Assessing Incentives to Increase Digital Payment Acceptance and Usage: A Machine Learning Approach

62 Pages Posted: 25 Jul 2022

See all articles by Jeff Allen

Jeff Allen

World Bank

Santiago Carbo-Valverde

Universitat de València

Sujit Chakravorti

Chakra Advisors LLC

Francisco Rodriguez-Fernandez

University of Granada - Department of Economic Theory and History

Oya Pinar Ardic

World Bank

Date Written: July 19, 2022

Abstract

An important step to achieve greater financial inclusion is to increase the acceptance and usage of digital payments. Although consumer adoption of digital payments has improved dramatically globally, the acceptance and usage of digital payments for micro, small, and medium retailers (MSMRs) remain challenging. Using random forest estimation, we identify 14 key predictors out of 190 variables with the largest predictive power for MSMR adoption and usage of digital payments. Using conditional inference trees, we study the importance of sequencing and interactions of various factors such as public sector initiatives, technological advancements, and private sector incentives. We find that in countries with low POS terminal adoption, killer applications such as mobile phone payment apps increase the likelihood of P2B digital transactions. We also find the likelihood of digital P2B payments at MSMRs increases when MSMRs pay their employees and suppliers digitally. The level of ownership of basic financial accounts by consumers and the size of the shadow economy are also important predictors of greater adoption and usage of digital payments. Using causal forest estimation, we find a positive and economically significant marginal effect for merchant and consumer fiscal incentives on POS terminal adoption on average. When countries implement financial inclusion initiatives, POS terminal adoption increases significantly and MSMRs’ share of P2B digital payments also increases. Merchant and consumer fiscal incentives also increase MSMRs’ share of P2B electronic payments.

Keywords: Payments, Incentives, Financial Inclusion, Regulation, Machine Learning, MSME, Tax Policy, Shadow Economy

JEL Classification: D4, E4, G2, O3

Suggested Citation

Allen, Jeff and Carbo-Valverde, Santiago and Chakravorti, Sujit and Rodriguez-Fernandez, Francisco and Pinar Ardic, Oya, Assessing Incentives to Increase Digital Payment Acceptance and Usage: A Machine Learning Approach (July 19, 2022). Available at SSRN: https://ssrn.com/abstract=4167356 or http://dx.doi.org/10.2139/ssrn.4167356

Jeff Allen

World Bank

Santiago Carbo-Valverde

Universitat de València ( email )

Departamento de Analisis Economico
Facultad de Economia, Campus Tarongers
Valencia, Valencia 46022
Spain

Sujit Chakravorti

Chakra Advisors LLC ( email )

3445 Deer Ridge Drive
Danville, CA 94506
United States

Francisco Rodriguez-Fernandez (Contact Author)

University of Granada - Department of Economic Theory and History ( email )

Granada
Spain

Oya Pinar Ardic

World Bank ( email )

1818 H Street NW
Washington, MD 20433
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
83
Abstract Views
418
Rank
551,886
PlumX Metrics