Can expert knowledge compensate for data scarcity in crop insurance pricing?
AbstractAlthough 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.
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Bibliographic InfoPaper provided by Agricultural and Applied Economics Association in its series 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. with number 149431.
Date of creation: 2013
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expert knowledge; data scarcity; crop insurance pricing; Bayesian estimation; Agricultural Finance; Farm Management; Research Methods/ Statistical Methods; Risk and Uncertainty; C14; Q19;
Other versions of this item:
- Zhiwei Shen & Martin Odening & Ostap Okhrin, 2013. "Can expert knowledge compensate for data scarcity in crop insurance pricing?," SFB 649 Discussion Papers SFB649DP2013-030, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
- 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
This paper has been announced in the following NEP Reports:
- NEP-AGR-2013-06-04 (Agricultural Economics)
- NEP-ALL-2013-06-04 (All new papers)
- NEP-IAS-2013-06-04 (Insurance Economics)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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, vol. 73(1), pages 88-101, April.
- Aleksey Min & Claudia Czado, 2010. "Bayesian Inference for Multivariate Copulas Using Pair-Copula Constructions," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 8(4), pages 511-546, Fall.
- 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.
- Martin Odening & Oliver Musshoff & Wei Xu, 2007. "Analysis of rainfall derivatives using daily precipitation models: opportunities and pitfalls," Agricultural Finance Review, Emerald Group Publishing, vol. 67(1), pages 135-156, May.
- Biener, Christian, 2013. "Pricing in Microinsurance Markets," World Development, Elsevier, vol. 41(C), pages 132-144.
- 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, 06.
- Wei Xu & Ostap Okhrin & Martin Odening & Ji Cao, 2010. "Systemic Weather Risk and Crop Insurance: The Case of China," SFB 649 Discussion Papers SFB649DP2010-053, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
- 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.
- repec:sae:ecolab:v:16:y:2006:i:2:p:1-2 is not listed on IDEAS
- 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.
- Raushan Bokusheva, 2011. "Measuring dependence in joint distributions of yield and weather variables," Agricultural Finance Review, Emerald Group Publishing, vol. 71(1), pages 120-141, May.
- 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.
- 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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