Age‐related taxation of bequests in the presence of a dependency risk
Bibliographic record
Abstract
Abstract This paper studies the properties of the optimal taxes on bequests when individuals differ in wage and in their risks of mortality and old‐age dependance. Survival is positively correlated to income but dependency is negatively correlated with it. The government cannot distinguish between bequests motives, that is whether bequests resulted from precautionary reasons or from pure joy of giving reasons. Instead, it observes the timing of bequests and the health status at death. Under the utilitarian social welfare criterion, we show that bequests taxation results from a combination of equity, insurance, and public revenue motives. If redistribution concerns dominate insurance concerns, it is desirable to tax the most bequests of those individuals living long in good health and to tax the least bequests of those dying early. This is a direct consequence of the socio‐demographic structure we assumed where richer agents live longer and in better health than poorer agents. To the opposite, if insurance concerns dominate redistributive concerns, early bequests should be the most taxed and, bequests under dependency the least taxed. Under the Rawlsian criterion, we find that early bequests should be the least taxed and bequests left by the healthy long‐lived individuals should be the most taxed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".