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Monetary union in West Africa: who might gain, who might lose, and why?

2005· article· en· W3123355417 on OpenAlexaffvenue
Xavier Debrun, Paul R. Masson, Catherine Pattillo

Bibliographic record

VenueCanadian Journal of Economics/Revue canadienne d économique · 2005
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSeigniorageEconomicsCurrencyMonetary economicsMonetary policyPrice of stabilityIncentiveInflation (cosmology)WelfareCurrency unionInternational economicsMarket economy

Abstract

fetched live from OpenAlex

Abstract. We develop a model in which governments’ financing needs exceed the socially optimal level because public resources are diverted to serve the narrow interests of the group in power. From a social welfare perspective, this results in undue pressure on the central bank to extract seigniorage. Monetary policy also suffers from an expansive bias, owing to the authorities’ inability to precommit to price stability. Such a conjecture about the fiscal‐monetary policy mix appears quite relevant in Africa, with deep implications for the incentives of fiscally heterogeneous countries to form a currency union. We calibrate the model to data for West Africa and use it to assess proposed ECOWAS monetary unions. Fiscal heterogeneity indeed appears critical in shaping regional currency blocs that would be mutually beneficial for all their members. In particular, Nigeria's membership in the configurations currently envisaged would not be in the interests of other ECOWAS countries unless it were accompanied by effective containment on Nigeria's financing needs. JEL classification: E58, E61, E62, F33

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0040.004
Open science0.0000.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.087
GPT teacher head0.167
Teacher spread0.079 · 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

Citations132
Published2005
Admission routes2
Has abstractyes

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