Data-driven analysis of central bank digital currency (CBDC) projects drivers

TL;DR

This study employs machine learning to analyze how economic and technological factors influence the development of CBDC projects across countries, highlighting financial development as a key predictor.

Contribution

It introduces a data-driven approach using machine learning to quantify predictors of CBDC project progression, validated across multiple time points.

Findings

Financial development index is the most important predictor.

GDP per capita and voice accountability are significant factors.

Results are consistent over different time periods.

Abstract

In this paper, we use a variety of machine learning methods to quantify the extent to which economic and technological factors are predictive of the progression of Central Bank Digital Currencies (CBDC) within a country, using as our measure of this progression the CBDC project index (CBDCPI). We find that a financial development index is the most important feature for our model, followed by the GDP per capita and an index of the voice and accountability of the country's population. Our results are consistent with previous qualitative research which finds that countries with a high degree of financial development or digital infrastructure have more developed CBDC projects. Further, we obtain robust results when predicting the CBDCPI at different points in time.