The Tax Effects of Intergenerational Transfers: A Quantitative Exercise.
Bibliographic record
Abstract
Economists predict transfer of wealth from one generation to the next due to increased global wealth. This transfer of wealth is predicted to widen already existing poverty gaps in some economies. Some economists have suggested the taxation of intergenerational transfers in a bid to increase government revenue and reduce the widening gaps between the poor and the rich. This study captures the key mechanisms at work in assessing the labour earnings reaction to changes in unearned income, emanating from gifts and bequests that heirs receive. Three theoretical models are developed to understand the labor earnings reaction with varying unearned income compositions. The tax effects on the transfer of intergenerational wealth are then decomposed. Further, the study uses specific parameters from the Canadian economy with other assumptions to quantitatively investigate the three theoretical models and examine the differences in utility, consumption and labour supply when gift and bequest taxes are applied to unearned income. The study concludes that heirs experience high levels of utility when gift and bequest taxes are applied by the government. Heirs tend to reduce labour hours supplied upon the receipt and taxation of transfers. An optimal combination of income and transfer taxes is necessary to maintain a productive work force in an economy.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.016 | 0.001 |
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".