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

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.

Ranking Crop Yield Models: A Comment

“…In fact, from 2000 to 2003, at least three articles have been published using the IHS model with the most common specification where the variance is a continuous polynomial function of time (Ramírez 2000;Ramírez, Misra, and Field 2003;Field, Misra, and Ramírez 2003). Yet, although NRL made substantial modifications to the gamma, beta, and nonparametric kernel models originally published in AJAE, they made no attempt to address this obvious shortcoming of the discrete variance specification used in the Ramírez (1997) IHS model.…”

Changes in Producers' Perceptions of Within-Field Yield Variability after Adoption of Cotton Yield Monitors

“…The beta distribution is used in this study because, relative to other nonnormal parametric distributions used in the literature (i.e., the gamma or Weibull), it is "flexible" enough to accommodate a wider range of skewness and kurtosis values and, thus, allows for varying degrees of asymmetry, which is not possible with the normal or other less flexible parametric distributions. Previous literature shows that temporal cotton yield distributions tend to be right-skewed, which can be easily accommodated by the beta distribution (e.g., Chen and Miranda, 2008;Field, Misra, and Ramirez, 2003;Ramirez, Misra, and Field, 2003). In addition, most of the empirical literature in agricultural economics over the past decade has used the beta distribution to model temporal crop yields (e.g., Babcock, Hart, and Hayes, 2004;Goodwin, 2009).…”