Global cities, ownership structures, and location choice
Bibliographic record
Abstract
Purpose This paper aims to develop a more nuanced view of subnational location choice with a particular focus on global cities. It is argued that multinational firms may use global cities to establish bridgeheads-subsidiaries at intermediate levels of the ownership chain that enable further international as well as subnational expansion. Design/methodology/approach Beyond those host country subsidiaries that are directly owned by a foreign multinational, the authors go deeper and focus specifically on the multi-tiered – “subsidiaries of subsidiaries” to examine how the geographic origins and destinations of these investments are associated with micro-location choices in a host country. Findings The authors find that there are substantial differences between the types, roles, activities and geographic origins of the firms locating in different areas, and in the ownership structures spanning them. The authors propose that this has managerial and theoretical implications which may be understood based on an organizing framework describing a tradeoff between the pursuit of global connectivity and local density on the one hand and cost control on the other. Research limitations/implications Empirical work on foreign location choices should take into account ownership structures and take a more fine-grained view of subnational variation. Practical implications Managers need to consider the trade-offs between connectivity, density and costs when making foreign location decisions. Social implications Policy makers should think about the unique contributions that various subnational regions such as global and ordinary cities can make to global value chains. Originality/Value The authors bridge the hitherto separate literatures pertaining to subsidiary mandates and subnational dimensions of foreign location choice by investigating the fine-grained roles and ownership structures from a supranational as well as subnational perspective.
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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 | 0.004 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".