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Living Wage Policies at the San Francisco Airport: Impacts on Workers and Businesses

2004· article· en· W1606459157 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

VenueIndustrial Relations A Journal of Economy and Society · 2004
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicAviation Industry Analysis and Trends
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsEarningsWageLiving wageRevenueLabour economicsWork (physics)Hourly wageEconomicsCost of livingBusinessDemographic economicsEconomic growthFinance

Abstract

fetched live from OpenAlex

This paper evaluates the costs, benefits and related impacts of living wage policies implemented at the San Francisco Airport (SFO). Unlike other living wage ordinances, the policies at SFO cover a large proportion of the low‐wage labor force in a distinct labor market. The authors find that about 73 percent of the ground‐based non‐managerial workers at SFO received substantial wage increases as a direct or indirect result of the policies; the proportion of these workers earning under $10 per hour fell from 55 percent to 5 percent, significantly reducing earnings inequality. Other benefits to workers included enhanced health benefits and an arrest of declines in quality of life indices. The costs of the policies to employers amounted to an average of 0.7 percent of fare revenue, or $1.42 per airline passenger. We observe a series of dynamic adjustments that reduced those costs, including dramatically reduced turnover, improved worker morale and greater work effort. We find some limited evidence of worker‐worker substitution, but no evidence of employment decline.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.118
Threshold uncertainty score0.409

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.047
GPT teacher head0.241
Teacher spread0.194 · 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