Multiple credit constraints and borrowing behavior of farm households: panel data evidence from rural Ethiopia

  • Hailu Elias Assisant Professor of Economics, Department of Economics and Institute of Development and Policy Research (IDPR), Addis Ababa University.


Promoting an inclusive rural credit market in developing countries is a reemerging and pressing development agenda, given its importance in the poverty reduction and economic growth process. Existing literature mainly focuses on the supply side of the market with little or no attention given to demand aspects. This paper analyzes both the demand and supply side factors affecting credit constraints and borrowing behavior of farmers. Two waves of survey data, which included about 1,200 randomly selected households from four zones of the Amhara region in Northern Ethiopia, were used for the analysis. The Generalized Linear Latent and mixed model (gllamm) was employed to account for unobserved heterogeneity and potential correlations across credit constraint categories. The results show that the probability of quantity rationing increased in the study area between the years 2011 and 2013. Exposure to climatic shocks, age, and lack of education were found to increase the probability of being constrained while female and married heads were relatively less constrained. The results further indicate that borrower's perceived probability of rejection due to strict lending policies and institutional rigidities; the transaction cost of borrowing; and risk aversion behavior of farmers highly reduced the probability of borrowing from the formal credit market. Compared to North Shewa, farmers living in South Wollo zone were found to be discouraged and hence did not prefer borrowing from the formal sector. However, farmers in West Gojjam were less discouraged and had a higher probability of participating in the formal credit market, signifying zonal variation in credit constraints and borrowing behavior. This suggests the need to work on more innovative lending approaches by giving attention to context-specific factors to build demand-driven, climate-smart, and inclusive rural credit market.


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eISSN: 1993-3681