India's Trade Policy Reforms and Industry Competitiveness in the 1980s
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it