Managing investment liberalization : the political economy of Japanese foreign direct investment in the United States, 1985-1993
Bibliographic record
Abstract
In this study, we will analyze the political economy of Japanese foreign direct investment (JFDI) in the United States during the period through 1985 to 1993 from the perspective of International Political Economy (IPE).Within the sphere of IPE, the relationship between political and economic forces is an important dimension which must be examined.The core question asked in this dissertation is why and how the United States managed the entry of Japanese FDI during the above period.Specifically, we attempt to center on U.S. investment policy change which was a result from the entry of JFDI.The period covered in this dissertation is the most important development stage in U.S. investment policy during the past four decades.In this case, the United States was the largest economy in that time, while the Japan was the second largest economy.To examine the impact of JFDI and U.S. policy adjustment, the unitary actor model in traditional International Relations is not sufficient to grasp the complex dynamics of the problem.Furthermore, U.S. investment policy adjustment is essentially a political process and therefore requires a political economy approach.To accomplish this objective, we construct a more detailed analytical framework help us specify the relationship between JFDI and U.S. policy adjustment.This framework was a slightly revised one provided by the second generation of IPE scholars in the new century.It emphasizes that interest and institution are two crucial variables to examine policy adjustment.In this framework, unitary actor assumption is only a special case.Based on rational choice institutionalism, the logic is that the incentives rooted in economic interest strongly compel individuals, societal groups and states act.At the same time, whether the policy demand that these actors asked would be realized partly depend on the policy supply framed by institution.As a result, what needs to be clarified is which kind of interest created by JFDI and under what institution circumstance?This dissertation argues that U.S. policy adjustment is partly due to the nature of JFDI, and partly depends on the institution background.At the national level, if the U.S. industries that JFDI entered would create national economic gap between the United States and Japan, U.S. government will strongly oppose I am deeply grateful to Professor Hatsue Shinohara for her insights, encouragements and arrangements.As my supervisor, Professor Shinohara has done great for me.The slogan "one page a day" suggested by Professor Shinohara is useful to identify my schedule in writing dissertation.The comments and suggestions I received from Professor Shinohara has produced a fascinating progress and helped understand the Japan-U.S. relationships.I am also especially grateful to Professor Shujiro Urata for his help to grasp the economic sense of my study.I expect I have learned much
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".