人力资本、社会网络对农民非农就业及收入的影响
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摘要
基于作者自己对上虞虞南地区部分农村农民的调研数据,本文主要考察了人力资本、社会网络资源在农民的非农就业决策和非农就业收入中扮演的角色,并使用调研数据对调研地农民的人力资本、社会网络、村庄区位条件等现状进行了统计分析。
大量文献研究表明人力资本和社会资本在非农就业决策中的正面作用,本文实证研究的第一部分正是尝试建立Probit模型来验证这些理论。首先对一些重要变量进行描述性统计分析,目的在于呈现调研地社会经济发展的概况；继而运用因子分析对社会网络资源这一关键变量的三个核心指标进行整合；最后对非农就业决策模型进行运算和解读。通过这一过程,本文证实了受教育程度在非农就业中的正向作用而职业培训并没有发挥重要影响,并发现社会网络资源与非农就业显著的正向相关性。此外,一些个人特征、家庭特征和村庄特征变量也显著地影响到了农民的非农就业决策。
在此基础之上,实证研究的第二部分深入分析非农就业收入与人力资本、社会网络资源之间的具体联系,模型主要参考明瑟尔收入方程并添加了研究需要的其他变量。模型拟合的结果表明：受教育程度对非农就业入有积极意义,调研样本的教育回报率大约为6.5%,但依然无法看到职业培训的效果；社会网络资源在对收入的提升上作用非常显著,但由于没有控制行业变量并考虑到收入和社会网络之间可能的双向因果关系,对这一结果应谨慎对待；村庄区位条件对非农就业收入影响不大,非农就业从某种程度上起到了抹平地区间收入差距的作用；女性可能在行业准入和职业晋升上受到歧视从而在收入上体现出与男性的巨大差距。
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.
大量文献研究表明人力资本和社会资本在非农就业决策中的正面作用,本文实证研究的第一部分正是尝试建立Probit模型来验证这些理论。首先对一些重要变量进行描述性统计分析,目的在于呈现调研地社会经济发展的概况；继而运用因子分析对社会网络资源这一关键变量的三个核心指标进行整合；最后对非农就业决策模型进行运算和解读。通过这一过程,本文证实了受教育程度在非农就业中的正向作用而职业培训并没有发挥重要影响,并发现社会网络资源与非农就业显著的正向相关性。此外,一些个人特征、家庭特征和村庄特征变量也显著地影响到了农民的非农就业决策。
在此基础之上,实证研究的第二部分深入分析非农就业收入与人力资本、社会网络资源之间的具体联系,模型主要参考明瑟尔收入方程并添加了研究需要的其他变量。模型拟合的结果表明：受教育程度对非农就业入有积极意义,调研样本的教育回报率大约为6.5%,但依然无法看到职业培训的效果；社会网络资源在对收入的提升上作用非常显著,但由于没有控制行业变量并考虑到收入和社会网络之间可能的双向因果关系,对这一结果应谨慎对待；村庄区位条件对非农就业收入影响不大,非农就业从某种程度上起到了抹平地区间收入差距的作用；女性可能在行业准入和职业晋升上受到歧视从而在收入上体现出与男性的巨大差距。
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.
引文
1数据出处：《中国农村统计年签—2010》
1数据出处：《中国农村统计年鉴—2010》
2数据出处：《上虞市统计年鉴2005》
1KMO值>0.9,非常适合；0.8-0.9之间,很适合；0.7-0.8之间,适合；0.6-0.7,比较适合；0.5-0.6,勉强；0.5以下则不适合。
1并非完全精确的天数,要求调研对象估计自己一年非农就业的工作月份并进行转换。
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1数据出处：《中国农村统计年鉴—2010》
2数据出处：《上虞市统计年鉴2005》
1KMO值>0.9,非常适合；0.8-0.9之间,很适合；0.7-0.8之间,适合；0.6-0.7,比较适合；0.5-0.6,勉强；0.5以下则不适合。
1并非完全精确的天数,要求调研对象估计自己一年非农就业的工作月份并进行转换。
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