Tax‐Motivated Relocations of Headquarters: The Role of Affinity Bias among Socially‐Responsible Blockholders and CEOs
Bibliographic record
Abstract
Abstract While socially‐responsible large shareholders have been shown to have a substantial impact on corporate leaders’ decisions on social responsibility, prior research remains silent on whether that impact is subject to bias among these two sets of actors. To shed light on this issue, we study the role of socially‐responsible blockholders as well as CEOs in the occurrence of tax‐motivated international relocations of corporate headquarters (HQs) – a key form of shareholder‐oriented behaviour. Drawing on stewardship theory and corporate governance research, we first hypothesize that responsible blockholders’ total equity stake in a firm is negatively related to a firm's propensity to undertake a tax‐motivated HQ relocation. Using complementary insights from social identity theory, we then propose that both socially‐responsible blockholders and CEOs tend to identify more strongly with compatriots than with foreigners. This leads us to hypothesize that (a) the stake of responsible domestic blockholders is more negatively related to a firm's relocation propensity than the stake of responsible foreign blockholders, and that (b) the stake of responsible blockholders that are compatriots of their firm's CEO is more negatively related to that propensity than the stake of responsible blockholders with a different nationality than the CEO's. Logit analyses of a sample of US firms covering the period 1998–2017 lend substantial support to our hypotheses, indicating that affinity bias among socially‐responsible blockholders and CEOs shapes the occurrence of a key form of shareholder‐oriented behaviour.
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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 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".