农地经营权抵押贷款试点政策效果仿真模拟研究——以宁夏平罗县658份农户数据为例
|
摘要
研究目的:构建农地经营权抵押贷款试点政策效果仿真模型,预测政策效果未来走势并寻找优化途径。研究方法:系统动力学、G1法、熵值法。研究结果:(1)家庭收入是政策效果最显著的正向影响因素,贷款可得性是政策效果最有效的正向影响因素;(2)贷款期限、贷款获批比例、农户信用等级均为政策效果的主要影响因素,其中,农户信用等级是政策效果的关键因素;(3)贷款利率是政策效果的较为敏感影响因素。中小型农户贷款响应对利率的提高有较强的敏感反应,而对利率降低敏感度不高。研究结论:提高贷款可得性、适度延长贷款期限、提升农户信用等级是优化农地经营权抵押贷款政策效果的有效途径。
The purpose of this study is to construct a simulation model for the effect of pilot policy for farmland management rights mortgage, to predict the future trend of policy effects and to find ways for optimization. The methods used include system dynamics, G1 method and entropy method. The results show that: 1)household income is the most significant positive influence factor of policy effect, while loan availability is the most effective positive influence factor of policy effect; 2)the loan period, the proportion of loans approved and the credit rating of farmers are the main influence factors to improve the policy effect, among which the credit rating of farmers is the key factor of policy effect; 3)the loan interest rate is a more sensitive factor of policy effect. The loan response of small and medium-sized farmers has a strong sensitivity to the increase of interest rates, but they have a low sensitivity to interest rate reduction. In conclusion, improving the availability of loans, moderately extending the loan period and upgrading the credit rating of farmers are effective ways to optimize the effect of the farmland management rights mortgage policy.
The purpose of this study is to construct a simulation model for the effect of pilot policy for farmland management rights mortgage, to predict the future trend of policy effects and to find ways for optimization. The methods used include system dynamics, G1 method and entropy method. The results show that: 1)household income is the most significant positive influence factor of policy effect, while loan availability is the most effective positive influence factor of policy effect; 2)the loan period, the proportion of loans approved and the credit rating of farmers are the main influence factors to improve the policy effect, among which the credit rating of farmers is the key factor of policy effect; 3)the loan interest rate is a more sensitive factor of policy effect. The loan response of small and medium-sized farmers has a strong sensitivity to the increase of interest rates, but they have a low sensitivity to interest rate reduction. In conclusion, improving the availability of loans, moderately extending the loan period and upgrading the credit rating of farmers are effective ways to optimize the effect of the farmland management rights mortgage policy.
引文
[1]韩俊,罗丹,程郁.信贷约束下农户借贷需求行为的实证研究[J].农业经济问题,2007,56(2):44-52,111.
[2]马九杰,沈杰.中国农村金融排斥态势与金融普惠策略分析[J].农村金融研究,2010(5):5-10.
[3]赵海.农村土地承包经营权抵押贷款问题分析——从农业经营主体的视角[J].农村工作通讯,2014(14):24-28.
[4]黄惠春.农村土地承包经营权抵押贷款可得性分析——基于江苏试点地区的经验证据[J].中国农村经济,2014(3):48-57.
[5]李韬,罗剑朝.农户土地承包经营权抵押贷款的行为响应——基于Poisson Hurdle模型的微观经验考察[J].管理世界,2015(7):54-70.
[6]曹瓅,罗剑朝.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[J].中国农村经济,2015(12):31-48.
[7]张欣,于丽红,兰庆高.农户农地经营权抵押贷款收入效应实证检验——基于辽宁省昌图县的调查[J].中国土地科学,2017,31(12):42-50.
[8]张珩,罗剑朝,王磊玲.农地经营权抵押贷款对农户收入的影响及模式差异:实证与解释[J].中国农村经济,2018(9):79-93.
[9] TURNER B L, WUELLNER M, NICHOLS T, et al.Development and evaluation of a system dynamics model for investigating agriculturally driven land transformation in the north central United States[J]. Natural Resource Modeling,2016, 29(2):179-228.
[10] MOMODU A S, ADDO A, AKINBAMI J F K, et al. Lowcarbon development strategy for the West African electricity system:preliminary assessment using system dynamics approach[J]. Energy Sustainability&Society, 2017, 7(1):11.
[11]杨阳,贺德方,佟贺丰.北京市私人载客小型和微型汽车的仿真模型及政策模拟[J].中国软科学,2012(6):78-89.
[12]董坤祥,侯文华,丁慧平,等.创新导向的农村电商集群发展研究——基于遂昌模式和沙集模式的分析[J].农业经济问题,2016,37(10):60-69,111.
[13]王静,庄鹏睿,罗良清.扶贫工作范式转换的系统仿真与政策模拟——基于江西省典型贫困地区贫困人口调查的研究[J].管理世界,2017(2):176-177.
[14]郭亚军.综合评价理论、方法及应用[M].北京:科学出版社,2007:33-38.
[15]杨婷怡,罗剑朝.农户参与农村产权抵押融资意愿及其影响因素实证分析——以陕西高陵县和宁夏同心县919个样本农户为例[J].中国农村经济,2014(4):42-57.
(1)资料来源:http://npc.people.com.cn/n1/2018/1224/c14576-30485180.html。
(1)由于本文研究试点地区农地经营权抵押政策模拟,按照全国人大授权,可不考虑法律等政策约束,未纳入政策变量。
(1)G1法是由东北大学郭亚军教授[14]提出的一种对AHP进行改良的赋权方法。
(1)由于篇幅限制,灵敏度分析曲线图未在文中报告,如果需要可向笔者索取。
(1)表3中4个子系统实际趋势的计算方法与前文定义一致。由于篇幅限制,2017年、2018年数据情况未在文中报告,如需要可向笔者索取。
[2]马九杰,沈杰.中国农村金融排斥态势与金融普惠策略分析[J].农村金融研究,2010(5):5-10.
[3]赵海.农村土地承包经营权抵押贷款问题分析——从农业经营主体的视角[J].农村工作通讯,2014(14):24-28.
[4]黄惠春.农村土地承包经营权抵押贷款可得性分析——基于江苏试点地区的经验证据[J].中国农村经济,2014(3):48-57.
[5]李韬,罗剑朝.农户土地承包经营权抵押贷款的行为响应——基于Poisson Hurdle模型的微观经验考察[J].管理世界,2015(7):54-70.
[6]曹瓅,罗剑朝.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[J].中国农村经济,2015(12):31-48.
[7]张欣,于丽红,兰庆高.农户农地经营权抵押贷款收入效应实证检验——基于辽宁省昌图县的调查[J].中国土地科学,2017,31(12):42-50.
[8]张珩,罗剑朝,王磊玲.农地经营权抵押贷款对农户收入的影响及模式差异:实证与解释[J].中国农村经济,2018(9):79-93.
[9] TURNER B L, WUELLNER M, NICHOLS T, et al.Development and evaluation of a system dynamics model for investigating agriculturally driven land transformation in the north central United States[J]. Natural Resource Modeling,2016, 29(2):179-228.
[10] MOMODU A S, ADDO A, AKINBAMI J F K, et al. Lowcarbon development strategy for the West African electricity system:preliminary assessment using system dynamics approach[J]. Energy Sustainability&Society, 2017, 7(1):11.
[11]杨阳,贺德方,佟贺丰.北京市私人载客小型和微型汽车的仿真模型及政策模拟[J].中国软科学,2012(6):78-89.
[12]董坤祥,侯文华,丁慧平,等.创新导向的农村电商集群发展研究——基于遂昌模式和沙集模式的分析[J].农业经济问题,2016,37(10):60-69,111.
[13]王静,庄鹏睿,罗良清.扶贫工作范式转换的系统仿真与政策模拟——基于江西省典型贫困地区贫困人口调查的研究[J].管理世界,2017(2):176-177.
[14]郭亚军.综合评价理论、方法及应用[M].北京:科学出版社,2007:33-38.
[15]杨婷怡,罗剑朝.农户参与农村产权抵押融资意愿及其影响因素实证分析——以陕西高陵县和宁夏同心县919个样本农户为例[J].中国农村经济,2014(4):42-57.
(1)资料来源:http://npc.people.com.cn/n1/2018/1224/c14576-30485180.html。
(1)由于本文研究试点地区农地经营权抵押政策模拟,按照全国人大授权,可不考虑法律等政策约束,未纳入政策变量。
(1)G1法是由东北大学郭亚军教授[14]提出的一种对AHP进行改良的赋权方法。
(1)由于篇幅限制,灵敏度分析曲线图未在文中报告,如果需要可向笔者索取。
(1)表3中4个子系统实际趋势的计算方法与前文定义一致。由于篇幅限制,2017年、2018年数据情况未在文中报告,如需要可向笔者索取。