社会网络对农户借贷的影响研究
发布时间：2018-08-20 17:05
【摘要】：我国农户借贷难问题一直困扰着农户自身和政府相关部门，并制约着农户提高收入和摆脱贫困，影响社会稳定。尽管政府、金融机构、学者及相关人士开始关注并探求解决农户融资难的相关对策措施，但大部分研究局限于现有农村金融体系，很少从社会网络视角来研究农户借贷问题。对于具有明显“差序格局”特征的我国农村地区，农户社会网络对农户借贷的影响研究就显得很有意义。 本文以农户为调查对象，以农户社会网络对农户借贷获得、各借贷渠道借贷获得及农户信贷配给的影响为研究内容，分析当前我国农户社会网络和农户借贷现状。基于社会网络理论，本文建立了以人情支出比、强关系网络、网络规模、网顶、网差、网络中心性、网络异质性为指标的社会网络测量模型，探究社会网络对农户借贷的影响机理；并通过调查问卷整理出的413份有效数据，，运用logit模型、MNL模型以及OLM模型分别检验了社会网络对农户借贷获得、农户各借贷渠道借贷获得以及农户信贷配给的影响研究假设。 研究结果表明：人情支出比越大、强关系网络规模越大、社会网络规模越大，农户获得非正规借贷、正规借贷和混合借贷的概率越大，越有助于农户获得借贷；网络异质性越高，有助于农户获得非正规借贷和混合借贷，越有助于农户获得借贷；人情支出比越大、强关系网络规模越小，农户受到信贷配给的概率越小；强关系社会网络规模越大，农户受到信贷配给越严重,主要是这些农户的需求不足导致。网顶和网差对农户获得借贷没有显著影响，但是网差大的农户在正规贷款方面有优势，网顶高、网差大、网络规模大的农户在信贷配给方面没有优势。网络中心性高的农户获得非正规借贷、正规借贷和混合借贷的可能性更小，农户获得借贷的概率也更低，但受到正规金融机构的信贷配给程度更小。 根据理论与实证研究结论，基于农户社会网络视角，为提高农户借贷获得、减轻农户信贷配给程度，从农户自身、国家政策层面和金融机构三方面，探讨了如何构建和利用好社会网络，从而使农户的借贷需求得到满足，减轻其信贷配给程度。
[Abstract]:The problem of rural households'difficulty in borrowing and lending has been plaguing the farmers themselves and the relevant government departments, restricting the farmers to raise their income and get rid of poverty, and affecting social stability. The system seldom studies peasant household lending from the perspective of social network, so it is very meaningful to study the impact of peasant household social network on peasant household lending in rural areas with obvious "differential order pattern".
Based on the social network theory, this paper establishes a human expenditure ratio, a strong relationship network, a network scale, and a network top. Social network measurement model with network gap, network centricity and network heterogeneity as indicators to explore the impact mechanism of social network on rural household lending; and 413 valid data collected by questionnaire, using logit model, MNL model and OLM model to test the access of social network to rural household lending, and rural household lending channels. Research hypotheses on the impact of acquisition and credit rationing on farmers.
The results show that the larger the ratio of human expenditure, the larger the scale of strong relationship network, the larger the scale of social network, the greater the probability of informal lending, formal lending and mixed lending, the more conducive to farmers'access to credit; the higher the heterogeneity of the network, the more conducive to farmers' access to informal lending and mixed lending, the more conducive to farmers'access to informal lending and mixed lending. The larger the ratio of human expenditure, the smaller the scale of strong relationship network, and the smaller the probability of farmers receiving credit rationing. The larger the scale of strong relationship social network, the more serious the farmers are subjected to credit rationing, mainly due to the insufficient demand of these farmers. Farmers with high network centrality are less likely to get informal lending, formal lending and mixed lending. Farmers with high network centrality have lower probability of obtaining loans, but they are less likely to receive credit rationing from formal financial institutions.
Based on the theory and empirical research, this paper discusses how to construct and make good use of the social network from three aspects: farmers themselves, state policies and financial institutions, so as to improve the farmers'access to credit and reduce their credit rationing.
【学位授予单位】：湖南大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：F832.43
本文编号：2194364
[Abstract]:The problem of rural households'difficulty in borrowing and lending has been plaguing the farmers themselves and the relevant government departments, restricting the farmers to raise their income and get rid of poverty, and affecting social stability. The system seldom studies peasant household lending from the perspective of social network, so it is very meaningful to study the impact of peasant household social network on peasant household lending in rural areas with obvious "differential order pattern".
Based on the social network theory, this paper establishes a human expenditure ratio, a strong relationship network, a network scale, and a network top. Social network measurement model with network gap, network centricity and network heterogeneity as indicators to explore the impact mechanism of social network on rural household lending; and 413 valid data collected by questionnaire, using logit model, MNL model and OLM model to test the access of social network to rural household lending, and rural household lending channels. Research hypotheses on the impact of acquisition and credit rationing on farmers.
The results show that the larger the ratio of human expenditure, the larger the scale of strong relationship network, the larger the scale of social network, the greater the probability of informal lending, formal lending and mixed lending, the more conducive to farmers'access to credit; the higher the heterogeneity of the network, the more conducive to farmers' access to informal lending and mixed lending, the more conducive to farmers'access to informal lending and mixed lending. The larger the ratio of human expenditure, the smaller the scale of strong relationship network, and the smaller the probability of farmers receiving credit rationing. The larger the scale of strong relationship social network, the more serious the farmers are subjected to credit rationing, mainly due to the insufficient demand of these farmers. Farmers with high network centrality are less likely to get informal lending, formal lending and mixed lending. Farmers with high network centrality have lower probability of obtaining loans, but they are less likely to receive credit rationing from formal financial institutions.
Based on the theory and empirical research, this paper discusses how to construct and make good use of the social network from three aspects: farmers themselves, state policies and financial institutions, so as to improve the farmers'access to credit and reduce their credit rationing.
【学位授予单位】：湖南大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：F832.43
【引证文献】
相关硕士学位论文 前1条
1 吴豪;基于复杂网络理论的农村信贷系统建模及仿真研究[D];西北农林科技大学;2013年
本文编号：2194364
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