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农户对农地经营权抵押贷款响应及其影响因素——基于零膨胀负二项模型的微观实证分析

曹瓅1 罗剑朝1,2

(1.西北农林科技大学经济管理学院)
(2.西北农林科技大学农村金融研究所)

【摘要】本文运用宁夏同心县、平罗县1272户农户调查数据,采用零膨胀负二项模型实证分析了市场主导型模式和政府主导型模式下农户对农地经营权抵押贷款的响应、差异及其影响因素。研究表明,户主性别、年龄、文化程度,农户家庭劳动力占比、土地经营面积、经营类型、家庭社会资本、金融机构业务办理积极性、农户贷款经历及农地经营权抵押贷款模式差异均对农户农地经营权抵押贷款响应频次具有显著影响。相较于政府主导型模式,市场主导型模式下农户对贷款的响应更为积极;户主性别和年龄、有无家庭成员或亲戚朋友担任村干部以及农户有无贷款经历是两种模式下农户对农地经营权抵押贷款响应的共同影响因素,其中,农户有无贷款经历是最重要的影响因素。

【关键词】 农地经营权;抵押贷款;贷款行为;零膨胀负二项模型;

【DOI】

【基金资助】 教育部“长江学者和创新团队发展计划”创新团队项目“西部地区农村金融市场配置效率、供求均衡与产权抵押融资模式研究”(项目编号:IRT1176); 国家自然科学基金面上项目“农村土地承包经营权抵押融资试点效果评价、运作模式与支持政策研究”(项目编号:71573210); 西北农林科技大学基本科研业务费人文社会科学项目“农村土地承包经营权抵押担保融资效果评价、运作模式与支持政策研究”(项目编号:2014RWZD01); 清华大学中国农村研究院博士论文奖学金项目(项目编号:201517)的资助;

Responses of the farm households to mortgaging operational rights to rural land and its influencing factors—a micro empirical analysis on the basis of the zero-inflated negative binomial model

CAO Li1 LUO Jianchao1,2

(1.The College of Economics & Management, Northwest A & F University)
(2.Institute of Rural Finance, Northwest A & F University)

【Abstract】This paper adopted survey data of 1272 farm households from Tongxin and Pingluo counties in Ningxia Hui Autonomous Region as well as the ZINB model to empirically analyze the farmers’ responses to, differences in and influencing factors on mortgaging operational rights to rural land under the market-dominating mode of Tongxin County and the government-dominating mode of Pingluo County. The study showed that the gender, ages and educational levels of household heads, proportions of labor force of the farm households, areas of operational land, operational types, social capital of households, enthusiasm for handling business of financial institutions, farmers’ borrowing experiences and differences in modes of mortgaging operational rights to rural land, all had significant effects on the frequency of responses to mortgaging operational rights to rural land. Farmers’ responses to loans under the market-dominating mode was more active than that under the government-dominating mode; meanwhile, the gender and age of the household head, any family member or relative as the village cadre and borrowing experience were common influencing factors on farmers' responses to mortgaging operational rights to rural land from the two regions. Among them, farmers’ borrowing experience was the most important influencing factor.

【Keywords】 operational rights to rural land; mortgages; the behavior of borrowing loans; the zero-inflated negative binomial model;

【DOI】

【Funds】 Supported by the Program for Changjiang Scholars and Innovative Research Team in University of the Ministry of Education of the People's Republic of China (IRT1176); Supported by the General Program of the National Natural Science Foundation of China (71573210); Supported by the Program of the Fundamental Research Funds for Humanities and Social Sciences of Northwest A & F University (2014RWZD01); Supported by the Doctoral Dissertation Scholarship of China Institute for Rural Studies, Tsinghua University (201517);

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    Footnote

    [1]. ① Source: the announced list of pilot counties for mortgaging operational rights to rural land and residential property in 2016, http://www.tuliu.com/read-20863.ht ml [^Back]

    [2]. ② Source: management measures on mortgaging operational rights to rural land of the Agricultural Bank of China (the trial version), http://www.caein.com/index.asp?xA ction=xR eadN ews&NewsI D=101221 [^Back]

    [3]. ① The process of shifting to (4) with logit as copula is complicated with many matrix transformations and intermediate variables. Therefore, this paper just shows the formula. [^Back]

    [4]. ① The incidence percentage reflects the percentage of the count dependent variable' change caused by per unit of change in the independent variable when other independent variables remain unchanged, namely, change of the farmers’ frequency of responses to mortgaging operational rights to rural land. When the independent variable has positive effects, the incidence percentage is (the incidence ratio – 1) X 100%; when the independent variable has negative effects, the incidence percentage is (1–the incidence ratio) X 100%. [^Back]

    [5]. ① 1.2658 = 1/0.7900. [^Back]

    [6]. ① 1/0.8012 = 1.2481. [^Back]

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This Article

ISSN:1002-8870

CN: 11-1262/F

Vol , No. 12, Pages 31-48

December 2015

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Article Outline

Abstract

  • 1 Introduction
  • 2 Literature review
  • 3 Research design and construction of models
  • 4 Data sources and selection of variables
  • 5 Results of measurement and analyses
  • 6 Research conclusions and policy implications
  • Footnote

    References