Bibliographic record
Abstract
Tax evasion analysis typically assumes that evasion involves individual taxpayers responding to some given policies. However, evading taxes could require the collaboration of at least two taxpayers. Detection depends on the costly avoidance activities of both transacting partners. An increase in sanctions leads to a direct increase in the expected cost of a transaction in the illegal sector, but it may also increase the incentive for the partners to cooperate in avoiding detection. The total cost of transacting in the illegal sector can fall, and tax evasion may increase. The policy implications of this phenomenon are considered. JEL Classification: H26 L’évasion fiscale collective. Dans les analyses de l’évasion fiscale, on suppose habituellement que le payeur de taxe fait face à un ensemble donné de politiques auxquelles il réagit. Pourtant, dans le cas des transactions marchandes, l’évasion fiscale n’est possible que si plusieurs agents coopèrent ensemble. La probabilité que l’évasion soit détectée dépend alors des efforts que chacun fait pour la cacher. Dans un tel contexte, de plus lourdes sanctions accroissent le coût espéré des transactions illégales, mais peuvent aussi, indirectement, accroître l’incitation pour les partenaires à coopérer pour cacher leur activité illégale. Il en résulte que le coût total des transactions illégales peut diminuer et l’évasion fiscale augmenter. Nous étudions les implications de ce phénomène.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.004 |
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".