Beggar Thy Neighbor or Beggar Thy Domestic Firms? Evidence from 2000-2011 Chinese Customs Data
Bibliographic record
Abstract
The premise of beggar-thy-neighbor policies and currency wars is that currency depreciations lead to export growth.This premise, however, is far from validated as the existing economic literature largely either fails to find significant trade flow effects of currency fluctuations or finds that these effects are only minor.We revisit the question of whether currency fluctuations are systematically associated with trade flows using rich and unique firm level Chinese customs data on China-US trade over the 2000 to 2011 period that allows us to consider firm involvement in processing trade and firm dynamics in both export and import markets.Our firm-level based estimation of trade elasticities suggest that the China-US trade balance strongly responds to changes in the CNY/USD rate.This finding is particularly pronounced when we distinguish between ordinary and processing firms.Our results thus suggest that the influence of exchange rates on trade flows is stronger than previously thought and add insights to the policy debate on beggar-thy-neighbor policies and currency wars by, at least in principle, validating the underlying premise of such policies.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".