On the benefits of index insurance in US agriculture: a large-scale analysis using satellite data

Stigler, Matthieu; Lobell, David B.

doi:10.48550/arxiv.2011.12544

Cited by 2 publications

(4 citation statements)

References 37 publications

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“…At the finest level (L3 wards), the zonal risk averaged over the 798 wards units is still 48%, meaning that the best local index is only capturing half of the local variations in yields. To put this number in context, Stigler and Lobell (2021) report that at the lowest administrative level in the United States-the county-the R 2 y ð Þ to the county average is 75%. Given the inequality…”

Section: Zonal Riskmentioning

confidence: 99%

“…In brief, the first is a quantile-based R 2 τ (Koenker & Machado, 1999), which we evaluate at the τ ¼ 0:3 quantile. The second is an expected utility measure comparing the utility of index insurance to individual farm-level insurance (Kenduiywo et al, 2021;Stigler & Lobell, 2021). Computing the expected utility measure requires us to make several assumptions regarding the utility function as well as to simulate data given the relatively small sample size we have.…”

Section: Relation To Other Measuresmentioning

confidence: 99%

“…Also, unlike measures that focus exclusively on downside risk, the linear correlation takes into account "positive" basis risk, which, besides unnecessarily raising the cost of insurance, was found to reduce demand (Vosper & Cecchi, 2022). Finally, as shown in Stigler and Lobell (2021), the simple metric based on the average R 2 between yields and the index is a good predictor of the expected utility of index insurance for maize and soybeans in the United States. We observe a similar result in our robustness check section, finding a high correlation between our linear measure of basis risk and more sophisticated metrics based on quantile regression or expected utility.…”

mentioning

confidence: 98%

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Optimal index insurance and basis risk decomposition: an application to Kenya

Stigler¹,

Lobell²

2023

American J Agri Economics

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Index insurance is a promising tool to reduce the risk faced by farmers, but high basis risk, which arises from imperfect correlation between the index and individual farm yields, has limited its adoption to date. Improving adoption will require reducing one or both of the two fundamental sources of basis risk: the intrinsic heterogeneity within an insurance zone (zonal risk), and the lack of predictive accuracy of the index (design risk). Previous work has focused mostly on design risk, conflating the quality of the index with the quality of the zone. Consequently, there is currently no way to distinguish a “good index in a bad zone” from a “bad index in a good zone”. Here we investigate the relative roles of zonal and design risk, with two main contributions. First, using a formal decomposition of basis risk, we show that the optimal index is the first principal component of the correlation matrix of yields between fields. This provides a simple upper bound on the insurable basis risk that any index can reach within a given zone. Second, we use 10 m resolution satellite data on maize yields in Kenya to provide the first large‐scale empirical analysis of the extent of zonal versus design risk. Our results show a strong local heterogeneity in yields, underscoring the challenge of implementing index insurance in smallholder systems and the potential benefits of low‐cost yield measurement approaches that can enable more local definitions of insurance zones.

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Section: Zonal Riskmentioning

confidence: 99%

Section: Relation To Other Measuresmentioning

confidence: 99%

mentioning

confidence: 98%

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Optimal index insurance and basis risk decomposition: an application to Kenya

Stigler¹,

Lobell²

2023

American J Agri Economics

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“…Second, this paper provides new perspectives on farmers' apparently low demand for area insurance plans in the United States. The existing literature generally compares AYP with YP in effects on yield variance (Barnett et al, 2005;Miranda, 1991;Stigler & Lobell, 2021) or certainty equivalent revenues (Awondo & Datta, 2018;Deng et al, 2007). These methods depend on assumptions about the yield distribution or the utility function and underestimate the effect of basis risk on farmers' AYP choices.…”

Section: Introductionmentioning

confidence: 99%

Systemic risk, relative subsidy rates, and area yield insurance choice

Gong

Hennessy

Feng

2022

American J Agri Economics

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We investigate the nature of crop yield systemic risk and its implications for farmers' area yield insurance choices. A theory-grounded, normalized measure of systemic risk, R 2 , is developed and made amenable to empirical analysis through the logit link. The measure is estimated on a large-scale, unit-level corn yield dataset. We find that systemic risk explains less than half of total unit yield variability on average, suggesting that the risk management effectiveness of area yield insurance is low. By relating natural resource endowments with systemic risk, we find that more excessive heat and drought events lead to larger while more excessive precipitation events lead to smaller systemic risk. We then study whether current area yield insurance subsidy rates provide farmers with sufficient subsidy transfers to compensate for the uncovered risk exposure associated with area yield insurance. A new concept, the threshold relative area subsidy rate (TRASR), quantifies a lower bound on the ratio of area yield insurance subsidy rate over individual yield insurance subsidy rate below which risk-averse farmers should always prefer individual yield insurance to area yield insurance. The calibrated TRASR values indicate that current area yield insurance subsidy rates discourage farmers from choosing area yield insurance over individual yield insurance, especially at low area yield insurance coverage levels. We also find that TRASR is positively correlated with systemic risk at the unit-level of analysis, suggesting that farmers who like area yield insurance's risk management features will dislike its transfer implications and vice versa.

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On the benefits of index insurance in US agriculture: a large-scale analysis using satellite data

Cited by 2 publications

References 37 publications

Optimal index insurance and basis risk decomposition: an application to Kenya

Optimal index insurance and basis risk decomposition: an application to Kenya

Systemic risk, relative subsidy rates, and area yield insurance choice

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