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Record W1555924088 · doi:10.34989/sdp-2009-2

Labour Shares and the Role of Capital and Labour Market Imperfections

2021· preprint· en· W1555924088 on OpenAlexaff
Lena Suchanek

Bibliographic record

VenueEconstor (Econstor) · 2021
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicLabor market dynamics and wage inequality
Canadian institutionsBank of Canada
Fundersnot available
KeywordsLabour economicsEconomicsCapital (architecture)Capital marketPhenomenonEmpirical evidenceGeography

Abstract

fetched live from OpenAlex

In continental Europe, labour shares in national income have exhibited considerable variation since 1970. Empirical and theoretical research suggests that the evolution of labour markets and labour market imperfections can, in part, explain this phenomenon. The author analyzes the role of capital market imperfections in the determination of the distribution of national income, comparing European and Anglo-Saxon countries. She uses a simple general-equilibrium model to trace the effects of credit and labour market imperfections on factor shares. Simulations indicate that improvements in capital markets can explain lower labour shares. An increase in the degree of employee power results in higher labour shares. Regression results confirm the author's findings. Improvements in credit markets and decreasing employee bargaining power have contributed to shrinking labour shares, especially in Europe. Openness is a negative determinant of labour shares.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.141
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.009
GPT teacher head0.204
Teacher spread0.195 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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