Epidemic Exposure, Financial Technology, and the Digital Divide

• Published date: 01 October 2022
• Author: ORKUN SAKA,BARRY EICHENGREEN,CEVAT GIRAY AKSOY
• Date: 01 October 2022
• DOI: http://doi.org/10.1111/jmcb.12945

DOI: 10.1111/jmcb.12945

ORKUN SAKA

BARRY EICHENGREEN

CEVAT GIRAY AKSOY

Epidemic Exposure, Financial Technology, and the

Digital Divide

We ask whether epidemic exposure leads to a shift in nancial technology

usage and who participates in this shift. We exploit a data set combining

Gallup World Polls and Global Findex surveys for some 250,000 individ-

uals in 140 countries, merging them with information on the incidence of

epidemics and local 3G Internet infrastructure. Epidemic exposure is asso-

ciated with an increase in remote-access (online/mobile) banking and sub-

stitution from bank branch based to ATM activity. The temporary nature

of the effects we identify is more consistent with a demand channel rather

than that of supply with high initial xed costs. Exploring heterogeneity us-

ing a machine learning–driven approach, we nd that young, high-income

earners in full-time employment have the greatest tendency to shift to on-

line/mobile transactions in response to epidemics. Baseline effects are larger

for individuals with better ex ante 3G signal coverage, highlighting the role

of the digital divide in adaption to new technologies necessitated by adverse

external shocks.

JEL codes: G20, G59, I10

Keywords: epidemics, ntech, banking

E     inducing changes in

economic behavior and accelerating technological and behavioral trends. The Black

We are grateful to seminar participants at CESifo Area Conference on the Economics of Digitization

(2021), Finest Autumn Workshop (2021), 7th Joint Bank of Canada and Payments Canada Symposium,

IFABSOxford Conference (2021), Sabanci University, Webinar series in Finance and Development(WE-

FIDEV), 2nd LTI@UniTO/Bankof Italy Conference on "Long-Term Investors’ Trends: Theory and Prac-

tice", MIT-CEBRA 2021 Annual Meeting as well as Carol Alexander, Ralph De Haas, Jonathan Fu, Sean

Higgins, Beata Javorcik, Thomas Lambert, Xiang Li, Nicola Limodio and Enrico Sette for their useful

comments and suggestions. Leon Bost and Pablo Zarate provided outstanding research assistance. Views

presented are those of the authors and not necessarily those of the EBRD. All interpretations, errors, and

omissions are our own. All authors contributed equally to this paper and the order of author names is ran-

domized via AEA Randomization Tool (code: AuWT141_jCPw). Orkun Saka acknowledgesthe funding

receivedfrom the British Academy through a Leverhulme Small Research Grant scheme (SRG21\211248).

O S is Assistant Professor of Economics at the City, University of London (E-mail:

orkun.saka.3@city.ac.uk). B E is Professor of Economics and PoliticalScience at the

University of California, Berkeley (E-mail: eichengr@berkeley.edu). Cevat Giray Aksoy is Associate Di-

rector at the European Bank for Reconstruction and Development (E-mail: cevat.aksoy@kcl.ac.uk).

Received November 7, 2021; and accepted in revised form March 30, 2022.

Journal of Money, Credit and Banking, Vol. 54, No. 7 (October 2022)

© 2022 The Authors. Journal of Money, Credit and Banking published by Wiley Periodicals

LLC on behalf of Ohio State University.

This is an open access article under the terms of the Creative Commons Attribution-NonCom-

mercial License, which permits use, distribution and reproduction in any medium, provided

the original work is properly cited and is not used for commercial purposes.

1914 :MONEY,CREDIT AND BANKING

Death, the mother of all epidemics, is thought to have sped the adoption of earlier

capital-intensive agricultural technologies such as the heavy plow and water mill by

inducing substitution of capital for more expensive labor (Senn 2003, Pelham 2017).

COVID-19, a more recent example, is said to haveincreased remote working (Brenan

2020), online shopping (Grashuis et al. 2020), and telehealth (Richardson et al. 2020).

Here we study these issues in the context of ntech adoption and usage.1We as k

whether past epidemics induced a shift toward remote-access nancial technologies

such as online banking and ATMs, and away from traditional brick-and-mortar bank

branches. We combine data on epidemics worldwide with nationally representative

Global Findex surveys of individualnancial behavior elded in more than 140 coun-

tries in 2011, 2014, and 2017. Matching each individual in Global Findex data set to

detailed background information about the same individual in Gallup World Polls

(GWP) allows us to control for socioeconomic factors at a granular level.

Holding constant individual-level economic and demographic characteristics and

country and year xed effects, we nd that contemporaneous epidemic exposure in-

creases the likelihood that individuals transact via the Internet and mobile bank ac-

counts, make online payments using the Internet, and complete account transactions

using an ATM instead of with a teller at a bank branch. Exposure to an epidemic

leads to 10.6 percentage point increase in online/mobile transactions using the In-

ternet and bank account, and a 4.5 percentage point increase in mobile transactions

using bank accounts. Given that the means of these outcome variables are 8.3% and

9.4%, respectively, the effect is large—doubles and triples the initial propensity. In

addition, we nd that separate impacts on ATMand in-branch transactions almost ex-

actly offset. This suggests that epidemic exposure mainly affects the form of banking

activity—digital or in person—without increasing or reducing its volume or extent.

Although the limited time span covered by our data allows for only a tentative

analysis of persistence, our results suggest that the impact of epidemic exposure is

felt mainly in the short run rather than persistently over time. This supports the con-

jecture that the effects we detect are driven by demand-side factors (i.e., consumer

preferences, as consumers shift back and forth between in-person and digital pay-

ments vehicles in response to changes in the risk of face-to-face contact) rather than

supply-side factors (as banks invest the sunk costs of permanently increasing the sup-

ply of, inter alia, ATMs in response to the increased risk of close personal contact).

Consistent with this interpretation, we fail to nd any effects of epidemics on banks’

provision of new technologies such as ATMs.2

Sensitivity analyses support these ndings. The results continue to hold when

we adjust for multiple outcomes (Anderson 2008). A test following Oster (2019)

1. We interchangeably use the terms “ntech adoption” and “ntech usage.” As will be clear later,

our measures of nancial technology adoption and usage at the individual level tend to be binary and

thus cannot speak to the intensive margin of ntech access (i.e., how much a technology is used by an

individual). In a sense, construction of variables based solely on the extensivemargin is more in line with

the notion of ntech adoption rather than ntech usage.

2. See Online Appendix Table 12.