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The purpose of this paper is to be an academic inquiry into rating issues confronted by the US Federal Crop Insurance program stemming from changes in participation rates as well as the weighting of data to reflect longer‐run weather patterns.

The authors investigate two specific approaches that differ from those adopted by the Risk Management Agency, building upon standard maximum likelihood and Bayesian estimation techniques that consider parametric densities for the loss‐cost ratio.

Both approaches indicate that incorporating weights into the priors for Bayesian estimation can inform the distribution.

In most cases, the authors' results indicate that including weighting into priors for Bayesian estimation implied lower premium rates than found using standard methods.

This article seeks to examine the reference yield calculation method used in crop insurance rating and provides recommendations that could potentially improve actuarial performance of the Federal crop insurance program.

Conceptual, numerical, and statistical analysis is utilized to evaluate the reference yield calculation method used in the US Federal crop insurance program.

The results suggest that reference yields, which at the time of this study are calculated using National Agricultural Statistics Service (NASS) data, do not accurately represent the average actual yields of the insured pool of producers in the Federal crop insurance program. In addition, it is found that not regularly updating these NASS‐based reference yields exacerbates this problem because these reference yields do not appropriately represent the current state of technological progress.

The empirical analysis leads this paper to recommend a reference yield calculation procedure that utilizes county‐average yields from the risk management agency (RMA) participation database and an approach that uses spatially aggregated average yields in cases when data for a particular county are sparse.

No previous study has investigated the reference yield calculation method in the Federal crop insurance program using both RMA and NASS data sets. Moreover, this study contributes to the small literature that examines various aspects of the actual production history (APH) rating platform and suggests refinements to improve actuarial performance.

The purpose of this paper is to investigate how experience in insurance contracts may influence participation in the Italian crop insurance market.

From Italian farm-level data, the author estimates a dynamic discrete choice model of participation to investigate the role of experience. The methodology, coupled with exploratory analysis of the data, allows one to compare the relevance of different sources of experience in the crop insurance decision-making process.

The author found that experience tends to be a catalyst for insurance participation. Policy implications are discussed, in particular, the author discusses on the importance of bolstering uptake to exploit the advantages of the inertia that emerge from experience, and the importance of initiatives to increase the knowledge of crop insurance instruments.

To the best of the author’s knowledge, the role of experience has been underinvestigated. The analysis has the specific contribution of modeling the potential role of experience (exploited after buying an insurance contract) on uptake in crop insurance programs.