Imitation of Location Choices for Tax-Motivated HQ Relocations: Uncertainty Reduction & Legitimation
Bibliographic record
Abstract
In recent decades an increasing number of firms has engaged in relocations of their HQ abroad to save on income taxes. Most of these tax-motivated HQ relocations have been targeted to the same relatively limited number of countries, raising the question whether this imitation tendency among relocating firms is purely explained by their desire to benefit from some countries’ greater tax friendliness or whether other corporate motives are also at play. Drawing on organizational institutionalism, we propose that two important additional motives for firms to imitate their peers’ most popular location choices for tax-motivated HQ relocations are to reduce outcome uncertainty and to legitimize the focal relocation to domestic non-market stakeholders. Separating a firm’s peers into domestic rivals and non-rival compatriots and controlling for countries’ tax friendliness, we find support for the existence of these dual motives in conditional logit analyses of a sample of 127 listed US firms that launched tax-motivated HQ relocations over the period 1995-2017. Our findings contribute to international strategy research on HQ relocations by shedding further light on why HQs that have mainly been relocated abroad for tax purposes are concentrated in a limited number of countries.
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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 | 0.029 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".