Abstract/Summary

Malaria is a problem in Africa. Thus, the aim of this research is to present a reliable measure of the farmers’ Willingness-To-Pay for malaria abatement in Africa. We develop a model that inputs the stochastic frontier model into the household production model. We analyse our model using the Bayesian Markov chain Monte Carlo techniques of Gibbs Sampling and Metropolis-Hastings. In order to arrive at a reliable posterior distribution for our Willingness To Pay, we multiply the individual households’ price for agricultural staples by the corresponding malaria estimates from our analysis together with a constant value. We apply our model to datasets from Nigeria, Ethiopia, and, Tanzania and report the corresponding Willingness To Pay point estimates and posterior distributions. Our results show that on the average, farmers in these three countries are willing to pay less than US$1 for a 100 per cent increase in malaria case per 1000 individual per annum. Policy makers can use these values to introduce minimum prices and gradual repayment schemes for prophylactic measures.