Global Cities, Connectivity, and the Location Choice of MNC Regional Headquarters
Bibliographic record
Abstract
Abstract Regional headquarters (RHQs) perform a crucial bridging function between corporate headquarters, regional affiliates, and other regional actors. Their bridging role and associated connectivity needs lead MNCs to locate their RHQs in highly connected ‘global cities’. We examine how the interplay between global city connectivity, geographic distance, and RHQ roles determine the likelihood that particular cities are chosen as a location for MNCs’ RHQ investments. Our inferences are based on an analysis of location choices for 1031 RHQs among 48 global cities. We find that while the geographic distance of a global city to the MNC's regional affiliates diminishes the likelihood that a given city is chosen, these distance effects disappear when the global city is highly connected. Well connected global cities, furthermore, attract investment in RHQs by MNCs from more distant countries‐of‐origin. On average, city connectivity is a more important characteristic for RHQs that have an entrepreneurial role.
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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 | 0.001 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".