Can central bank digital currencies help advance financial inclusion?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Central banks around the world are considering how retail central bank digital currencies (CBDCs) may help to advance financial inclusion. While CBDCs are not a magic bullet, they could be a further tool to promote universal access to payments and other financial services if this goal features prominently in the design from the get-go. In particular, central banks can consider design options to: (1) promote innovation in the two-tiered financial system (eg allowing for non-bank payment service providers); (2) offer a robust and low-cost public sector technological basis (with novel interfaces and offline payments); (3) facilitate enrolment (via simplified due diligence and electronic know-your-customer processes) and data portability; and (4) foster interoperability (both domestically and across borders). Together, these features can address a range of specific barriers to financial inclusion: geographic remoteness, institutional and regulatory factors, economic and market structure issues, characteristics of vulnerability, lack of financial literacy and low trust in existing financial institutions. This paper draws on interviews with nine central banks with advanced work on CBDCs and financial inclusion — the Central Bank of the Bahamas, Bank of Canada, People’s Bank of China, Eastern Caribbean Central Bank, Bank of Ghana, Central Bank of Malaysia, Bangko Sentral ng Pilipinas, National Bank of Ukraine and Central Bank of Uruguay. It gives concrete examples from the central banks’ work and discusses challenges, risks and regulatory and legal implications. It argues that while CBDCs hold promise for furthering financial inclusion, CBDC issuance may also require new laws and regulations to be enacted, or existing laws to be revised.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it