Determinant Factors of Credit Use and Its Impact on Small Farmer’s Income: A Study in Lemo Woreda, Hadiya Zone, Southern Region, Ethiopia

Nuri Lefebo Toramo


Delivering productive credit to the rural farmer has been a hotly pursued. Farmers in Ethiopia cannot implement improved agricultural technologies out of their own funds. They need credit to implement the new technologies. However, the achievement of credit is controversial. Therefore, the major concern of this study was to assess the impact of credit on gross farm income and to identify factors affecting credit use by small farmers. Primary data were collected from randomly selected farmers. A total of 118 households comprising 85 credit users’ and 35 non-users were included in the final analysis. In addition, secondary data were collected from relevant organizations and pertinent documents. Descriptive statistics such as mean, standard deviation and percentages were used for analyzing the data. Moreover, t-test and - test were employed to compare credit users and non-users with respect to the hypothesized and other related variables. A binary logit model was employed to examine factors influencing agricultural credit use. In this regard, a total of eleven explanatory variables were included in the empirical model of which six were significant, while propensity score matching technique was employed to analyze the impact of credit on gross farm income. Fertility status of the soil, total household expenditure, and frequency of contact of DA were highly important in influencing agricultural credit use as evidenced by the model output. The other critical variables include farm size and gross farm income. While cultivated area, number of draft oxen, credit and total livestock unit were highly important factors of production contributing to gross farm income. The other critical variables include family labor and frequency of contact of DA. Therefore, considering of factors affecting credit use, impact of credit on gross farm income and livelihood of the farmer are vital in providing information to undertake effective measures with the aim of extending loans and increasing access to credit.

Keywords: Farm credit, farm income, Impact, Propensity Score Matching, Ethiopia.

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