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In this paper, we use a variety of machine learning methods to quantify theextent to which economic and technological factors are predictive of theprogression of Central Bank Digital Currencies (CBDC) within a country, usingas our measure of this progression the CBDC project index (CBDCPI). We findthat 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 ofthe country's population. Our results are consistent with previous qualitativeresearch which finds that countries with a high degree of financial developmentor digital infrastructure have more developed CBDC projects. Further, we obtainrobust results when predicting the CBDCPI at different points in time.

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