Attitudes, Incentives, and Tax Compliance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it