[论文解读] On the benefits of index insurance in US agriculture: a large-scale analysis using satellite data

Matthieu Stigler, David B. Lobell|arXiv (Cornell University)|Nov 25, 2020

Agricultural risk and resilience参考文献 42被引用 4

一句话总结

本研究利用美国玉米带600个县近180万块玉米和大豆田的高分辨率卫星估算单产数据，模拟并比较了指数保险与农场层面保险。研究发现，模拟中30%至40%的农民需求指数保险，出人意料地高；然而其效益在很大程度上取决于所选基准：在时间变异性高的县，指数保险优于无保险，但在相同地区却因基差风险而表现不如农场层面保险，凸显了指数设计中时空变异性的关键作用。

ABSTRACT

Index insurance has been promoted as a promising solution for reducing agricultural risk compared to traditional farm-based insurance. By linking payouts to a regional factor instead of individual loss, index insurance reduces monitoring costs, and alleviates the problems of moral hazard and adverse selection. Despite its theoretical appeal, demand for index insurance has remained low in many developing countries, triggering a debate on the causes of the low uptake. Surprisingly, there has been little discussion in this debate about the experience in the United States. The US is an unique case as both farm-based and index-based products have been available for more than two decades. Furthermore, the number of insurance zones is very large, allowing interesting comparisons over space. As in developing countries, the adoption of index insurance is rather low -- less than than 5\% of insured acreage. Does this mean that we should give up on index insurance? In this paper, we investigate the low take-up of index insurance in the US leveraging a field-level dataset for corn and soybean obtained from satellite predictions. While previous studies were based either on county aggregates or on relatively small farm-level dataset, our satellite-derived data gives us a very large number of fields (close to 1.8 million) comprised within a large number of index zones (600) observed over 20 years. To evaluate the suitability of index insurance, we run a large-scale simulation comparing the benefits of both insurance schemes using a new measure of farm-equivalent risk coverage of index insurance. We make two main contributions. First, we show that in our simulations, demand for index insurance is unexpectedly high, at about 30\% to 40\% of total demand. This result is robust to relaxing several assumptions of the model and to using prospect theory instead of expected utility.

研究动机与目标

• 利用高分辨率田块级数据评估美国农业部门指数保险的真实效益与需求。
• 调查600个美国县中指数保险适用性的决定因素，特别是时空单产变异性的角色。
• 评估不同绩效指标（与无保险或农场层面保险相比）如何影响指数保险的感知价值。
• 检验在替代行为模型（包括前景理论）下的结果稳健性。
• 利用田块级卫星数据量化区域-单产指数保险中的基差风险，超越以往基于县汇总数据的估计。

提出的方法

• 本研究使用1998年至2017年美国玉米带600个县中180万块玉米和大豆田的卫星估算单产预测数据。
• 基于县平均单产构建基于区域的指数保险赔付，当单产低于某一阈值时触发赔付。
• 使用效用函数的二阶泰勒展开近似计算田块级期望效用，风险规避通过效用函数的二阶导数捕捉。
• 分析比较在指数保险、农场层面赔偿保险和无保险情况下的期望效用，采用期望效用理论与累积前景理论。
• 在县层面计算时空单产方差，并通过回归模型中的相对重要性度量（lmg）评估其在预测保险需求中的相对重要性。
• 基差风险通过经验累积分布估计：即田块单产低于某一给定阈值但县指数未触发赔付的概率。

实验结果

研究问题

• RQ1 当使用真实单产数据在田块层面进行模拟时，美国指数保险的需求水平如何？
• RQ2 与无保险相比，指数保险的感知效益在与农场层面赔偿保险比较时有何差异？
• RQ3 县内时间与空间单产变异性的程度在多大程度上影响指数保险的适用性？
• RQ4 结果对替代行为模型（如累积前景理论）的稳健性如何？
• RQ5 在美国玉米带，玉米和大豆区域-单产指数保险中的基差风险程度如何？

主要发现

• 模拟中约30%至40%的农民需求指数保险，表明尽管存在基差风险，其采纳率仍出人意料地高。
• 与无保险相比，指数保险在时间单产变异性最高的县中效益最大。
• 与农场层面保险相比，指数保险在相同高时间变异性县中效益最低，原因在于基差风险。
• 绩效指标的选择——尤其是与无保险还是农场层面保险比较——可能导致对指数保险价值的相反结论。
• 基差风险仍然显著：玉米为24.7%的田块在单产低于第90百分位数时遭受损失，但县指数未触发赔付；大豆则上升至46.3%。
• 研究发现，空间单产方差会降低指数保险的效益，而时间单产方差会提高其效益，强调在指数设计中必须同时考虑两者。