Stronger together: Country‐of‐origin agglomeration and multinational enterprise location choice in an adverse institutional environment
Bibliographic record
Abstract
Abstract Research Summary Research suggests that multinational enterprises (MNEs) are attracted to locations with concentrated firms from the same home country to benefit from interactions with market forces, but it remains an open question whether such agglomeration facilitates MNEs' interactions with nonmarket actors such as the host government. We submit that since country‐of‐origin agglomeration can enable collective actions and create collective gains, colocation with compatriot firms will help MNEs navigate an adverse institutional environment. In line with this reasoning, we hypothesize that MNEs are more attracted to locations with country‐of‐origin agglomeration when MNEs face an exogenous shock that increases their regulatory burden in the host country. Our analysis offers corroborative evidence. The study adds to research on agglomeration, institutional environment, and location strategy. Managerial Summary Why do multinational enterprises (MNEs) locate near compatriot firms in a foreign location? The commonly recognized benefits include resource access and knowledge spillover from interactions with market forces such as suppliers and customers. We submit that colocation with compatriot firms can also help MNEs navigate an adverse institutional environment by generating “stronger‐together” benefits. Colocation can enable collective actions and create collective gains for MNEs in their interactions with the host government. We find that after a diplomatic dispute, Korean MNEs are more attracted to locations in China that already have a cluster of Korean firms, whether in the same/related industries or in unrelated industries; this is particularly the case for small MNEs and in locations with weak institutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".