Institutional Distance as a Source of Arbitrage: Ownership Decisions in Foreign Acquisitions
Bibliographic record
Abstract
In this study, we deviate from the pervasive view that cross-border distance is a source of liability and suggest that institutional distance offers learning and arbitrage benefits for multinational corporations (MNCs). We test this view on equity ownership decisions in cross-border mergers and acquisitions (M&As). We argue that because institutional distance offers arbitrage benefits to MNCs, foreign acquirers are likely to prefer a shared ownership arrangement to better exploit these benefits as institutional distance increases. However, maintaining shared ownership at very high institutional distances is costly, therefore, the relationship between institutional distance and equity ownership is non-linear. Further, we propose that when firms are more familiar with the external environment (as measured by geographic, cultural and linguistic proximity), they can better derive arbitrage benefits due to institutional differences, and therefore more likely to opt for shared ownership. We test our arguments on a sample of 37,588 cross-border M&As involving 52 home and 54 host countries over 17 years (1996-2013).
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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 | 0.010 |
| 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 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".