Mobile money adoption, input use, and farm output among smallholder rice farmers in Ghana

Abstract

Financial exclusion continues to be a major challenge to smallholder farmers' participation in agricultural value chains in developing countries. Digitizing procurements and other forms of transactions using mobile money technology among value chain actors is essential for ensuring financial inclusion and enhancing agricultural value chain transformation. This study examines the factors influencing the adoption of mobile money technology and the impact of the technology on production input use and farm output, utilizing data from a cross-sectional survey of smallholder rice farmers in northern Ghana. A linear regression with endogenous treatment effects method is employed to account for both observable and unobservable selection bias. The results reveal positive and significant marginal effect of mobile money technology on input use and farm output. Adopters of the technology applied 18% and 13% more fertilizer and herbicides, respectively than nonadopters. The output increased by about 4% for the adopters. The results also show that mobile money technology adoption, input use and farm output are significantly influenced by education, farmer-based organization (FBO) membership, access to credit, input prices, and location fixed effects. Expansion of mobile technology networks, increased investment in education, credit facilities, and FBOs can be quite relevant in promoting the adoption of mobile money technology in Ghana.

Smartphone use, off‐farm employment, and women's decision‐making power: Evidence from rural China

“…The PSM, AIPW, and IPWRA cannot address selection bias associated with unobserved factors. For this study, we employ the ETR model for its substantial advantage in accounting for selection bias arising from both observed and unobserved factors (Abdul‐Rahaman & Abdulai, 2021; Ma et al, 2020; Ma & Abdulai, 2017; Stata Press, 2019).…”

“…The ETR model jointly estimates Equations () and (), thereby generating a parameter capturing the correlation between the two error terms. If the is statistically significant, an endogeneity issue related to smartphone use is indicated (Abdul‐Rahaman & Abdulai, 2021; Hübler & Hartje, 2016).…”

“…First, we use the endogenous treatment regression (ETR) model to investigate whether smartphone use increases women's decision‐making power. Relying on a valid instrumental variable, the ETR model addresses selection bias associated with observed and unobserved factors (Abdul‐Rahaman & Abdulai, 2021; Ma, Nie, Zhang, & Renwick, 2020; Ma & Abdulai, 2017; Stata Press, 2019). This study focuses on smartphones rather than mobile phones.…”

Mobile Money, Agricultural Intensification, and Household Welfare: Panel Evidence from Rural Uganda

“…Kikulwe, Fischer, and Qaim (2014) find that Kenyan farmers who have adopted mobile money spend 30 USD and 15 USD more on chemical fertilizer and pesticides, respectively. In their study on smallholder rice farmers in Ghana, Rahaman and Abdulai (2022) show that adopters of mobile money applied 18% and 13% more fertilizers and herbicides, respectively, than nonadopters.…”

Effet du mobile money sur la résilience des ménages exerçant des activités génératrices de revenus au Cameroun

“…Ces auteurs affirment que le mécanisme sous‐jacent est l'augmentation de la réception des transferts de fonds et la diversité des expéditeurs en raison de la réduction des coûts de transaction favorisées par le mobile money. Abdul‐Rahaman et Abdulai (2021) en se basant sur une régression linéaire avec une méthode de traitement endogène visant à prendre en compte le biais de sélection, révèlent un effet marginal positif et significatif du mobile money sur l'utilisation des intrants agricoles et la production agricole. En effet, les adoptants du mobile money ont utilisé 18% et 13% de plus d'engrais et d'herbicides, respectivement, que les non adoptants.…”