Punishing in the Public Interest: Exploratory Canadian Evidence Pertaining to Convictions and Incarcerations for Tax Offences
Bibliographic record
Abstract
Although naming and shaming is a deterrence strategy used by tax authorities, ostensibly to increase tax compliance, the contents of tax conviction notices in which taxpayers are named and shamed have not been investigated in empirical research. To this end, this study uses a sample of 2,570 taxpayers convicted of tax offences by the Canadian tax authority over a ten-year period (2006 through 2015) to identify key characteristics of convictions and incarcerations for tax crimes, and to understand how key conviction characteristics are associated with incarceration. Over this period, findings show that 55% of tax convictions in Canada relate to failure to file tax returns, and that 14% of convicted individuals are incarcerated for an average of 17 months. The mean unreported income per convicted taxpayer is $89,978, the mean unremitted excise tax per convicted taxpayer is $15,330, and the mean fine amount per convicted taxpayer is $48,201. Males are more likely to be convicted of a tax crime than females. Further, professionals are far more likely to be incarcerated than non-professionals. Results also indicate that underreporting related to excise tax as a form of tax evasion is more likely to result in harsher sentencing than underreporting related to income tax. Lastly, we observe a downward trend in convictions and incarceration over the 10-year span, such that the total convictions and incarcerations at the end of the sample period are roughly one-third of the convictions at the beginning. Implications for public policy are discussed.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.016 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".