IDEAS home Printed from https://ideas.repec.org/h/spr/lnopch/978-3-031-18679-0_6.html
   My bibliography  Save this book chapter

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

In: Mathematical Research for Blockchain Economy

Author

Listed:
  • Toshiko Matsui

    (Imperial College London)

  • Daniel Perez

    (Imperial College London)

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.

Suggested Citation

  • Toshiko Matsui & Daniel Perez, 2023. "Data-Driven Analysis of Central Bank Digital Currency (CBDC) Projects Drivers," Lecture Notes in Operations Research, in: Panos Pardalos & Ilias Kotsireas & Yike Guo & William Knottenbelt (ed.), Mathematical Research for Blockchain Economy, pages 95-108, Springer.
    • Handle: RePEc:spr:lnopch:978-3-031-18679-0_6
    DOI: 10.1007/978-3-031-18679-0_6
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:lnopch:978-3-031-18679-0_6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.