Demand for Payment Services and Consumer Welfare: The Introduction of a Central Bank Digital Currency
Bibliographic record
Abstract
In recent years, there have been rapid technological innovations in retail payments. Such dramatic changes in the economics of payment systems have led to questions regarding whether there is consumer demand for cash. The entry of these new products and services has resulted in significant improvements in the characteristics of existing methods of payment, such as tap-and-go technology or contactless credit and debit cards. In addition, the introduction of decentralized digital currencies has raised questions about whether there is a need for a central bank digital currency (CBDC) and, if so, what its essential characteristics should be. To address these questions, we develop and estimate a structural model of demand for payment instruments. Our model allows for rich heterogeneity in consumer preferences. Identification of the distribution of consumer heterogeneity relies on observing individual-level consumer decisions at the point of sale. Using parameter estimates, we conduct a counterfactual experiment of an introduction of CBDC and simulate post-introduction consumer adoption and usage decisions. We also provide insights into the potential welfare implications of the introduction of new payment instruments.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".