Browsing by Author "Addey, K.A."

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Adoption of crop insurance in Ghana: an application of the complementary log-log truncated Poisson double-hurdle model
(Emerald Publishing Ltd, 2021) Addey, K.A.; Jatoe, J.B.D.; Kwadzo, G.T.
Purpose – The aim of this paper is to identify the factors that influence rice farmers’ decisions to adopt crop insurance and premium payments (willingness to pay [WTP] amounts). The paper also demonstrates the usefulness of the complementary log-log (cloglog) truncated Poisson double-hurdle model as an alternative hurdle model. Design/methodology/approach – The study first investigated the nature of the dependent variable, which had non-normal residuals and was overdispersed. The probit truncated normal regression double-hurdle model was tried but it failed the normality and homoscedasticity tests; hence, the cloglog truncated Poisson double hurdle model was employed in the study. Findings – An estimated 61% of respondents would purchase crop insurance, despite farmers not having prior experience with this product. Amongst others, the factors that influence insurance adoption amongst rice farmers are the share of rice in total income, reliability perception of crop insurance schemes and the probability of failure to achieve target yields. The latter helps the authors to address adverse selection, a central issue to the viability of such an insurance programme. The determinants of farmers’ WTP are also identified. Research limitations/implications – Sampling was limited to farmers using irrigation and living in one region of Ghana, which may limit the study’s wider applicability. Originality/value – As far as the authors are aware, this study is the first to select the appropriate hurdle model based on established properties of the dependent variable on this topic – crop insurance decisions.
Implications of crop yield distributions for multiperil crop insurance rating in Ghana: a lasso model application
(Agricultural Finance Review, 2024) Addey, K.A.; Jatoe, J.B.D.
Purpose – The objective of this paper is to examine crop yield predictions and their implications on MPCI in Ghana. Farmers in developing countries struggle with their ability to deal with agricultural risks. Providing aid for farmers and their households remains instrumental in combatting poverty in Africa. Several studies have shown that correctly understanding and implementing risk management strategies will help in the poverty alleviation agenda. Design/methodology/approach – This study examines the importance of crop yield distributions in Ghana and its implication on multiperil crop insurance (MPCI) rating using the Lasso regression model. A Bonferroni test was employed to test the independence of crop yields across the regions while the Kruskal-Wallis H test was conducted to examine statistical differences in mean yields of crops across the ten regions. The Bayesian information criteria and k-fold cross-validation methods are used to select an appropriate Lasso regression model for the prediction of crop yields. The study focuses on the variability of the threshold yields across regions based on the chosen model. Findings – It is revealed that threshold yields differ significantly across the regions in the country. This implies that the payment of claims will not be evenly distributed across the regions, and hence regional disparities need to be considered when pricing MPCI products. In other words, policymakers may choose to assign respective weights across regions based on their threshold yields. Research limitations/implications – The primary limitation is the unavailability of regional climate data which could have helped in a better explanation of the variation across the regions. Originality/value – This is the first study to examine the implications of regional crop yield variations on multiperil crop insurance rating in Ghana.