Study On Relationship Between Education And Income Of Chinese Farmers

The process of rural poverty in China, how to help farmers out of poverty as quickly as possible? How to enhance farmers’ ability to withstand the risk of poverty? How to find stable and effective mechanism for increasing farmers’ income? How to achieve a virtuous cycle of self-development of the farmers? This series of questions, both theoretical urgency, but also has real urgency. From the theoretical point of view, the economy is based on income is the core. From a practical sense, "the first strong power to teach," "the first anti-poverty and ignorance." Education system from the perspective of farmers’ income is a quite meaningful work, of course, a challenging task.In this paper, qualitative analysis and quantitative analysis, specification analysis and empirical analysis of a combination of methods, the main innovation of this paper include data innovation, methodology, and innovative ideas in three areas. Specifically: First, data innovation - the use of micro-panel data. The data used in this article from the "Chinese Nutrition and Health Survey" (China Health and Nutrition Survey, referred to as CHNS) panel database (Longitudinal Master Files), the time span from 1989, 1991, 1993, 1997, 2000 , 2004, 2006-2009, eight observations constitute a micro panel data.Second, the method of innovation - the use of micro-econometrics. In this paper, a micro-econometric analysis of a variety of tools, but also cutting-edge micro-econometric research areas, such as the use of panel VAR model, the variance decomposition and impulse response function of the relationship between education and income test; use of kernel density function estimation farmers’ income distribution; the use of quantile regression and counterfactual decomposition methods and analysis of factors affecting the income distribution decomposition; use panel fixed-effect model analysis on the rate of return migrant education structure and institutional factors; use panel fixed-effect model analysis farmer education on sustainable income; the use of double-difference analysis of rural compulsory education policy for farmers affected by the impact of years of education; using quantile regression analysis of farmers’ income on the direct impact of years of education.Third, view innovation - The relationship between education and income. In this paper, education and income, two-way relationship between the research, that is, not only to study the impact of education on income, but also studied the impact of income on education.Chapter 1 Introduction In this paper, as the opening, based on the issues raised, to further elaborate the study theoretical significance and practical significance, then this paper, literature review in Chapter 2 and Chapter 3, the relationship between farmers’ education and income related to the theory As a paper-based literature and theoretical basis. Predecessors, both based on extensive research and brilliant ideas, this paper, Chapter 4 Analysis of farmers’ education and income, the use of macro-and micro-panel data summary data chart analysis, the main part of the next paper - quantitative analysis, to provide a relationship between education and income intuitive description. In order to study the relationship between farmers ’education and income, it is necessary first to test the relationship between the two, this is the thesis of Chapter 5, the relationship between farmers’ education and income test, measurement methods used in this chapter include the stationary panel data test, the panel VAR model, generalized impulse response functions, variance decomposition and Granger causality test. Test results show that the existence of education and income Granger causality. Then, the paper from Chapter 6 to Chapter 9, using a variety of quantitative analysis tools, respectively, studied the impact of education on income and income on education: education for farmers in Chapter 6 of the income distribution effects of quantitative analysis, this chapter first use of nuclear density function estimate the distribution of income of farmers, and then use the quantile regression and decomposition dynamics of income distribution were analyzed; Chapter 7 farmer education on income structure and institutional factors affecting quantitative analysis in this chapter Chapter 6, based on adding a number of structural and institutional control variables, targeted gender, urban-rural and other structural and institutional factors were analyzed; Chapter 8 permanent income effect on farmers’ education and the measurement analysis, and Chapter 6 7 can be seen as the impact of education on the analysis of current income, and this chapter to further analysis of education for the future impact of the expected permanent income. Chapter 9 of the paper is to analyze the impact of income on education, including two levels: using quantile regression to examine the direct effects of income on education, with the double-difference method to examine the indirect effects of income on education. In summary, the main thesis in Chapter 10 Conclusions and policy recommendations, summarized the main conclusions of this paper and made several targeted policy recommendations.The main conclusions of this paper are: First, all sub-sites from the regression results, level of education, experience and constant coefficient is positive, experience squared coefficient is negative, all coefficient values are 99% significance level . Sites were investigated for each component of the case: the rate of return to education are significantly positive, but in the sub-sites are very obvious differences, there is no simple upward or downward trend. In other words, the rate of return to education does not appear as conditions improved and sub-sites increase, or sub-sites as conditions improve and decline in such a clear trend, but showing an irregular movements. It can be concluded that: in the low-scoring position return return to education is higher than the score-bit return. 6 counterfactual analysis also indicates that the higher level of education to improve the positive impact on income.Second, the urban-rural dummy variable coefficient is significantly positive, indicating that urban residents than rural residents by years of education. Gender dummy variable coefficient is significantly positive, indicating that residents of rural male years of education than rural female residents, but in all sub-locus, variable coefficients are high and low, did not show a clear trend, in general, point of view, low-income level of farmers, the variable coefficient is more prominent, that is, low income levels of rural male residents of years of education than women years of higher education. Regional dummy variable coefficient is significantly positive. On the whole, middle-income level of farmers, the variable coefficient is more prominent, showing inverted U-shaped change.