Get in the zone: The risk-adjusted welfare effects of data-driven vs. administrative borders for index insurance zones
Journal of Development Economics · 2026 · Ella Kirchner、Elinor Benami、Andrew Hobbs、Michael R. Carter、Zhenong Jin
中文摘要
农业指数保险旨在保护生产者免受特定区域内大范围冲击的影响,此类区域通常以行政边界划定。然而,这些边界可能难以准确反映产量变异,从而给投保人和公众双方带来成本。地理空间数据可及性的提升与聚类技术的进步,为划定更具同质性的保险分区提供了可能。本研究评估了在肯尼亚西部根据实测农业环境条件重新划定分区边界的影响。我们利用超过13,000个作物实割测产观测数据以及基于卫星的生长条件估计,在模拟的区域产量指数保险计划中评估数据驱动分区如何影响生产者福利。尽管在分区数量固定的条件下,部分数据驱动分区能适度提升风险降低效果,但在分区数量与田间抽样强度上赋予更大灵活性,更有助于在绩效与成本之间取得平衡。我们提出一个概念模型并开展实证模拟,以刻画在资源受限环境中,分区设计与数据收集强度如何共同塑造保险结果。
Abstract
Agricultural index insurance aims to protect producers from widespread shocks within defined areas, often based on administrative boundaries. However, these boundaries may poorly reflect yield variation, imposing costs on both policyholders and the public. Advances in geospatial data accessibility and clustering techniques offer potential to define more homogeneous insurance zones. This study evaluates the impact of redrawing zone boundaries based on observed agri-environmental conditions in western Kenya. Using over 13,000 crop cut observations with satellite-based estimates on growing conditions, we assess how data-driven zones affect producer welfare in a simulated area-yield index insurance program. While some data-driven zones modestly improve risk reduction with a fixed number of zones, greater flexibility in zone number and field sampling intensity offers more potential to balance performance and cost. We present a conceptual model and empirical simulations to characterize how zone design and data collection intensity jointly shape insurance outcomes in resource-constrained settings.