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Estimating bivariate yield distributions and crop insurance premiums using nonparametric methods

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  • Qiujie Zheng
  • H. Holly Wang
  • Qing Hua Shi

Abstract

Modelling crop yield distribution is crucial in crop insurance premium setting. The correlation between different crop yields due to rotations or systemic risks requires estimation of joint yield distribution for multiple crops. In this article, we apply a nonparametric method to estimate bivariate yield distributions using farm-level yield data of wheat and corn in Shandong Province in China. Then, the simulated yields are used to evaluate the expected indemnity of one traditional and one hypothetical crop insurance programme. Our results reveal that the nonparametric bivariate method is very flexible in shaping the yield probability density functions to estimate local idiosyncrasies and correlation between two crops. It is also feasible to simulate the nonparametric yield distributions at a satisfying level of accuracy. The simulation results show that the hypothetical two-crop insurance contract can be more affordable to farmers than traditional individual crop insurance contracts.

Suggested Citation

  • Qiujie Zheng & H. Holly Wang & Qing Hua Shi, 2014. "Estimating bivariate yield distributions and crop insurance premiums using nonparametric methods," Applied Economics, Taylor & Francis Journals, vol. 46(18), pages 2108-2118, June.
    • Handle: RePEc:taf:applec:v:46:y:2014:i:18:p:2108-2118
    DOI: 10.1080/00036846.2014.894630
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    1. Paul Gallagher, 1987. "U.S. Soybean Yields: Estimation and Forecasting with Nonsymmetric Disturbances," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 69(4), pages 796-803.
    2. Richard E. Just & Quinn Weninger, 1999. "Are Crop Yields Normally Distributed?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 81(2), pages 287-304.
    3. Gallagher, Paul W., 1987. "U.S. Soybean Yields: Estimation and Forecasting with Non-Symmetric Disturbances," Staff General Research Papers Archive 10779, Iowa State University, Department of Economics.
    4. C. Robert Taylor, 1990. "Two Practical Procedures for Estimating Multivariate Nonnormal Probability Density Functions," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 72(1), pages 210-217.
    5. Cuiping Xu & H. Holly Wang & Qinghua Shi, 2012. "Farmers’ Income and Production Responses to Rural Taxation Reform in Three Regions in China," Journal of Agricultural Economics, Wiley Blackwell, vol. 63(2), pages 291-309, June.
    6. Alan P. Ker & Barry K. Goodwin, 2000. "Nonparametric Estimation of Crop Insurance Rates Revisited," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 82(2), pages 463-478.
    7. Alan P. Ker & Keith Coble, 2003. "Modeling Conditional Yield Densities," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 85(2), pages 291-304.
    8. Charles B. Moss & J. S. Shonkwiler, 1993. "Estimating Yield Distributions with a Stochastic Trend and Nonnormal Errors," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 75(4), pages 1056-1062.
    9. Bailey Norwood & Matthew C. Roberts & Jayson L. Lusk, 2004. "Ranking Crop Yield Models Using Out-of-Sample Likelihood Functions," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(4), pages 1032-1043.
    10. Goodwin, Barry K. & Mahul, Olivier, 2004. "Risk modeling concepts relating to the design and rating of agricultural insurance contracts," Policy Research Working Paper Series 3392, The World Bank.
    11. Barry K. Goodwin & Alan P. Ker, 1998. "Nonparametric Estimation of Crop Yield Distributions: Implications for Rating Group-Risk Crop Insurance Contracts," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 80(1), pages 139-153.
    12. Steven D. Hanson & Robert J. Myers & J. Roy Black, 1998. "The Effects of Crop Yield Insurance Designs on Farmer Participation and Welfare," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 80(4), pages 806-820.
    13. Funing Zhong & Manxiu Ning & Li Xing, 2007. "Does crop insurance influence agrochemical uses under current Chinese situations? A case study in the Manasi watershed, Xinjiang," Agricultural Economics, International Association of Agricultural Economists, vol. 36(1), pages 103-112, January.
    14. Turvey, Calum G. & Zhao, Jinhua, 1999. "Parametric And Non-Parametric Crop Yield Distributions And Their Effects On All-Risk Crop Insurance Premiums," Working Papers 34129, University of Guelph, Department of Food, Agricultural and Resource Economics.
    15. Carl H. Nelson & Paul V. Preckel, 1989. "The Conditional Beta Distribution as a Stochastic Production Function," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 71(2), pages 370-378.
    16. Ralph R. Botts & James N. Boles, 1958. "Use of Normal-Curve Theory in Crop Insurance Ratemaking," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 40(3), pages 733-740.
    17. Vitor Ozaki & Barry Goodwin & Ricardo Shirota, 2008. "Parametric and nonparametric statistical modelling of crop yield: implications for pricing crop insurance contracts," Applied Economics, Taylor & Francis Journals, vol. 40(9), pages 1151-1164.
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    2. Yuehua Zhang & Ying Cao & H. Holly Wang, 2018. "Cheating? The Case of Producers’ Under‐Reporting Behavior in Hog Insurance in China," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 66(3), pages 489-510, September.

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