Hostname: page-component-78c5997874-xbtfd Total loading time: 0 Render date: 2024-11-03T04:55:08.415Z Has data issue: false hasContentIssue false
  • English
  • Français

Evaluating Crop and Revenue Insurance Products as Risk Management Tools for Texas Cotton Producers

Published online by Cambridge University Press:  28 April 2015

James E. Field ,
Sukant K. Misra  and
Octavio Ramirez
James E. Field
Affiliation:
College of Agricultural Sciences and Natural Resources, Texas Tech University, Lubbock, TX
Sukant K. Misra
Affiliation:
College of Agricultural Sciences and Natural Resources, Texas Tech University, Lubbock, TX
Octavio Ramirez
Affiliation:
Department of Agricultural and Applied Economics, Texas Tech University, Lubbock, TX
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper develops and illustrates the application of a procedure to evaluate and compare the cost effectiveness of alternative crop insurance products for cotton in terms of their effect on expected producer net returns and the variation of net returns. Farm unit-level cotton yields and state-level price distributions are estimated by a multivariate nonnormal parametric modeling procedure and used to simulate the net returns to alternative crop insurance products over a 10-year planning horizon. The ranking of alternative insurance products using third-degree stochastic dominance is presented for Texas cotton producers.

Keywords

cottoncrop insurancemultivariate nonnormal parametric modelingstochastic dominanceC5Q1
Type
Articles
Information
Journal of Agricultural and Applied Economics , Volume 35 , Issue 1 , April 2003 , pp. 39 - 52
Copyright
Copyright © Southern Agricultural Economics Association 2003

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

Coble, K.H., Heifner, R.G., and Zuniga, M.. “Implications of Crop Yield and Revenue Insurance for Producer Hedging.” Journal of Agricultural and Resource Economics 25(2):432–52.Google Scholar
Duncan, J., and Myers, R.J.. “Crop Insurance Under Catastrophic Risk.” American Journal of Agricultural Economics 82(November 2000):842–55.CrossRefGoogle Scholar
Federal Crop Insurance Corporation-Risk Management Agency. Actuarial Document Site. United States Department of Agriculture. Internet site: http://www.rma.usda.gov/tools/actdoc/index.html (Accessed April 27, 2000).Google Scholar
Gallagher, P. “U.S. Corn Yield Capacity and Probability: Estimation and Forecasting with Non-Symmetric Disturbances.” North Central Journal of Agricultural Economics 8(January 1986):109–22.CrossRefGoogle Scholar
Gallagher, P. “U.S. Soybean Yields: Estimation and Forecasting with Nonsymmetric Disturbances.” American Journal of Agricultural Economics 71(November 1987):796–803.CrossRefGoogle Scholar
Goodwin, B.K., and Smith, V.H.. The Economics of Crop Insurance and Disaster Aid. Washington, DC: The AEI Press, 1995.Google Scholar
Hennessy, D.A., Babcock, B.A., and Hayes, D.J.. “Budgetary and Producer Welfare Effects of Revenue Insurance.” American Journal of Agricultural Economics 79(1997):1024–34.CrossRefGoogle Scholar
Miranda, M.J., and Glauber, J.W.. “Systematic Risk, Reinsurance, and the Failure of Crop Insurance Markets.” American Journal of Agricultural Economics 79(1997):206–15.CrossRefGoogle Scholar
Mjelde, J.W., and Cochran, M.J.. “Obtaining Lower and Upper Bounds on the Value of Seasonal Climate Forecasts as a Function of Risk Preferences.” Western Journal of Agricultural Economics 13,2(1988):285–93.Google Scholar
National Agricultural Statistics Service. Agricultural Statistics. Washington, DC: U.S. Department of Agriculture, 1999.Google Scholar
Ramirez, O.A. “Estimation and Use of a Multivariate Parametric Model for Simulating Heteroscedastic, Correlated, Nonnormal Random Variables: The Case of Corn Belt Corn, Soybean, and Wheat Yields.” American Journal of Agricultural Economics 79(February 1997):191–205.CrossRefGoogle Scholar
Ramirez, O.A., and Somarriba, E.. “Modeling and Simulation of Autocorrelated, Non-Normal Time Series for Analyzing Risk in Diversified Agricultural Systems.” Journal of Agricultural and Resource Economics 25,2(2000):653–68.Google Scholar
Ramirez, O.A., Misra, S., and Field, J.. “Are Crop Yields Normally Distributed?: A Reply.” Lubbock, TX: Texas Tech University, College of Agricultural and Applied Economics publication T-1-512, 2000.Google Scholar
Ramirez, O.A., Moss, C.B., and Boggess, W.G.. “Estimation and Use of the Inverse Hyperbolic Sine Transformation to Model Non-Normal Correlated Random Variables.” Journal of Applied Statistics 21,4(1994):289–303.CrossRefGoogle Scholar
Skees, J.R., Harwood, J., Somwaru, A., and Perry, J.. “The Potential for Revenue Insurance Policies in the South.” Journal of Agricultural and Applied Economics 30,1(July 1998):47–61.CrossRefGoogle Scholar
Taylor, C.R. “Two Practical Procedures for Estimating Multivariate Nonnormal Probability Density Functions.” American Journal of Agricultural Economics 72(February 1990):210–17.CrossRefGoogle Scholar
Texas Agricultural Extension Service. Texas Crop and Livestock Budgets. Internet site: http://agecoext.tamu.edu/budgets/list.htm (Accessed March 21, 2000).Google Scholar
Texas Agricultural Statistics Service. Texas Agricultural Statistics. Austin, TX: U.S. Department of Agriculture and Texas Department of Agriculture, 1970-1998.Google Scholar
Texas Agricultural Statistics Service. Crop Report with Comparisons. Austin, TX: U.S. Department of Agriculture and Texas Department of Agriculture, January 12, 2000.Google Scholar
Vercammen, J.A. “Constrained Efficient Contracts for Area Yield Crop Insurance.” American Journal of Agricultural Economics 82(November 2000):856–64.CrossRefGoogle Scholar
Wang, H.H., Hanson, S.D., Myers, R.J., and Black, J.R.. “The Effects of Crop Yield Insurance on Farmer Participation and Welfare.” American Journal of Agricultural Economics 80(November 1998):806–20.CrossRefGoogle Scholar