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Record W2753367413 · doi:10.12735/jfe.v6n1p22

Unemployment Orthodoxy: Fiscal or Monetary Policy? Case Study of France

2017· article· en· W2753367413 on OpenAlexvenueno aff
Nahid Kalbasi Anaraki

Bibliographic record

VenueJournal of Finance & Economics · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsnot available
Fundersnot available
KeywordsOrthodoxyUnemploymentEconomicsMonetary policyKeynesian economicsFiscal policyMonetary economicsMacroeconomicsPhilosophyTheology

Abstract

fetched live from OpenAlex

France's economy has suffered from an unprecedented unemployment rate of above 10% over the past decade. The topic is widely debated among economists; while monetary economists argue contractionary monetary policy and austerity plans are the roots of high unemployment rate, New Keynesians believe fiscal policy and high corporate tax rates are the roots of problem. Lucas critique conjectures that monetary policy has only short term effects on real variables including unemployment. This study tests the hypothesis whether fiscal policy plays a more important role than monetary policy in shaping unemployment in France. The paper implements several econometric models to find out which group of policy variables is more effective in combating unemployment. Indeed, the study tests the hypothesis whether New Keynesian models have a better prediction power in explaining unemployment rate than New Classical models. Implementing quarterly data for the period of 1980-2015 and using OLS and GMM techniques the study finds out fiscal policy variables are the most important factors in shaping unemployment rate in France, supporting New Keynesian proposition.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.174
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.109
GPT teacher head0.293
Teacher spread0.183 · 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 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

Citations0
Published2017
Admission routes1
Has abstractyes

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