Abstract Preview:
EXECUTIVE SUMMARY Agricultural financing plays a critical role in enhancing crop productivity and fostering rural development, particularly in agrarian economies. This study aims to empirically investigate the impact of agricultural financing on the crop productivity of smallholder farmers and assess its implications on income inequality. To achieve this objective, a mixed research approach was adopted, integrating both qualitative and quantitative methodologies. An explanatory research design was employed to explore the causal relationship between access to agricultural financing and crop output. Primary data were collected from a sample of 800 crop-producing households using structured questionnaires, interview schedules, and focus group discussions, selected through simple random sampling. Secondary data were obtained through document reviews from relevant institutional sources. The data were analysed using descriptive statistics and econometric modelling techniques, specifically Propensity Score Matching (PSM), to estimate the Average Treatment Effect on the Treated (ATT). A binary logit model was used to identify the determinants of access to agricultural financing. The results revealed that age, seed cost, labor cost, tractor access (as a proxy for infrastructure), education level, and farming experience significantly influenced whether a household received agricultural financing. Conversely, variables such as plant protection, fertilizer use, and landholding size were found to have an insignificant effect. The covariate balancing indicators and balance plots confirmed that the matching algorithms were successful in reducing selection bias, with mean standardized bias decreasing by up to 77.5% and pseudo R² values dropping significantly post-matching. The common support condition was also satisfied, ensuring the reliability of the PSM estimates. The PSM results confirmed that agricultural financing had a statistically significant and positive impact on crop productivity. On average, farmers with access to financing produced ETB 3,942.53 to ETB 6,251.01 more per season compared to those without access, depending on the matching algorithm used (NNM-1, NNM-5, and KBM). The study concludes that agricultural financing significantly enhances crop productivity but may unintentionally exacerbate income inequality. It recommends targeted financial inclusion strategies to ensure equitable access to credit. Furthermore, it advocates for practical, hands-on training programs tailored to farmers‘ realworld needs, rather than theoretical lectures. Such capacity-building initiatives should focus on improving technical expertise, efficient input utilization, and financial literacy, thereby maximizing the benefits of agricultural financing for sustainable rural development. Keywords: Agricultural Financing, Crop Productivity, Propensity Score Matching, Logit, Gini
Full Abstract:
EXECUTIVE SUMMARY Agricultural financing plays a critical role in enhancing crop productivity and fostering rural development, particularly in agrarian economies. This study aims to empirically investigate the impact of agricultural financing on the crop productivity of smallholder farmers and assess its implications on income inequality. To achieve this objective, a mixed research approach was adopted, integrating both qualitative and quantitative methodologies. An explanatory research design was employed to explore the causal relationship between access to agricultural financing and crop output. Primary data were collected from a sample of 800 crop-producing households using structured questionnaires, interview schedules, and focus group discussions, selected through simple random sampling. Secondary data were obtained through document reviews from relevant institutional sources. The data were analysed using descriptive statistics and econometric modelling techniques, specifically Propensity Score Matching (PSM), to estimate the Average Treatment Effect on the Treated (ATT). A binary logit model was used to identify the determinants of access to agricultural financing. The results revealed that age, seed cost, labor cost, tractor access (as a proxy for infrastructure), education level, and farming experience significantly influenced whether a household received agricultural financing. Conversely, variables such as plant protection, fertilizer use, and landholding size were found to have an insignificant effect. The covariate balancing indicators and balance plots confirmed that the matching algorithms were successful in reducing selection bias, with mean standardized bias decreasing by up to 77.5% and pseudo R² values dropping significantly post-matching. The common support condition was also satisfied, ensuring the reliability of the PSM estimates. The PSM results confirmed that agricultural financing had a statistically significant and positive impact on crop productivity. On average, farmers with access to financing produced ETB 3,942.53 to ETB 6,251.01 more per season compared to those without access, depending on the matching algorithm used (NNM-1, NNM-5, and KBM). The study concludes that agricultural financing significantly enhances crop productivity but may unintentionally exacerbate income inequality. It recommends targeted financial inclusion strategies to ensure equitable access to credit. Furthermore, it advocates for practical, hands-on training programs tailored to farmers‘ realworld needs, rather than theoretical lectures. Such capacity-building initiatives should focus on improving technical expertise, efficient input utilization, and financial literacy, thereby maximizing the benefits of agricultural financing for sustainable rural development. Keywords: Agricultural Financing, Crop Productivity, Propensity Score Matching, Logit, Gini