Research on BOPs of China’s Tea Trade Based on the Marshall-Lerner Condition
Bibliographic record
Abstract
The Marshall-Lerner Condition is one of the important theories to explore the impact of exchange rates on BOPs.This paper uses the annual data of China’s tea import and export from 1986 to 2008 to analyze whether China’s tea trade meets the Marshall-Lerner Condition or not.In order to achieve this purpose,we calculate the demand elasticity of China’s tea import and export,and build the econometrics model to analyze the relationship between the real exchange rate of RMB and China’s tea BOPs.The result explains that the tea trade between China and other countries shows different effects to the changes of RMB exchange rates.On the whole,the rise of tea export price will give rise to the tea export value,while the decrease of tea import price will increase the import value.The appreciation of RMB will promote China’s tea BOPs,while the devaluation of RMB will worsen BOPs,The demand elasticity of imports and exports is less than 1,so basically speaking,China’s tea trade is consistent with the Marshall-Lerner Condition.
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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 | 0.005 |
| 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 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".