An empirical analysis of agricultural credit's impact on wheat productivity and farm income in India

Abstract

This study aims to assess the impact of agricultural credit on productivity and farm income on wheat in India. Based on national sample survey data for the year 2018–19, the endogenous switching regression (ESR) model was employed to estimate the true effect of the credit access. Our findings reveal that while credit access helps to boost productivity, it does not proportionally increase income due to external factors like market conditions and price fluctuations. The study highlights a negative selection bias, where lower-yield farmers access credit but may not fully benefit. To estimate impact of access to farm credit on farmers productivity and income, various empirical models have been used in the literature, including OLS regression, Propensity Score Matching (PSM) and ESR. Each method has its strengths and limitations, with ESR being well-suited for this type of analysis. This model estimates separate equations for treated and untreated groups, incorporating a selection equation that captures decision to participate in the treatment. ESR is particularly effective in situations where unobserved factors influence both treatment and outcome, as it includes a stochastic error term to control the latent variables. Access to credit has potential to increase productivity as well as farm income of wheat. The estimated treatment effect on the treated (ATT) reveals a remarkable increase in the productivity of farmers who had access to credit. This highlights the crucial role that credit availability plays in enhancing the productivity of wheat farmers. However, while examining the impact of credit access on farm income, the estimation of ATT was found to be non-significant. The findings reveal that credit access enhances wheat productivity, as it enables farmers to invest in modern inputs like improved seeds, fertilizers and machinery. However, this increase in productivity does not translate into a proportional rise in farm income. Additionally, household characteristics like age, education, gender and social status differentially influence credit access. To address these challenges, policy measures should include targeted financial products for disadvantaged groups, gender-inclusive lending practices, training programs on financial literacy and strengthening agricultural cooperatives are crucial for ensuring that productivity gains from credit access leads to sustained income for wheat farmers. It is crucial to examine the real impact of institutional credit on wheat farming. Wheat, being one of the most important staple crops in India, not only sustains millions of farmers but also plays a vital role in the country's food security. Therefore, understanding how credit access influences wheat productivity and the income of wheat-growing farmers is of paramount importance. This study aims to address this critical gap by identifying the major determinants of credit access for wheat production and assessing its effects on the productivity and income of wheat farmers at the farm household level in India.