Is There Always a Sticky Floor Effect in China?

Transcription

1 Is There Always a Sticky Floor Effect in China? The Analysis on the Distribution of Clerical Support Workers Gender Wage Gap under Different Employment Forms in China Dinghong Yi, Yan Yu (School of Labor and Human Resources, Renmin University of China, Beijing, ) Abstract: Global studies on the gender wage gap are paying more and more attention on the wage distribution instead of the average wage differentials, and conclusions are various from country to country. This paper is based on the wageindicator data from China, studying the distribution of Clerical Support Workers gender wage gap under different employment forms. We first separately describe the distribution characteristics of gender wage rate for regular employees and labor dispatching ones. In contrast with the Sticky Floor Effect found by other Chinese scholars, we find the evolution of Clerical Support Workers gender gap hides an intriguing composition effect. Then we use the quantile decomposition method proposed by Machado and Mata (2005) to do the quantile regression and counterfactual analysis on gender wage data under both employment forms, in order to find out the key factors affecting the gender wage gap, and to see whether gender discrimination or individual characteristics contribute more to gender wage gap. Furthermore, we test the quality of the wageindicator data and put forward recommendations to improve the data collection in the future. Keywords: Gender Wage Gap; Clerical Support Workers; Labor Dispatching Employees; Sticky Floor Effect ; Quantile Decomposition I. INTRODUCTION The inequity between men and women in the labor market is always one of the most classical topics in the labor and employment field. Especially, the gender wage gap has aroused interest of scholars all over the world. And in recent years, the researches based on wage distribution have tended to be more and more popular, which have replaced the researches on the average wage to become the worldwide mainstream. Many researches based on wage distribution have been done but the conclusions about how the degree of gender wage gap varies with the positions of wage distribution are totally different. Gender wage gap shows the Glass Ceiling Effect in some countries, while the Sticky Floor Effect in other counties. Here, Glass Ceiling Effect refers to a phenomenon that the gender gap is bigger at the top of the wage distribution, which is often used to describe the inequality degree between the male and the female, indicating that some intangible, man-made obstacles are set to prevent some qualified women from being promoted to a specific position and obtaining higher pay. In contrast, the Sticky Floor Effect refers to the phenomenon that wage gap is bigger at the bottom of wage distribution. For the wage distribution in China, many researches show that the gender wage gap appears to be the Sticky Floor Effect, which indicates the degree of gender wage gap decreases with the position of wage distribution rises. And nowadays, Chinese labor market is under the background of labor dispatching abuse, because enterprises are trying to save labor costs and avoid the trouble from labor dispute, which leads to a great deal of implicit phenomena of the same work with different pay in China. In this paper, we take the background of Chinese labor market into consideration, and use the wageindicator data from China, separately describe and analyze the

2 distribution of gender wage gap for dispatching employees and regular ones. Then with our research result, we raise the following question: Is There Always a Sticky Floor Effect in China? Besides, this paper focuses on the Clerical Support Workers as the objective occupations of our research, mainly because of the existing database. In the wageindicator data, the sample of Clerical Support Workers is relatively large and the key information is comprehensive so that the data could be easily described and further analyzed, thus a more representative conclusion could be reached. The main research goals of this paper are as following. Frist, we try to figure out the distribution and contributing factors of the Clerical Support Workers gender wage gap under different employment forms in China. Second, we plan to compare the conclusion with other relevant researches, in particular, to examine whether gender wage gap shows the Sticky Floor Effect in China concluded by other researches. Third, the wageindicator data in China is used for the first time in this paper, so we would test the quality of data (such as authenticity, reliability) to achieve some further enlightenments for the data collection. The paper proceeds as follows. Section II is a literature review about the research on the gender wage gap based on the wage distribution from different countries, especially that from China. In Section III, we introduce the data used in this paper and describe its statistical characteristics. In Section IV, we present the model for quantile regression and the key variables. Section V is quantile regression and decomposition based on the extension of Mincer wage equation. Section VI draws conclusions, putting forward the implications of this paper. And Section VII points out the limitations and offers suggestions for future researches. II. LITERATURE REVIEW Studies of European countries have found significant differences in the gender gap at different quantiles of the log wage distribution. And whether it shows Sticky Floor Effect or Glass Ceilings Effect varies from country to country. Bonjour and Gerfin (2001) first Used micro data for Switzerland to analyze the distribution of the gender wage gap and found an over proportional part of wage gap at low wages was due to discrimination. On the basis of them, other authors considered the differences of wage distribution between countries. Datta (2003), Ronald and Nina compared how U.S. and Danish gender wage gaps had developed between 1983 and They found Women at the highest decline in Denmark faced the biggest increase in the gap, while in the U.S., the decline was largest at the top and at the middle of the distribution. Rica (2008) found the difference between workers in different education levels in Spain. For highly educated workers, the gap was in line with the glass ceil, and for less-educated workers, the gap decreased. Then Kunze (2008) reviewed the empirical literature on the gender wage gap. He thought whether a 'Glass Ceiling' exists was an important question and a quantile regression technique was one avenue of research that may help resolve this question. After that more factors have been taken into account to influence the wage distribution. Wahlberg(2009) examined differences in wage distributions between natives, non-refugees, and refugees in Sweden. He found that the wage differentials between natives and non-refugee immigrants decreased across the distribution, while those between natives and refugee immigrants increased. And Albrechet (2009) extended the Machado Mata technique to account for sample selection to analyze the gender log wage gap for full-time workers in the Netherlands In China, studies about gender wage differential also have aroused wide attentions among scholars. Since Meng, X. wrote the first paper in 1998 about gender wage gap in township enterprises in rural China, many more

3 researches were made in the related areas, and most of them were in term of average wage. However, in recent 5 years, gender wage gap studies in China followed the international research trend by using a counterfactual analysis based on quantile regression. Ge (2007) first examined gender wage gap in urban China using this new methodology. He ran quantile regression of gender differential on wage distribution by using data from UHS (Urban Household Survey) between 1988 and Then he used improved Machado&Mata (2005) quantile decomposition method to study factors that affect gender wage gap, and made counterfactual analysis. He found that the degree of gender wage gap varied with the positions of wage distribution: the gap was big in lower positions and small in higher positions of the wage distribution. Moreover, the trend of the gap varies with the positions too: the gap was bigger and bigger in lower positions and the opposite occurs in higher positions.the reason why the gap was bigger and bigger in lower positions was that female workers there face more and more discrimination. The reason why the gap was smaller and smaller in higher positions was that the difference of productivity characteristics ( such like education) between male and female workers got smaller and smaller in higher positions. Compared to the Sticky Floor Effect that was proved in Ge s research, other scholars have come to different conclusions by using different databases. Chen and Duan (2009) firstly used some descriptive statistical methods to investigate the distributional characteristics of the related gender wage data from the database of CHNS (China Health and Nutrition Survey) between 1988 and 2005 in cities of China. Then, they employed approaches of quantile regression modeling and decomposition respectively to analyze gender wage gap in China. They divided wages into three levels (low, median and high), and found that: in 1988, the gap in each level became bigger with wage increasing; in 1996, it was large in low and high wage levels and small in median wage level; in 2005, it was reversed. Later on, many scholars did their researches based on previous studies about distribution level. Zhang and Guo (2009) regressed the wage equations of male and female at different quantiles and decomposed the gender wage gap that came from the quantile regression based on the micro data of northeast urban labor market in China in The results showed that there was a gender wage gap in the Northeast urban labor market. With the male and female wages rose, the gender wage gap was shrinking, but the extent of gender wage discrimination had continued to expand. Qi and Liu (2009) used adult data from CHNS to explore the gender wage gap between state sector and non-state sector in China. Quantile regression analysis showed that a Sticky Floor Effect rather than Glass Ceiling Effect was detected. In addition, the gender wage gap in state sector is lower than non-state sector. Furthermore, the decomposition indicates that the gender wage gap was attributable to the coefficients effect (discrimination) rather than human resource endowment differences between the two genders. Wang (2010) analyzed data from migrant worker survey conducted in Dalian Shanghai, Wuhan, Shenzhen and Chongqing by IECASS in 2006 and data from urban household sample survey conducted by NBS in He found that gender wage gap among migrant workers presented as Glass Ceiling Effect, while gender wage gap among urban employees shown as Sticky Floor Effect Liu (2012) added a new explanatory variable, health human capital, that never been considered before into the equation in order to analyze the effect of health human capital on wage differential. The results showed that this new factor has a bigger gender wage gap effect in low wage level. From the above, Ge s study laid the foundation of researches about gender wage gap in term of quantile distribution, and papers since then were extensions from four aspects: 1) control the geological area variable, target on certain places, such as Northeast area; 2) compare differences between different types of enterprises, such as state owned and non-state owned; 3) target on certain group of people, such as migrant workers; 4) Add new explanatory variable, such as health human capital. Above researches mainly used big sample data from CHNS or UHS. However, because those data are not obtained for studying gender wage purpose, scholars have to make certain assumptions and estimates before they

4 process data, which inevitably decreases the accuracy of regression equations. It is interesting that most of studies agree on the finding that gender wage gap in China shows Sticky Floor Effect, whereas different conclusions can be made if the analysis focus on different groups. This makes people wonder that does Sticky Floor Effect always exists in China? Will the result be the same if we add some new terms? This paper is trying to answer the above questions. By using more targeted, time-sensitive, and detailed data, this paper will study gender wage gap and effect of gender discrimination under different employment forms among clerical support workers in China. III. DATA Data Declaration The data used in this paper comes from Survey of Chinese wage work (wageindicator.cn) online survey database. The number of questionnaire sample based on the job characteristics of the selected positions of Clerical Support Workers is 3094 in total. The survey time is mainly distributed in 2011 January to September, especially in March. Due to the relative concentration of the investigation time, the data set can be regarded as cross-sectional data, without considering the influence of time. Because the data of the research is relatively timely and targeted, our study will also get some conclusions which are convincing, as well as close to reality. In order to avoid the deviation from the real data, however, we have eliminated the samples which have omitted the very information that is important for this study on the premise of large sample size, so that we can get a complete data set. By data screening and cleaning, we finally got 1155 effective samples. The samples of regular employees are total up to 936, which include 266 males and 670 females. The samples of labor dispatching employees are 219 with 68 male samples and 151 female samples in them. According to the state of gender, age, marriage and children, as well as the size of industry and firm, and other indicators, the distribution of these samples can be taken as follows. TABEL 1 Distribution of the samples Regular Employees Labor Dispatching Employees Indicators Number Percent(%) Number Percent(%) Female Gender Male Age Single, no children Married, no children Marriage and Married, with children children Single, With children Industry Agriculture, manufacturing,

5 construction Firm Size Commercial services Trade, transport, hospitality Public sector, health care, education employees employees employees employees >500 employees It can be seen from the distribution of these samples that females take the majority of the Clerical Support Workers, which is related to the nature of the occupation. This survey data shows that there isn t serious occupation discrimination in Clerical Support Work. Accordingly, the analysis of ensuing paragraphs will focus on whether there is wage discrimination between different genders. The male-to-female ratio of labor dispatching employees as well as regular employees is basically the same. The proportion of males in the labor dispatching employees is slightly higher than that in regular ones. Obviously, the age distribution of labor dispatching employees gradually gets younger: more than 95% of the population is under the age of 30, others are all under the age of 33 except a person who is 47 years old. It is not only due to the fact that the form of labor dispatching has begun to flourish in recent years, but also related to the applicable objects of the form of employment. Since the labor dispatching is mainly practiced in temporary, auxiliary or substitute posts, the applicable object is usually the lower level employees who have just entered the workplace. The ages of these employees are generally young. The Descriptive Statistics of Wage Distribution We perform descriptive statistics on male and female s log hourly wages at different quantiles for regular and labor dispatching employees separately. The results are as follows: TABEL 2 Descriptive statistics on the distribution of log hourly wage 10% 25% Median 75% 90% Mean std.dev. Female Regular Employees Labor Dispatching Employees Male Gap Female Male Gap From the table above we can be see that the average number of four sets of data is greater than the median, which illustrates that the distribution of wage income is biased. That is to say, more than half of all workers wages are lower than the average wage. The fluctuation in wages of regular employees is greater than that of labor dispatching employees, and males wage of regular employees fluctuate more acute than females, while females

6 wages in labor dispatching employees have higher volatility than males. From the point of view of the gender wage gap in log hourly wage, the gap of regular employees starting to trend upward overall from the median. That is to say, the wage gap between genders becomes larger with the increase of wage quantiles. On the contrary, it receives the trend of decline in the mass of labor dispatching employees, which has risen until it gets to the 90th quantile, proving that with the increase of wage quantiles, the gender wage gap will be shrinking. This conclusion is not consistent with that of studies before. In order to analyze the trend of gender wage gap between two employment forms closely, it must make an overall description about the distribution of wage gap. The core of this study lies in the distribution of the gender wage gap in different employment forms. So the first thing to do is to investigate the gender wage gap between labor dispatching employees and regular ones. How to select the index that can reflect the gap is the key point. The wage level changes with the fluctuation of quantile. High quantiles have a larger cardinal numbers. The absolute value of the wage gap must be greater than that of the low quantiles, results in the fact that the absolute value of the wage gap can't accurately reflect the gender wage gap in different levels of quantile. In order to measure the relative difference of gender wage in different level of quantiles more accurately, as well as to make the concept of this index clear, we get the description about gender wage gap as follows: First, taking the log of hourly wages between male and female, get the log wage differential (lnw m -lnw f ), which means ln (W m /W f ), and then the wage gap in different levels of quantiles has been amended already. On this basis, in order to make the meaning of gender wage gap more clear, we remove the logarithm and get the wage rates between male and female(w m /W f ). According to this index, we draw a diagram about the ratio distribution of gender wage, as shown in Figure 1. FIGURE 1 Gender wage rate The conclusions of this paper are not the same with most of the domestic scholars who get the result of Sticky Floor Effect though large sample data, such as CHNS, UHS, and so on. The figure above shows that the gender wage gap has smooth fluctuations under the 80% quantiles for the labor dispatching employees (quantiles below the lowest 5% and above the highest 5% are not to be considered as the bottom and top of quantile are not stable). The gender wage gap begins to decrease when quantiles reach to above the 80%. So the gender wage gap of the labor dispatching employees shows the Sticky Floor Effect For regular employees, the gender wage gap below 70% and above 80% segments with the increase of quantile, which shows the Glass Ceiling Effect overall. So far as the two groups are concerned, the wage difference of labor dispatching employees is higher than that of regular employees in lower quantiles (under 60th quantile). On the contrary, when in high quantiles, the wage difference of regular employees is higher than that of labor dispatching ones. In most cases, the labor dispatching employees receive a higher wage differences. Thus, the gender wage gap doesn t always present Sticky Floor Effect in China. The overall trend of labor

7 dispatching employees shows the Sticky Floor Effect, while that of regular employees shows Glass Ceiling Effect, especially after investigating the different forms of employment forms. In addition, it fluctuates in some different quantiles, which is not a single downward trend concluded by other scholars. It may contain many economic meanings behind the complex trend of this fluctuation. We will make further analysis though the regression and decomposition of the data. IV. MODEL AND VARIABLES Research Model In this paper, the regression model is based on an extended Mincer wage equation. On the basis of the Mincer equation and according to the data s characteristics, some more variables are included in the model. The specific model can be expressed as follows. For a given quantile, the male and the female are given the following regression respectively: logw i = C + β 1 S i + β 2 exp i + β 3 expi 2 + β 4 gen i + β 5 X i + ε i In which, logw i is logarithmic hourly wage, S i is educational level, exp i is working experience, gen i is gender variable, and Xi is a summary of other vectors which can reflect personal characteristics, including marriage and children, industry, firm size and so on. Variable Description Most Chinese large sample databases provide yearly wage data, other scholars estimated hourly wage only by assuming years working hours. However, the wageindicator data provides the accurate hourly wage, making our study to be more accurate. The use of the log wage income can make the distribution of wage income is closer to normal, and can eliminate some heterogeneity and special points interference, so we take log hourly wage as the dependent variable. For the independent variables, we select the level of education, working experience, the squared of working experience, gender, marriage and children, industry and firm size.taking into account the endogenous independent variables, we haven t taken the age variable, whose information has been included in the level of education and working experience. The processing instructions for each independent variable are as follows 1. Level of education We regard the years of education as measure index of education level. According to the provisions of years of education in China,we process the variable of the highest degree in the original data, 6 years corresponding to the highest level of education is primary school, 9 years corresponding to the highest level of education is junior high school, 12 years corresponding to the highest level of education is high school (or secondary education), 14 years corresponding to the highest level of education is junior college, 16 years corresponding to the highest level of education is the college, 18 years corresponding to the highest level of education is postgraduate, 21 years corresponding to the highest level of education is Doctor. 2. Working Experience China's school rules is six years old, so for the educated people, working experience = Age - years of schooling -

8 6. For the uneducated population, According to the rule of Labor law of the People's Republic of China 1 and Minor protection law of the People's Republic of China 2, Employers only can recruit the person who under the age of sixteen. So on the processing of the people who are educated, working experience = age The squared of working experience According to the life cycle theory, the wages will increase with age and show inverted U-shaped trend, namely, wages and working experience may exist quadratic nonlinear relationship, so the square of working year will also be taken as one of the independent variables. 4. Gender Gender serves as a dummy variable, and we take male as 1, and take female as The status of marriage and Children According to the combination of the status of marriage and children, this sample was divided into four groups: single without children, married without children, married with children and single with children. Among them, the single includes widowed, divorced and unmarried three cases. 6. Industry According to the standard of NACE2000, Employees of the industry will be divided into four categories, including: agriculture, manufacturing, construction; commercial services; trade, transport, services industry; public sector, health care, education. 7. The firm size The firm size is measured through the number of employees, mainly divided into five dimensions: 0-10, 11-50, , , and> 500. V. ANALYSIS BASED ON THE QUANTILE REGRESSION The Result of Quantile Regression In this paper, we group the samples into two parts by the employment forms, one is labor dispatching employees and the other one is regular employees. Regressing at the 10th, 25th, 50th, 75th, and 90th quantiles, we use OLS regressions to estimate the general observation as a benchmark. Among them, the regression results of regular employees were shown in table 3. TABEL 3 The quantile regression results of regular employees VARIABLES OLS 10% 25% median 75% 90% lnwage lnwage lnwage lnwage lnwage lnwage Education 0.108*** 0.100*** 0.115*** 0.109*** ** (0.0241) (0.0161) (0.0131) (0.0191) (0.0430) (0.0475) Experience *** *** 0.102*** 0.109*** (0.0209) (0.0187) (0.0142) (0.0188) (0.0388) (0.0385) 1 The article 15 of the People's Republic of China labor law regulates: it is forbidden for employers to employ persons under the age of sixteen. If literature and art, sports and special units want to employ persons under the age of sixteen, they must be in accordance with relevant state regulations, fulfill the formalities for examination and approval, and safeguard their rights to receive compulsory education. 2 The article 49 of The minors of the People's Republic of China regulates: if enterprises and institutions, individual business employ persons under the age of sixteen it's illegal, the labor department shall be ordered to correct and imposed a fine. If the circumstances are serious, the industrial and commercial administrative department should revoke the business license.

9 Expeirence ** *** *** *** ( ) ( ) ( ) ( ) ( ) ( ) Married, no Children (0.111) (0.0977) (0.0728) (0.0979) (0.195) (0.193) Married, with children * *** ** (0.131) (0.100) (0.0808) (0.113) (0.243) (0.233) single, with children *** *** * (0.564) (0.154) (0.334) (0.394) (0.811) (0.335) Commercial services * *** ** (0.0695) (0.0662) (0.0509) (0.0668) (0.132) (0.133) Trade, transport, hospitality (0.0940) (0.0812) (0.0623) (0.0817) (0.162) (0.151) Public sector, health care, education (0.129) (0.110) (0.0849) (0.113) (0.220) (0.211) ** (0.124) (0.119) (0.0897) (0.120) (0.234) (0.226) (0.130) (0.126) (0.0953) (0.127) (0.251) (0.243) * (0.120) (0.114) (0.0865) (0.117) (0.227) (0.217) > ** (0.123) (0.117) (0.0886) (0.118) (0.229) (0.218) Gender (0.0712) (0.0642) (0.0483) (0.0636) (0.126) (0.123) Constant 0.966** * 3.050*** 2.432*** (0.393) (0.259) (0.225) (0.319) (0.712) (0.771) *** p<0.01, ** p<0.05, * p<0.1 As we can see from figure 3, the coefficient of independent variables education, experience, expeirence 2 are statistically significant, especially for the lower quantiles (such as 10th, 25th and 50th quantiles). That means income level has a significant positive relationship with education and working experience at the low quantiles. In addition, the coefficient of education is much more significant than working experience at high quantiles, which implies education plays a dominated role in determining salaries for the high income people. The distribution of working expeirence2 followed a U shaped curve, which is consisted with the hypothesis of life cycle theory. Compared with the single and no child group, the married group needs to raise children generally earn less money because they have to spend partial energy to keep their work-life balance. Additionally, the wage of single parents is dramatically higher than sing no child group. One possible reason could be single parents have to work harder due to the heavy burden of bringing up children. And it is probable the single parents get subsidies granted by their companies. Figure 2 shows the variation of each coefficient.

10 FIGURE 2 The regression coefficients of labor dispatching employees In order to find the income differences between genders of regular employees, we then separately make regressions the wages of male and female regular employees on these independent variables at various quantiles. Figure 3 shows the regression coefficient in different conditions. FIGURE 3-1 The regression coefficients of male labor dispatching employees

11 FIGURE 3-2 The regression coefficients of female labor dispatching employees The wage differences of regular employees come from education reward and working experience reward by comparing Figure 3-1 and Figure 3-2. Generally speaking, the salaries of male are significant higher than female at each quantiles, because it is easier for male to get much more information or social resources than the female. Furthermore, the regression coefficients of education and working experience for the male are relatively stable contrast to female which can be concluded as the main factor to explain the various wage ratios fluctuations at different quantiles. The slight decline of regular employees' income difference from 70th to 80th quantile can be explained by the variation of coefficients in the female's quantile regression equations. There is a small drop of female education reward from 50th to 70th quantile. Then it increases from 70th to 90th quantile. Additionally, the reward of working experience rises up in the 75th to 80th period. Because of the rising of working experience reward and education reward, the gap between male and female income falls dramatically in the high quantiles. TABEL 4 The quantile regression results of labor dispatching employees VARIABLES OLS 10% 25% 50% 75% 90% lnwage lnwage lnwage lnwage lnwage lnwage Education * (0.0344) (0.0444) (0.0473) (0.0338) (0.0767) (0.0649) Experience (0.0426) (0.0502) (0.0569) (0.0428) (0.0895) (0.0957) Expeirence ( ) ( ) ( ) ( ) ( ) ( ) Married, no Children (0.219) (0.217) (0.338) (0.248) (0.452) (0.501)

12 Married, with children (0.184) (0.337) (0.378) (0.231) (0.446) (0.394) Commercial services (0.131) (0.183) (0.184) (0.133) (0.294) (0.267) Trade, transport, hospitality (0.151) (0.200) (0.224) (0.157) (0.354) (0.314) Public sector, health 0.539** * ** care, education (0.271) (0.324) (0.304) (0.226) (0.506) (0.383) (0.253) (0.312) (0.337) (0.246) (0.556) (0.489) (0.259) (0.341) (0.356) (0.265) (0.577) (0.507) (0.243) (0.314) (0.330) (0.239) (0.536) (0.457) > (0.246) (0.308) (0.326) (0.242) (0.536) (0.464) Gender (0.119) (0.149) (0.172) (0.123) (0.267) (0.241) Constant 2.586*** 1.528* 1.352* 1.845*** 5.160*** 3.830*** (0.588) (0.807) (0.809) (0.560) (1.238) (1.176) *** p<0.01, ** p<0.05, * p<0.1 Combined with regression data of regular employees and labor dispatching ones, we can see in comparison that education level and work experience on the quantile variables of regular employees are more significant, which in labor dispatching employees are not significant at all. That means the labor dispatching employees salaries depend largely on the human capital investment, including education, experience and so on. While the correlation of labor dispatching employees wages and human capital investment is very small, which may depend on other personal factors. Compared with the reality, wages of some labor dispatching employees engaged in clerical support work, such as labor dispatching customer managers in banks, are largely depends on its performance, in which determinant is not personal ability, but social resources, which has less direct relation with their education level and work experience. There are some female labor dispatching employees unable to get a promotion or salary raise because their unwillingness to supervise others or to travel. On the other hand, salaries of labor dispatching employees are associated with their industries, which is not obvious in the regression. Only Public sector, health care, education s virtual variables are significant at high scores, in which wages of labor dispatch employees are significantly higher than the agricultural, manufacturing industry and construction. However, other two industries virtual variables are not significant, which may be related to the loose data processing for the classification of the industry in this article. Further research can be subdivided to the industry, investigating industry impact situation on labor dispatching clerical support workers income. Meanwhile, unexpectedly, the gender variables are not significant in the quantile regression of both regular employees and labor dispatching ones. While, researches of other scholars, in which they do not distinguish different occupations when they study on employees in whole China, have shown gender variables are fairly significant. This means, in China, overall speaking, gender affects wages hugely. However, for clerical support

13 workers, gender s influence is comparatively smaller. This result is related to the intensity and the content of the job. Specifically, most of the works for clerical workers are: communication, reading and writing. The intensity is small, and most of the work will be done in offices, which requires less physical energy. Especially for labor dispatching employees, they are engaged in the basic clerical work, which are rather easy, and simple in thinking, thus consequently requires less knowledge and skills either. Therefore, gender s difference and education background do not play an important role there. Nevertheless, for the clerical support work, which is comparatively more suitable for women, wages do differ on genders for some level, and in most of the situations, male have higher wages than female. What is the reason for this difference? Is it because of the personal characteristics of male and female differs, such as on educational level, limits on working years, marital status, the industry they belong, or the scale of the enterprise, etc.? Or is that market discrimination still exists somehow in clerical profession? This is the question we will focus on later. So next, in this article, we will decompose the wage difference of genders on different quantiles for both regular employees and clerical dispatching employees, to see how many differences are due to the gender discrimination from labor market, and how many are because of the difference from individual characteristics. Quantile Decomposition In this part, we use the quantile technique proposed by Machado and Mata (2005) to decompose the gender wage gap separately for labor dispatching employees and regular ones. The gaps at different percentile points in the wage distribution are decomposed into the components due to differences in characteristics and differences in gender. The idea of the MM technique is to generate two counterfactual densities. The first is the female log wage density that would arise if women were given men s labor market characteristics, but were paid wage derived from the female sample regression, X M β F (θ). The second is the density that would arise if women retained their characteristics, but were given wage derived from the male sample regression, X F β M (θ). The decomposition of the raw gap can then be written as follows. (X M X F ) βm (θ) +X F (β M (θ) β F (θ)) Here, according to the MM quantile decomposition, we simplify the steps by adding a dummy variable of gender, which only needs to do a return towards all samples including men and women, rather than quantile regressions separately for men and women. As all the samples for both men and women, including the quantile regression can be assumed that for all men and women regression coefficients in each quartile except dummy variables of gender are the same. Thus, the coefficients before the gender dummy variable are correspond wage gap between different genders after "filtering" out individual characteristics, that is the gender wage gap men and women bring in each quantiles brought from different coefficients. Based on the coefficients in front of dummy variables, we can easily get the counterfactual gender wage gap in contrast to the fact, which reflects the result from discrimination on the gender; but their actual gender wage gap mainly reflects the difference on the individual characteristics between men and women--certainly including some parts not fully explained. The total gap, gaps and personal characteristics gap coefficients under non-dispatch system and the dispatch system were shown in Figure 4, Figure 5.

14 FIGURE 4 The wage gap decomposition of regular employees As is shown above, the solid line reflects the coefficient differences on gender wages of regular employees, which means the pure differences on genders on the assumption that all other variables coefficients are under the same conditions or the gender discrimination in the labor market. The dotted line reflects a regular employees difference in individual characteristics, namely the parts of the gender wage gaps which can mainly be explained by personal characteristics (including level of education, work experience, marital status, involved industry, size of companies etc.). All in all, the individual characteristics below the median are the main factor of gender wage differences. And gender discrimination is not very serious in this range, even showing negative effects (gender variable coefficient is negative), which means the market shows even preference to women instead of discrimination. The impact of gender discrimination starts highlighting from 50% to 80%, especially when some sex discrimination to become a major factor in the wage gap from 50% to 60%. After 80%, differences in individual characteristics have again become the main influencing factors of the gender wage gap, and with the quantiles increase, gender discrimination suggests a narrowing trend in the high-income groups. The results are similar with conclusions from other scholars, indicating that high-income women in the labor market will receive more equal treatment. FIGURE 5 The wage gap decomposition of labor dispatching employees For the labor dispatching employees, the situation is not the same. 35% before and 90% later, gender discrimination has been in a dominant position. While in the middle part, some fluctuations only exist between 45%

15 and 60%, and gender wage gap in some other quantiles mainly explained by the individual characteristics differences largely explain the gender wage gap. This result implies that there is no statistical significant difference between male and female incomes of the labor dispatching employees. The gap between genders was generally caused by individual character differences, which is one kind of non-human capital factors. The not significant income differences results do not imply the less gender discriminations existing for labor dispatching employees. Actually, labor dispatching is one form of discriminations, people doing same work with different payments, which decisively determines the employees' salary. In this condition, the effect of gender discrimination is not so crucial. To sum up, for the regular employees at higher than 80th quantile and less than the median, differences of human capital is the main reason of their gender wage gap. Between the median with 80th quantile, the effect of gender discrimination is significant. On the contrary, the sexism of labor dispatching employees is not so obviously, because of the existing discrimination in labor deal for labor dispatching employees covers the gender discrimination effects. VI. CONCLUSIONS According to the above empirical researches and analyses, we can get the following conclusions about the different forms of employments of Chinese clerical support workers. First, the gender wage gaps at different quantiles are not always Sticky Floor Effect Generally speaking, labor dispatching employees wage gap between genders is greater than regular ones. At the quantiles less than 70th, labor dispatching employees wage gaps between genders present Sticky Floor Effect, but the wage gaps of regular employees present Glass Ceilings effect. In the higher 70th quantile case, both of the two employment relationships show larger fluctuation. Therefore, we have strong evidences to believe that the wage gaps of various genders in China are not always a single Sticky Floor Effect Second, compared with the other kinds of occupations, the wage gap between genders of clerical support workers is not significant, which can be attributed to the simplification and low labor intensity of the clerical support work. However, no matter regular or labor dispatching, the wages of the male are generally higher than the female's. Third, There is an obviously positive correlation relationship between the average income of labor dispatching employees and human capital investment (such as education level, work experience, etc.), Labor dispatching employees are significant homogeneity. In this case, the differences of human capital characteristics are not so significant, the determinant of wages are some other non-human capital characteristics. Fourth, for regular employees, the female's ability in acquiring information and working experience accumulating is not as good as the male's ability. Additionally the education investment reward and working experience reward of the male is relatively more stable than the female's in different quantiles. Fifth, the gender discrimination of income levels is not so obvious in the regular employees coming from less than the median and higher than 80th quantile. Variations in individual characteristics, especially the variations of human capital which are the main causes of wage gap between genders. There is an existing discrimination in the range of upper 50th quantile and under 80th quantile. However, it is totally different for the labor dispatching employees from both end of the samples (less than 35th quantiale and higher than 90th quantile). To a great extent, the income gap of different genders in this group can be explained by gender discrimination. But, the gender discrimination can account for little about the income differences for the labor dispatching employees around the median between male and female. Discrimination against labor dispatching employees has covered the effect of the gender discrimination. Thus, it is reasonable to let the enterprises execute the revised labor contract law to

16 make the labor dispatching employees have the equal payment and rights with the regular ones. Based on the application and test of the data, we have strong evidence to believe the authenticity and the reliability of the data. During the research about the gender discrimination in different working types, we have some suggestions about data collection of Wageindicator Corporation. Firstly, developing a uniform global standard of statistical working will be helpful to compare the different situation through different countries. What's more, the adjustment of information providing can be made according the real national conditions, for example, the state-owned enterprise and private enterprises in China are totally different, roughly categorized may ignore the significant information. Another example is about making stratified sampling. Generally, we the divided the samples by geographic region which may not appropriate in China, according to economic strength of different cities may be more convincing. In addition, the distributions of the data need our attentions. Figure 1 plots the stable tendency of wages for regular employees and labor dispatching employees from less than 70th quantile and apparent fluctuation in higher than 70th quantile cases. The same change in both two curves should be caused by the distribution of the samples. Relatively fewer samples at the high quantile can't reflect the real character of the overall samples. It's reasonable to propose a new method in the random sampling to make the characters of the samples consisted with the population distribution. VII. LIMITATIONS AND FUTURE RESEARCHES The Limitations To accurately understand of the results of this study, it is important to recognize several limitations. Firstly, with the limitations of data, especially the quantity and quality of the samples, we only study the Clerical Support Workers gender wage gap without taking into account many other occupations whose wage may greatly influenced by employment forms, such as sales staff, drivers and so on. Secondly, In order to standardize the statistical data, the questionnaire design of wageindicator considered little about special national conditions of China, so that some of the independent variables which have an important impact on dependent variable log wage has not been taken into account, such as ownership. Specifically, the state-owned enterprises and private enterprises are not distinguished clearly in this survey, resulting in a limited explanatory power of the regression equation. Last but not least, for labor dispatching employees, who are not influenced greatly by the level of education, working experience and other factors of human capital, we have not further discussed the influence of non-human capital factors, owing to the limitations of original data, as well as avoiding excessive decentralization of the research topic. DIRECTIONS FOR FUTURE RESEARCH The results of the present study pose a number of interesting directions for future research. First, if we are going to do further research on the gender wage gap, factors to consider should not be limited to the traditional ones, such as industries, regions, and so on. According to the national reality and the present hot topics, many interesting factors can be taken into account. The employment forms in this paper may bring some enlightenment.

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