The Decreasing Trend in U.S. Cash Effective Tax Rates: The Role of Growth in Pre-Tax Income
Bibliographic record
Abstract
ABSTRACT We develop a linear corporate tax function where taxes paid are regressed on pre-tax income and an intercept. We show that if the intercept is positive, cash ETRs are a convex function of pre-tax income. We present large-sample evidence consistent with this ETR convexity. Thus, although firms may have stable linear tax functions (i.e., constant parameters in the linear tax model) representing stable tax avoidance behavior, ETRs can change over time because of growth in pre-tax income. Consequently, simply examining changes (or differences) in cash ETRs is nondiagnostic about whether tax avoidance has changed over time (or differs across firms). We illustrate our argument by showing that all of the observed downward trend in cash ETRs documented by Dyreng et al. (2017) can be explained by growth in pre-tax income. The wholesale concern about increased tax avoidance over time might be overstated. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: G39; H20; H25; H26.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".