Reconsideration of the Effects of Political Factors on FDI: Evidence from Japanese Outward FDI
Bibliographic record
Abstract
This paper empirically examines the role of political factors in the Japanese outward Foreign Direct Investment (FDI) activities with a panel data of 30 developed or developing countries for the period of 1995-2009. The estimation model is constructed on the basis of the OLI (ownership, location and internalization advantages) and knowledge-capital models. Political factors, which represent multiple dimensions of each host country including important institutional assessments, are included as additional explanatory variables with market potential, wages, skilled workforce endowments, investment cost, and openness. It is found that Political factor perception by Japanese MNCs is sensitive to different levels of initial political stability in the host countries. Thus, the model with political factors and traditional explanatory variables reasonably explains recent Japanese outward FDI flows and reveals new patterns in its behavior.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".