Exploring the relationship between willingness to participate in insurance and bank loan approval for coffee farmers in Dak Lak Province: A Bayesian Model Averaging approach

Abstract

This study has employed Bayesian Model Averaging (BMA) to identify the most suitable model for assessing the eligibility of Vietnamese coffee farmers for bank loans, effectively avoiding overfitting and ensuring that only the most crucial variables were considered in the analysis. Findings from the study indicate that factors such as ethnicity, labor, yield, land ownership, and willingness to participate (WTP) in coffee insurance significantly influenced the farmers' eligibility for bank loans. Moreover, the study suggests that banks and insurance companies should also take into account additional factors, such as socio-economic context, household size and composition, land ownership, and risk-sharing programs, to enhance access to credit. With this valuable information, banks can forge partnerships with insurance companies to craft highly effective loan programs and insurance products tailored to Vietnamese farmers' unique needs. The simplicity, practicality, and strong predictive ability of the model chosen by BMA make it a valuable tool for guiding policy decisions.