Premature deaths, accidental bequests, and fairness*
Bibliographic record
Abstract
Abstract While there is little agreement regarding the taxation of bequests in general, there is a widely held view that accidental bequests should be subjected to a confiscatory tax. We re‐examine the optimal taxation of accidental bequests by introducing a concern for compensating individuals for a premature death. Assuming that individuals care about what they leave to their children, we show that, whereas the 100 percent tax view holds under the utilitarian criterion, the ex post egalitarian criterion (giving priority to the worst‐off ex post ) implies subsidizing accidental bequests so as to compensate the short‐lived. In a second‐best setting, compensating the short‐lived justifies taxing total bequests at a rate increasing with the age of the deceased. Finally, when the model is extended to an intergenerational setting, accidental bequests can no longer be used as a redistributive tool, so that ex post egalitarianism rejoins the 100 percent tax view.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| 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".