Optimal Fiscal Policy in a Model with Uninsurable Idiosyncratic Income Risk
Bibliographic record
Abstract
Abstract We study optimal fiscal policy in a standard incomplete-markets model with uninsurable idiosyncratic income risk, where a Ramsey planner chooses time-varying paths of proportional capital and labour income taxes, lump-sum transfers (or taxes), and government debt. We find that (1) short-run capital income taxes are effective in providing redistribution since the tax base is relatively unequal and inelastic; (2) an increasing pattern of labour income taxes over time mitigates intertemporal distortions from capital income taxes; (3) the optimal policy increases overall transfers, calibrated initially to the US welfare system, by roughly $50\%$; (4) two-thirds of the welfare gains come from redistribution and the remaining third come mostly from insurance; and (5) redistribution also leads to a more efficient allocation of labour via wealth effects on labour supply—lower productivity households can afford to work relatively less.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".