Data-driven analysis of central bank digital currency (CBDC) projects drivers
This provides a data-driven analysis for policymakers and researchers to understand drivers of CBDC adoption, but it is incremental as it quantifies known qualitative findings.
The paper tackled the problem of predicting Central Bank Digital Currency (CBDC) project progression using economic and technological factors, finding that a financial development index was the most important predictor, followed by GDP per capita and voice and accountability indices, with results robust over time.
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.