Who Gets to Play Dirty? Using Legitimacy Theory to Examine Investor Reactions to Differing Modes of Corporate Tax Minimization*
Bibliographic record
Abstract
ABSTRACT This study examines how information about a corporation's tax minimization activities and primary operations jointly influence investor behavior. Prior research identifies fear of investor backlash as a primary curb on corporations' tax minimization. However, evidence for such reactions remains mixed. Drawing on the psychological framework of legitimacy theory, we predict and find that the interaction between operations‐level validity and tax‐level propriety influences investor behavior. For companies with lower perceived validity in their primary operations, perceived improper tax minimization elicits strong negative reactions from investors, while proper tax minimization partially compensates for a lack of validity. Conversely, companies with greater perceived operational validity are mostly insulated from negative reactions to tax strategies deemed improper. Thus, management concerns over the reputational risks of tax minimization may be misplaced in some contexts, as companies whose primary operations are more valued by society may be afforded more leeway in their tax strategies.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".