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Record W653542324 · doi:10.1257/app.20160510

Why Has Urban Inequality Increased?

2018· article· en· W653542324 on OpenAlexaff
Nathaniel Brandt Baum-Snow, Matthew Freedman, Ronni Pavan

Bibliographic record

VenueAmerican Economic Journal Applied Economics · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional Economics and Spatial Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEconomicsComplementarity (molecular biology)Wage inequalityInequalityEconomies of agglomerationContext (archaeology)WageCapital (architecture)Labour economicsMicroeconomicsGeography

Abstract

fetched live from OpenAlex

This paper examines mechanisms driving the more rapid increases in wage inequality in larger cities between 1980 and 2007. Production function estimates indicate strong evidence of capital–skill complementarity and increases in the skill bias of agglomeration economies in the context of rapid skill-biased technical change. Immigration shocks are the source of identifying variation across cities in changes to the relative supply of skilled versus unskilled labor. Estimates indicate that changes in the factor biases of agglomeration economies rationalize at least 80 percent of the more rapid increases in wage inequality in larger cities. (JEL J24, J31, O33, R23)

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)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0010.002
Scholarly communication0.0030.004
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0110.001

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 head0.028
GPT teacher head0.214
Teacher spread0.186 · 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

Citations145
Published2018
Admission routes1
Has abstractyes

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