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How Much Can We Learn About Producers' Utility Functions from Their Production Data?

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  • Lence, Sergio H.

Abstract

A thought experiment is designed to investigate whether the structure of risk aversion (i.e., the changes in absolute or relative risk aversion associated with changes in wealth) can be estimated with reasonable precision from agricultural production data. Findings strongly suggest that typical production data are unlikely to allow identification of the structure of risk aversion. A flexible utility parameterization is found to slightly worsen technology parameter estimates. Results also indicate that even under a restricted utility specification, utility parameter estimates are biased. Further, their quality is much worse when shocks are not large or samples are small.

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Bibliographic Info

Paper provided by Regional Research Committee NC-1014: Agricultural and Rural Finance Markets in Transition in its series Proceedings:2008 Agricultural and Rural Finance Markets in Transition, September 25-26, 2008, Kansas City, Missouri with number 119534.

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Date of creation: 2008
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Handle: RePEc:ags:nc1008:119534

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Web page: http://www.agfin.ifas.ufl.edu/

Related research

Keywords: expected utility; risk preferences; production analysis; risk attitudes; Production Economics; Risk and Uncertainty; C13; D24; D81; Q12;

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  1. Koundouri, Phoebe & Nauges, Celine, 2005. "On Production Function Estimation with Selectivity and Risk Considerations," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 30(03), December.
  2. Murat Isik & Madhu Khanna, 2003. "Stochastic Technology, Risk Preferences, and Adoption of Site-Specific Technologies," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 85(2), pages 305-317.
  3. Hart, Chad E. & Lence, Sergio H., 2004. "Financial Constraints and Farm Investment: A Bayesian Examination," Staff General Research Papers 11469, Iowa State University, Department of Economics.
  4. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119.
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  6. Abdullahi Abdulkadri, 2003. "Estimating risk aversion coefficients for dry land wheat, irrigated corn and dairy producers in Kansas," Applied Economics, Taylor & Francis Journals, vol. 35(7), pages 825-834.
  7. Robison, Lindon J. & Shupp, Robert S. & Myers, Robert J., 2010. "Expected utility paradoxes," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 39(2), pages 187-193, April.
  8. Geweke, John, 1988. "Antithetic acceleration of Monte Carlo integration in Bayesian inference," Journal of Econometrics, Elsevier, vol. 38(1-2), pages 73-89.
  9. Bar-Shira, Z. & Just, R. E. & Zilberman, D., 1997. "Estimation of farmers' risk attitude: an econometric approach," Agricultural Economics, Blackwell, vol. 17(2-3), pages 211-222, December.
  10. Barry T. Coyle, 1999. "Risk Aversion and Yield Uncertainty in Duality Models of Production: A Mean-Variance Approach," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 81(3), pages 553-567.
  11. Harwood, Joy L. & Heifner, Richard G. & Coble, Keith H. & Perry, Janet E. & Somwaru, Agapi, 1999. "Managing Risk in Farming: Concepts, Research, and Analysis," Agricultural Economics Reports 34081, United States Department of Agriculture, Economic Research Service.
  12. Subal C. Kumbhakar, 2002. "Specification and Estimation of Production Risk, Risk Preferences and Technical Efficiency," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 84(1), pages 8-22.
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