Bibliographic record
Abstract
Shrinking budgetary allocations for tax enforcement at the U.S. federal level have placed an unprecedented premium on low cost policies that promote voluntary tax compliance. In other jurisdictions, tax administrators have experimented with rewarding taxpayers for voluntarily complying with tax laws, but there has been an absence of reward-focused policy experimentation in the United States. To explore the efficacy of rewards among U.S. taxpayer populations, a multi-period online tax reporting experiment was conducted featuring a simple reward intervention: a token monetary amount pre-announced and provided to participants who were audited and found to have fully complied. The reward failed to increase average post-audit compliance levels as compared to the no-reward control condition, regardless of whether random audits or non-random (i.e., conditional on past detected evasion) audits were used. However, the reward treatment condition in combination with random audits was strikingly effective with respect to an alternative measure of tax compliance: “consistent compliance,” or the outcome in which a participant voluntarily reports all of her income in each and every period of the experiment. When used in conjunction with random audits, the reward treatment caused consistent compliance to rise by 89% as compared to the no-reward control condition (statistically significant at the 5% level). These results suggest that pairing token monetary rewards with random audits may help maintain taxpayers’ commitments to voluntary compliance over time. Such findings may justify conducting field experiments to better understand the effects of reward programs on real-world taxpayer populations.
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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 | 0.000 | 0.000 |
| 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) | 0.003 | 0.005 |
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; both teacher heads agree on what is shown here.
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".