Strategic FDI and industrial ownership structure
Bibliographic record
Abstract
We argue that different industrial ownership structures generate different incentives for firms to engage in FDI. A comparison is made between (partially) cooperative structures such as the Japanese kieretsu and Korean chaebol systems and competitive structures such as U.S. firms. It is found that ownership structure has significant implications for the probability of initial FDI. Whether or not a cooperative structure is also coordinated turns out to be crucial in predicting FDI behaviour. This has further implications for the optimal FDI incentives of potential host countries and for how empirical studies might be designed. JEL classification: F10, F21, F23 Stratégie d’investissements directs à l’étranger et structure de propriété de l’industrie. Les auteurs suggèrent que des structures différentes de propriété dans l’industrie engendrent des incitations diverses pour les entreprises à investir directement à l’étranger. On fait des comparaisons entre des structures partiellement coopératives comme celles des kieretsus au Japon et des chaebol en Corée, et des structures concurrentielles comme celles qui existent aux Etats–Unis. Il appert que la structure de propriété a un impact significatif sur la probabilité de faire initialement un investissement direct à l’étranger. Le fait que la structure de coopération engendre aussi une coordination s’avère d’une importance centrale pour prédire le comportement d’investissement direct à l’étranger. Voilà qui a des implications pour le design des incitations à investir par les pays hôtes potentiels, et pour le design des études empiriques de ces phénomènes.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".