Bibliographic record
Abstract
In this paper, we focus on the pass-through of exchange rate fluctuations into prices of final goods and services and examine whether contrasting pass-through rates are associated with regional and/or product-specific characteristics. Using CPI micro-data from 2002 to 2010, we estimate industry-specific rates of pass-through across regions in Mexico. By looking at within-country price responses, we alleviate shortcomings of cross-country studies that assess pass-through determinants. The results indicate that pass-through rates differ across regions and industries: low pass-through regions exhibit nearly one-quarter of the elasticity shown by high pass-through regions after twelve months. This heterogeneity prevails at longer horizons. The findings suggest that full pass-through is rejected for all regions and industries. Most of these differences in transmission rates are explained by regional and product characteristics: demand conditions, economic development, distance to the US border, import intensity, price change dispersion and expenditure share play a clear role in increasing pass-through, whereas market density dampens pass-through rates. The evidence confirms pricing-to-market theories and has implications for the design of monetary and exchange rate 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".