Empirical Estimates for how Changes in China’s Foreign Reserves Are Hurting Chinese Exports and Helping US Exports
Bibliographic record
Abstract
This paper estimates the change in China's exports and the change in US exports due to a one dollar increase in China's foreign reserves. The statistical technique used produces reduced form estimates that capture the influence of omitted variables without having to construct and estimate complex structural models. I find that in August 2000 China's accumulation of 621 million dollars of foreign reserves is correlated with China's exports increasing by 151 million and the US's exports falling by 628 million dollars. In contrast, in November 2016, China spending 69 billion dollars of its foreign reserves supporting the value of the yuan is correlated with China's exports falling by 4.77 billion and the US's exports rising by 2.42 billion. Donald Trump's accusation that China is suppressing the yuan exchange rate to help Chinese exports at the expense of US exports did not fit the facts between August 11, 2015 and December 31, 2016.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".