Fischer, Bart Lynn (2016-08). Evaluating Optimal Crop Insurance Coverage for Cotton Producers. Doctoral Dissertation. Thesis uri icon

abstract

  • In 2002, Brazil filed a complaint against U.S. cotton policy in the World Trade Organization (WTO). After years of litigation, the 2014 Farm Bill eliminated upland cotton as a covered commodity. Instead, cotton producers were given the opportunity to purchase a new area-wide crop insurance policy known as the Stacked Income Protection Plan (STAX). The Supplemental Coverage Option (SCO)--a similar area-wide crop insurance policy--was made available for all crops, including cotton. Producers were also given the opportunity to choose different crop insurance coverage levels for both irrigated and non-irrigated crops, and they were allowed to exclude yields from their Actual Production History (APH) database in years where the county yield is more than 50 percent below the 10-year county average yield. These tools were evaluated on 16 representative cotton farm/practice combinations in 6 states using a stochastic simulation model and an expected utility framework to rank the risky alternatives. Results indicate that the ability to choose separate coverage levels by practice on its own had a negligible impact, although that changed somewhat when combined with the other tools. By contrast, both the Yield Exclusion and the supplemental area-wide policies individually improved upon the optimal policy combination in the baseline. When looking collectively across all of the crop insurance tools available, STAX was a component of the optimal crop insurance policy combination on 15 of the 16 farm/practice combinations; the remaining farm/practice combination preferred SCO. For 5 of the 16 farm/practice combinations, the interaction between the various tools yielded an optimal policy valued higher than any tool provided on its own. The results of this research emphasize the importance of building models that mirror the experience of the producer when trying to estimate optimal producer decisions. Specifically, this means building models that take the futures price at planting as known and that utilize the Risk Management Agency (RMA) premium-rating methodology. For example, while conventional wisdom suggests purchasing Revenue Protection policies, those policies never factored into the optimal crop insurance policy combinations in this analysis.
  • In 2002, Brazil filed a complaint against U.S. cotton policy in the World Trade Organization (WTO). After years of litigation, the 2014 Farm Bill eliminated upland cotton as a covered commodity. Instead, cotton producers were given the opportunity to purchase a new area-wide crop insurance policy known as the Stacked Income Protection Plan (STAX). The Supplemental Coverage Option (SCO)--a similar area-wide crop insurance policy--was made available for all crops, including cotton. Producers were also given the opportunity to choose different crop insurance coverage levels for both irrigated and non-irrigated crops, and they were allowed to exclude yields from their Actual Production History (APH) database in years where the county yield is more than 50 percent below the 10-year county average yield. These tools were evaluated on 16 representative cotton farm/practice combinations in 6 states using a stochastic simulation model and an expected utility framework to rank the risky alternatives.

    Results indicate that the ability to choose separate coverage levels by practice on its own had a negligible impact, although that changed somewhat when combined with the other tools. By contrast, both the Yield Exclusion and the supplemental area-wide policies individually improved upon the optimal policy combination in the baseline. When looking collectively across all of the crop insurance tools available, STAX was a component of the optimal crop insurance policy combination on 15 of the 16 farm/practice combinations; the remaining farm/practice combination preferred SCO. For 5 of the 16 farm/practice combinations, the interaction between the various tools yielded an optimal policy valued higher than any tool provided on its own.

    The results of this research emphasize the importance of building models that mirror the experience of the producer when trying to estimate optimal producer decisions. Specifically, this means building models that take the futures price at planting as known and that utilize the Risk Management Agency (RMA) premium-rating methodology. For example, while conventional wisdom suggests purchasing Revenue Protection policies, those policies never factored into the optimal crop insurance policy combinations in this analysis.

publication date

  • August 2016