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

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Mathematical Research for Blockchain Economy (MARBLE 2022)

Part of the book series: Lecture Notes in Operations Research ((LNOR))

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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.

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Notes

  1. 1.

    We took 12-week moving average of Google Trends search results.

  2. 2.

    The updated CBDC projects status is available in an online annex of [4] (See https://www.bis.org/publ/work880.htm). The information is said to have been collected through desk research and with the help of contacts at several individual central banks.

  3. 3.

    See Financial Development Index Database by IMF for more information. (https://data.imf.org/?sk=f8032e80-b36c-43b1-ac26-493c5b1cd33b).

  4. 4.

    The dataset includes all projects announced as of 1 December 2020. For more information, see https://www.bis.org/publ/work880.htm.

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Author information

Authors and Affiliations

  1. Department of Computing, Imperial College London, London, United Kingdom

    Toshiko Matsui & Daniel Perez

Authors
  1. Toshiko Matsui
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  2. Daniel Perez
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Corresponding author

Correspondence to Toshiko Matsui .

Editor information

Editors and Affiliations

  1. Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos Pardalos

  2. CARGO Lab, Wilfrid Laurier University, Waterloo, ON, Canada

    Ilias Kotsireas

  3. Data Science Institute, Imperial College London, London, UK

    Yike Guo

  4. Department of Computing, Imperial College London, London, UK

    William Knottenbelt

A Appendix

A Appendix

This annex gives additional tables, regression results and figures to complement the paper. See main text for further discussion.

1.1 A.1 CBDC Projects Status

Below shows the part of the updated project score of global CBDC development efforts, relating to [4] (as of December 2020).Footnote 4 Note that only the countries with index of 3 (live CBDC) and 2 (pilot) as of December 2020 are listed here.

Country

   Overall*

   Overall (Aug 20)

   Retail*

   Wholesale*

Bahamas

   3

   2

   3

   0

Canada

   3

   2

   1

   2

Switzerland

   3

   1

   1

   2

Euro area (ECB)

   3

   2

   1

   2

France

   3

   2

   1

   2

Japan

   3

   2

   1

   2

South Africa

   3

   1

   1

   2

United Arab Emirates  

   2

   2

   0

   2

Australia

   2

   1

   1

   1

China

   2

   2

   2

   0

Ecuador

   2

   2

   2

   0

Eastern Caribbean

   2

   2

   2

   0

United Kingdom

   2

   2

   1

   1

Hong Kong

   2

   2

   0

   2

Indonesia

   2

   1

   1

   1

India

   2

   0

   1

   1

South Korea

   2

   2

   2

   0

Saudi Arabia

   2

   2

   0

   2

Sweden

   2

   2

   2

   0

Singapore

   2

   2

   0

   2

Swaziland

   2

   1

   1

   1

Thailand

   2

   2

   0

   2

Ukraine

   2

   2

   2

   0

Uruguay

   2

   2

   2

   0

*As of December 2020.

1.2 Top 10 Features for the Random Forest Classifier with Aggregated Data

Tables 7 and 8 give the 10 most important independent variables for the random forest classifier with aggregated data (data averaged over the period 2014–19, subject to data availability), with August 2020 and December 2020 CBDCPI data as an objective variable, respectively.

Table 7 Most important features for the random forest classifier (Aug 2020)
Full size table
Table 8 Most important features for the random forest classifier (Dec 2020)
Full size table

1.3 A.3 Top 10 Features for the Random Forest Classifier with Full Data

Tables 9 and 10 show the 10 most important index for the random forest classifier with full data, with August 2020 and December 2020 CBDCPI data as an objective variable, respectively.

Table 9 Most important features for the random forest classifier (Aug 2020)
Full size table
Table 10 Most important features for the random forest classifier (Dec 2020)
Full size table

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Matsui, T., Perez, D. (2023). Data-Driven Analysis of Central Bank Digital Currency (CBDC) Projects Drivers. In: Pardalos, P., Kotsireas, I., Guo, Y., Knottenbelt, W. (eds) Mathematical Research for Blockchain Economy. MARBLE 2022. Lecture Notes in Operations Research. Springer, Cham. https://doi.org/10.1007/978-3-031-18679-0_6

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