Do remotely-sensed vegetation health indices explain credit risk in agricultural microfinance?
Do remotely-sensed vegetation health indices explain credit risk in agricultural microfinance?
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DOI：
10.1016/j.worlddev.2019.104771
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发表时间：
2020-03
影响因子：
6.9
通讯作者：
Johannes Möllmann;M. Buchholz;W. Kölle;O. Musshoff
中科院分区：
文献类型：
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作者：
Johannes Möllmann;M. Buchholz;W. Kölle;O. Musshoff
农民易受不利天气事件的影响，而气候变化可能会增加不利天气事件的频率和程度，这是充分信贷供应的主要障碍。除其他因素外，小农获得信贷对生产率和产出增长至关重要。指数保险可以帮助贷款人弥补在天气状况恶劣的年份缺乏分期付款的情况，因此被认为可以加快农业贷款。利用马达加斯加一家小额信贷机构(MFI)提供的一个独特的借款人数据集，我们分析了遥感植被健康指数是否能够解释MFI农业贷款组合的信用风险。因此，我们利用序贯Logit模型和分位数回归。更具体地说，我们将遥感植被状况指数、温度状况指数和植被健康指数作为单个分支机构和聚合银行级别的自变量。这些指数可在全球范围内使用，并可能通过降低基数风险(指数与基础风险敞口之间的不完全相关性)来提高指数保险的有效性，这是指数保险的一大缺点。此外，我们认为借款人的贷款和社会人口变量是额外的自变量。结果表明，MFI的信用风险在很大程度上是由植被健康指数来解释的。此外，分位数回归的结果表明，植被健康指数的解释能力随着信用风险的增加而增强。因此，利用遥感植被健康指数设计指数保险对小额信贷机构对冲其农业贷款组合的信用风险可能特别有价值。面对较低的违约率，小额信贷机构可能会降低利率。因此，遥感指数保险可以增加获得信贷的机会，促进研究区域的可持续发展。
Farmers’ vulnerability to adverse weather events, which are likely to increase in frequency and magnitude due to climate change, is a major impediment to a sufficient credit supply. Smallholder farmers’ access to credit is, among other factors, crucial for productivity and output growth. Index insurance could help lenders to compensate for lacking installment payments in years with severe weather conditions and, thus, is considered to accelerate agricultural lending. Using a unique borrower dataset provided by a Microfinance Institution (MFI) in Madagascar, we analyze whether remotely-sensed vegetation health indices can explain the credit risk of the MFI’s agricultural loan portfolio. Therefore, we utilize sequential logit models and quantile regressions. More specifically, we consider the remotely-sensed Vegetation Condition Index, Temperature Condition Index and the Vegetation Health Index as independent variables at the individual branch and the aggregated bank level. These indices are available globally and can potentially enhance the effectiveness of index insurance by reducing basis risk (imperfect correlation between the index and the underlying exposure), a major drawback of index insurance. Moreover, we consider loan- and socio-demographic variables of the borrowers as additional independent variables. Our results show that the credit risk of the MFI is explained, to a large extent, by the vegetation health indices. Moreover, the results from quantile regressions show that the explanatory power of the vegetation health indices increases with increasing credit risk. Thus, utilizing remotely-sensed vegetation health indices for index insurance designs might be particularly valuable for MFIs to hedge the credit risk of their agricultural loan portfolio. Facing lower default rates, MFIs could reduce interest rates. Remotely-sensed index insurance could therefore enhance access to credit, contributing to sustainable development in the study region.