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Can expert knowledge compensate for data scarcity in crop insurance pricing?

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  • Shen, Zhiwei
  • Odening, Martin
  • Okhrin, Ostap

Abstract

Although there is an increasing interest in index-based insurances in many developing countries, crop data scarcity hinders its implementation by forcing insurers to charge higher premiums. Expert knowledge has been considered a valuable information source to augment limited data in insurance pricing. This article investigates whether the use of expert knowledge can mitigate model risk which arises from insufficient statistical data. We adopt the Bayesian framework that allows for the combination of scarce data and expert knowledge, to estimate the risk parameter and buffer load. In addition, a benchmark for the evaluation of expert information is created by using a richer dataset generated from resampling. We find that expert knowledge reduces the parameter uncertainty and changes the insurance premium in the correct direction, but that the effect of the correction is sensitive to different strike levels of insurance indemnity.

Suggested Citation

  • Shen, Zhiwei & Odening, Martin & Okhrin, Ostap, 2013. "Can expert knowledge compensate for data scarcity in crop insurance pricing?," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 149431, Agricultural and Applied Economics Association.
    • Handle: RePEc:ags:aaea13:149431
    DOI: 10.22004/ag.econ.149431
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    References listed on IDEAS

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    1. Barry K. Goodwin, 2001. "Problems with Market Insurance in Agriculture," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 83(3), pages 643-649.
    2. Ostap Okhrin & Martin Odening & Wei Xu, 2013. "Systemic Weather Risk and Crop Insurance: The Case of China," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 80(2), pages 351-372, June.
    3. Raushan Bokusheva, 2011. "Measuring dependence in joint distributions of yield and weather variables," Agricultural Finance Review, Emerald Group Publishing Limited, vol. 71(1), pages 120-141, May.
    4. Aleksey Min & Claudia Czado, 2010. "Bayesian Inference for Multivariate Copulas Using Pair-Copula Constructions," Journal of Financial Econometrics, Oxford University Press, vol. 8(4), pages 511-546, Fall.
    5. H. Holly Wang & Hao Zhang, 2003. "On the Possibility of a Private Crop Insurance Market: A Spatial Statistics Approach," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 70(1), pages 111-124, March.
    6. Mario J. Miranda & Katie Farrin, 2012. "Index Insurance for Developing Countries," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 34(3), pages 391-427.
    7. Julia I. Borman & Barry K. Goodwin & Keith H. Coble & Thomas O. Knight & Rod Rejesus, 2013. "Accounting for short samples and heterogeneous experience in rating crop insurance," Agricultural Finance Review, Emerald Group Publishing Limited, vol. 73(1), pages 88-101, May.
    8. Biener, Christian, 2013. "Pricing in Microinsurance Markets," World Development, Elsevier, vol. 41(C), pages 132-144.
    9. Martin Odening & Oliver Musshoff & Wei Xu, 2007. "Analysis of rainfall derivatives using daily precipitation models: opportunities and pitfalls," Agricultural Finance Review, Emerald Group Publishing Limited, vol. 67(1), pages 135-156, May.
    10. Mary Kynn, 2008. "The ‘heuristics and biases’ bias in expert elicitation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(1), pages 239-264, January.
    11. John Duncan & Robert J. Myers, 2000. "Crop Insurance under Catastrophic Risk," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 82(4), pages 842-855.
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    Cited by:

    1. Poeschel, Friedrich, 2012. "Assortative matching through signals," IAB-Discussion Paper 201215, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    2. Fabio Gaetano Santeramo, 2018. "Imperfect information and participation in insurance markets: evidence from Italy," Agricultural Finance Review, Emerald Group Publishing Limited, vol. 78(2), pages 183-194, February.
    3. Yong Liu & A. Ford Ramsey, 2023. "Incorporating historical weather information in crop insurance rating," American Journal of Agricultural Economics, John Wiley & Sons, vol. 105(2), pages 546-575, March.
    4. Kjersti Aas, 2016. "Pair-Copula Constructions for Financial Applications: A Review," Econometrics, MDPI, vol. 4(4), pages 1-15, October.
    5. Nguyen, Giang & Nguyen, Trung Thanh, 2020. "Exposure to weather shocks: A comparison between self-reported record and extreme weather data," Economic Analysis and Policy, Elsevier, vol. 65(C), pages 117-138.
    6. Erwin Bulte & Rein Haagsma, 2021. "The Welfare Effects of Index-Based Livestock Insurance: Livestock Herding on Communal Lands," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 78(4), pages 587-613, April.
    7. Fabio G Santeramo, 2019. "I Learn, You Learn, We Gain Experience in Crop Insurance Markets," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 41(2), pages 284-304, June.

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    More about this item

    Keywords

    Agricultural Finance; Farm Management; Research Methods/ Statistical Methods; Risk and Uncertainty;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • Q19 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Other

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