Trade-Policy Dynamics: Evidence from 60 Years of U.S.-China Trade
Bibliographic record
Abstract
We study the growth of Chinese exports to the United States, from autarky during 1950-1970 to 15 percent of overall U.S. imports in 2008, taking advantage of the rich heterogeneity in trade policy and trade growth across products during this period.Central to our analysis is an accounting for the dynamics of trade flows, observed trade policy, and expectations about future policy.In our empirical analysis, we estimate the dynamics of the elasticity of Chinese exports to (i) past tariff changes and (ii) the risk of future tariff hikes.We find that Chinese exports responded slowly to the tariff changes that occurred when China was granted Most Favored Nation status in 1980, and that policy uncertainty was more important in the immediate aftermath of this liberalization than in the lead-up to China's 2001 accession to the World Trade Organization.It is difficult, however, to separately identify these two effects using data alone.In our quantitative analysis, we disentangle these effects by using a structural model to estimate a path of trade-policy expectations.We find that the 1980 reform was largely a surprise and initially had a high probability of being reversed.The likelihood of reversal dropped considerably during the mid 1980s but changed little throughout the late 1990s and early 2000s despite China's accession to the World Trade Organization in 2001.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".