Data-Driven Analysis of Central Bank Digital Currency (CBDC) Projects Drivers

Toshiko Matsui () and Daniel Perez ()
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Toshiko Matsui: Imperial College London
Daniel Perez: Imperial College London

A chapter in Mathematical Research for Blockchain Economy, 2023, pp 95-108 from Springer

Abstract: 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). By extracting and aggregating cross country data provided by several international organisations, we find that the 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.

Keywords: Central bank digital currency (CBDC); Digital currency; CBDC project index; Machine learning; Multilayer perceptron; Random forest (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-3-031-18679-0_6

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