Bibliographic record
Abstract
The constituency statutes, passed mainly in the U.S. in the last century, allow firm directors to consider the interests of stakeholders other than shareholders (i.e., non-financial stakeholders) when making business decisions. One type of critical decisions managers make pertains to corporate tax planning, which creates value for the shareholders at the expense of the public interest or social welfare. In this paper, we investigate whether this law change with a permissive nature affects directors, and hence, managers' attitude towards corporate tax avoidance. Employing a staggered difference-in-difference method, we find that firms incorporated in the states that have adopted constituency statutes exhibit significantly higher ETRs based on current tax expense, but not total tax expense or cash tax paid. This causal relationship suggests that managers, with the permission to consider the social impact of tax avoidance, become less aggressive in tax planning. We further find that the effect of adoption is stronger for financially unconstrained firms and firms in retail businesses, where the demand (cost) for tax avoidance is lower (higher). Finally, we show that our main results are driven by firms located in states with a high sense of social responsibility and the firms with high levels of tax avoidance prior to the adoption. Overall, the findings in this paper suggest a positive social impact brought by the passage of constituency legislations.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".