The Interrelationship between Agglomeration and Internationalization
Bibliographic record
Abstract
Geographic distance that hinders internationalization is constant, but when firms internationalize, geography changes across space and time. Internationalization decisions should thus consider geography rather than focusing solely on geographic distance. Drawing from the literature on agglomeration, an important construct in geography, I hypothesize that a firm tends to internationalize into a geographically distant country, when it has learned from inward foreign direct investments (FDIs) and other firms involved in outward FDIs that are both agglomerated in the firm’s home location. When a firm has learned that some other firms from the same home location have conducted outward FDIs in a geographically distant country by applying the knowledge acquired from agglomerated inward FDIs from the country, the firm is likely to follow the others by entering the distant country. I further hypothesize that when a firm has become experienced in such learning, it is even more likely to choose a geographically distant country once it internationalizes. In order to test these hypotheses empirically, I collected a unique sample of 351 firms, which chose their first host countries between 2011 and 2015, and whose home locations had different levels of agglomeration. The findings provide support to show that changing geography affects internationalization.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".