农户行为与农村金融改革——基于随机自然实验与计算实验的研究

项目名称： 农户行为与农村金融改革——基于随机自然实验与计算实验的研究

项目编号： No.71271109

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 管理科学

项目作者： 肖斌卿

作者单位： 南京大学

项目金额： 56万元

中文摘要： 从2004年到2012年，中央一号文件连续九年强调农村金融改革的重要性。而农户既是改革中心，也是其终极目标。中国农村金融改革必须从其微观基础-农户的认知、态度和行为-破题。本研究基于行为金融理论，创新性采用随机自然实验和计算实验方法，辅以历史追溯法、质地研究法及计量实证检验：从"诱导性制度变迁"视角，结合内生性金融理论，构建我国农村内生性金融的发展逻辑和理论体系，为基于农户行为的农村金融分析提供理论基础；拓展现有农户行为决策模型，构建基于中国情境的农户行为决策模型；基于决策模型，探索行为对农户投资、储蓄及借贷等金融行为的影响机制，挖掘农户金融需求特征；梳理总结当前农村金融机构服务创新成功实践，基于农户行为特征，运用新方法评价农村金融宏观政策制定及微观机制创新有效性。本研究旨在拓展农村金融研究方法，深化农村金融理论研究，优化农村金融改革政策，为破解中国农村金融改革难题提供切实可行的措施建议。

中文关键词： 内生性金融；农户行为决策模型；行为偏差；随机自然实验；计算实验

英文摘要： From 2004 to 2012, a Central File of CCP for nine consecutive years, emphasizes the importance of rural financial reform. The farmers are not only the center of reform, but the ultimate goal. Chinese rural financial reform must be from its foundation - farmer's cognition, attitude and behavior. This study based on the behavioral finance theory is carried out with Randomized Natural Field Experiment and computational experiments, supplemented by the historical method, Qualitative research method and empirical test. From the perspective of "induced institutional change", combined with the endogenous financial theory, to construct the theory system of China's Rural Endogenous Financial Development, which is based on the behavior of farmers; to expand the existing farmer behavior decision-making model based on Chinese situation; based on that decision model, exploratory the influence mechanism of behavior on farmer investment, savings and loans behavior, to mine the farmer's financial demand characteristics; to review the current successful practice of rural financial service innovation basing on farmers behavioral characteristics, using the new method for the evaluation the validity of macroscopical policy and microcosmic mechanism. This study aims to expand rural finance research method, deepen rural finance theor

英文关键词： Endogenous Finance；Decision-making Model of Famers Behavior；Behavioral Bias；Randomized Natural Field Experiment；Agent-based Computational Experiment