Preferences for Crop Insurance Programs among Smallholder Cocoa Farmers in Ghana

• Applicant: Professor Dr. Awudu Abdulai
• Subject Area: Agricultural Economics, Agricultural Policy, Agricultural Sociology
• Term: from 2017 to 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 345646117

The study aims at contributing to the literature on crop insurance programs by analyzing farmers preferences for various cash crop insurance programs, particularly the traditional insurance programs. Given the significance of weather uncertainty and imperfect markets, we develop a theoretical framework to show how production risks and liquidity constraints affect farmers crop insurance choices. To the extent that data on crop insurance purchase decsions are not available in Ghana, we intend to conduct a choice experiment survey to elicit farmers preferences for various crop insurance programs in the Ashanti and Western regions in the country. Preferences for crop insurance programs will then be investigated in a two-stage process. The first-stage of the study will consider the decision to participate or not to participate, using a discrete choice model and then the choice among alternate crop insurance programs, given that the farmer has expressed the willingness to participate in it, using a multinomial logit model. The second-stage of the analysis will use the choice experiment data to examine farmers preferences and willingness-to-pay (WTP) for crop insurance attributes. We will employ the hybrid latent variable model framework proposed by Hess and Stathopoulos (2013), in which data on indicators of survey engagement are jointly modeled with respondents answers to the stated choice questions. This approach distinctly accounts for scale heterogeneity caused by potentially different levels of engagement among the respondents, while also overcoming the problems of endogeneity bias and measurement errors. Finally, we will examine preference heterogeneity and the sources of heterogeneity among the farmers, using the hybrid latent class approach developed by Mariel et al. (2015).

• DFG Programme: Research Grants