Shaming of Tax Evaders: Empirical Evidence on Perceptions of Retributive Justice and Tax Compliance Intentions
Bibliographic record
Abstract
Abstract Although naming-and-shaming (shaming) is a commonly used tax enforcement mechanism, little is known about the efficacy of shaming tax evaders. Through two experiments, this study examines the effects of shaming tax evaders on third-party observers’ perceptions of retributive justice and tax compliance intentions, and whether the salience of persuasion of observers moderates these relationships. Based on insights from defiance theory, the message learning model, and persuasive communications, this study predicts and finds that shaming evaders increases observers’ tax compliance intentions. Furthermore, the results show that higher persuasion, which includes sanction and normative appeals, affects observers’ tax compliance intentions. This study also suggests that shaming has a positive effect on perceptions of retributive justice. Importantly, the results reveal that perceptions of retributive justice in shaming punishment mediate the effect of shaming on tax compliance intentions. The implications for theory and practice are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".