Cory Walters

Input Use under Crop Insurance: The Role of Actual Production History

&NA; The impact of crop insurance on changes in input use has attracted much attention by economists. While there are a number of studies on this topic, they frame moral hazard in inputs use in a static model. However, when agricultural producers are forward‐looking, they would make input allocation decisions realizing that their decisions would affect their future actual production history. This, in turn, affects the probability and size of future indemnity payments. Thus, moral hazard should be framed in a dynamic input use decision model. We first show theoretically that under certain feasible conditions, a static analysis always results in lower optimal input use when compared to a dynamic one with endogenous actual production history. This is because static models fail to recognize the role of actual production history. We then run numerical simulations using nitrogen application rates as a case study. We find that static models indicate significant reduction in nitrogen use compared to the no‐insurance scenario, whereas the dynamic models with a role for actual production history indicate almost no reduction in applied nitrogen. The dynamic analysis not only suggests a near absence of moral hazard, but, for low coverage rates, it results in an optimal nitrogen rate higher than that under the no‐insurance scenario. These findings illustrate the importance of recognizing the role of actual production history in mitigating moral hazard possibilities in crop insurance and the dynamic nature of moral hazard in crop insurance.

Risk Implications from the Selection of Rainfall Index Insurance Intervals

Purpose n n n n nThe purpose of this paper is to empirically examine the financial outcomes from forage production and RI-PRF insurance interval for two locations in Nebraska. Both locations provide historical forage production and precipitation data, allowing the authors to examine the relation between RI-PRF net income and forage production. n n n n nDesign/methodology/approach n n n n nThe authors focus on evaluating the producer net income and risk (measured as variance of net income) by examining the relation between farm precipitation and production and comparing multiple insurance intervals to no insurance. Each insurance interval will likely have a different relation (basis risk) between observed production and return from insurance and, therefore, a different impact on the variance of net incomes. The impact on variance of net incomes identifies the risk-reducing aspects of RI-PRF insurance intervals. The authors then rank each scenario into four mutually exclusive zones that describe the risk-reducing effectiveness and whether the subsidy is working correctly. n n n n nFindings n n n n nThe authors found both risk increasing and decreasing insurance intervals exist at both locations. One insurance scenario (low in BBR) provided the highest net income while increasing risk, suggesting a profit maximizing opportunity. RI-PRF reduces net income risk with intervals insuring during high expected precipitation (growing season); while net income risk increases with intervals insuring low expected precipitation (non-growing season, winter months). The farmer would want to insure during the high expected precipitation months, which coincides with the growing season, since RI-PRF lowers the net income risk. For the government, removing net income risk increasing intervals improves the allocation of government resources. n n n n nOriginality/value n n n n nIn this paper, the authors modeled the relation between RI-PRF interval selection using the historical forage production data at two locations in Nebraska. The use of historical forage production data allowed the authors to precisely identify the risk-reducing effectiveness of RI-PRF interval selection.

Net income risk, crop insurance and hedging

Purpose - At the beginning of the production year producers face a complex risk management decision environment given by risks specific to their operation, multiple crop insurance contracts and hedging opportunities. The purpose of this paper is to provide a producer-level framework for risk management decision making, focusing on the interaction between crop insurance and hedging. Design/methodology/approach - The authors develop a Monte Carlo simulation model that generates a producer’s net income (NI) distribution that incorporates historical producer risk, price-yield correlation via a copula, price risk, and production costs. The authors evaluate the NI distribution through a modified Modern Portfolio Theory (MPT) decision framework. The authors use the modified MPT decision framework to explore tradeoffs between expected NI and farm ruin (defined as 1 or 5 percent expected shortfall) from different crop insurance contracts and pre-harvest hedging options. Findings - Only revenue protection and the highest two levels of coverage level exist on the efficient frontier. The level of hedging on the efficient frontier ranges from 0 to 55 percent of Actual Production History. The authors find that increasing coverage level 5 percent (from 80 to 85 percent) negatively impacts the optimal hedging amount by 26 percentage points (from 35 to 9 percent). Originality/value - The model provides the precise identification of financial benefits from different risk management strategies by incorporating producer-level historical yield data, using a copula to capture yield-price dependency structure and producer production cost in generating the NI distribution. This model can be applied to any producer’s characteristics and data.