Assessing incentives to increase digital payment acceptance and usage: A machine learning approach

Using random forest estimation, the authors identify 14 key predictors out of 190 variables with the largest predictive power for micro, small, and medium-sized retailer (MSMR) adoption and usage of digital payments. Using conditional inference trees, they study the importance of sequencing and interactions of various factors such as public policy initiatives, technological advancements, and private sector incentives. The authors find that in countries with low point of sale (POS) terminal adoption, killer applications such as mobile phone payment apps increase the likelihood of P2B digital transactions. They 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, they 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 person-to-business (P2B) digital payments also increases. Merchant and consumer fiscal incentives also increase MSMRs’ share of P2B electronic payments.

  • Date: 18/01/2022
  • Sector: Any
  • Topics: Digital technology, Inclusive finance, Sustainable enterprises
  • Regions: Global
  • Resource type: Publications
  • Institutions: World Bank