What Determines Effective Tax Rates? The Relative Influence of Tax and Other Factors*†
Bibliographic record
Abstract
ABSTRACT Many studies use GAAP effective tax rates (ETRs) as a proxy for tax avoidance and assume that very low (high) ETRs represent the greatest (least) tax avoidance, yet ETRs can be affected by items unrelated to tax avoidance. Despite awareness of the potential limitations of ETRs versus other factors as a measure of tax avoidance, the literature lacks consistent evidence on the extent to which ETRs capture tax avoidance. We take a step toward filling this void using income tax footnote disclosures from 2008 through 2016 to investigate how well ETRs versus other factors capture cross‐sectional differences in tax avoidance. We document that ETRs below 5% and above 40% are significantly influenced by items largely unrelated to tax avoidance, such as valuation allowances and goodwill impairments. Truncating ETRs at zero and one, controlling for standard determinants of tax avoidance, and using industry‐size‐adjusted ETRs or multiyear GAAP ETRs do not eliminate the clustering of factors largely unrelated to tax avoidance in the tails of the ETR distribution. Cash ETRs attenuate but do not eliminate this clustering. Researchers can use ETR rate reconciliation data to construct an adjusted ETR that removes the influence of factors largely unrelated to tax avoidance. Our findings inform researchers about factors largely unrelated to tax avoidance that drive significant deviations in ETRs from the statutory tax rate. This is of increasing importance as the number of studies examining the consequences of very high and very low ETRs grows.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".