The impact of the average crop revenue election (ACRE) program on the effectiveness of crop insurance Academic Article uri icon


  • © Emerald Group Publishing Limited. Purpose – The purpose of this paper is to analyze the effect of the 2008 Farm Bill’s average crop revenue election (ACRE) program on the risk-reducing effectiveness of crop insurance products. Design/methodology/approach – Three crop/region combinations are examined, representing regions with both high and low price-yield correlation regions. Actual production history (APH) and crop revenue coverage (CRC) insurance instruments are considered separately under the 2002 Farm Bill and under ACRE. Monte Carlo simulations, combined with the copula approach, are used to simulate net wealth distributions and to calculate the corresponding expected utilities. The outcomes are evaluated using certainty-equivalent wealth based on different risk premium assumptions. Findings – Crop insurance contracts appear to be more effective under the 2002 Farm Bill than under ACRE, especially for crops characterized by low yield-price correlation. CRC insurance is found to be more effective than APH insurance for all crop/region combinations considered. Research limitations/implications – The paper only considers a static framework and farm-level insurance contracts. Further research could investigate how ACRE affects decoupled income support, whether the results change if Supplemental Revenue Assistance is included, or how different the outcomes might be for multiple-crop farms. Practical implications – The results suggest that risk-reducing effectiveness decreases under ACRE and that no reasonable adjustment to APH base price can make APH competitive with CRC for any crop/regions considered. Originality/value – The risk-reducing effectiveness of the 2008 Farm Bill’s ACRE program is analyzed, and as a methodological contribution the copula approach is used to model the multivariate distribution of yields and prices.

author list (cited authors)

  • Power, G. J., Vedenov, D. V., & Hong, S.

citation count

  • 7

publication date

  • November 2009