Market Discriminatory Impact On Wage Differentials Between Urban Informal Employment And Formal Employment

In China we often see this phenomenon:those who work in the form of formal employment stability, enjoying higher unit benefits, with job promotion, education and training priorities, rather than those who work in the form of loose informal employment, they have little welfare benefits even have nothing They do not have the advantage in promotion and education and training, all have led to different wage levels. Generally, employment discrimination may be caused due to the following two aspects:first, informal employment and informal employment characteristics of different coefficients, for example, years of work experience in formal employment is different to informal employment. Second, informal employment discrimination in the labor market. It specifically refers to informal employment, even informal employment has the same individual characteristics, the wage level of informal employment remains below the formal employment And in both cases corresponding policy implications are different. The first means that by improving the individual characteristics of informal employment, to narrow the wage gap. Second, it means that you must to narrow the wage gap by reducing informal employment discrimination against persons.Fewer domestic scholars studied it, Most researchers use Logarithmic wage differential, excessive emphasis on mean analysis, proposed policy which is very simple. as the above deficiencies, This paper introduces the concept of the ratio of logarithm wages between formal employment and informal employment and use quantile regression and M-M (2005) wage decomposition model. By these two methods we studied wage differentials on the entire wage distribution between formal employment and informal employment. Finally, according to research findings, From the government, enterprises and workers in three aspects proposed measures and recommendations to reduce employment discrimination.Based on CHNS database, in accordance with certain criteria we divide the sample into a formal employment and informal employment categories, and gives both the average wage, average work experience level and education level experience packet information. Using the OLS regression model, quantile regression model gives wage determination equation (which gives10quantile regression quantile,25percentile and50percentile,75percentile and90th percentile of the regression results), and factor (rate of return) wage equation gives a brief description and analysis. Machado and Mata (2005) proposed a model (M-M model) counterfactual wage distribution was studied in the discrimination existence, the same year a different variation of the median market discrimination and the different year the same time quantile market on discrimination changing. By OLS regression and M-M quantile regression model analysis, the following conclusions:First, OLS regression results show that returns to education and retum on work experience are not equal and exist gender wage gap. Second, quantile regression results show that both the formal employment or informal employment is estimated coefficient returns to schooling has reached a significant level of1%. Absolute value of the difference between the two, first rose up then falling; Returns of formal employment work experience is significantly higher than the return of informal employment; gender wage gap is pervasive and women have to suffer wage discrimination; Marriage variable coefficient is significantly positive. Third, the Machado-Mata (2005) decomposition results are two corresponding conclusions. One section of the data analysis showed that the number of wage differentials for2009,with the quantile increase, there is a shrinking trend;For characteristic differences, with the improvement of quantile the proportion of the characteristic differences continuous improvement; coefficient differences, but on the contrary, with the increase of the wage distribution, the proportion of the coefficient differences continue to reduce. Second time series data analysis showed that trend coefficient differences and trend characteristic difference are similar, The total difference and Coefficient differences decreases monotonically from low-end to high-end, It explains that market Discrimination of the low end of the wage distribution is higher than the high end of the wage distribution. Coefficient difference (market discrimination), the low score was significantly higher than the high score. market Discrimination of the low end of the wage distribution has Interannual fluctuations.Employment discrimination on the low end of the wage income is more obvious, so the government, in addition to develop appropriate anti-discrimination laws, should also focus on improving the level of education and improving the policies and subsidies on informal employment of low wages; For companies should assume certain aspects of corporate social responsibility, both in terms of recruitment or employment to the spirit of "fair and just" attitude, treat every employee; aspects of workers to understand the situation, through a variety of channels and methods to continuously improve their core competencies, while focusing on training their awareness of rights and advocacy skills.