TRADE DEFICIT IN NEPAL: A REVIEW ON CURRENT TRADE DEFICIT, CAUSES AND SOLUTIONS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it