India's Trade Policy Reforms and Industry Competitiveness in the 1980s
Bibliographic record
Abstract
The paper proposes a method of measuring and analyzing competitiveness, and applies it to Indian manufacturing data of 1980/81, 1987/88 and 1991/92. The method consists of computing a unit cost ration and breaking it down into various components, ditinguishing between comparative advantage measured at shadow prices, and competitive advantage measured at market prices. The difference, equal to the sum of all price ditortions, may enhance or diminish competitiveness, depending on whether the distortions are cost‐increasing or ‐decreasing. The study reviews first the limited trade reforms of the 1980s and examines whether they have led to increased competitiveness. Although the present study is limited to less than the full potential of the method, due to lack of adequate data, it demonstrates, that the policy changes of the 1980s have failed to enhance the competitiveness of the industrial sector as a whole, while some industries have undergone substantive changes. In three industry case studies the results are compared with the findings of earlir studies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".