Fintech, financial inclusion and income inequality: a quantile regression approach

Abstract

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.

Highlights

• Harnessing the potential of FinTech to reduce financial exclusion and income inequality has been proposed by the UN and G20.

• We posit that FinTech affects income inequality directly and indirectly through financial inclusion.

• We invoke quantile regression analysis to investigate whether the effects of FinTech differ across countries with different levels of income inequality.

• We find that financial inclusion is a key channel through which FinTech reduces income inequality, at all quantile levels, primarily among higher-income countries.

Keywords:

• FinTech
• financial inclusion
• income inequality
• quantile regression

JEL Classifications:

• D31
• D63
• F02
• 011
• 015

Acknowledgement

We acknowledge useful comments from participants at the Financial Inclusion and Fintech Conference at the School of Finance and Management, SOAS University of London, on 25–26 March 2019, in particular Gerhard Kling and Desire Kanga. Useful comments were also received from participants at the 4th Workshop on Macroeconomic Policies in Emerging and Developing Countries, at Loughborough University on 11–12 June 2019, in particular Christopher Green. We also thank two anonymous referees and the editor of the European Journal of Finance for their constructive comments. We acknowledge financial support from the ESRC-NSFC (ES/P005241/1) Research Grant on ‘Developing financial systems to support sustainable growth in China – The role of innovation, diversity and financial regulation’, the DFID-ESRC (ES/N013344/2) Research Grant on ‘Delivering Inclusive Financial Development and Growth’, and the AXA Research Fund. We are responsible for all surviving errors

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 See Beck (2016) on the measurement problems associated with the use of supply-side indicators of financial inclusion. As he points out, the indicators give ‘a rather blurred picture of the ultimate metric we are interested in: the share of population in a country that uses different types of financial services’ (Beck 2016, 12).

2 There is a parallel and closely related strand of literature on the relationship between financial development and income inequality. See, for example, Beck, Demirgüç-Kunt, and Levine 2007; Claessens and Perotti 2007; Demirgüç-Kunt and Levine 2009; De Haan and Sturm 2017.

3 For instance, Dabla-Norris et al. (2015b, 2015a) indicate that different financial inclusion strategies may imply a trade-off between growth and inequality. They develop a model in which an increase in financial inclusion may lead to a reduction in inequality, if it is achieved through policies focused on increasing access (reducing participation costs) for people who have been left out of the formal financial system (e.g., reducing documentation requirements to open an account or obtain a loan). In contrast, inequality may increase if the policy focus is on relaxing the borrowing constrains faced by people who already have access to credit and other services from formal financial institutions (e.g., reducing collateral requirements).

4 See Demirgüç-Kunt, Klapper, and Singer (2017), Beck (2015) and Cull, Ehrbeck, and Holle (2014) for excellent reviews of this literature.

5 Results from a meta-analysis of 27 randomised controlled trials show that saving promotion interventions in Sub-Saharan Africa have helped households increase their savings and have had significant ‘trickle-down’ effects in terms of increasing household incomes and expenditures as well as food security and returns from family businesses (Steinert et al. 2018).

6 As a skill-biased technological change, the ICT revolution can also contribute to increasing wage disparities between skilled and unskilled labour (e.g., Jaumotte, Lall, and Papageorgiou 2013; Richmond and Triplett 2018). However, recent findings by Dabla-Norris et al. (2015c) indicate that the effects of skill-biased technological change vary across countries at different levels of economic development, and that an increase in the skill premium is strongly associated with widening disparities in advanced economies but not in emerging and developing economies.

7 The standardised income inequality measures are fully discussed in Solt (2009).

8 Description of variables are provided in Appendix, Table A1

9 We did not include GDP per capita in the estimations due to the high correlation between this variable and our measures of financial inclusion.

10 The instruments used for FinTech (mobile finance) are mobile phone penetration and fixed broadband penetration (Ghosh 2016; Andrianaivo and Kpodar 2012; Demirgüç-Kunt et al. 2018). The definition, summary statistics and correlation matrix of these instruments and our FinTech, financial inclusion and income inequality variables are provided in Appendix Tables A3–A6.

11 Asongu and Odhiambo (2018) investigate the relationship between mobile banking and income inequality

but only for a cross-section of developing countries in 2011. Furthermore, they do not explore the interrelationship between Fintech, financial inclusion and income inequality.

12 To this end, we use the World Bank’s income classification of countries discussed in Section 3.2.

Additional information

Funding

We acknowledge financial support from the ESRC-NSFC [ES/P005241/1] Research Grant on ‘Developing financial systems to support sustainable growth in China – The role of innovation, diversity and financial regulation’, the DFID-ESRC [ES/N013344/2] Research Grant on ‘Delivering Inclusive Financial Development and Growth’ under the Growth Research Programme (DEGRP) Call 3, and the AXA Research Fund; Department for International Development; Economic and Social Research Council.