Export-led Growth in India and the Role of Liberalisation
Bibliographic record
Abstract
The literature on export-led growth in India is voluminous but inconclusive. This study re-examines the export–output relationship over the 1960–2009 period, and finds strong evidence of export-led growth for India. While there is no long-run cointegration relationship between India’s exports and output, tests of causality and impulse responses show a significant positive impact of export growth on output growth in the short run. Autoregressive models of India’s output growth also reveal the significant role of export growth over the same period. This result remains robust regardless of testing exports with different measures of output. The short-run impact of export on output, however, becomes insignificant in all types of estimation once the sample is reduced to the pre-liberalisation era from 1960 to 1991. Hence, liberalisation appears to have significantly contributed to export-led growth in India. We work with quarterly data as well over the liberalisation regime from 1996Q2 to 2010Q4 and find consistent results on export-led growth for India. Thus, export-led growth appears to be a liberalisation phenomenon for India. These findings have implications for other developing economies that aspire to grow fast but confront dilemmas with trade liberalisation policy. JEL Classification: F41, F43, C32, O53
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".