Corporate Tax Avoidance and Sales: Microevidence and Aggregate Implications
Bibliographic record
Abstract
This paper examines the effect of corporate tax avoidance (CTA) on U.S. firm-level sales and its aggregate implications. In theory, CTA gives a competitive edge to avoiding firms, which affects the distribution of sales in the economy. In practice, we find a causal impact of CTA on firm-level sales using a broad set of measures of tax avoidance and different identification strategies. Combining microestimates and the model, we assess how changes in CTA over the past two decades have shaped the distribution of sales across U.S. industries. Although the effects vary by sector, rising CTA among large firms has reinforced their dominant positions, contributing to increased concentration, in several key industries. This paper was accepted by Maria Guadalupe, business strategy. Funding: Financial support from the ENS Paris-Saclay Booster’s program, from the SSHRC [Insight Development Grant 207-2018-2019-Q1-00406], from the UQAM research chair on the local impact of multinationals, and from the Fonds de la Recherche Scientifique – FNRS [Grant No. F.4533.23] is gratefully acknowledged. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2024.05773 .
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".