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Record W3043371987

The Great Separation:Top Earner Segregation at Work in High-Income Countries

2020· preprint· en· W3043371987 on OpenAlexaboutno aff
Olivier Godechot

Bibliographic record

VenueEconstor (Econstor) · 2020
Typepreprint
Languageen
FieldSocial Sciences
TopicUrban, Neighborhood, and Segregation Studies
Canadian institutionsnot available
FundersNorges ForskningsrådAlexander von Humboldt-StiftungAgence Nationale de la RechercheNational Science Foundation
KeywordsSeparation (statistics)Work (physics)EconomicsDemographic economicsLabour economicsMathematicsStatisticsPhysicsThermodynamics
DOInot available

Abstract

fetched live from OpenAlex

Analyzing linked employer-employee panel administrative databases, we study the evolving isolation of higher earners from other employees in eleven countries: Canada, Czechia, Denmark, France, Germany, Hungary, Japan, Norway, Spain, South Korea, and Sweden. We find in almost all countries a growing workplace isolation of top earners and dramatically declining exposure of top earners to bottom earners. We compare these trends to segregation based on occupational class, education, age, gender, and nativity, finding that the rise in top earner isolation is much more dramatic and general across countries. We find that residential segregation is also growing, although more slowly than segregation at work, with top earners and bottom earners increasingly living in different distinct municipalities. While work and residential segregation are correlated, statistical modeling suggests that the primary causal effect is from work to residential segregation. These findings open up a future research program on the causes and consequences of top earner segregation.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.526
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.002
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.024
GPT teacher head0.288
Teacher spread0.264 · 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; both teacher heads agree on what is shown here.

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

Citations4
Published2020
Admission routes1
Has abstractyes

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