Let the punishment fit the crime: How retributive fairness perceptions influence observers’ tax compliance intentions
Bibliographic record
Abstract
The purpose of my research is to investigate how perceptions of retributive justice influence tax compliance. I address this objective by proposing two research questions: (1) How do taxpayers perceive the propriety of punishment for tax evasion? (2) How and when are observers’ compliance intentions influenced by perceptions of fairness disclosures about a specific retributive outcome, and the presence of a fairness-relevant disclosure?\nTo address the first research question, I conduct a survey of 331 adult Canadian taxpayers to ascertain the extent to which taxpayers perceive punishments for tax evasion as fair or unfair. I find that an appropriate punishment is viewed as a fine equal to the amount of taxes evaded.\nTo address the second research question, I conduct an experiment using 400 adult Canadian taxpayers. In this experiment, I examine two levels of retributive justice disclosures regarding punishment outcomes where punishments for offences are perceived as too lenient (unfair) versus just right (fair), and I also examine how the combination of fairness disclosures about a specific retributive outcome, and the presence of a justice-relevant disclosure influence tax compliance. I find that when taxpayers are presented with multiple justice disclosures, they anchor on the disclosure presented first. Specifically, when the justice-relevant disclosure precedes the retributive justice disclosure the differential effect on observers' compliance from the retributive justice disclosure will be suppressed. However, when a retributive justice disclosure precedes another justice-relevant disclosure, observers will anchor on the retributive justice disclosure, and the justice-relevant disclosure will not significantly alter their initial fairness judgment.\nMy research extends the retributive justice and tax compliance literatures by carefully examining retributive justice disclosures at a more fine-grained level, and provides an incremental contribution to the retributive justice literature by demonstrating the differential impact of retributive justice perceptions on subsequent behaviour. Moreover, this research is the first to examine when and how retributive justice disclosures or other tax-related justice-relevant disclosures can act as an anchor in decision making. Thus, my research contributes to existing research that examines how justice judgments can be used as an anchor which may influence subsequent tax reporting behaviour.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".