Bibliographic record
Abstract
Abstract We study optimal fiscal and monetary policy in a Ramsey economy where firms learn from their production experience and incur a real cost in changing their prices. Two central results emerge from our study. First, optimal tax policy is counter-cyclical – tax rates fall during recession and rise during boom. This finding contrasts with pro-cyclical tax results obtained in standard sticky price Ramsey models. In presence of learning-by-doing (LBD) mechanism, the Ramsey planner finds it relatively more costly to raise taxes in response to a negative technology shock. Higher taxes would reduce hours, output, and hence future level of organizational capital which will magnify the shock further by lowering future productivity. Hence, in response to a negative productivity shock, the planner finds it optimal to lower taxes in order to raise the after tax return to work and minimize the welfare-reducing effects of the shock. Second, optimal inflation is very stable and persistent over the business cycle. We show that while a dynamic link between current production and future productivity generates the inflation persistence, the real cost of price adjustment is the key factor behind the very low volatility in optimal inflation. Both of these mechanisms work through the monopolistic firms’ optimal pricing condition – namely the New Keynesian Philips Curve.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".