Bibliographic record
Abstract
In macroeconomics, life-cycle models are typically used to address exclusively life-cycle issues. This paper shows that modeling the life-cycle may be important when addressing public policy issues, in this case the welfare costs of inflation. In the representative agent model, the optimal inflation rate is characterized by the Friedman rule: deflate at the real interest rate. In the corresponding life-cycle model, the optimal inflation rate is quite high: for the benchmark calibration, it is around 95% per annum. Much of the paper is concerned with understanding this result. Briefly, in the life-cycle model there are distributional consequences of injecting money via lump-sum transfers. The net effect is to transfer income from old, rich agents to young, poor ones. These transfers twist the age-utility profile in a way that agents find desirable from a lifetime utility point of view. A second issue concerns how to assess the costs of inflation in a life-cycle model. Metrics that are equivalent in the representative agent model can give very different answers in a life-cycle model.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".