Total factor productivity spillovers from trade reforms in India
Bibliographic record
Abstract
Abstract It is often argued that the positive effects of policies or institutional reforms on the productivity of firms are amplified by positive spillovers to other firms. In this paper, we use a comprehensive dataset of Indian manufacturing firms and employ spatial econometric techniques to estimate the strength of inter‐firm total factor productivity (TFP) spillovers within India following trade reforms. We use externally imposed tariff reductions as an exogenous source of variation in TFP (following Topalova and Khandelwal) and test for spillovers arising from observation, labour mobility and intermediate input use. We find no evidence of TFP spillovers between Indian manufacturing firms after trade liberalization on average. However, initially productive firms seem to benefit from TFP increases in other highly productive firms located in their vicinity. Résumé Effets de débordement via la productivité total des facteurs des réformes commerciales en Inde. On suggère souvent que les effets positifs des politiques ou réformes institutionnelles sur la productivité des firmes sont amplifiés par les effets positifs de débordement sur d’autres firmes. Dans ce mémoire, on utilise une base de données compréhensive de firmes manufacturières indiennes et des techniques économétriques spatiales pour calibrer la force de cet effet de débordement entre firmes via la productivité totale des facteurs (PTF) à l’intérieur de l’Inde à la suite de réformes commerciales. On utilise des réductions de tarifs imposées de l’extérieur comme source exogène de variation dans la PTF (suivant en cela Topalova et Khandelwal 2011) et on calibre les effets de débordement par observation, mobilité du travail, et utilisation d’inputs intermédiaires. On ne détecte aucun effet de débordement de la PTF entre les firmes manufacturières après la libéralisation du commerce en moyenne. Cependant les firmes initialement productives semblent bénéficier de l’accroissement de la PTF d’autres firmes productives dans les environs.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".