Attitudes, Incentives, and Tax Compliance
Bibliographic record
Abstract
Data published by the Canada Revenue Agency show that tax compliance at the most basic level (filing and remitting on time) is quite satisfactory, while the more significant levels of tax compliance, as measured by the proportion of taxpayers judged to be at “substantive risk of non-compliance”, shows more problems. Whether increased audits and penalties are the best way to deal with this non-compliance depends on the reasons why taxpayers fail to comply. If taxpayers care only about incentives and are “playing the audit lottery”, increasing penalty and audit rates should improve compliance. But if psychological factors (including moral and ethical concerns) are important, improved compliance might instead be achieved by strategies that change taxpayers ’ attitudes towards the tax system such as increasing its perceived fairness and making it easier to comply with the tax laws. This study contributes to the compliance literature by studying the appropriate measure of tax compliance to use in research aimed at determining those changes in tax laws which would encourage higher levels of compliance. In the past some studies have used measures based on questions about tax compliance in hypothetical situations while others have used measures based on the experimental economics methodology. This paper finds that experimental economics
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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 | 0.006 | 0.046 |
| 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 | 0.002 |
| 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".