Returns to Education in China:1989-2011

Using China Health and Nutrition Survey(CHNS) data between 1989 and 2011, this paper measures the returns to education in China based on the Mincer earnings function and reaches the following findings through an analysis of the tendency of continuous variations over a long timeframe: returns to education are on the rise within the range of samples both under relative and absolute scenarios; returns to different levels of education are characterized by increasing marginal return; no significant difference exists between the returns to junior middle school and the returns to primary school education. Further discussions consider that the requirements of job positions for the overall competence of personnel, differentiated decline of corporate demand for recruitment, lack of an evaluation system in the labor market, information asymmetry in the job market, the development strategy adopted in a particular stage of history and the current slow progress of economic transition have jointly led to the underemployment of college graduates and the great enthusiasm of parents investing in higher education for their children. Conclusions of this paper not only have important practical relevance to the ongoing implementation of China's innovation-driven development strategy, but offer inspirations for the new round of educational reform as well.

Education, Employment and Human Capital Investment in China: Reality and Expectation

With the national population census data and sample survey data of 1990,2000,2005,2010 and 2015,this paper examines the evolving relationship between education and unemployment,and carries out an empirical analysis on how such correlation affects the expected return on education.Our findings suggest that education can significantly reduce the risk of unemployment,but this effect varied over time during 1990-2015.Education’s effect in reducing unemployment significantly increased from 1990 to 2000,but diminished from 2005 to 2015,particularly for the young group.This paper arrives at the ex post return on education estimated with 1995,2002,2007 and 2013 data of the Chinese Household Income Project(CHIP)and calculates the expected return on education based on such data.Results indicate that from 1995 to 2007,the expected return on education kept on the rise until it peaked in 2007.For the young group aged 22-29,the expected return on education started to decline after 2002.This result is of great significance to unravelling return on education during China’s transition period and households’decisions to invest in education.

The dilemma of social security on the retired education youth who back to Shanghai

The Shanghai government to help return Shanghai retired educated youth to overcome economic and social life in various difficulties introduced many help policy, but due to historical and realistic reasons, retired to Shanghai social security of the educated youth is always a trouble, it is difficult to get the perfect solution, it seriously affected the Shanghai social harmonious stability. This article attempts through the government-led level of retirement return Shanghai educated youth with all-sides help to solve their difficulties, through to the educated youth to compensate, to set up the educated youth mutual guarantee fund, broken educated youth and children between the problem of intergenerational transmission of retired return Shanghai social security dilemma of educated youth broken, in order to improve their level of social security in Shanghai.

Returns to education in rural China

Based on one of the most widely used datasets by foreign-based sociologists,this paper examines the rate of returns to education in rural China.Compared with the previous studies that showed rather low rates in rural areas throughout the 1980s,this study finds a considerably higher rate in 1996.A chief contributor is the rapid non-agricultural development,which creates enormous upward mobility opportunities,particularly for the more educated.Due to the uneven economic development nationwide,the rate of returns to education varies widely across regions.In areas with less developed non-agricultural sectors,it remains low.In contrast,where off-farm employment is widespread,it is much higher.In addition,the labor market is functioning to allocate the more educated to better-paid jobs,but has yet to produce higher returns to education in non-agricultural sectors than in the agricultural sector.However,changes may be occurring in coastal regions.

Improving the Accuracy of Estimated Returns to Education in China--Based on Employment Rate, Career Length, and Income Growth

Most empirical studies on the returns to education use current income to proxy for lifetime income due to the lack of longitudinal data. This simplification is found to cause biased estimates and the result is conditional on being employed. This paper quantifies the returns to education with heterogeneity in employment rates, career lengths, and income growth rates. Using data from China, this paper attempts to account for these differences across the life-cycle and estimates the returns to education in terms of lifetime income when actual lifetime earnings data are not available. The model clarifies the mathematical relationship between conditional current returns to education, unconditional current returns to education, and unconditional lifetime returns to education. This new approach explains how employment rates, career lengths, and income growth rates affect the direction and magnitude of the bias in estimating the returns to education.

Factors Influencing Migrant Workers’ Employment and Earnings——The Role of Education and Training

In the urban labor market, the rural labor force can choose whether to become self- employed or work for wages depending on their stock of human capital and local labor market conditions. A simple Mincer earnings regression shows that the rate of return to schooling for wage earners is two percentage points higher than that for the self-employed. After correcting for bias in sample selection, the expanded Mincer earnings equation estimated the rate of return to schooling for wage earners at between 5.3 and 6.8 percent. From the standpoint of training, we found that the simplest form of training, short-term training and formal training played an important role in promoting migrant workers’ repeat mobility. However, the simplest form of training did not have a significant effect on earnings,whereas short-term and formal training played an important determining role in this respect. Moreover, rights protection issues such as wage arrears also had an important effect on migrant workers’ repeat mobility. In handling heterogeneity and endogenous educational variables among migrant workers, the authors found that the years of schooling of the parents of migrant workers were not an ideal instrumental variable.

The impact of overeducation on earnings in China

The paper estimates the returns to overeducation by the Over-Required and Undereducation(ORU)model.The estimated results indicate that the returns to overeducation are positive,but lower than the returns to required education,which suggests that while overeducated employees’earnings are diminished,they still can benefit from it.The paper also attempts to estimate the returns to overeducation by occupations,industries and regions.The result shows that in the field where educational level has much to do with the skills required by employers,education-job match has a greater effect on one’s earnings,such as professionals and skilled persons.On the contrary,education-job mismatch has little effect on one’s earnings,such as non-skilled employees,administrative and clerical employees.In addition,the returns to overeducation are lower or insignificant for those working in competitive but lower paid industries and areas.Conversely,the returns to overeducation are higher for those working in the highly monopolized and highly paid industry and area.It can be argued that regardless of the incidence of overeducation,those with higher level of education prefer to choose the lower level of job in these industries and areas.