Evaluating the quality of remote sensing products for agricultural index insurance.

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 indi...

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Autores principales:	Benson K Kenduiywo, Michael R Carter, Aniruddha Ghosh, Robert J Hijmans
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a33d18889a514c96a63df23a9a6f1e7a
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spelling	oai:doaj.org-article:a33d18889a514c96a63df23a9a6f1e7a2021-12-02T20:17:10ZEvaluating the quality of remote sensing products for agricultural index insurance.1932-620310.1371/journal.pone.0258215https://doaj.org/article/a33d18889a514c96a63df23a9a6f1e7a2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0258215https://doaj.org/toc/1932-6203Agricultural 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.Benson K KenduiywoMichael R CarterAniruddha GhoshRobert J HijmansPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10, p e0258215 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Benson K Kenduiywo Michael R Carter Aniruddha Ghosh Robert J Hijmans Evaluating the quality of remote sensing products for agricultural index insurance.
description	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.
format	article
author	Benson K Kenduiywo Michael R Carter Aniruddha Ghosh Robert J Hijmans
author_facet	Benson K Kenduiywo Michael R Carter Aniruddha Ghosh Robert J Hijmans
author_sort	Benson K Kenduiywo
title	Evaluating the quality of remote sensing products for agricultural index insurance.
title_short	Evaluating the quality of remote sensing products for agricultural index insurance.
title_full	Evaluating the quality of remote sensing products for agricultural index insurance.
title_fullStr	Evaluating the quality of remote sensing products for agricultural index insurance.
title_full_unstemmed	Evaluating the quality of remote sensing products for agricultural index insurance.
title_sort	evaluating the quality of remote sensing products for agricultural index insurance.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/a33d18889a514c96a63df23a9a6f1e7a
work_keys_str_mv	AT bensonkkenduiywo evaluatingthequalityofremotesensingproductsforagriculturalindexinsurance AT michaelrcarter evaluatingthequalityofremotesensingproductsforagriculturalindexinsurance AT aniruddhaghosh evaluatingthequalityofremotesensingproductsforagriculturalindexinsurance AT robertjhijmans evaluatingthequalityofremotesensingproductsforagriculturalindexinsurance
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Evaluating the quality of remote sensing products for agricultural index insurance.

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