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Record W4381194222 · doi:10.3386/w31366

The Spillover Effects of Top Income Inequality

2023· report· en· W4381194222 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueNational Bureau of Economic Research · 2023
Typereport
Languageen
FieldSocial Sciences
TopicRegional Development and Policy
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaInstitut für Arbeitsmarkt- und BerufsforschungUniversität ZürichEuropean CommissionMinisterio de Economía y CompetitividadW.E. Upjohn Institute for Employment Research
KeywordsSpillover effectEconomic inequalityInequalityEconomicsDemographic economicsMathematicsMicroeconomics

Abstract

fetched live from OpenAlex

Top income inequality in the United States has increased considerably within occupations.This phenomenon has led to a search for a common explanation.We instead develop a theory where increases in income inequality originating within a few occupations can "spill over" through consumption into others.We show theoretically that such spillovers occur when an occupation provides non-divisible services to consumers, with physicians our prime example.Examining local income inequality across U.S. regions, the data suggest that such spillovers exist for physicians, dentists, and real estate agents.Estimated spillovers for other occupations are consistent with the predictions of our theory.

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.

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)0.015
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.777
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.519
GPT teacher head0.607
Teacher spread0.088 · 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