Tax compliance and obedience to authority at home and in the lab: A new experimental approach
Bibliographic record
Abstract
Abstract In most experimental studies of tax evasion, participants are instructed that they may report any amount of income from zero up to the amount they actually earned or received. This amounts to an invitation to gamble. In contrast, real-world tax authorities unambiguously demand compliance. We develop two new settings for conducting tax experiments. Both involve an explicit demand for compliance. Thus, we can determine whether knowing that the experimental authority would regard evasion as wrongful disobedience will influence compliance decisions. We demonstrate that simply telling people that they are required to pay a “participation fee” analogous to a tax produces remarkably high compliance rates and less sensitivity to changes in economic variables than in the earlier experimental literature using invitation-to-gamble language. This suggests that many people pay taxes despite the financial attraction of non-compliance because they are strongly inclined towards obeying authority. Furthermore, we show that giving participants a week to make their reporting decisions at home without an authority figure physically present overcomes the inclination to obey for some people, significantly lowering compliance rates. However, the majority still complies, even after the audit rate falls from 25% to 1%, which would make noncompliance extremely attractive if it were viewed only as a simple matter of risk and expected return.
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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 | 0.014 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".