While a significant contributor to economic development, agriculture is an underserved sector by financial institutions. This study seeks to investigate the short and long-term impacts of digital credit tailored to the needs of smallholder Kenyan agricultural producers. The implementing partners will begin by designing a non-traditional credit-scoring algorithm, which will combine information on aggregate weather-related risks with individual financial transactions data. To identify the impact of the improved credit score on loan uptake, this project will randomly notify farmers of their eligibility to apply to receive a digital agricultural loan with encouragement. Moreover, by adopting various methodological strategies, including instrumental variables, a phased-in randomization and a regression discontinuity, it will help identify both the short and long-term effects of digital credit and the marginal impact of different loan terms. With a large sample size, heterogeneous treatment effects across borrower characteristics will be estimated. Finally, it will assess the effectiveness of loans backed by lender-level weather index insurance as a consumer protection measure to minimize default and over-indebtedness. The findings will support the decision-making on the feasibility of digital credit as an agricultural development tool for millions of agricultural producers. Results forthcoming.
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