Foreign Direct Investment and the Flying Geese Model: Japanese Electronics Firms in Asia-Pacific
Bibliographic record
Abstract
This paper is a critical examination of the ‘flying geese’ and ‘billiard ball’ models of foreign direct investment (FDI) and their ability to explain the spatial expansion of Japanese electronics multinationals (MNCs) in Asia-Pacific countries from 1985 to 1996. Data on Japanese FDI are analyzed in this region at the aggregate, sectoral, and firm level. The paper commences with a review of the flying geese model, especially that version which interprets Japanese FDI as a catalyst for Asian development, and the billiard ball metaphor which suggests a mechanism for host countries to ‘catch up’ with Japan. The authors then turn to an analysis of Japanese FDI in Asia-Pacific together with employment data for fourteen major firms. This allows an evaluation of the two models in terms of recent geographical patterns of investment and employment growth by electronics MNCs. A special case study of Matsushita Electric Industrial Co. Ltd (MEI) helps flesh out the evolving geography of Japanese electronics firms in Asia-Pacific. Although the results support the overall patterns suggested by the two models, the authors argue that metaphors and analogies such as flying geese and billiard balls should not be used casually and as a substitute for analysis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".