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Record W4396720914 · doi:10.3368/jhr.0223-12761r2

Who Paid Los Angeles’ Minimum Wage?

2024· article· es· W4396720914 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.

Bibliographic record

VenueThe Journal of Human Resources · 2024
Typearticle
Languagees
FieldSocial Sciences
TopicUrban, Neighborhood, and Segregation Studies
Canadian institutionsNickel Institute
Fundersnot available
KeywordsMinimum wageWageLabour economicsEconomicsDemographic economics

Abstract

fetched live from OpenAlex

<h3>Abstract</h3> We analyze how the incidence of minimum wage increases falls on customers by studying two minimum wages in Los Angeles County that remained unequal for over five years. Because Los Angeles’ municipal borders are porous, the case resembles a natural experiment. Using a novel 5-year price survey dataset, we show that the full incidence of the higher minimum wage fell on customers in high-income neighborhoods, and that none of the incidence fell on customers in low-wage neighborhoods. Additionally, exposure to competitors subject to a lower minimum wage mitigated these effects, and lower-wage restaurants exposed to higher-wage competitors received monopoly rents.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.329
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.320
Teacher spread0.290 · 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