Experimental Evidence on Mixing Modes in Income Tax Evasion
Bibliographic record
Abstract
Taxpayers unlawfully trying to avoid income tax in most countries can mis-report a wide variety of line items, including income sources, exemptions, deductions, and credits. Such portfolio opportunities, or “modes,” for evasion raise important policy questions. For example, increasing the probability of detection in underreporting of income may increase compliance in terms of income reporting but may decrease compliance as a result of increased evasion through over reporting of deductions. It is possible that the resulting increase in revenue from the mode targeted for increased enforcement effort will be partially, or even fully, offset by deteriorating compliance in other modes. In this article, data from a series of laboratory experiments are used to investigate the compliance behavior of individuals when evasion can be accomplished via multiple items. The findings suggest that increasing enforcement for a single item may lead to revenue declines as evasion increases in other items as an offset.
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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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".