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Record W2149636102 · doi:10.1177/097380101100600101

Export-led Growth in India and the Role of Liberalisation

2012· article· en· W2149636102 on OpenAlexaff
Biru Paksha Paul, Anupam Das

Bibliographic record

VenueMargin The Journal of Applied Economic Research · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Theory and Policy
Canadian institutionsMount Royal University
Fundersnot available
KeywordsLiberalizationCointegrationEconomicsInternational economicsExport performanceEstimationGranger causalityShort runMonetary economicsInternational tradeEconometricsMarket economy

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.553

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.278
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2012
Admission routes1
Has abstractyes

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