Side-by-side Chinese-English


曹瓅1 罗剑朝1,2



【关键词】 农地经营权;抵押贷款;贷款行为;零膨胀负二项模型;


【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;


【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);

Download this article

    [1]. ① Source: the announced list of pilot counties for mortgaging operational rights to rural land and residential property in 2016, ml [^Back]

    [2]. ② Source: management measures on mortgaging operational rights to rural land of the Agricultural Bank of China (the trial version), 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]


    [1] Lambert, D.: Zero-inflated Poisson Regression with an Application to Defects in Manufacturing, Technometrics, 34(1):1–14, 1992.

    [2] Greene, W.: Accounting for Excess Zeros and Sample Selection in Poisson and Negative Binomial Regression Models, working paper (EC-94-10), Department of Economics, New York University, 1994.

    [3] Tan, R. & Qu, F. Chinese Journal of Population, Resources and Environment (中国人口·资源与环境), (3) (2007).

    [4] Luo, Y. Contemporary Economic Research (当代经济研究), (6) (2009).

    [5] Li, T. & Luo, J. Management World (管理世界), (7) (2015).

    [6] Huang, H. Chinese Rural Economy (中国农村经济), (3) (2014).

    [7] Zeng, Q. Journal of Central University of Finance & Economics (中央财经大学学报), (11) (2010).

    [8] Zheng, M. & Fan, J. Chinese Rural Economy (中国农村经济), (12) (2012).

    [9] Yu, L., Chen, J. & Lan, Q. Issues in Agricultural Economy (农业经济问题), (3) (2014).

    [10] Yang, T. & Luo, J. Chinese Rural Economy (中国农村经济), (4) (2014).

    [11] Lin, L. & Shen, Y. Finance & Economics (财经科学), (4) (2015).

    [12] Wang, C. Sociological Studies (社会学研究), (5) (2010).

    [13] Cao, L. & Luo, J. Journal of Nanjing Agricultural University (Social Sciences Edition) (南京农业大学学报 (社会科学版)), (5) (2015).

    [14] Fang, Q., Luo, J. & Cao, L. Journal of South China Agricultural University (Social Science Edition) (华南农业大学学报 (社会科学版)), (3) (2015).

    [15] Zeng, W. & Cai, X. Issues in Agricultural Economy (农业经济问题), (9) (2011).

    [16] He, S. China Rural Survey (中国农村观察), (1) (2008).

    [17] Pan, H., Zhai, F. & Liu, D. Finance and Trade Research (财贸研究), (5) (2011).

This Article


CN: 11-1262/F

Vol , No. 12, Pages 31-48

December 2015


Article Outline


  • 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