774*** (0.018) 1.323*** (0.008) 1.154*** (0.010) 0.731*** (0.008) 0.325*** (0.009) 0.459*** (0.009) 0.431*** (0.010) 0.340*** (0.008) 0.262***

0.305*** (0.019) 0.0555** (0.026) 1.560*** (0.013) 1.382*** (0.016) 0.831*** (0.012) 0.308*** (0.013) 0.390*** (0.014) 0.477*** (0.015) 0.281*** (0.013) 0.222***

0.316*** (0.013) 0.0451*** (0.017) 1.243*** (0.008) 1.159*** (0.010) 0.769*** (0.008) 0.389*** (0.008) 0.518*** (0.009) 0.461*** (0.010) 0.386*** (0.008) 0.325***

0.139*** (0.026) −0.186*** (0.038) 1.204*** (0.026) 1.189*** (0.028) 0.893*** (0.021) 0.613*** (0.019) 0.343*** (0.020) 0.312*** (0.022) 0.320*** (0.036) 0.215***

0.263*** (0.015) −0.018 (0.022) 1.146*** (0.014) 1.030*** (0.016) 0.756*** (0.012) 0.490*** (0.010) 0.316*** (0.012) 0.328*** (0.013) 0.285*** (0.021) 0.144***

0.271*** (0.012) −0.008 (0.018) 1.239*** (0.011) 1.067*** (0.012) 0.812*** (0.009) 0.482*** (0.008) 0.377*** (0.009) 0.400*** (0.010) 0.321*** (0.017) 0.184***

0.271*** (0.013) 0.029 (0.020) 1.380*** (0.012) 1.123*** (0.014) 0.857*** (0.010) 0.487*** (0.009) 0.460*** (0.010) 0.461*** (0.011) 0.351*** (0.019) 0.224***

0.241*** (0.016) 0.0656*** (0.025) 1.637*** (0.015) 1.318*** (0.017) 0.952*** (0.012) 0.501*** (0.011) 0.560*** (0.012) 0.521*** (0.014) 0.420*** (0.024) 0.284***

0.231*** (0.012) −0.018 (0.018) 1.342*** (0.011) 1.189*** (0.012) 0.875*** (0.009) 0.527*** (0.008) 0.416*** (0.009) 0.425*** (0.010) 0.343*** (0.017) 0.219***

(0.016) −0.0844*** (0.026) −0.0541*** (0.009) 7.009*** (0.032)

(0.011) −0.102*** (0.017) −0.0589*** (0.006) 7.347*** (0.020)

(0.008) −0.0291** (0.013) −0.0559*** (0.004) 7.557*** (0.015)

(0.009) 0.138*** (0.014) −0.0533*** (0.005) 7.756*** (0.016)

(0.014) 0.371*** (0.021) −0.0448*** (0.007) 8.016*** (0.025)

(0.009) 0.0732*** (0.014) −0.0462*** (0.005) 7.428*** (0.016)

(0.041) −0.197*** (0.027) −0.115*** (0.012) 6.686*** (0.037)

(0.024) −0.198*** (0.015) −0.116*** (0.006) 7.130*** (0.020)

(0.020) −0.232*** (0.012) −0.126*** (0.005) 7.382*** (0.016)

(0.022) −0.201*** (0.014) −0.115*** (0.006) 7.533*** (0.018)

(0.028) −0.0945*** (0.017) −0.0966*** (0.007) 7.705*** (0.023)

(0.020) −0.155*** (0.012) −0.104*** (0.005) 7.255*** (0.016)

Notes: Standard errors are in parentheses. The coefficient on female education is larger than that of males; the coefficient on female experience is smaller than that of males; and the coefficient on the constant term of females is smaller than that of males.

Y. Ge et al. / China Economic Review 22 (2011) 151–164

Machine operators and assemblers

0.215*** (0.025) −0.112*** (0.033) 0.987*** (0.017) 1.072*** (0.021) 0.697*** (0.016) 0.447*** (0.017) 0.450*** (0.017) 0.356*** (0.019) 0.402*** (0.015) 0.376***

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Y. Ge et al. / China Economic Review 22 (2011) 151–164

.3 .2 0

.1

log wage gap

.4

.5

158

0

20

40

60

80

100

quantile Fig. 2. Coefficients effect vs. characteristics effect. The solid line is the true gender earnings differentials, while the dotted line is the gender earnings differentials if females are paid like males. Notes: The coefficients effect can be seen from the distance between the solid line and the dotted line. The characteristics effect can be seen from the distance between the dotted line and the 0 line. We can see that the coefficients effect is larger in lower positions, while the characteristics effect is larger in higher positions.

            m m f f f m f f f m f f f f f f m f f f f f f ff βm + ff β1 ; β2 ; X1 ; X2 −ff β1 ; β2 ; X1 ; X2 1 ; β2 ; X1 ; X2 −ff β1 ; β2 ; X1 ; X2 = ff β1 ; β2 ; X1 ; X2 −ff β1 ; β2 ; X1 ; X2 |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl} |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl} ð5Þ

ð4Þ

ð6Þ

The third part denotes the effect of the first characteristic (X1) and the fifth part denotes the effect of returns to the first characteristic. The same applies for the fourth part and the sixth part to the second characteristic (X2). 4. Empirical Results We use figures and tables to present our results in this section. Using figures, we present the results on percentiles ranging from the 2nd to the 98th percentile. Using tables, we present the specific results for the 10th, 25th, 50th, 75th, and 90th percentiles. For comparison, at the bottom of each table, we also give the corresponding mean results. Table 2 presents the quantile regression results for the 10th, 25th, 50th, 75th, and 90th percentiles. We can see that females have higher returns to education than males, and that both for males and females, those at higher positions have higher returns to education. With regard to experience coefficients, males generally have higher returns to experience than females. A surprise is found in gender difference in the constant term, which is even larger than the true gender earnings gaps. For example, at the 10th percentile, the true gender earnings gap is 0.318 log points, but gender difference in the constant term is 0.323 (7.009–6.686) log points. Based on the aforementioned coefficient results, we can roughly conclude that (1) gender difference in education coefficient reduces gender earnings gaps; (2) gender difference in experience coefficient enlarges gender earnings gaps; and (3) gender difference in constant term enlarges gender earnings gaps. To obtain more precise results, decomposition should be performed. Gender earnings gaps are decomposed using the method proposed in Section 3. The results are shown in Fig. 2. The solid line is the predicted gender gap across the distribution. This line is derived by predicting the earnings distribution of males and females using their own quantile regression coefficients, respectively. The dotted line is the gender earnings gaps if females are paid like males (i.e., predicting the earnings distribution of females using the quantile regression coefficients of males). The distance between these two lines is denoted as the “coefficients effect,” while the distance between the dotted line and the “0 line” indicates the “characteristics effect.”8 In Fig. 2, it can be seen that the “coefficients effect” explains most of the gender earnings gaps in lower positions, while the “characteristics effect” is negligible. However, in higher positions, the “characteristics effect” explains much of the gender earnings gaps. This result is also evident in Table 3. At the 10th percentile, the proportion explained by the coefficients takes up 85.90%. At the 90th percentile, the proportion explained by the coefficients takes up only 35.56%. This is an interesting finding. Although gender earnings gaps are both higher in lower and higher positions, the driving forces behind them are different. In lower positions, gender earnings gaps are much explained by the coefficients effect, while in higher positions, the coefficients effect explains less than half of the gender earnings gaps. Oaxaca (1973) argued that the coefficients effect is generally regarded as discrimination. Based on this argument, our results reveal that females with the lowest earnings may face the most serious form of discrimination in Hong Kong.9 This implies that the government should prioritize helping females with the lowest earnings. 8 If the characteristics of females are distributed just like the characteristics of males, and their returns to characteristics are the same as that of males, the gender gap will be zero. 9 One anonymous referee pointed out that gender differences in occupation may actually reflect labor market discrimination. As a robustness check, we have decomposed the gender gaps—without occupation— in the regression. At the 10th percentile, the proportion explained by coefficients takes up 103.11%; at the 90th percentile, the proportion explained by coefficients takes up 66.39%. Thus, results found in this paragraph are reasonably robust.

Y. Ge et al. / China Economic Review 22 (2011) 151–164

159

Table 3 General decomposition results. Percentile

Original gap

Predicted gap

Explained by coefficients

10

0.318

25

0.288

50

0.181

75

0.223

90

0.286

Mean

0.233

0.278 (0.023) 0.238 (0.017) 0.186 (0.015) 0.181 (0.022) 0.328 (0.033) 0.233

0.239 (0.013) 0.19 (0.009) 0.135 (0.008) 0.095 (0.010) 0.117 (0.016) 0.153

Explained by characteristics 85.90% (0.065) 80.10% (0.062) 72.97% (0.059) 52.87% (0.072) 35.56% (0.085) 65.74%

0.039 (0.021) 0.047 (0.016) 0.05 (0.015) 0.085 (0.022) 0.212 (0.036) 0.080

Residual 14.10% (0.065) 19.90% (0.062) 27.03% (0.059) 47.13% (0.072) 64.44% (0.085) 34.26%

0.040 (0.023) 0.05 (0.017) −0.004 (0.015) 0.043 (0.022) −0.042 (0.033) 0

Notes: The standard errors (in parentheses) are obtained through a bootstrapping method. At the 10th percentile, the proportion explained by coefficients takes up 85.90%. At the 90th percentile, the proportion explained by coefficients only takes up 35.56%.

We proceed to analyze the effects of specific coefficients and specific characteristics in Figs. 3 and 4. The solid line is the gender earnings gap before adjusting a relevant coefficient (or characteristic), and the dotted line is the gender earnings gap after adjustment. If the dotted line is above the solid line, it means that the situation of females becomes worse if they are paid like males (or if their characteristics are distributed like males). This implies that females are initially at an advantage as far as this coefficient (or characteristic) is concerned. In the following figures, a series of counterfactual analyses is conducted. One by one, the coefficients (or characteristics) of females are substituted with the coefficients (or characteristics) of males. Each subsequent figure is based on the change in the previous figure—the solid line in the subsequent figure is the same as the dotted line in the previous subfigure. This stepwise analysis attempts to isolate the effects of specific coefficients or characteristics.10 Fig. 3 presents the effects of specific coefficients on education, experience, industry, occupation, immigrant status, and constant term.11 Fig. 3-1 shows the effect of the education coefficient. At all percentiles, the solid line is below the dotted line, indicating that the gender earnings gaps will increase if female workers are paid the same returns to education as males. The reason is that female's returns to education are higher than males', as mentioned in Table 2. The third column of Table 4 presents the same result. At all percentiles, the effects of the education coefficient are negative. This implies that replacing the returns to education for female workers with that of male workers is not a good solution to bridging the gender earnings gap. Fig. 3-2 presents the effect of the experience coefficient. At all percentiles, the solid line is above the dotted line, indicating that gender earnings gaps will be reduced if female workers are paid according to the returns to experience for males. This result is also found in the fourth column of Table 4: at all percentiles, the effects of the experience coefficient are positive. This implies that raising the returns to experience for female workers can help reduce gender earnings gaps. Fig. 3-6 shows the effect of the constant term. At all percentiles, the solid line is above the dotted line. More importantly, the gap between the solid line and the dotted line is very large. Similar results are found in the eighth column of Table 4. The size of the effect of the constant term is bigger than that of any other coefficients. Table 4 gives another finding: the effect of the constant term is biggest at the 10th percentile (0.295 log points).12 This result has important meanings for academic research and government policies—special attention should be paid to gender earnings gaps in lower positions. At these positions, a large part of the gender earnings gaps remains unexplained, which is reflected in the effect of the constant term. There are three possible reasons behind this finding. First, as noted earlier, there is no information on working hours in the data. We expect that female workers at lower positions may engage in part-time jobs, and their monthly working hours may be less than that of their male counterparts. We thus realize that results in this paper may overstate gender earnings gaps, although this paper partly deals with the problem of the lack of working hours and finds that working hours do not seem to be a major source of gender earnings gaps (Appendix 1 appears to suggest that working hours are not significantly different by gender).13 However, a precise statement on the role of working hours requires a separate study. Second, the absence of a minimum wage law may explain higher gender earnings gaps at lower positions. In the literature, a “glass ceiling” rather than a “sticky floor” is usually found in other developed economies. The absence of a minimum wage law may make Hong Kong's labor market different from other developed economies. We expect that this difference may be another explanation for gender earnings gaps at lower positions. Third, females at lower positions may be affected by Confucian philosophy more seriously. They may value the household more than their performance in the labor market. Thus, it is not difficult to understand why the effect of the constant term is bigger in lower positions. 10 Like the Oaxaca–Blinder decomposition, our method is also cursed by the decomposition order problem, which is often known as the “index number problem.” 11 The effect of experience coefficients is obtained by substituting the experience and experience2 coefficients of females with the experience and experience2 coefficients of males at one time. The same method applies for the effects of industry coefficients, occupation coefficients, and immigrant coefficients. 12 Caution should be noted here that the estimated coefficient of the constant term will change if using different base groups for industry and occupation. 13 We carry out the following steps to deal partly with the problem of the lack of information on working hours. First, run earnings regressions for male and female workers, respectively. Second, predict each worker's earnings based on the regression results. Third, generate a variable DIFF to describe the difference of true log earnings and predicted log earnings (lnearningstrue-lnearningspredict). Finally, drop persons with DIFF b −1. This treatment can partly solve the problem that females are more likely to be part-time workers than males. If DIFF b −1, a person's true earnings will be less than 37% of his/her predicted earnings. This person is likely to be a part-time worker.

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Figure 3-2 Effect of Experience Coefficients

.5 .2

.2

.3

.4

log wage gap

.4 .3

log wage gap

.5

.6

.6

Figure 3-1 Effect of Education Coefficient

20

40

60

80

100

0

20

40

60

80

quantile

quantile

Figure 3-3 Effect of Industry Coefficients

Figure 3-4 Effect of Occupation Coefficients

100

.2

.2

.3

.4

log wage gap

.4 .3

log wage gap

.5

.5

.6

0

0

20

40

60

80

100

0

20

40

quantile

60

80

100

quantile

Figure 3-6 Effect of Constant Coefficient

.3 .2

log wage gap

.4

0

.2

.1

.3

log wage gap

.5

.4

.5

.6

Figure 3-5 Effect of Immigrant Coefficients

0

20

40

60

quantile

80

100

0

20

40

60

80

100

quantile

Fig. 3. Effects of specific coefficients. Note: The solid line is the gender earnings gaps before adjustment, while the dotted line is the gender earnings gaps after adjustment.

The rest of Fig. 3 shows the effects of industry coefficients, occupation coefficients, and immigrant coefficients. The size of these effects is relatively small. We will not discuss these effects in detail. It is perhaps surprising that the effect of the immigrant coefficient is not large. Understandably, gender difference in the proportion of immigrants is not large enough to influence the labor market significantly. Based on Table 1, the proportion of immigrants is 0.301 (0.020 + 0.281) for males and 0.299 (0.032 + 0.267) for females. The difference is only about 0.002.

Y. Ge et al. / China Economic Review 22 (2011) 151–164

Figure 4-2 Effect of Experience

.2

log wage gap

0

0

.1

.2 .1

log wage gap

.3

.3

.4

.4

Figure 4-1 Effect of Education

161

20

40

60

80

100

0

40

60

quantile

Figure 4-3 Effect of Industry

Figure 4-4 Effect of Occupation

100

80

100

.2

.3

80

0

.1

log wage gap

.2 .1 0

log wage gap

20

quantile

.3

0

0

20

40

60

80

100

0

20

40

60

quantile

quantile

0 -.005 -.01

log wage gap

.005

.01

Figure 4-5 Effect of Immigration

0

20

40

60

80

100

quantile Fig. 4. Effects of specific characteristics. Note: The solid line is the gender earnings gaps before adjustment, while the dotted line is the gender earnings gaps after adjustment.

Fig. 4 presents the effects of specific characteristics, namely, education, experience, industry, occupation, and immigrant status. Fig. 4-1 presents the effect of gender differences in education. For most percentiles, the distance between the solid line and the dotted line is small. Although the education of females is higher than males on average, the difference is small and its effect on the gender earnings gap is likewise small.

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Y. Ge et al. / China Economic Review 22 (2011) 151–164

Table 4 Specific decomposition results of coefficients. Percentile

Explained by coefficients

Education

Experience

Industry

Occupation

Immigrant

Constant

10

0.239 (0.013 ) 0.190 (0.009) 0.135 (0.008) 0.095 (0.010) 0.117 (0.016) 0.153

−0.134 (0.009) −0.129 (0.008) −0.145 (0.011) −0.163 (0.014) −0.171 (0.017) −0.116

0.117 (0.009) 0.103 (0.006) 0.107 (0.006) 0.094 (0.006) 0.088 (0.007) 0.126

−0.037 (0.008) −0.018 (0.005) −0.004 (0.006) 0.010 (0.007) 0.025 (0.010) 0.001

−0.032 (0.010) −0.032 (0.007) −0.061 (0.006) −0.114 (0.007) −0.079 (0.010) −0.055

0.031 (0.006) 0.027 (0.004) 0.033 (0.004) 0.031 (0.004) 0.015 (0.006) 0.023

0.295 (0.009) 0.239 (0.005) 0.205 (0.005) 0.238 (0.008) 0.238 (0.010) 0.174

25 50 75 90 Mean

Notes: The standard errors (in parentheses) are obtained through a bootstrapping method. The effect of the constant term is larger in lower positions of the earnings distribution.

Table 5 Specific decomposition results of characteristics. Percentile

Explained by characteristics

Education

Experience

Industry

Occupation

Immigrant

10

0.039 (0.021) 0.047 (0.016) 0.050 (0.015) 0.085 (0.022) 0.212 (0.036) 0.080

0.007 (0.014) −0.004 (0.011) 0.036 (0.010) −0.009 (0.013) −0.011 (0.018) −0.007

0.041 (0.017) 0.067 (0.013) 0.015 (0.011) 0.035 (0.013) 0.024 (0.020) 0.049

0.006 (0.010) −0.024 (0.007) −0.005 (0.007) −0.015 (0.009) −0.026 (0.013) −0.010

−0.015 (0.016) 0.009 (0.012) 0.008 (0.012) 0.070 (0.017) 0.224 (0.031) 0.049

0.000 (0.005) 0.000 (0.004) −0.003 (0.003) 0.003 (0.004) 0.001 (0.007) −0.002

25 50 75 90 Mean

Notes: The standard errors (in parentheses) are obtained through a bootstrapping method. In lower positions, the effect of occupational segregation has little impact on the gender earnings gaps; in higher positions, it tends to enlarge the gender earnings gaps.

Fig. 4-2 presents the effects of gender differences in experience. At all percentiles, the solid line is above the dotted line. It means that gender differences in experience enlarge gender earnings gaps. This implies that raising the working experience of females is an effective way to reduce gender earnings gaps. Fig. 4-3 presents the effect of gender differences in industries. The distance between the solid line and the dotted line is small too. This means that although there is a large difference in industries between genders, its effect on gender earnings gaps is small. An interesting result is found in the effect of gender difference in occupations. Fig. 4-4 shows that in lower positions, it has little impact on gender earnings gaps; in higher positions, it enlarges gender earnings gaps greatly. This result is also found in the sixth column of Table 5. This is different from related mean-level analyses. Some previous studies focusing on the mean level (e.g., Sung, Zhang and Chan, 2001; Meng and Miller, 1995) assert that occupational segregation is not important to the gender earnings gap compared with their earnings gap within occupations. It is not difficult to understand the difference between our results and those in previous studies. In Fig. 4-4, the effect of occupational segregation varies with percentiles: at lower percentiles, it has little impact (negative impact at some percentiles); at higher percentiles, it has a positive impact. If solely focused on the mean analysis, it would not be surprising to obtain the result that occupational segregation is not important to gender earnings gaps.14 From this aspect, it is clear that it is better to examine gender gaps from the whole earnings distribution rather than from the mean level. The result in this paper implies that, to reduce gender earnings gaps in higher positions, the government should deal with the gender occupational segregation problems. It has been more than 10 years since the Equal Opportunities Commission (EOC) was established, and since then the Sex Discrimination Ordinance was implemented. One might say that the EOC and relevant policies should focus on the concept of “Equal Pay for Equal Work” because Hong Kong has done well to guarantee the rights of females in choosing their occupations. However, our findings show that there is still much room for EOC to improve the rights of females in choosing occupations, especially for females with higher quality.

14 If we focus on the gender earnings gap at the mean level in Hong Kong, we can obtain results that are similar with aforementioned studies—that occupational segregation is not important. This can be seen at the bottom of Table 5. At mean-level analysis, occupational segregation accounts for only about 0.049 log points in the gender earnings gap.

Y. Ge et al. / China Economic Review 22 (2011) 151–164

163

5. Conclusions This paper uses data from the 2006 by-census to examine gender earnings gaps across the whole earnings distribution in Hong Kong. We find many interesting results hidden in the traditional mean-level analysis. We narrow down these findings to three points. First, gender gaps are larger both in lower positions and in higher positions in the earnings distribution, which means that both the “glass ceiling effect” and the “sticky floor effect” exist in Hong Kong's labor market. Second, gender earnings gaps in lower positions are mainly caused by coefficients effects, while gender earnings gaps in higher positions are much caused by gender differentials in characteristics. Third, the effect of occupational segregation on gender earnings gaps varies with specific positions in the earnings distribution. In lower positions, occupational segregation has little impact on gender earnings gaps. In higher positions, on the other hand, occupational segregation favors male workers and enlarges gender earnings gaps. There are a few policy implications from the empirical results in this paper. First, we find that gender earnings gaps in lower positions are mainly caused by coefficients effects. According to Oaxaca (1973), this part may be closely related to discrimination. Thus, the government may intensify efforts in anti-discrimination laws. Second, we find that both female working experience and its return are lower than males are. There appear to be two options to help female workers in this regard. One is to increase the returns to working experience of females, implying that the quality of female workforce or their work effort should be enhanced and/or any discrimination should be reduced. The other is to increase the working experience of females, calling for the encouragement of female participation in the labor market. The government can provide some public babysitting services to ensure that female workers could stay longer in the labor market. Third, solving gender occupational segregation problems is very important in narrowing gender earnings gaps in higher positions. The government should make an effort to reduce access barriers to better occupations for females.

Appendix 1. Median hours of work* of employed persons by major occupation group of main employment and sex in Hong Kong (2006) Sources: Quarterly Report on General Household Survey: October to December 2006. Quarterly Report on General Household Survey: January to March 2007. Quarterly Report on General Household Survey: April to June 2007. Q1

Q2

Q3

Q4

Male

Female

Male

Female

Male

Female

Male

Female

Major occupation of main employment

Hours

Hours

Hours

Hours

Hours

Hours

Hours

Hours

Managers and administrators Professionals Associate professionals Clerks Service workers and shop sales workers Craft and related workers Plant and machine operators and assemblers Elementary occupations Other Occupations Overall

45 44 44 44 54 45 50 48 48 48

45 44 44 44 48 45 44 54 48 45

45 44 44 44 54 45 50 48 48 48

44 42 42 42 48 45 42 54 42 45

48 45 45 45 54 48 54 48 50 48

45 44 44 44 50 48 48 56 48 48

48 45 45 44 54 47 50 48 50 48

45 44 44 44 50 48 45 54 48 47

Notes: Referring to median hours of work during the seven days before enumeration.

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