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A Macroeconomic Analysis of EU Accession under Alternative Monetary Policies

2003· article· en· W2146527967 on OpenAlexaff
Michael B. Devereux

Bibliographic record

VenueJCMS Journal of Common Market Studies · 2003
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAccessionEconomicsExchange rateMonetary economicsMonetary policyInflation (cosmology)Fiscal policyInternational economicsInflation targetingBoomMacroeconomicsEuropean union

Abstract

fetched live from OpenAlex

Abstract This article provides an analytical discussion of the adjustment to EU accession for an economy under alternative assumptions about monetary policy rules. The post‐accession phase is characterized by rapid capital inflows and real exchange rate appreciation. If accession is combined with membership of the euro area and a pegged exchange rate, then the post‐accession period exhibits excessive foreign borrowing, high wage inflation, an excessive stock market boom, and much too rapid growth in the non‐traded sector at the expense of the exportable goods sector. Alternative monetary policies can be used to eliminate the inefficiencies of the post‐accession adjustment, but some bring real costs in terms of lower growth and unemployment. We find that the best policy is one of flexible inflation targeting with some weight on exchange rate stability. In the absence of exchange rate adjustment, fiscal policy could be used, but this requires complicated time‐varying expenditure taxes. While the analytical discussion emphasizes the benefits of exchange rate adjustment, a later section of the article explores some more recent arguments regarding non‐traditional costs of exchange rate volatility.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0060.002
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.122
GPT teacher head0.321
Teacher spread0.199 · 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 designSimulation or modeling
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

Citations12
Published2003
Admission routes1
Has abstractyes

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