Beggar thy neighbor or beggar thy domestic firms? evidence from 2000-2011 Chinese customs data
Bibliographic record
Abstract
A premise of beggar-thy-neighbor policies is that currency depreciations lead to export growth. This premise, however, does not seem validated as there is no consensus in the empirical literature regarding the impact of exchange rate changes on trade flows. We reexamine whether currency fluctuations are systematically associated with trade flows using a rich and unique Chinese customs dataset spanning the universe of bilateral Chinese transaction level trades over the 2000 to 2011 period. This dataset allows us to consider firm-level involvement in processing trade and firm-level dynamics in both export and import markets. Key findings of our firm-level estimations of trade elasticities include that the response of Chinese firms to exchanges rate changes depends strongly on the extent to which firms are involved in processing trade, i.e. heterogeneity in the extent of processing trade is crucial to understanding trade elasticities, and that the Chinese trade balance responds strongly to changes in the relative value of the Chinese Yuan, thereby implying that the influence of exchange rates on trade flows is significant and that currency depreciations do in fact lead to export growth and trade balance improvement.
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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 | 0.006 |
| 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 | 0.001 |
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".