Profitability of Foreign Direct Investment in Global Cities and Co- Ethnic Clusters
Bibliographic record
Abstract
This paper compares the profitability of foreign direct investment (FDI) in global cities (GCs), their metropolitan areas (metros), and other locations; and examines the impact of co-ethnic and co- industry FDI concentrations. GCs, metros, and clusters offer multinational enterprises (MNEs) a range of economic, institutional, and ecosystem advantages, but may also present substantial cost and competitive challenges. We use a sample comprising 1,832 unique Japanese subsidiaries in North America across 1,263 MNEs over the years 1990-2013. We apply a multi-level longitudinal analysis model and determine spatially significant clusters using geo-coding, proximal distance, and density analysis. We find that subsidiaries in GCs and metros are about twice as likely to be profitable relative to those in other locations. Services subsidiaries in GCs, and manufacturing subsidiaries in metros outperform peers elsewhere. Co-ethnic clusters improve subsidiary profitability in GCs and metros, but not in other locations. Our study responds to calls to examine the performance of FDI in global cities, and to bridge international business research with economic geography. It informs the subsidiary performance literature and the eclectic paradigm on fine-grained location specific advantages; and provides a large sample, longitudinal baseline to aid subsequent theoretical and empirical research.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".