Indian Manufacturing Productivity: What Caused the Growth Stagnation before the 1990s?
Why this work is in the frame
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Bibliographic record
Abstract
This article addresses the question of why productivity growth in Indian manufacturing was slow in the pre-reform period and analyzes how economic reforms in the 1990s accelerated productivity growth. The answer lies in two subtle but important distortion-inefficiency mechanisms, which affected productivity growth by distorting intermediate input allocation. The interaction of quantitative restriction policies and inflexible labour laws resulted in lower than optimal materials per worker usage. The combination of high inflation and unavailability of credit exacerbated this factor distortion and lowered productivity growth further. Using a panel dataset on Indian industries, this article finds widespread underutilization of materials compared to labour until recently, and this sub-optimal materials per worker usage lowered productivity growth.
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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 | 0.004 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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