Rise of the Central Bank Digital Currencies: Drivers, Approaches and Technologies

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

“…In the experiment, we find random forest performs better than MLP, and that the financial development index [23] is particularly the most important feature for our model, followed by the GDP per capita and the voice and accountability, when explaining the CBDCPI drivers for August 2020. This corresponds [3], which concluded that more developed CBDC projects can be found in countries with higher financial development index, digital infrastructure, GDP, and institutional characteristics. As a robustness check, we have performed the same analysis with full and aggre-gated data and with December 2020 CBDCPI.…”

“…In practice, several central banks issued their CBDCs between August and December 2020 (see Section 2.2). Further, a few central banks are aiming to issue their CBDCs in the next few years, which has attracted a lot of attention [3]; for example, beginning of this year saw the closing of the ECB digital euro consultation with record level of public feedback [13]. This move is consistent with the observation that the introduction of a CBDC can present a significant innovation in money and banking history [14].…”

“…However, a majority of CBDCs are still in research or pilot stage, although a survey in early 2020 showed more than 80% of central banks were studying the subject [24]. Further, the drivers of the CBDC projects are yet to be thoroughly investigated [3].…”

“…The previous research include the potential risk and benefits of introducing the CBDC, and quantitatively, the welfare gains of introducing CBDC into the economy [11]. Of the relevant research that exists in this vein, [3] suggests by taking an ordered probit approach [18] for comprehensive cross country database that the majority of the CBDC projects are found in digitised economies with a high capacity for innovation. They conclude that some of the potential drivers of CBDC development are related to factors affecting a country's digital infrastructure, innovation capacity, institutional quality, development and financial inclusion, public interest in CBDCs, and cross-border transactions.…”

“…Our primary objective is to improve understanding the dominant drivers for CBDCs and the factors that increases the possibly of a country to accelerate this effort. We use the CBDC project index (CBDCPI) [3] as our objective variable and factors affecting a country's digital or technological capability and government effectiveness as independent variables, in order to reduce the problem to identifying the independent variables with the most predictive power. To accomplish this, we utilise machine learning techniques to predict the CBDCPI and pick the most important variables for our model.…”