Model Structure and the Combined Welfare and Trade Effects of China's Trade Related Policies
Bibliographic record
Abstract
Because China’s economic structure is different from that in OECD countries, using conventional neo-classical competitive trade models to analyze the welfare and trade impacts of trade related policy change can be misleading. In particular, both the exchange rate regime and output and pricing policies of state owned enterprises (SOE’s) will have effects on trade and welfare which differ from a classical competitive model. This paper present a numerical model that captures the combined and interactive effects of three policy elements in prototype form of tariffs, policy towards SOEs in the industrial sector, and an exchange rate regime supporting large trade surpluses and additions to foreign reserves. The model has non neutral monetary features, endogenous trade imbalances and average product pricing of labor in goods. We do not claim it to be fully representative of modern China, but it does go some way beyond simple competitive models used elsewhere and points to different conclusions of policy impact. We calibrate our model to 2006 data, and then evaluate the impacts both singly and in combination of: tariff liberalization, a move to more freely floating exchange rates, and SOE enterprise reform. Results show that large differences in policy have a different impact relative to a classical competitive model. SOE reform and a freely floating Chinese exchange rate have more impact on China’s welfare than tariff liberalization. Policies of RMB appreciation and increasing China’s money stock reduce China’s trade surplus. In the traditional competitive model, trade liberalization impacts both imports and exports, while in our central case model, with endogenously determined trade surplus, trade liberalization has little effect on exports. Most of the policy impact is on imports and the trade surplus. SOE reform of China’s manufacturing sector significantly decreases production of China’s manufacturing sector and increases production in China’s other sectors.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.005 | 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".