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Record W4383163047 · doi:10.1016/j.latcb.2023.100100

Heterogeneous exchange rate pass-through in Mexico: What drives it?

2023· article· en· W4383163047 on OpenAlexaboutno aff
Diego Solórzano

Bibliographic record

VenueLatin American Journal of Central Banking · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsnot available
Fundersnot available
KeywordsExchange-rate pass-throughEconomicsExchange rateQuarter (Canadian coin)Monetary economicsEconometricsInterest rateProduct (mathematics)Price elasticity of demandInternational economicsMicroeconomicsGeographyMathematics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.159
Threshold uncertainty score0.826

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.050
GPT teacher head0.242
Teacher spread0.192 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2023
Admission routes1
Has abstractyes

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