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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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    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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