Heads I win; Tails you lose: Asymmetry in Exchange rate Pass-through into Import Prices
Bibliographic record
Abstract
Summary We analyse exchange rate pass-through into import prices for a large group of 33 emerging and developed economies from 1980, quarter 1, to 2010, quarter 4. Our error correction models permit asymmetric pass-through for currency appreciations and depreciations over three horizons of interest: on impact, in the short run and in the long run. We find that depreciations are typically passed through more strongly than appreciations in the long run, suggesting that exporters may exert a degree of long-run pricing power. This asymmetry is stronger in economies which are more import dependent but is moderated by freedom to trade and a positive output gap. Given that this pass-through asymmetry is welfare reducing for consumers in the destination market, a key macroeconomic implication is that import-dependent economies, in particular, can benefit from trade liberalization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".