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 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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".