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Purpose -The purpose of this paper is to explain the factors affecting crop insurance purchases by farmers in Inner Mongolia, China. Design/methodology/approach -A survey of farmers in Inner Mongolia, China, is undertaken. Selected variables are used to explain crop insurance purchases, and a probit regression model is used for the analysis. Findings -Results show that a number of variables explain crop insurance purchases by farmers in Inner Mongolia. Of the eight variables in the model, seven are statistically significant. The eight variables used to explain crop insurance purchases are: knowledge of crop insurance, previous purchases of crop insurance, trust of the crop insurance company, amount of risk taken on by the farmer, importance of low crop insurance premium, government as the main information source for crop insurance, role of head of village, and number of family members working in the city. Research limitations/implications -A possible limitation of the study is that data includes only one geographic area, Inner Mongolia, China, and so results may not always fully generalize to all regions of China, for all situations. Practical implications -Crop insurance has been recently expanded in China, and the information from this study should be useful for insurance companies and government policy makers that are attempting to increase the adoption rate of crop insurance in China. Social implications -Crop insurance may be a useful approach for stabilizing the agricultural sector, and for increasing agricultural production and food security in China. Originality/value -This is the first study to quantitatively model the factors affecting crop insurance purchases by farmers in Inner Mongolia, China.

For the autoregressive fractionally integrated moving-average (ARFIMA) processes which characterize both long-memory and short-memory behavior in time series, we formulate Bayesian inference using Markov chain Monte Carlo methods. We derive a form for the joint posterior distribution of the parameters that is computationally feasible for repetitive evaluation within a modi®ed Gibbs sampling algorithm that we employ. We illustrate our approach through two examples.

Firms within various sectors of an economy are often faced with a number of risks. These risks can be relatively sudden and large, especially if they are weather related.

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