Gendered investment differences among smallholder farmers: evidence from a microcredit programme in western kenya

Keiji Jindo (), Jens A. Andersson, Foluke Quist-Wessel, Jackonia Onyango and Johannes W. A. Langeveld
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Keiji Jindo: Wageningen University & Research
Jens A. Andersson: Wageningen University & Research
Foluke Quist-Wessel: Biomass Research
Jackonia Onyango: Advance Consulting
Johannes W. A. Langeveld: Biomass Research

Food Security: The Science, Sociology and Economics of Food Production and Access to Food, 2023, vol. 15, issue 6, No 6, 1489-1504

Abstract: Abstract The advent of microcredit programmes in sub-Saharan Africa provides opportunities for rural households to acquire agricultural inputs and consumer goods. This study analysed gender differences in investment behaviour and repayment performance using a unique dataset—the complete client database (21,386 clients) of a microcredit programme operating in Western Kenya. Products purchased via the microcredit programme include seeds, fertilisers, post-harvesting technologies (drying sheets, storage bags, and pesticides), chicken feed packages, and different solar panel products. A machine learning-based basket analysis identified combinations of products purchased by male and female clients. Our results showed that female farmers usually made smaller investments, had higher repayment rates, and purchased more post-harvesting technologies than male farmers. In addition, female farmers used their loans to purchase less expensive products, whereas male farmers usually purchased more fertiliser and expensive solar panel products. The basket analysis revealed that female farmers purchased multiple products simultaneously more often than male farmers did. Finally, households without mobile phones had low repayment capabilities. Collectively, our findings show that microcredit programmes serving smallholder farmers can capitalise on their business data to learn about their clients’ gendered investment preferences and repayment behaviour.

Keywords: Microcredit programme; Gender equality; Food security; Smallholder farmers; Women empowerment; Machine learning (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s12571-023-01394-0

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