Bibliographic record
Abstract
A fixed exchange rate limits the ability of the real exchange rate to adjust to shocks, and tends to raise the volatility of real GDP. But adjustment may be enhanced if internal prices are more flexible under a fixed exchange rate. This Paper develops a model in which price setters incur a cost to retain the option of ex-post price flexibility. The benefit of flexibility is increasing in the variance of demand facing price-setters. We ask whether fixing the exchange rate is likely to increase price flexibility. For a unilateral peg followed by one country alone, the answer is yes. Moreover, because there is a strategic complementarity in the choice of price flexibility, the increase in flexibility following an exchange rate peg can be very large. It is even possible that the increase in internal flexibility following an exchange rate peg is so great that it overturns the direct effect, and GDP is more stable after a peg. On the other hand, when an exchange rate peg is supported by bilateral participation of both monetary authorities (such as a monetary union), the degree of price flexibility may actually be less than under freely floating exchange rates. The model also allows for multiple, self-fulfilling equilibria in the degree of price flexibility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".