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Weather Information and the Potential for Intertemporal Adverse Selection in Crop Insurance


  • Haiping Luo
  • Jerry R. Skees
  • Mary A. Marchant


This study investigates the potential usefulness of early-season weather information in forecasting corn yields for the Midwest. To the extent that farmers are able to forecast yields prior to sales closing dates for crop insurance, farmers may use such information in deciding which years to purchase crop insurance and which years not to purchase insurance. Such intertemporal adverse selection would result in increased losses for the crop insurance program. Three yield forecasting models were developed using early-season weather information: (1) simple weather forecasts; (2) yield-weather regression models; and (3) yield-weather discriminant functions. This study presents procedures that would allow farmers to predict low corn yields before the purchasing deadline of corn insurance on 95 percent of planted acreage in the Midwest.

Suggested Citation

  • Haiping Luo & Jerry R. Skees & Mary A. Marchant, 1994. "Weather Information and the Potential for Intertemporal Adverse Selection in Crop Insurance," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 16(3), pages 441-451.
  • Handle: RePEc:oup:revage:v:16:y:1994:i:3:p:441-451.

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    Cited by:

    1. Quaas, Martin F. & Baumgärtner, Stefan, 2008. "Natural vs. financial insurance in the management of public-good ecosystems," Ecological Economics, Elsevier, vol. 65(2), pages 397-406, April.
    2. Hong Wu & Donald Wilhite, 2004. "An Operational Agricultural Drought Risk Assessment Model for Nebraska, USA," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 33(1), pages 1-21, September.
    3. Stefan Baumgärtner & Martin F. Quaas, 2007. "Agro-biodiversity as natural insurance and the development of financial insurance markets," Working Paper Series in Economics 61, University of Lüneburg, Institute of Economics.
    4. Martin, Steven W. & Barnett, Barry J. & Coble, Keith H., 2001. "Developing And Pricing Precipitation Insurance," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 26(1), pages 1-14, July.
    5. Miguel A. Carriquiry & Walter E. Baethgen, 2016. "Seasonal Climate Forecasts and Agricultural Risk Management: Implications for Insurance Design," Documentos de Trabajo (working papers) 16-03, Instituto de Economia - IECON.
    6. Osgood, Daniel E. & Suarez, Pablo & Hansen, James & Carriquiry, Miguel & Mishra, Ashok, 2008. "Integrating seasonal forecasts and insurance for adaptation among subsistence farmers : the case of Malawi," Policy Research Working Paper Series 4651, The World Bank.
    7. Carriquiry, Miguel A. & Osgood, Daniel E., 2006. "Index Insurance, Production Practices, and Probabilistic Climate Forecasts," 2006 Annual meeting, July 23-26, Long Beach, CA 21463, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    8. Jensen, Nathaniel D. & Mude, Andrew G. & Barrett, Christopher B., 2018. "How basis risk and spatiotemporal adverse selection influence demand for index insurance: Evidence from northern Kenya," Food Policy, Elsevier, vol. 74(C), pages 172-198.
    9. Keith H. Coble & Robert Dismukes & Joseph W. Glauber, 2007. "Private Crop Insurers and the Reinsurance Fund Allocation Decision," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 89(3), pages 582-595.
    10. Barry Smit & Mark Skinner, 2002. "Adaptation options in agriculture to climate change: a typology," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 7(1), pages 85-114, March.

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