Bibliographic record
Abstract
Inheritance and estate tax rates are highly heterogeneous across countries. Indeed, the lack of a broadly accepted model of optimal inheritance taxation is reflected in statutory tax rates ranging from 0% in several countries including Australia, Canada and Sweden to 55% in Japan. In addition, tax expenditures (TEs) through reduced rates, exemptions, and deductions in the context of inheritance taxes are significant, and hence exacerbate the variation in effective tax rates. Moreover, these schemes are often costly, opaque, and raise several concerns with regard to their effectiveness as well as efficiency in reaching their stated goals. Inheritance TEs relating to the transfer of family businesses are a case in point. Taxes on inheritance can create liquidity constraints for heirs that force them to sell parts or all of the business. In this context, many countries have introduced tax privileges to lower inheritance tax liability for the transfer of family businesses. At the same time, neither the theory nor empirical evidence substantiate the underlying assumption that keeping family businesses within the family enhances welfare. In addition, several observers argue that liquidity constraints as a result of inheritance taxation may be less of an issue for most businesses than commonly assumed. Against this background, the goal of this discussion note is threefold: i) to provide an overview on inheritance taxation across OECD countries; ii) to discuss its interconnections with family business succession and; iii) to introduce the reader to specific TEs for inheritance taxation, and to assess their alignment with a broad sustainability agenda, in particular with regard to job creation and social mobility, as well as their effectiveness and efficiency.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".