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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".