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Demir, Ayse, Pesque-Cela, Vanesa, Altunbas, Yener and Murinde, Victor (2022) 'Fintech, financial inclusion and income inequality: A quantile regression approach.' The European Journal of Finance, 28 (1). pp. 86-107.

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Although theory suggests that financial market imperfections – mainly information asymmetries, market segmentation and transaction costs – prevent poor people from escaping poverty by limiting their access to formal financial services, new financial technologies (FinTech) are seen as key enablers of financial inclusion. Indeed, the UN 2030 Agenda for Sustainable Development (UN-2030-ASD) and the G20 High-Level Principles for Digital Financial Inclusion (G20-HLP-DFI) highlight the importance of harnessing the potential of FinTech to reduce financial exclusion and income inequality. This paper investigates the interrelationship between FinTech, financial inclusion and income inequality for a panel of 140 countries using the Global Findex waves of survey data for 2011, 2014 and 2017. We posit that FinTech affects inequality directly and indirectly through financial inclusion. We invoke quantile regression analysis to investigate whether such effects differ across countries with different levels of income inequality. We uncover new evidence that financial inclusion is a key channel through which FinTech reduces income inequality. We also find that while financial inclusion significantly reduces inequality at all quantiles of the inequality distribution, these effects are primarily associated with higher-income countries. Overall, our results support the aspirations of the UN-2030-ASD and G20-HLP-DFI.

Item Type: Journal Article
Keywords: Fintech, financial inclusion, income inequality, quantile regression
SOAS Departments & Centres: Departments and Subunits > School of Finance & Management
Subjects: H Social Sciences > HG Finance
ISSN: 1351847X
Copyright Statement: © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
DOI (Digital Object Identifier):
Date Deposited: 05 Jun 2020 09:43
Funders: Economic and Social Research Council, Economic and Social Research Council

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