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Evaluating the quality of remote sensing products for agricultural index insurance

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  • Benson K Kenduiywo
  • Michael R Carter
  • Aniruddha Ghosh
  • Robert J Hijmans

Abstract

Agricultural index insurance contracts increasingly use remote sensing data to estimate losses and determine indemnity payouts. Index insurance contracts inevitably make errors, failing to detect losses that occur and issuing payments when no losses occur. The quality of these contracts and the indices on which they are based, need to be evaluated to assess their fitness as insurance, and to provide a guide to choosing the index that best protects the insured. In the remote sensing literature, indices are often evaluated with generic model evaluation statistics such as R2 or Root Mean Square Error that do not directly consider the effect of errors on the quality of the insurance contract. Economic analysis suggests using measures that capture the impact of insurance on the expected economic well-being of the insured. To bridge the gap between the remote sensing and economic perspectives, we adopt a standard economic measure of expected well-being and transform it into a Relative Insurance Benefit (RIB) metric. RIB expresses the welfare benefits derived from an index insurance contract relative to a hypothetical contract that perfectly measures losses. RIB takes on its maximal value of one when the index contract offers the same economic benefits as the perfect contract. When it achieves none of the benefits of insurance it takes on a value of zero, and becomes negative if the contract leaves the insured worse off than having no insurance. Part of our contribution is to decompose this economic well-being measure into an asymmetric loss function. We also argue that the expected well-being measure we use has advantages over other economic measures for the normative purpose of insurance quality ascertainment. Finally, we illustrate the use of the RIB measure with a case study of potential livestock insurance contracts in Northern Kenya. We compared 24 indices that were made with 4 different statistical models and 3 remote sensing data sources. RIB for these indices ranged from 0.09 to 0.5, and R2 ranged from 0.2 to 0.51. While RIB and R2 were correlated, the model with the highest RIB did not have the highest R2. Our findings suggest that, when designing and evaluating an index insurance program, it is useful to separately consider the quality of a remote sensing-based index with a metric like the RIB instead of a generic goodness-of-fit metric.

Suggested Citation

  • Benson K Kenduiywo & Michael R Carter & Aniruddha Ghosh & Robert J Hijmans, 2021. "Evaluating the quality of remote sensing products for agricultural index insurance," PLOS ONE, Public Library of Science, vol. 16(10), pages 1-24, October.
  • Handle: RePEc:plo:pone00:0258215
    DOI: 10.1371/journal.pone.0258215
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    References listed on IDEAS

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    1. Yuma Noritomo & Kazushi Takahashi, 2020. "Can Insurance Payouts Prevent a Poverty Trap? Evidence from Randomised Experiments in Northern Kenya," Journal of Development Studies, Taylor & Francis Journals, vol. 56(11), pages 2079-2096, November.
    2. Janic Bucheli & Tobias Dalhaus & Robert Finger, 2021. "The optimal drought index for designing weather index insurance," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 48(3), pages 573-597.
    3. Jing Cai, 2016. "The Impact of Insurance Provision on Household Production and Financial Decisions," American Economic Journal: Economic Policy, American Economic Association, vol. 8(2), pages 44-88, May.
    4. Elinor Benami & Michael R. Carter, 2021. "Can digital technologies reshape rural microfinance? Implications for savings, credit, & insurance," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 43(4), pages 1196-1220, December.
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    Cited by:

    1. Bucheli, Janic & Dalhaus, Tobias & Finger, Robert, 2022. "Temperature effects on crop yields in heat index insurance," Food Policy, Elsevier, vol. 107(C).
    2. Michael R. Carter, 2022. "Can digitally‐enabled financial instruments secure an inclusive agricultural transformation?," Agricultural Economics, International Association of Agricultural Economists, vol. 53(6), pages 953-967, November.
    3. Shin, Soye & Magnan, Nicholas & Mullally, Conner & Janzen, Sarah, 2022. "Demand for Weather Index Insurance among Smallholder Farmers under Prospect Theory," Journal of Economic Behavior & Organization, Elsevier, vol. 202(C), pages 82-104.

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