GAMING THE IRS’S THIRD-PARTY REPORTING SYSTEM: EVIDENCE FROM PARI-MUTUEL WAGERING
Bibliographic record
Abstract
This study examines whether taxpayers intentionally avoid IRS third-party reports. In 2017 an IRS amendment created an exogenous shock that impacted how third parties report gambling winnings to the IRS. In thoroughbred racing, this shock had a substantial impact on certain types of wagers. This paper considers how gamblers reallocated their money following the shock. Using a difference-in-differences research design that compares U.S. tracks to Canadian tracks, I find that gamblers increased their investment in wager types that had become less likely to trigger third-party reports by 27 percent. In the U.S., over $400 billion in tax revenue goes uncollected annually, largely due to unreported income. Third-party IRS reporting is considered the most effective way to reduce underreporting, but there is limited understanding of how taxpayers interact with third-party reporting rules. This paper provides evidence on this interaction, showing that taxpayers purposefully avoid third-party reports to facilitate tax evasion.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".