Bayesian ratemaking procedure of crop insurance contracts with skewed distribution

Over the years, crop insurance programs became the focus of agricultural policy in the USA, Spain, Mexico, and more recently in Brazil. Given the increasing interest in insurance, accurate calculation of the premium rate is of great importance. We address the crop-yield distribution issue and its implications in pricing an insurance contract considering the dynamic structure of the data and incorporating the spatial correlation in the Hierarchical Bayesian framework. Results show that empirical (insurers) rates are higher in low risk areas and lower in high risk areas. Such methodological improvement is primarily important in situations of limited data.

Spatio-temporal modeling of agricultural yield data with an application to pricing crop insurance contracts

This article presents a statistical model of agricultural yield data based on a set of hierarchical Bayesian models that allows joint modeling of temporal and spatial autocorrelation. This method captures a comprehensive range of the various uncertainties involved in predicting crop insurance premium rates as opposed to the more traditional ad hoc, two-stage methods that are typically based on independent estimation and prediction. A panel data set of county-average yield data was analyzed for 290 counties in the State of Parana (Brazil) for the period of 1990 through 2002. Posterior predictive criteria are used to evaluate different model specifications. This article provides substantial improvements in the statistical and actuarial methods often applied to the calculation of insurance premium rates. These improvements are especially relevant to situations where data are limited.

Parametric and nonparametric statistical modelling of crop yield: implications for pricing crop insurance contracts

This article considers alternative methods to calculate the fair premium rate of crop insurance contracts based on county yields. The premium rate was calculated using parametric and nonparametric approaches to estimate the conditional agricultural yield density. These methods were applied to a data set of county yield provided by the Statistical and Geography Brazilian Institute (IBGE), for the period of 1990 through 2002, for soybean, corn and wheat, in the State of Paran. In this article, we propose methodological alternatives to pricing crop insurance contracts resulting in more accurate premium rates in a situation of limited data.

Wildlife damage, insurance/compensation for farmers and conservation: Sri Lankan elephants as a case

The interference with agriculture has been recognised as the main cause for the current conflict between farmers and wild elephants in Sri Lanka, as elsewhere in the Asian elephant range. Thus compensating farmers for the damages caused by elephants is essential, if this endangered species is to survive in the long run. This paper explores the practicality of establishing an improved publicly funded insurance/compensation scheme to recompense farmers for the elephant damages. It does so by analysing results from two contingent valuation surveys undertaken in Sri Lanka. We find that possible public support of farmers plus urban dwellers significantly exceeds the financial requirement of the insurance scheme proposed in this study for perpetuity. The article also shows that it is often inappropriate from an economic viewpoint to analyse crop insurance as if it only involves the insurance of a private good because important positive externalities can arise from ‘crop’ damages by wildlife, e.g. elephants. The use of agricultural land by some species is essential for their long-term survival and this is often positively valued by the community as a whole.