Bibliographic record
Abstract
This paper shows an avenue through which a numerical inflation target ensures low inflation and high credibility; one that is independent of the usual Walsh incentive contract. Our novel game theoretic framework - a generalization of alternating move games - formalizes the fact that since the target is explicit/legislated, it cannot be frequently reconsidered. This ‘explicitness’ therefore serves as a commitment device. There are two key results. First, it is shown that if the inflation target is sufficiently rigid (explicit) relative to the public’s wages, low inflation is time consistent and hence credible even if the policymaker’s output target is above potential. Second, it is found that the central banker’s optimal explicitness level is decreasing in the degree of her patience/independence (due to their substitutability in achieving credibility). Our analysis therefore offers an explanation for the ‘inflation and credibility convergence’ over the past two decades as well as the fact that inflation targets were legislated primarily by countries that had lacked central bank independence like New Zealand, Canada, and the UK rather than the US, Germany, or Switzerland. We show that there exists fair empirical support for all the predictions of our analysis.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".