Redistribution and Optimal Retirement Financing∗
Bibliographic record
Abstract
We study optimal redistribution in life-cycle economy with privately observed permanent permanent earning ability types and mortality types. The novel feature of our analysis is that earning ability and mortality are negatively correlated. We char-acterize pareto optima and show that efficient allocation must satisfy an inverse euler equation (despite ability types being permanent). Hence, all ability types face positive saving distortions at all ages as long as the correlation between ability and mortality is negative. We calibrate our model and show that the efficient inter-temporal distor-tions are large. We propose a simple tax policy that implements the efficient allocation. Implementing the optimal tax policy has increase (steady state) ex ante welfare by 4.56 percent (with the lowest earning decile gain as much at 35 percent). The contribution of differential mortality is about 0.5 percent in terms of consumption. ∗We would like to thank Alex Bick, Domenico Ferraro, Berthold Herrendorf, Bart Hobijn, Natalia Kovrijnykh, Todd Schoellman and Galina Verschagina as well as participants at the SED meetings in Toronto and the Midwest Macro Meetings in St Louis for valuable comments and discussions. Please visit
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".