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Record W4392003120 · doi:10.1080/01446193.2024.2314079

Prevailing wages, school construction costs, and bids by out-of-state contractors: evidence from the Minneapolis–Saint Paul metropolitan area

2024· article· en· W4392003120 on OpenAlexaboutno aff
Kevin Duncan, Adam Case, Frank Manzo

Bibliographic record

VenueConstruction Management and Economics · 2024
Typearticle
Languageen
FieldDecision Sciences
TopicAuction Theory and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsMetropolitan areaSAINTState (computer science)Labour economicsEconomicsBusinessEngineeringArtHistoryComputer scienceArt historyArchaeology

Abstract

fetched live from OpenAlex

In the United States, prevailing wage laws authorize minimum remuneration by locality and occupation for public construction. The policy’s goal of leveling the playing field between local and lower wage, nonlocal builders is shared by fair wage policies in Canada and posted worker rules in the European Union. This is the first paper to test if the wage policy reduces bid disparities between these two types of contractors. The statistical analysis of over 600 subcontractor bids for schools built within the Minnesota’s largest metropolitan area examines differences in low, winning bids between Minnesota-based contractors and those from neighboring states with lower average construction wages. Findings indicate that prevailing wage requirements substantially reduce bid disparity between in- and out-of-state subcontractors. Additional results illustrate estimation issues related to measuring the influence of prevailing wage laws and unionized construction labor on construction costs.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.188
Threshold uncertainty score0.375

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.053
GPT teacher head0.308
Teacher spread0.255 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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