The Political Economy of Automotive Industrialization in East Asia
Bibliographic record
Abstract
Abstract This book offers a political economy explanation for the striking cross-national differences in strategies and performance among East Asia’s automotive industries. Some countries—China, South Korea, and Taiwan—have successfully pursued “intensive” growth strategies by increasing local value added based on domestic inputs and technological competencies. Malaysia has attempted but failed to pursue this path. In contrast, Thailand has become a champion of “extensive” growth, relying on foreign assemblers and their suppliers to achieve an impressive expansion of production, assembly, and exports. Latecomer Indonesia has followed Thailand with some success, whereas the Philippines has remained an automotive backwater. Through cross-case and within-case analyses of the seven countries, the book argues that variation is a function of the institutional and political contexts in which firms operate. Different strategies require different institutions and institutional capacities. Intensive development is especially institutionally demanding. Effective institutions emerge when political leaders face severe claims on resources (security threats and domestic pressures for welfare improvement) in the absence of easily accessible revenues to satisfy such needs. Brief comparisons with Brazil, Mexico, and other developing countries confirm the utility of the analytic framework. This explanation is superior to neoclassical accounts. It is consistent with but provides more insight than other prominent approaches to development: national innovation systems, global value chains, and developmental states. New challenges facing auto assemblers and suppliers, such as the transition to electric and autonomous vehicles, will call heavily upon the institutional capacities highlighted in this book.
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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 | 0.001 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".