Core or periphery? The effects of country-of-origin agglomerations on the within-country expansion of MNEs
Bibliographic record
Abstract
textabstractWe show how the initial subnational entry location of foreign multinational enterprises (MNEs) in China influences their subsequent within-country location choices and expansion speed. We distinguish between MNEs that establish their first subsidiary in co-ethnic cores – dense agglomerations of other firms from the same country of origin – and MNEs that locate their first subsidiary in the periphery, i.e., outside of these co-ethnic cores. To identify co-ethnic cores in China, we employ a geo-visualization methodology, which draws the boundaries of cores organically and dynamically over time. We contrast our findings with the prevailing approach of using static administrative boundaries for identifying agglomerations. Our results provide evidence of path dependency, in that (a) entry through subnational locations with strong co-ethnic communities is followed by expansion into other locations where co-ethnic communities are present, and that (b) entry through co-ethnic communities accelerates the pace at which MNEs establish additional subsidiaries in China. We also find that co-ethnic community effects continue to influence within-country MNE activities over time, despite a host of economic, institutional, and investment developments.
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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 | 0.008 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.001 |
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".