Assessment of Export-Led Growth Hypothesis: The Case of Bangladesh, China, India and Myanmar
Bibliographic record
Abstract
The Asian countries, particularly Bangladesh, China, India and Myanmar, have been witnessing impressive economic growth rates due to their trade performance in the international market. Although export-led growth assumption is functional in these economies, existing pieces of literature hardly considered them in their studies. Against this backdrop, the present study investigates the export-led growth hypothesis for four South Asian countries — Bangladesh, China, India, and Myanmar — covering country-specific different time ranges. This research employs the autoregressive distributed lag (ARDL) bounds testing approach to co-integration and the MWALD Granger causality test to determine the causal relationship between variables. The results obtained from the autoregressive distributed lag method confirm the co-integration among the variables. In addition, the Granger causality test explores both the export-led and growth-led export hypotheses in Bangladesh and India as per the bidirectional causation between exports and economic development. Only the export-led growth theorem is relevant to China, and the growth-led export hypothesis is valid in the case of Myanmar based on the unidirectional causation between these variables. Therefore, any joint footstep of BCIM countries is critical to promoting exports by penetrating new destinations with diversified export goods and services. The obtained findings also indicate the potential for utilising these countries’ unused resources to encourage exports to uplift the existing growth trajectory.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".