Academic journal article Agricultural and Resource Economics Review

Can Crop Insurance Premiums Be Reliably Estimated?

Academic journal article Agricultural and Resource Economics Review

Can Crop Insurance Premiums Be Reliably Estimated?

Article excerpt

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.

Key Words: agricultural subsidies, crop insurance premium estimation, loss-cost procedures, Risk Management Agency

(ProQuest: ... denotes formulae omitted.)

The U.S. crop insurance program is a joint effort of the federal government and private insurance companies that sell policies to farmers backed by reinsurance provided by the Federal Crop Insurance Corporation. The Risk Management Agency (RMA), a division of the U.S. Department of Agriculture, administers this insurance program. The traditional product offered by the RMA, which is the focus of this paper, is a farm-level, multipleperil, crop yield insurance policy [called the MPCI or Actual Production History (APH) policy]. This policy protects against low yield and crop quality losses due to adverse weather and unavoidable damage from insects and disease (Barnett 2000).

During the past 15 years, the federal government has increasingly looked to crop insurance as a possible alternative to the historical "disaster relief" payments that are made to farmers when crop yields are drastically reduced due to widespread bad weather, pest outbreaks, or other adverse events. Therefore, through the RMA, it has tried to promote participation by subsidizing the premiums paid by farmers. In 2009, the U.S. crop insurance program covered close to 265 million acres, assuming nearly $80 billion in liabilities. This breadth of coverage has been obtained through increased subsidies over time, with producers as a whole now paying only about 40 percent of the total premiums required to keep the program solvent.1 The need for such large subsidies to achieve high levels of producer participation has for the most part been attributed to "adverse selection" (Harwood et al. 1999).

Specifically, it has been hypothesized that farmers are better able to ascertain what their actuarially fair premiums are than the RMA, and they tend to participate only if they feel that it is to their economic advantage. As a result, the program is loaded with producers whose fair premiums are lower than what they are being charged. In short, the root of the adverse selection problem is the RMA's inability to precisely estimate the actuarially fair premiums that should be charged to individual producers. In addition to their impact on the actuarial performance of the crop insurance program, incorrect rates can affect the producers' economic welfare and the incentives and returns to the private companies that sell federal crop insurance at those rates.

Although an extensive and highly relevant body of work on crop insurance program rating has been published in the agricultural economics literature to date, the question of how accurately crop insurance premiums can be estimated through historical loss-cost procedures (i.e., the basic approach used by RMA) and other proposed methods remains largely unanswered. Specifically, no study has quantified the magnitude of the inaccuracy in the premium estimates obtained under these alternative rating methods. The reason for this gap in the literature might be that, in principle, the "true" (i.e., actuarially correct) premium corresponding to any particular farmer is unknown, making an actual comparison between estimated and true rates unfeasible. …

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