The Impacts of the Shrinkage in Goods Exports on Chinese Economy: A CGE Model-Based Scenario Analysis
Bibliographic record
Abstract
Focusing on the shrinkage in goods exports, this paper quantitatively analyzes the impacts of Sino-US trade war on growth, trade, industrial production of China. The method used here is a dynamic simulation for the period from 2019 to 2030 based on a recursively dynamic CGE model of 18 industries. The impacts are analyzed and assessed by providing 5 alternative scenarios and by comparing their deviations from the baseline scenario. Three alternative scenarios are diffident forms of reduction in goods exports, and two alternative scenarios are diffident hedging measures to the impacts. A comparison of alternative scenarios reveals that the reduction in goods exports will significantly affect the nominal GDP but cause trouble for the real GDP of China. As the hedging measure, the currency depreciation ultimately only affects the price, and the effect on the real GDP is very limited. To increase the domestic real investment will result in the increase in imports and significantly hedge the loss of nominal GDP and cause a larger-scale trade deficit, and therefore need to be used with caution.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".