Does a J-Curve Effect Exist in Nepal-India Trade?
Bibliographic record
Abstract
Nepal is facing a persistent negative trade balance with India. One of the ways a nation can improve its trade balance is by imposing or raising tariffs on its import. An import tariff raises the price of imports, lowers its domestic demand, and ultimately lowers its import, which leads to an improvement in the country’s trade balance in the long run. However, consumers take time to change their habit or find a substitute in response to a price rise, a tariff led price increase of imports only increases the import bills, thereby, worsens the nation’s trade balance in the short run. Thus, the short-run deterioration and the long-run improvement of trade balance following the imposition of an import tariff produce a J-curve phenomenon. This study tests the presence of a J-curve effect, if any, on Nepal-India trade. Our study defines BOT as a dependent variable and measures it as Nepal’s export to India minus Nepal’s import from India. Our independent variables include RRGDP (ratio of real GDP) measured as Nepal’s real GDP divided by India’s real GDP, and RP (relative price) measured as the ratio of Nepal’s consumer price index to India’s consumer price index. We estimate an unrestricted vector autoregressive model (VAR). The coefficient of the variables
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".