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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".