Flying Geese In Asia: The Impacts of Japanese MNCs as a Source of Industrial Learning
Bibliographic record
Abstract
ABSTRACT Pacific Asia has looked to direct foreign investment (DFI) to achieve economic growth and technological catch‐up, and Japanese multinational corporations (MNCs) have responded massively. This paper evaluates Japanese MNCs as a source of industrial learning and technological transfer in the region, drawing from a large research literature and from the authors’ own surveys of Japanese DFI in the electronics sector. Japan's historic learning‐based approach to industrialisation is captured by the flying geese metaphor of structural transformation. As an explanation of the transfer of technological know‐how from Japan to Pacific Asia, however, the flying geese model is problematical. This paper reflects on the effectiveness, problems and dilemmas of Japanese MNCs in transferring such know‐how to the region from a political economy perspective summarised as a ‘reverse product cycle model’. This model portrays DFI as a ‘bargain’ between Japanese MNCs and host countries, and which becomes more difficult to negotiate as DFI moves from low‐skilled manufacturing to more innovative activities. The bases for this hypothesis relate to the increased complexity of industrial know‐how and the conflicting motivations between MNCs and host countries in early stages of the product life cycle. In practice, however, this ‘bargain’ has developed differently among Asian countries, and we illustrate these differences by comparing the experiences of South Korea, Taiwan and Malaysia.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".