TRADE DEFICIT IN NEPAL: A REVIEW ON CURRENT TRADE DEFICIT, CAUSES AND SOLUTIONS
Bibliographic record
Abstract
With globalization, world trade has been growing at a rapid pace. In most developing countries like Nepal, the problem of trade deficit has always been a part of the concern. The objective of this paper is to articulate the historical trend of the trade deficit in Nepal, the major imports and exports, the causes of the trade deficit, and some recommendations to solve the trade deficit. Nepal expanded its trade relationship after becoming a member of WTO on 23rd April 2004. Nepal mainly exports readymade garments, pashmina products, leather products, pulses, handicrafts, spices, medicinal herbs. The main imports are cereals, vehicles, pharmaceuticals, Mineral fuels, oils, iron & steel, plastics, gems, machinery. Major trading partners of Nepal are India, China, the USA, UAE, Canada, Indonesia, Argentina, France, Malaysia, and Ukraine. In the fiscal year 2019/20, imports decreased by 15.63%, and export increased by 0.62%. As a result, the total trade deficit decreased by 16.83%. Landlockedness, higher production cost, political instability, devaluation of currency are the factors impeding Nepal from coming out from the labyrinth of trade deficit. Fortification of the agricultural sector, focus on hydropower, improvement of infrastructures, modified trade policy, prioritization on export potential goods can solve the trade deficit. The country should strive towards specialization, strengthening the rural economy, gaining economies of scale, exploiting entrepreneurial and management skills of the labor force.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".