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Record W2947657566 · doi:10.5539/ijef.v11n7p45

Profit Shares as Virtual Equity: Short-Run Isomorphism of Share & Wage Systems

2019· article· en· W2947657566 on OpenAlexvenueno aff
Vikram Kumar

Bibliographic record

VenueInternational Journal of Economics and Finance · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Theory and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsProfit sharingEquity (law)ExternalityMicroeconomicsWageLabour economicsFinance

Abstract

fetched live from OpenAlex

An important argument in favor of public policy to promote profit-sharing arrangements – and one that distinguishes it from the canonical wage system – is that it creates a macroeconomic externality in the form of short-run excess demand for labor. In this paper we provide insights new in the literature to show that the two systems are isomorphic. We consider the most plausible basis for the distribution of the profits between labor and capital to be one that is conceptually consistent with the functional role of labor as a residual claimant. We postulate a sharing rule that is based on the recognition that in a profit-sharing system a portion of labor’s contribution is a form of equity – virtual equity – analogous to shareholder equity. With this interpretation, if the share parameter of worker pay is endogenously determined then we show that, eschewing any independent productivity effects, a profit-sharing system is not consistent with said macroeconomic externality. This analysis provides a framework to assess recent public policy initiatives and legislative proposals on both sides of the Atlantic, arguing that their advocation can be based on distributive but not efficiency grounds.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0040.007
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.000

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 head0.045
GPT teacher head0.270
Teacher spread0.226 · 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 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
Published2019
Admission routes1
Has abstractyes

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