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Record W7029533213

Is aggregate demand wage-led or profit-led?
\nNational and global effects

2012· report· en· W7029533213 on OpenAlexaboutno aff

Bibliographic record

VenueGoldsmiths (University of London) · 2012
Typereport
Languageen
FieldAgricultural and Biological Sciences
TopicMediterranean and Iberian flora and fauna
Canadian institutionsnot available
Fundersnot available
KeywordsWageWage shareCompetition (biology)Aggregate demandLow wageWage growthGlobal imbalances
DOInot available

Abstract

fetched live from OpenAlex

This paper estimates the effects of a change in the wage share on growth in the G20 countries using a post-Keynesian/post-Kaleckian model, analyses the interactions among different economies, and calculates the global multiplier effects of a simultaneous decline in the wage share. At the national level, a decrease in the wage share leads to lower growth in the euro area, Germany, France, Italy, UK, US, Japan, Turkey, and Korea, i.e. these economies are wage-led, whereas it stimulates growth in Canada, Australia, Argentina, Mexico, China, India, and South Africa; thus the latter group of countries are profit-led. However, a simultaneous decline in the wage share in all these countries leads to a decline in global growth. Furthermore, Canada, Argentina, Mexico, and India also contract when they decrease their wage-share along with their trading partners. Thus the global economy in aggregate is wage-led. The policy conclusions of the paper shed light on the limits of strategies of international competitiveness based on wage competition in a highly integrated global economy, and point at the possibilities to correct global imbalances via coordinated macroeconomic and wage policy, where domestic demand plays an important role. There is room for a wage-led recovery in the global economy based on a simultaneous increase in the wage shares, where global GDP as well as all individual countries can grow.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.768
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.227
Teacher spread0.197 · 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 designOther design
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
Published2012
Admission routes1
Has abstractyes

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