Can Crop Insurance Premiums Be Reliably Estimated?

Abstract

This paper develops and applies a methodology to assess the accuracy of historical loss-cost rating procedures, similar to those used by the U.S. Department of Agriculture's Risk Management Agency (RMA), versus alternative parametric premium estimation methods. It finds that the accuracy of loss-cost procedures leaves much to be desired, but can be markedly improved through the use of alternative methods and increased farm-level yield sample sizes. Evidence suggests that the high degree of inaccuracy in crop insurance premium estimations through historical loss-cost procedures identified in the paper might be a major factor behind the need for substantial government subsidies to keep the program solvent.

Premium estimation inaccuracy and the actuarial performance of the US crop insurance program

“…Note that although the yield data utilized to compute those premium estimates is simulated, the mean, variance, skewness and kurtosis parameters of the five distributions used to simulate that data are empirically-grounded, i.e. they correspond to those of the five original distributions estimated by Ramirez et al (2011) on the basis of long-term farm-level yield data collected by the University of Illinois Endowment Farms program.…”

“…This matrix is composed of 90 (100 £ 15) sub-matrices containing the PPRs and LRs associated with each D-M-SS-CC-MAD-BIAS combination, for PSRs ranging from 0 to 99 percent. As in Ramirez et al (2011), the MAD and BIAS are scaled in relation to a TP of 10.…”

“…The question of how accurately can crop insurance premiums be estimated via the RMA procedures and other statistical methods was recently tackled by Ramirez et al (2011) through empirically‐grounded simulation analyses. Specifically, they use long‐term farm‐level yield data collected by the University of Illinois Endowment Farms program to estimate five probability density function (pdf) shapes that should be generally representative of farm‐level corn yield distributions observed across that state.…”

“…Semi-parametric methods show significant potential because they encapsulate the advantages of the parametric and non-parametric approaches while mitigating their disadvantages (Ker and Coble, 2003;Norwood et al, 2004). Ramirez et al (2011) recently assessed the accuracy of select rating methods under different field conditions encompassing sample size, the number of farms from which data is available, and the level of yield correlation across farms. The rating methods evaluated by these authors include non-parametric historical loss-cost procedures that rely on indemnity data (similar to those currently used by the RMA) and flexible parametric methods which use simulations from estimated yield distribution models to compute the premiums.…”

A bootstrap approach for pricing crop yield insurance

“…While researchers agree that the procedure of yield detrending is a necessary step to remove the impact of the structural breaks and technological changes that may have an effect on the historical loss experience, the methodologies used to remove the yield trends are controversial (Ye et al , 2015). In addition, there is also lack of agreement over the appropriate yield distribution model, and the PR estimates are likely to be quite sensitive to these assumptions (Just and Weninger, 1999; Goodwin and Mahul, 2004; Ramirez et al , 2011). Furthermore, issues surrounding lack of data, as well as the credibility of the data, can add to additional challenges in producing accurate crop insurance premiums.…”