Changes to hukou discrimination in China’s labor market: employment and wages of rural migrant workers
【Abstract】The transfer of rural surplus labor is associated with China’s economic growth. Furthermore, labor reconfiguration makes a great contribution to economic growth. Rural migrant workers have been an important part of China’s labor market. China’s urban labor market has experienced great change. Labor market supply-demand changes may affect employment, wages and discrimination. Therefore, the aim of this paper is to analyze the change in the determination mechanism of employment and wages for rural migrant workers in China’s urban labor market. Although numerous studies have analyzed the discrimination against rural migrant workers in China’s urban labor market, most of them have used cross-sectional data, with little emphasis on comparative research. These studies can be classified into two groups. Studies conducted before 2002 have generally found that the explanatory power of discrimination with regard to the wage gap between urban local workers and rural migrant workers is approximately 50%. Those conducted after 2002 have found much lower explanatory power of discrimination. It is worth noting that the comparison of these results cannot directly show the change in discrimination in China’s urban labor market, as the methods and data used in these studies are different. We find that few studies have discussed the change in discrimination and that employment segregation has not been considered. Nevertheless, some researchers have found that system segregation and occupation segregation still exist when rural migrant workers enter the urban labor market. Therefore, we analyze the discrimination faced by rural migrant workers in two aspects, using the dual labor market and crowding effect theories: (1) whether workers can enter the labor market; and (2) whether discrimination exists after workers enter the market and how wages are determined. To address the first issue, we analyze the factors of employment for urban local workers and rural migrant workers, using the multinomial logit model and the 2001 and 2010 Chinese Urban Labor Survey. We then discuss the employment distribution of rural migrant workers in the self-employment, non-public and public sectors, and they are considered urban local workers by counterfactual analysis. For the second part, we discuss the change in the wage determination mechanism and wage discrimination in each sector. Based on the Mincer function, we analyze the effects of several factors on wage in each sector for urban local workers and rural migrant workers, using unconditional quantile regression. Furthermore, we pay more attention to the effect of education on wage and the change in the returns to education from 2001 to 2010. We then decompose the wage gap between urban local workers and rural migrant workers into two parts with the Firpo-Fortin-Lemieux decomposition method proposed by Firpo et al. (2007), and compare the change in discrimination faced by rural migrant workers in each sector from 2001 to 2010. The main conclusions are as follows. Firstly, the employment segregation discrimination faced by rural migrant workers decreased in 2010 compared with 2001. The education of rural migrant workers had a significant effect on the possibility of entering the public sector in 2010, but this effect did not exist in 2001. Furthermore, the effect of age on employment decreased in terms of both the significance and the degree. Secondly, the results of decomposing the wage gap between urban local workers and rural migrant workers in each sector show that the wage discrimination faced by rural migrant workers decreased in 2010 compared with 2001. The results also indicate that the wage gap is mainly affected by differences in individual characteristics, not by discrimination. In view of the results about employment segregation and wage discrimination, we conclude that employment segregation still exists when rural migrant workers enter the public sector. However, in 2010, once they entered the public sector, wage discrimination sharply decreased. Given these findings, we suggest that policymakers further enhance recruitment and employment norms to change the dual labor market between urban local workers and rural migrant workers into a fairer, more normative and more uniform labor market.
【Keywords】 China’s urban labor market; rural migrant workers; occupational segregation; wage discrimination;
. ① Data source: http://www.stats.gov.cn/tjsj/zxfb/201702/t20170228_1467424.html
. ① For an introduction of the methods and data sample of the CULS, see Wang (2005), Qu (2014) and the website http://iple.cssn.cn/ldjjx/yjsj/
. ② Wang (2005) divides occupations into self-employment, employees of public sector organizations, ordinary workers of non-public sector organizations, and administrative personnel and professional technicians of non-public sector organizations. Wang (2005) also discusses non-local people including rural migrant workers and the labor that flows among cities. This paper only discusses rural migrant workers and thus loses some samples. Since only few rural migrant workers are employed as administrative personnel or professional technicians of non-public sector organizations, this paper regards the ordinary workers, administrative personnel and professional technicians of non-public sector organizations jointly as employees of non-public sector organizations. In addition, according to the classification in this paper, public sector organizations still include collectively-owned enterprises and collectively-controlled enterprises. However, after the reform of state-owned enterprises, collective enterprises have basically completed their transformation and their ownership has been basically the same as that of non-public sector organizations. If public sector organizations include a large number of collective enterprises, the difference with non-public sector organizations will be weakened. In view of this, this paper collects statistics on the different types of enterprises that fell within the category of public sector organizations in 2010, and finds that the urban local labor employed by collective enterprises accounted for 8.2% of the urban local labor employed by public sector organizations and the rural migrant workers employed by collective enterprises accounted for 26.5% of the rural migrant workers employed by public sector organizations. It can be seen that the proportion of collective enterprises in public sector organizations was not high. Moreover, although the reform of state-owned enterprises approached the end in 2001, it has not been completely finished. Therefore, it is more reasonable to divide organizations into public sector organizations and non-public sector organizations. In order to compare the results about 2001 and 2010 under a unified approach, this paper still classified employment into self-employment, public sector organizations, and non-public sector organizations.
. ① Data source: CULS1 and CULS3 conducted by the Institute of Population and Labor Economics of the Chinese Academy of Social Sciences.
. ② Data source: http://www.chinajob.gov.cn/FAQs/content/2003-03/26/content_375641.htm
. ③ This is a counterfactual analysis method. The counterfactual analysis assumes premises and discusses how the results will change when the premises change. This requires a clear relationship between the assumptions and the results. When the changes brought by changes in the premises are more complex and diverse, the credibility of the impact of the premise changes on the results will be lower. As pointed out by Li (1982), the analysis made by Fogel et al. about the impact of railways on the US economy ignores the indirect effects of railways, because railways not only reduce transportation costs but also strengthen the links between different sectors as well as between different regions. In counterfactual analysis, such indirect effects are common. In general, when the external social and political conditions remain unchanged, the results estimated by counterfactual analysis will be more reliable if the relationship between the assumed premises and the results is clearer. This paper assumes rural migrant workers as urban local labor, and thus also faces other indirect effects brought by “city” in addition to the identity change in terms of hukou. Viewed from the labor market employment studied in this paper, when the labor market is not discriminatory, the employment of people is mainly determined by individual characteristics; when discrimination exists, different identities will cause different groups to face different employment environments. The relationship between the assumption and conclusion is thus clear. However, it should be admitted that counterfactual estimation is unable to be completely accurate and we can only improve its credibility. Therefore, the individual characteristics of people are controlled in the model, which reduces the indirect effects in the counterfactual estimation and improves the credibility of the estimation. In addition, this paper focuses on the changes in discrimination between 2001 and 2010. Although the counterfactual estimation has certain bias, the same baseline model is adopted in the comparison. Moreover, during this decade, the hukou system had no fundamental change: although rural migrant workers could participate in the social security system and their children could receive compulsory education in the places where they were hired, their participation rate in the social security system was low and they also faced difficulties in the compulsory education for their children. Therefore, in the comparison, the city-related factors that have not been considered were largely common without significant change during the studied period, and the comparison results are capable of reflecting the discrimination caused by hukou.
. ① Data source: CULS3 conducted by the Institute of Population and Labor Economics of the Chinese Academy of Social Sciences.
. ① Data source: CULS3 conducted by the Institute of Population and Labor Economics of the Chinese Academy of Social Sciences.
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