Some Firm Level Evidence from India ∗
Bibliographic record
Abstract
Based on a survey of over 1000 manufacturing establishments across a number of states in India, this paper analyzes the effect of key aspects of the investment climate on firm productivity. We find that, controlling for establishment size and industry, value added per worker is about 44 percent lower in states that entrepreneurs consider to be of relatively poor investment climate. Most of this gap is due to lower total factor productivity (TFP) of firms in these states. We then relate firm productivity to objective indicators of the investment climate. We trace about a quarter of the TFP gap to inferior power supply and poorer internet connectivity in poor-climate states. About a tenth is due to higher regulatory burden in the same states. The TFP disadvantage of poor-climate states would have been even higher if labor market rigidities were not a stronger drag on productivity in the better-climate states. Not only does this regional pattern in productivity confirm managers ’ subjective ratings of investment climates, but it also matches a similar pattern in capital formation. The average rate of net fixed investment is less than 2 % for firms sampled from poorclimate states against a figure of about 8 % in good-climate states. This evidence suggests that local governance – working through the regulatory environment and infrastructure – plays a significant role in investment and productivity growth. The findings and interpretations expressed in this paper are those of the authors and do not necessarily reflect official views of the World Bank, the members of its Board of Executive Directors, or the countries they represent
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".