Tax regulations and corporate social responsibility: Evidence from the adoption of addback statutes
Bibliographic record
Abstract
Different U.S. states have enacted addback statutes at various times to close tax avoidance loopholes, significantly reducing the after-tax income of companies headquartered in adopting states. Leveraging these statutes as exogenous shocks to taxable income, we investigate how changes in tax regulations affect firms’ corporate social responsibility (CSR) performance. Using a difference-in-differences approach, we find that firms in states subject to addback statutes experience a significant decline in their CSR performance scores. In cross-sectional analyses, we further show that this association is more pronounced among firms with high levels of intangibles—such as growth firms, those with significant R&D spending, and firms with a high number of patents—as well as among firms with high investment opportunities and financial constraints, which are more likely to be affected by the addback statutes. Moreover, the significant effect is evident across both CSR strengths and concerns, spans most dimensions, and is consistent across different providers of environmental, social, and governance (ESG) indicators. Overall, these findings carry important policy implications: they highlight an unintended consequence of tax regulations and reinforce the notion that increased financial burdens from taxes can limit the capital available for CSR/ESG investments.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".