Hostname: page-component-78c5997874-fbnjt Total loading time: 0 Render date: 2024-11-10T18:16:25.661Z Has data issue: false hasContentIssue false

Crop Revenue and Yield Insurance Demand: A Subjective Probability Approach

Published online by Cambridge University Press:  26 January 2015

Saleem Shaik
Affiliation:
North Dakota State University, Fargo, ND
Keith H. Coble
Affiliation:
Department of Agricultural Economics at Mississippi State University, Mississippi State, MS
Thomas O. Knight
Affiliation:
Department of Agricultural Economics at Texas Tech University, Lubbock, TX
Alan E. Baquet
Affiliation:
Department of Agricultural Economics, University of Nebraska, Lincoln, NE
George F. Patrick
Affiliation:
Department of Agricultural Economics at Purdue University, West Lafayette, IN

Abstract

A multinomial logit is utilized to model the choice of whether to purchase yield or revenue insurance using subjectively elicited survey data. Our results indicate that the demand for crop insurance is inelastic (−0.40), consistent with most earlier yield elasticity estimates, but the elasticity for choices between yield and revenue insurance is found to be relatively more elastic (−0.88).

Type
Research Article
Copyright
Copyright © Southern Agricultural Economics Association 2008

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Barnett, B.J., and Skees, J.R.Region and Crop Specific Models of the Demand for Federal Multiple Peril Crop Insurance.Journal of Insurance Issues 19(October 1995):4765.Google Scholar
Coble, K.H., Knight, T.O., Pope, R.D., and Williams, J.R.Modeling Farm-Level Crop Insurance Demand with Panel Data.American Journal of Agricultural Economics 78(May 1996):439-47.Google Scholar
Coble, K.H., Knight, T.O., Patrick, G.F., and Baquet, A.E.Crop Producer Risk Management Survey: A Preliminary Summary of Selected Data.” Agricultural Economics Information Report 99-001. Department of Agricultural Economics, Mississippi State University, September 1999.Google Scholar
Dillman, D. Mail and Telephone Surveys: A Total Design Method. NewYork: John Wiley Publishing, 1979.Google Scholar
Eales, J.S., Engel, B.K., Hauser, R.J., and Thompson, S.R.Grain Price Expectations of Illinois Farmers and Grain Merchandisers.American Journal of Agricultural Economics 72(August 1990):701-8.Google Scholar
Glauber, J.W.Crop Insurance Reconsidered.American Journal of Agricultural Economics 86(December 2004): 1179-95.Google Scholar
Goodwin, B.K.An Empirical Analysis of the Demand for Crop Insurance.American Journal of Agricultural Economics 75(May 1993): 425-34.Google Scholar
Goodwin, B.K., and Kastens, T.L.Adverse Selection, Disaster Relief, and the Demand for Multiple Peril Crop Insurance.” Contract report for the Federal Crop Insurance Corporation, May 1993.Google Scholar
Goodwin, B.K., and Smith, V.H. The Economics of Crop Insurance and Disaster Aid. Washington DC: American Enterprise Institute Press, 1995.Google Scholar
Greene, W.H. Econometric Modeling Guide, Volume 1 and 2. New York: Econometric Software Inc., 2002.Google Scholar
Grisley, W., and Kellogg, E.D.Farmers' Subjective Probabilities in Northern Thailand: An Elicitation Analysis.American Journal of Agricultural Economics 65(February 1983):7482.Google Scholar
Hennessy, D.A., Babcock, B.A., and Hayes, D.J.The Budgetary and Producer Welfare Effects of Revenue Assurance.American Journal of Agricultural Economics 79(August 1997): 1024-34.Google Scholar
Knight, T.O., and Coble, K.H.A Survey of Literature on U.S. Multiple Peril Crop Insurance Since 1980.Review of Agricultural Economics 19(Spring-Summer 1997): 128-56.CrossRefGoogle Scholar
Lau, H., Lau, A.H., and Zhang, Y.An Improved Approach for Estimating the Mean and Standard Deviation of a Subjective Probability Distribution.Journal of Forecasting 16(March 1997):8395.Google Scholar
Maddala, G.S. Limited Dependent and Qualitative Variables in Econometrics. New York: Cambridge University Press, 1983.Google Scholar
Mishra, A., and Goodwin, B.K.Adoption of Crop versus Revenue Insurance: A Farm-Level Analysis.Agricultural Finance Review 63(Fall 2003): 143-55.Google Scholar
Norris, P.E., and Kramer, R.A.The Elicitation of Subjective Probabilities with Applications in Agricultural Economics.Review of Marketing and Agricultural Economics 58(August, December 1990): 127-47.Google Scholar
Schnitkey, G.D., Sherrick, B.J., and Irwin, S.H.Evaluation of Risk Reductions Associated with Multi-Peril Crop Insurance Products.Agricultural Finance Review 63(Spring 2003): 121.Google Scholar
Serra, R., Goodwin, B.K., and Featherstone, A.M.Modeling Changes in the U.S. Demand for Crop Insurance during the 1990s.Agricultural Finance Review 63(Fall 2003):109-25.Google Scholar
Shaik, S., and Atwood, J. A.Demand for Optional Units in Crop Insurance.” Selected paper presented at AAEA Meetings, Montreal, Quebec, July 27-30, 2003.Google Scholar
Sherrick, B.J., Barry, P.J., Ellinger, P.N., and Schnitkey, G.D.Factors Influencing Farmers' Crop Insurance Decisions.American Journal of Agricultural Economics 86(February 2004): 103-14.Google Scholar
Skees, J.R., and Reed, M.R.Rate Making for Farm-Level Crop Insurance: Implications for Adverse Selection.American Journal of Agricultural Economics 68(August 1986):653-9.Google Scholar
Smith, V.H., and Baquet, A.The Demand for Multiple Peril Crop Insurance: Evidence from Montana Wheat Farms.American Journal of Agricultural Economics 78(February 1996):189210.Google Scholar
Smith, J., and Mandac, A.M.Subjective versus Objective Yield Distributions as Measures of Production Risk.American Journal of Agricultural Economics 77(February 1995): 152-61.Google Scholar