Globalization and Sustainability of Japan’s Internal Labor Markets: Foreign Direct Investment (FDI) and Wages at Japanese Manufacturing Firms
Bibliographic record
Abstract
Both inward and outward foreign direct investment (FDI) have implications for the wage rates of home-country workers. Such implications have been particularly noteworthy in Japan where the traditional internal labor-market practices, which value long-term sustainability of employment and wages, collide with the pressure for change in the globalizing Japanese economy on many fronts. In this paper we estimate the impacts of FDI on workers’ wages in Japanese manufacturing industries. We find that Japanese employees benefit, in the form of wage gains, from their employers’ association with both inward and outward FDI operations. These wage effects differ systematically depending on gender and worker ranks within their employer firms and are likely to weaken the mechanisms underlying the sustainability of Japanese firms’ traditional internal labor markets. The presence of FDI effects on worker wages also implies an increasing disparity between the incomes of workers who work for successfully globalizing firms and workers who do not, jeopardizing Japan’s traditional policy objective to sustain harmonious economic growth across all economic sectors. This would also deepen the structural divide including the wage gap of the Japanese economy that exists between large firms and small- and medium-size enterprises (SMEs) since firms which get involved in FDIs are mostly large firms.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".