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Inflation and Economic Performance in the CFA Franc Zone

2013· book-chapter· en· W2475864927 on OpenAlexaff
Komlan Fiodendji, Bernadette Dia Kamgnia, Nasser Ary Tanimoune

Bibliographic record

VenueAdvances in finance, accounting, and economics book series · 2013
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsEconomicsInflation (cosmology)Economic and monetary unionMonetary policyMonetary economicsEconomic stabilityInvestment (military)Inflation targetingMacroeconomicsInternational economicsEuropean unionPolitical science

Abstract

fetched live from OpenAlex

This chapter examines the relationship between inflation and economic performance in the CFA franc zone over the period 1991-2009 and studies the mechanism through which inflation affects long-term economic growth. Using a threshold model, the evidence strongly supports the view that the relationship between inflation and economic growth is nonlinear with a unique threshold. The most striking difference between West Africa Economic and Monetary Union (WAEMU) zone and Central African Economic and Monetary Community (CEMAC) zone is that the coefficients of inflation are all significantly negative for all inflation regimes, while for WAEMU zone, the coefficient of inflation is positive for the low and high inflation regimes. Further investigation suggests that for the WAEMU countries, but not for the CEMAC countries, the level of investment is the channel through which inflation nonlinearly affects economic growth. One of the main contributions of this chapter is to enable the policymakers, specifically central bankers in each zone, to concentrate on those policies that keep the target of inflation, which may be helpful for the achievement of sustainable economic growth. Low inflation is also helpful for minimizing the uncertainties in the financial market, which in turn boost the investment in the country.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.872
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.005
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.015
GPT teacher head0.187
Teacher spread0.172 · 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.

Study designTheoretical or conceptual
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

Citations0
Published2013
Admission routes1
Has abstractyes

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