Exports and Imports-Led Growth: Evidence from a Small Developing Economy
Bibliographic record
Abstract
This paper examines equilibrium relationships and dynamic causality between economic growth, exports, and imports in Nepal using time-series data between 1965 and 2020. This research examines the impact of exports and imports on the economic growth of Nepal and documents empirical evidence in exports-led growth, imports-led growth, growth-led exports, and growth-led imports hypotheses in both the short and long run. The test results show no evidence favoring the exports-led growth and growth-led exports hypotheses in both the short and long run. However, the study finds evidence supporting the imports-led growth hypothesis in the short term and the growth-led imports hypothesis in the long term. Overall, this paper finds no evidence in favor of the notion that foreign trade supports the economic growth of Nepal in the long run. The research findings may have important implications for policymakers in Nepal. The paper contributes to trade and economic growth literature by investigating the relationship between exports, imports, capital, and gross domestic products in a small economy such as Nepal, where exports make a minimal and imports make an extensive contribution to gross domestic products by using cointegration and the vector error correction model.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".