In All Fairness: A Meta-Analysis of the Tax Fairness–Tax Compliance Literature
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT We conduct a meta-analysis of the tax fairness–tax compliance literature from its inception in 1976 through 2021. We use an organizational justice perspective (Colquitt 2001) to differentiate between the dimensions of fairness that dominate tax fairness research. We find that the aggregate effect size of the fairness-compliance association is positive and of medium strength. We also find that distributive fairness has the strongest effect on taxpayers’ compliance and is largely driven by the subdimension of exchange equity. Other dimensions of fairness, namely, interactional (interpersonal and informational) and procedural, have smaller effect sizes. We also find a moderating effect of methodology. Our findings suggest both the importance of ensuring that tax dollars are used in ways that taxpayers value, while downplaying the effect of interactional aspects of tax administration, and the importance of carefully considering methodology when conducting tax fairness research.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it