MétaCan
Menu
← Back to cohort
Record W4409422060 · doi:10.3386/w33687

Labor Market Polarization and Inequality: A Roy Model Perspective

2025· report· en· W4409422060 on OpenAlexfundno aff
Andrés Erosa, Luisa Fuster, Gueorgui Kambourov, Richard Rogerson

Bibliographic record

VenueNational Bureau of Economic Research · 2025
Typereport
Languageen
FieldEconomics, Econometrics and Finance
TopicLabor market dynamics and wage inequality
Canadian institutionsnot available
FundersAgencia Estatal de InvestigaciónSocial Sciences and Humanities Research Council of Canada
KeywordsPerspective (graphical)InequalityPolarization (electrochemistry)EconomicsNeoclassical economicsSociologyMathematical economicsLabour economicsComputer scienceMathematicsArtificial intelligenceMathematical analysisChemistry

Abstract

fetched live from OpenAlex

We study the forces driving polarization and higher wage inequality since 1980 using a structural model of occupation choice in the tradition of Roy (1951).In our model, changes in relative occupational skill prices proxy for changes in relative demand for occupational labor services.Our analysis yields three main findings.First, although changes in skill prices have quantitatively important effects on employment shares and mean wages, they play essentially no role in accounting for the sharp rise in wage inequality.Second, changes in relative wages are driven by changes in higher order moments and do not reflect changes in relative demand.Third, changes in the variance of idiosyncratic within occupation productivity are the dominant factor behind the sharp rise in inequality.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.246
GPT teacher head0.459
Teacher spread0.212 · 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 designSimulation or modeling
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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueNational Bureau of Economic Research→Same topicLabor market dynamics and wage inequality→French-language works237,207→