Asymmetric and threshold effects of FinTech on poverty in SSA countries

Noha Emara

57 citations0/0 files storedJournal of Economic Studies

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FinTech, Crowdfunding, Digital Finance Microfinance and Financial Inclusion Economic Growth and Development

Abstract

Purpose The purpose of this paper is to analyze the dynamic asymmetric relationship between financial technology (FinTech) adoption and poverty alleviation on annual data for the Sub-Saharan Africa (SSA) region over the period from 2004 to 2020. Design/methodology/approach This study adopted the general method of moments (GMM) method on annual data for 127 countries including 45 countries from the SSA region over the period from 2004 to 2020. Findings The study’s findings show that improvement in FinTech may initially decrease the rate of extreme poverty, leading to a decrease in total poverty as a percent of the population. While there is an initial decrease in the rate of extreme poverty with improvements of FinTech, once the FinTech index reaches its threshold level of 37.18 points, further improvement in FinTech tends to decrease as penetration increases, giving rise to an decrease in the rate of poverty alleviation. Research limitations/implications Policymakers should design more aggressive and comprehensive policies directed at recouping the maximum gains of FinTech adoption, with a reasonable threshold target. Practical implications Policymakers in the SSA region must be aware of a FinTech threshold level of 37.18 points. To ensure the highest reduction in extreme poverty, policymakers must keep investing in FinTech to reach this threshold level. Social implications FinTech improvement leads to poverty alleviation. Policymakers in the SSA region can fully recoup the benefits of FinTech by achieving a pre-set threshold level. Originality/value This paper addresses that gap in the literature by studying the impact of FinTech, instead of the traditional financial inclusion measures, on poverty in the 45 countries in the SSA region, exploring the potential dynamic asymmetry of this poverty-FinTech link, and testing the presence and statistical significance of the threshold level of FinTech.

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References

1995Another look at the instrumental variable estimation of error-components models 2005Understanding Interaction Models: Improving Empirical Analyses 2007Finance, inequality and the poor 2017Digital Finance and FinTech: current research and future research directions 2016The long-run poverty and gender impacts of mobile money 2013Risk Sharing and Transactions Costs: Evidence from Kenya's Mobile Money Revolution 1983Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets 2000Estimation in dynamic panel data models: improving on the performance of the standard GMM estimator

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