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Record W3011350033 · doi:10.1016/j.jinteco.2022.103570

Invoicing and the dynamics of pricing-to-market: Evidence from UK export prices around the Brexit referendum

2022· article· en· W3011350033 on OpenAlexfundno aff
Giancarlo Corsetti, Meredith A. Crowley, Lu Han

Bibliographic record

VenueJournal of International Economics · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsnot available
FundersEconomic and Social Research CouncilInstitute of Social and Economic Research, Memorial University of NewfoundlandEuropean Central BankUniversity of Wisconsin-Madison
KeywordsBrexitReferendumInvoiceEconomicsDepreciation (economics)Monetary economicsCurrencyExchange rateExchange-rate pass-throughInternational economicsProduct (mathematics)Shock (circulatory)European unionMarket economy

Abstract

fetched live from OpenAlex

We provide micro-econometric evidence that, following the large and persistent sterling depreciation after the Brexit referendum, on impact, exchange rate pass-through (ERPT) was complete for transactions invoiced in producer currency and low for sales invoiced either in a vehicle or in the destination market currency. Yet these differences strikingly narrowed within six quarters. A weaker currency did not translate into a persistent gain in price competitiveness for UK exports. At a granular level we find that UK exporters invoice in multiple currencies—even when shipping a product to the same destination—and switch currencies over time. Remarkably, we fail to detect significant changes in the relative shares of invoicing currencies in response to the Brexit shock. Last but not least, we find that UK firms price-to-market, i.e., adjust markups to bilateral exchange rate and CPI movements, only when they invoice sales in the destination-market currency.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.030
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.066
GPT teacher head0.242
Teacher spread0.176 · 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 source (direct Gemma or distilled Codex), 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

Citations34
Published2022
Admission routes1
Has abstractyes

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