Storming the Beachhead: An Examination of Developed and Emerging Market Multinational Strategic Location Decisions in the U.S.
Bibliographic record
Abstract
Entering a foreign market is challenging given the fierce competition posed by local incumbents. The literature suggests that when entering a foreign market, it is advantageous to locate where there are agglomeration benefits. Given the dynamic nature of regional development, foreign firms have multiple location options. While the literature has primarily focused on developed country multinationals’ (DMNEs) location decisions, emerging market multinationals (EMNEs) are increasingly becoming influential in high-tech industries. Due to differences in DMNE and EMNE resource endowments, they may consider alternative options when locating abroad and, thus, we examine these nuances. Using multinomial logistic regression, we investigate domestic and foreign location patterns of firms within the U.S. biopharmaceutical industry as of 2018. We constructed a unique dataset of 19,962 U.S. locations and examined the location patterns of DMNEs and EMNEs from 61 countries and territories. Given the heterogeneity of regional development in the U.S., we developed a typology that stratifies regions into four categories (developed, growth, transitioning, and nascent). Counterintuitively, we find that foreign multinationals are more likely to be attracted to less developed regions than domestic firms and have different location patterns, not only compared to domestic firms, but also with respect to each other.
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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 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".