Balancing Industrial Concentration and Competition for Economic Development in Asia: Insights from South Korea, China, India, Indonesia and the Philippines
Bibliographic record
Abstract
In pursuit of economic growth and development, countries have tried to strike a balance between competition and industrial policies across time. This paper will review the empirical evidence on industrial concentration and its economic correlates (notably firms' performance as measured by profitability, factor productivity and innovation). It will also analyze how the introduction of competition policies and laws in South Korea, China, India, Indonesia and the Philippines affected industrial concentration. It will examine at what point in their industrialization and economic development these economies implemented these laws and policies. The empirical literature suggests that industrial concentration could exhibit an inverted-U-shaped relationship as far as its link to certain economic indicators of success, such as productivity and innovation. This suggests a role for recalibrating policies to adjust the balance between industrial concentration and competition, so that the over-all outcomes are net welfare enhancing. Indeed, country policy experiences reviewed here appear to demonstrate this recalibration, notably following privatization and liberalization policies.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".