Agricultural Finance's Impact on Productivity in Tanzanian Smallholder Farming

Abstract

It's clear that agricultural finance in developing countries falls short, posing significant credit constraints for smallholder farm households. Yet, understanding how credit access affects agricultural productivity in Tanzania remains limited. This study delves into this issue, exploring the impact of credit access on productivity in Tanzania, a nation with weak agricultural finance and substantial credit constraints. Using secondary cross-sectional data from 1042 smallholder farms during the 2017-2018 farming season, we employ an Endogenous Switching Regression model to address endogeneity and selection bias, with Propensity Score Matching as a robustness check. Both models consistently show that credit access boosts crop productivity for smallholder farms. Remarkably, farmers without credit could potentially increase productivity by 15.09% if credit constraints were removed. These findings underscore the importance of improving smallholders' credit access, which can significantly impact food security and poverty reduction by enhancing crop productivity and advancing the Sustainable Development Goals (SDGs).

Keywords: Credit access, credit constraints, agricultural productivity, endogenous switching regression model, propensity score matching, smallholder, impact analysis