Bit Reunification Of China's Gender Wage Gap Study

During the times of planned economy, China had practiced the united system ofwage and welfare, allocated labors planned, so, during that times, China was one ofthese Country who had the smallest gender wage gaps. Since the 1990's, the gap havebroadened drastically. At the same time, the studies on gender wage gaps areincreasing, but most of them are on gross and mean. Based on the gross and meangender wage differences of urban labor market, analyzed specifically the gender wagegaps on five quantiles, and concluded that gender wage gaps are different in everyquantile, so there will be deviation if used one gap to instead all. In general, theincomes of urban workers are polarized into two classes seriously. The gross gaps areobvious and the middle class is absent. In the urban labor market, the highest wage isas much 240 times as the lowest wage. The rate of gender wage gaps is 20%.Women's wages are generally lower than men's, which is right in every centiles. Menearn far more than women in the high centiles compared with the lowers. However,gender wage gaps are not equal to discrimination, as the gaps can also be resulted incharacter differences. Discrimination is only one factor. This paper estimates thecoefficients of gender wage discrimination on urban labor market both on gross leveland centiles. On theory and empirical analysis, this paper proves that genderdiscrimination is definitely existing and differentiates between quantiles. Characterdifferences can explain the gender wage differences partly, which is 16% of the grossgaps inthis paper, while, the discriminated coefficient is 0.2146 that is lower than therate of unexplained gender wage differences, which indicates that the gender wagegaps include both discriminations and unexplained factors. The discriminatedcoefficient in the 1% centile is 0.4304, 0.1585 in the 25% centile, 0.1398 in the 50%centile, 0.2208 in the 75% centile, 0.5370 in the 99% centile, which illustrate thatgender discrimination is in the highest degrees in the lower and higher quantiles., andin these two levels, women's treatments are more liable to lowered. This paper alsoestimated the influences of the variables of education, age, sector ownership andoccupation on gender wage gaps, the influences also vary between quantiles.