Cryptocurrencies and Central Bank Digital Currencies in Global Perspective
Bibliographic record
Abstract
This study investigates the relationship between cryptocurrency adoption rates (CARs) and the development of central bank digital currencies (CBDCs) using a global panel of 109 countries from 2020 to 2024. The analysis employs pooled OLS, fixed effects, ordered logistic regression and GMM models with robust controls for macroeconomic indicators, institutional quality, and technological readiness. CBDC status is measured as an ordinal variable representing five development stages, while CAR is derived from the Chainalysis Crypto Adoption Index. The empirical results show that higher CAR significantly increases the probability of a country progressing to more advanced CBDC stages. Margins analysis further indicates that increases in CAR substantially reduce the likelihood of remaining in early CBDC phases and raise the probability of reaching the pilot or launched stages. Heterogeneity analysis reveals that this relationship is strongest in low- and middle-income economies and in countries with low levels of financial inclusion, where cryptocurrencies present greater competition to traditional financial systems. The study contributes new large-sample evidence to the debate on digital currencies and provides policy-relevant insights: central banks in financially constrained economies appear to adopt CBDCs as developmental tools to enhance financial access and preserve monetary sovereignty in the face of growing cryptocurrency adoption.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".