The effects of population aging on optimal redistributive taxes in an overlapping generations model
Bibliographic record
Abstract
The impact of population aging on the steady state solution to a Ordover-Phelps (1979) overlapping generations optimal nonlinear income tax problem with two types of workers and quasilinear-in-leisure preferences is investigated. A decrease in the rate of population growth, which leads to an aging population, increases the relative price of consumption per person in retirement, which tends to decrease optimal consumption for retirees of both skill types. It is also shown that the optimal steady state rate of interest equals the rate of population growth. As a result, the steady state interest rate unambiguously declines when the rate of population growth declines. The resulting adjustments in production plans has an ambiguous effect on the aggregate wage rate. This article identifies factors contributing to an increase in the aggregate wage when the population ages, namely normality of consumption in retirement, complementarity between capital and labor in production, and a large capital deepening effect relative to the increase in dependency owing to demographic change. Depending on the sign of this wage effect, ambiguities may arise in the direction of change in the optimal steady state consumption and production plans. It is also shown that the optimal marginal income tax rates are independent of the rate of population growth.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".