Core or periphery? The effects of country-of-origin agglomerations on the within-country expansion of MNEs
Why this work is in the frame
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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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it