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The role of credit scoring models, weather index-based insurance, and asymmetric information in agricultural microfinance

信用评分模型、基于天气指数的保险和不对称信息在农业小额信贷中的作用

基本信息

批准号：
249506740
负责人：
Professor Dr. Oliver Mußhoff
金额：
--
依托单位：
Lehrstuhl für Landwirtschaftliche Betriebslehre
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2014
资助国家：
德国
起止时间：
2013-12-31 至 2020-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/249506740?language=en
关键词：
role credit scoring models weather

项目摘要

Many studies have shown that the microfinance approach based on cash-flow analyses is a time-consuming and thus cost-intensive assessment of the credit default risks of credit applicants. In contrast, credit scoring models, which entail automated identification of credit default risks, may reduce the costs of (agricultural) microfinance. In addition, the inclusion of weather variables into an agriculture-specific scoring model seems like a promising approach to devise credit scoring models for this clientele in particular. Indeed, weather index-based insurance, which has been intensively discussed in recent years, can diminish the comparably high weather-related credit default risk of agricultural microfinance. Lastly, another important issue in microfinance remains the prevention of asymmetric information. Yet, the effect of asymmetric information on credit default has never been investigated directly.Bearing in mind that there exist only few studies on credit scoring models, index-based insurance and asymmetric information in agricultural microfinance, the project pursues the following objectives:1. Evaluating the potential of credit scoring models (including weather risks) in agricultural microfinance2. Analyzing the influence of weather variables on credit granting and credit default risk in agricultural microfinance, as well as the related risk-reducing potential of weather index-based insurance3. Measuring the effect of asymmetric information on credit defaultThus, the research project provides important findings for the further development of microcredit for agricultural enterprises, and more particularly for the so far poor development of the financial system in the rural areas of developing countries.

许多研究表明，基于现金流量分析的小额金融办法是一种对信贷申请人的信贷违约风险进行评估的费时费力的办法。相比之下，信用评分模式需要自动识别信用违约风险，可降低（农业）小额金融的成本。此外，将天气变量纳入农业评分模型似乎是一种很有前途的方法，特别是为这一客户设计信用评分模型。事实上，近年来得到广泛讨论的基于天气指数的保险可以降低农业小额信贷中与天气有关的极高的信用违约风险。最后，小额金融的另一个重要问题仍然是防止信息不对称。然而，信息不对称对信用违约的影响还没有得到直接的研究，考虑到目前对农业小额信贷中的信用评分模型、指数保险和信息不对称的研究还比较少，本课题的研究目标如下：1.评估信贷评分模型（包括天气风险）在农业小额信贷中的潜力2。分析天气变量对农业小额信贷授信和信用违约风险的影响，以及基于天气指数的保险降低相关风险的潜力3。因此，该研究项目为进一步发展农业企业的小额信贷，特别是为发展中国家农村地区迄今为止发展不佳的金融系统提供了重要的研究结果。