  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.
