Abstract
The digital credit revolution, expanding access to agricultural credit, may have the perverse effect of increasing farmers’ debt to unsustainable levels, particularly when crop damage from extreme weather makes it difficult to repay loans. This study analyzes whether credit disbursed based on a novel credit-scoring model, bundled with crop insurance, expands rural borrowing and investments in agricultural technologies, yet simultaneously protects farmers from default and over-indebtedness. We will do so by implementing a cluster randomized trial during the winter (Rabi) season of 2022, the monsoon (Kharif) season of 2023, and the Rabi season of 2023, targeting 2,280 households from 120 villages in the states of Maharashtra and Odisha, India. Villages will be randomly assigned to a control group; a treatment group in which farmers are offered digital agricultural credit; or a group in which digital agricultural credit is bundled with picture-based crop insurance. We hypothesize that our implementing partners’ novel credit-scoring model is less discriminatory towards women and landless households compared to standard methods of issuing credit for smallholder farmers, and that our experimental treatments will hence increase credit utilization. We additionally hypothesize that farmers with bundled credit-insurance products experience lower levels of default and indebtedness. Findings will have immediate relevance for our implementing partner and policymakers.