| Based on the author's survey data on the peasants in part of the villages in the southern region of ShangYu County, this research mainly focus on the role of human capital and social network in the non-agricultural employment decision and income of peasants. The research also statistically analyzed the current situation of the human capital, social network and geographic conditions of the peasants through these survey data.The positive role of human capital and social capital played in the non-agricultural employment decision has already been demonstrated by a large quantity of documents. The first part of the empirical research in this paper is to build Probit model to verify these theories. First the author conducted some descriptive statistical analysis on some important variables, aiming at demonstrating the general situation of the socioeconomic development of the research region. Then the author used factor analysis to integrate the three core indicators of the key variable called "social network resources". In the end, the research calculated and explained the non-agricultural employment decision model. Through the process, this part of the research proved that peasants'education level played a positive role in their non-agricultural employment and vocational training did not have an important influence on their non-agricultural employment. Besides, some variables like personal characteristics, family characteristics and the characteristics of the villages also had a significant impact on peasants' non-agricultural employment decision.On that basis, the second part of the empirical research continues to conduct in-depth analysis on the actual correlation between non-agricultural income and human capital as well as social network resources. The model mainly refers to Mincer Wage Equation. The author also added some other variables indispensable in the research. The result of model fitting shows that peasants' education level has a positive effect on their non-agricultural income. In the research sample, the rate of return to education is about6.5%. But we still cannot see the effect of vocational training. Social network resources have a significant effect in raising income. But without the control variable like "industry difference" and the consideration of possible bidirectional causality between income and social network, we should treat this result cautiously. The geographic and economic conditions of the villages have little effects on non-agricultural income. Non-agricultural employment plays a role in equalize the regional income gap in some degree. Because of the discrimination towards women in terms of industry access and career promotion, female's income has a large margin compared to those of males. |
