Asymmetric Information and Adverse Selection in Nigerian Agricultural Credit Markets

Agricultural credit markets in developing economies are characterised by severe informational frictions that generate adverse selection problems, whereby lenders cannot effectively distinguish high-risk from low-risk borrowers, leading to credit rationing and market inefficiency. This study empirically investigates the existence and magnitude of adverse selection in Nigerian agricultural credit markets, using data from smallholder farmers in Katsina and Niger States, representing the North West and North Central geopolitical zones respectively. A stratified random sample of 600 farm households is surveyed in two rounds, gathering data on loan applications, approvals, loan terms, borrower characteristics, productivity outcomes, and lender assessment practices. The study employs a credit rationing model and a switching regression framework to identify selection effects and estimate the counterfactual loan performance of unserved borrowers. The theoretical framework draws on the Stiglitz-Weiss model of credit rationing under asymmetric information, the costly state verification model, and the relationship lending literature that examines how repeated interactions reduce informational barriers. The study evaluates whether the National Collateral Registry, the agricultural insurance programmes of the Nigerian Agricultural Insurance Corporation, and digital credit scoring tools have measurably reduced adverse selection in the markets studied. Existing Nigerian literature documents widespread credit rationing in agricultural credit markets but rarely tests the adverse selection mechanism directly or quantifies its welfare costs. This study fills that empirical gap and contributes findings relevant to the Central Bank of Nigeria's agricultural credit guarantee scheme review and the Bank of Agriculture's loan product design. Keywords: adverse selection, agricultural credit, information asymmetry, Nigeria, credit rationing