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Record W2614034046 · doi:10.1111/caje.12262

Managerial capital, occupational choice and inequality in a global economy

2017· article· en· W2614034046 on OpenAlexvenueno aff
Elias Dinopoulos, Bulent Unel

Bibliographic record

VenueCanadian Journal of Economics/Revue canadienne d économique · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsnot available
Fundersnot available
KeywordsLabour economicsEconomicsUnemploymentProductivityWelfareIncome distributionEconomic inequalityDistribution (mathematics)Capital (architecture)Human capitalInequalityMarket economyMacroeconomics

Abstract

fetched live from OpenAlex

Abstract This study proposes a simple theory of trade with endogenous firm productivity, occupational choice and income inequality. Individuals with different managerial talent choose to become entrepreneurs or workers. Entrepreneurs enhance firm productivity by investing in managerial capital. The model generates three income classes: low‐income workers facing the prospect of unemployment, middle‐income entrepreneurs managing domestic firms and high‐income entrepreneurs managing global firms. Trade liberalization policies raise unemployment and improve welfare. A reduction in per‐unit trade costs raises top incomes and generates labour‐market polarization. A reduction in fixed exporting costs has an ambiguous effect on top incomes and personal income distribution. Policies reducing labour‐market frictions or the costs of managerial‐capital acquisition create more jobs and improve welfare. The income distributional effects of labour‐market policies depend on which policy is implemented.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.247
GPT teacher head0.215
Teacher spread0.032 · 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 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

Citations20
Published2017
Admission routes1
Has abstractyes

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