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Challenging Belief in the Law of Small Numbers

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  • Coble, Keith H.
  • Barnett, Barry J.
  • Riley, John Michael

Abstract

Introduction: The context of row crop risk management continues to grow more complex. While the magnitude of price and yield risk changes over time, the development of sophisticated risk management tools and complex government policies may improve growers’ ability to manage risk -- if these instruments are used correctly. Conversely, these instruments may actually increase risk exposure if used incorrectly. Gone are the days when growers had access only to individual yield insurance and national triggered price programs. In 1996, revenue insurance became available for many crop growers. For most major crops, the acreage covered by revenue insurance now far exceeds that covered by yield insurance. The 2008 farm bill created the complex risk policies of ACRE and SURE (Ubilava et al.). Mitchell et al. argue that ACRE, which subsumed multiple revenue risks and integrated with other risk instruments, was difficult for growers to understand and difficult for county USDA officials to implement. Current farm bill proposals are now focused on various shallow loss programs such as Agricultural Risk Coverage (ARC), Stacked Income Protection Plan (STAX) and Supplemental Coverage Option (SCO) which layer risk protection on top of crop insurance. Thus, producers are likely to continue to be confronted with complex risk management tools which may overlap or leave gaps in risk protection. Further, the decision becomes even more complex when one considers the possibility of also using futures or forward contracts.

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Bibliographic Info

Paper provided by Agricultural and Applied Economics Association in its series 2013 AAEA: Crop Insurance and the Farm Bill Symposium, October 8-9, Louisville, KY with number 156958.

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Date of creation: Sep 2013
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Handle: RePEc:ags:aaeaci:156958

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Keywords: Agricultural and Food Policy; Risk and Uncertainty;

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  1. Anderson, John D. & Harri, Ardian & Coble, Keith H., 2009. "Techniques for Multivariate Simulation from Mixed Marginal Distributions with Application to Whole-Farm Revenue Simulation," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, Western Agricultural Economics Association, vol. 34(1), April.
  2. Matthew Rabin., 2000. "Inference by Believers in the Law of Small Numbers," Economics Working Papers, University of California at Berkeley E00-282, University of California at Berkeley.
  3. Peterson, Hikaru Hanawa & Tomek, William G., 2007. "Grain Marketing Strategies Within and Across Lifetimes," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, Western Agricultural Economics Association, vol. 32(01), April.
  4. Shapira, Zur & Venezia, Itzhak, 2008. "On the preference for full-coverage policies: Why do people buy too much insurance?," Journal of Economic Psychology, Elsevier, Elsevier, vol. 29(5), pages 747-761, November.
  5. Keith H. Coble & Barry J. Barnett, 2013. "Why Do We Subsidize Crop Insurance?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, Agricultural and Applied Economics Association, vol. 95(2), pages 498-504.
  6. Mitchell, Paul D. & Rejesus, Roderick M. & Coble, Keith H. & Knight, Thomas O., 2011. "Analyzing Farmer Participation Intentions and Enrollment Rates for the Average Crop Revenue Election (ACRE) Program," Staff Paper Series, University of Wisconsin, Agricultural and Applied Economics 560, University of Wisconsin, Agricultural and Applied Economics.
  7. Galarza, Francisco B. & Carter, Michael R., 2010. "Risk Preferences and Demand for Insurance in Peru: A Field Experiment," 2010 Annual Meeting, July 25-27, 2010, Denver, Colorado, Agricultural and Applied Economics Association 61871, Agricultural and Applied Economics Association.
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