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

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Listed:
  • Zhiwei Shen
  • Martin Odening
  • Ostap Okhrin

Abstract

Although there is an increasing interest in area yield insurance in many developing countries, crop data scarcity hinders its implementation by imposing 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 arising from insufficient statistical data. We adopt a Bayesian framework that allows for the combination of scarce crop data, expert knowledge and weather information, to estimate the loss distribution. We find that expert knowledge reduces the parameter uncertainty and changes the insurance premium in the correct direction.

Suggested Citation

  • Zhiwei Shen & Martin Odening & Ostap Okhrin, 2016. "Can expert knowledge compensate for data scarcity in crop insurance pricing?," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 43(2), pages 237-269.
    • Handle: RePEc:oup:erevae:v:43:y:2016:i:2:p:237-269.
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    File URL: http://hdl.handle.net/10.1093/erae/jbv015
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    1. 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.
    2. 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.
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    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.
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    Cited by:

    1. 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.
    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. 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.
    4. 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.
    5. Kjersti Aas, 2016. "Pair-Copula Constructions for Financial Applications: A Review," Econometrics, MDPI, vol. 4(4), pages 1-15, October.
    6. 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.
    7. Poeschel, Friedrich, 2012. "Assortative matching through signals," VfS Annual Conference 2012 (Goettingen): New Approaches and Challenges for the Labor Market of the 21st Century 62061, Verein für Socialpolitik / German Economic Association.

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    More about this item

    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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