Can expert knowledge compensate for data scarcity in crop insurance pricing?

Abstract

Although there is an increasing interest in area yield insurance in many developing countries, crop data scarcity hinders its implementation by imposing higher premiums. Expert knowledge has been considered a valuable information source to augment limited data in insurance pricing. This article investigates whether the use of expert knowledge can mitigate model risk arising from insufficient statistical data. We adopt a Bayesian framework that allows for the combination of scarce crop data, expert knowledge and weather information, to estimate the loss distribution. We find that expert knowledge reduces the parameter uncertainty and changes the insurance premium in the correct direction.