Gaming the IRS’ Third‐Party Reporting System: Evidence from Pari‐Mutuel Wagering
Bibliographic record
Abstract
ABSTRACT This study examines whether taxpayers intentionally avoid Internal Revenue Service (IRS) third‐party reports. In 2017 an IRS amendment created a quasi‐exogenous shock that reduced third‐party tax reporting of pari‐mutuel gambling winnings from certain types of wagers. I consider the effect that this rule change had on taxpayer behavior. Using a difference‐in‐differences research design comparing thoroughbred racing in the United States to Canada, I find a 27% increase in gambler's investment into wager‐types that became less likely to trigger third‐party reports. Further, I provide evidence that this effect was because of third‐party reporting, not withholding, and was stronger in more informed gambling populations. These findings suggest that taxpayers knowingly avoid third‐party reports, enabling underreporting of income to the IRS. This has important policy implications because underreported individual income is the largest driver of the $496 billion annual gap between legal tax liability and actual tax collections in the United States.
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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 | 0.015 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".