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Record W4294734212 · doi:10.1086/742712

The Impact of Unions on Nonunion Wage Setting: Threats and Bargaining

2022· report· en· W4294734212 on OpenAlexaff
David A. Green, Benjamin Sand, Iain G. Snoddy

Bibliographic record

VenueJournal of Political Economy · 2022
Typereport
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsGroup for Research in Decision AnalysisYork UniversityUniversity of British Columbia
Fundersnot available
KeywordsNonunionWageLabour economicsWage bargainingLow wageBusinessCollective bargainingEconomicsMedicine

Abstract

fetched live from OpenAlex

In this paper we provide new estimates of the impact of unions on nonunion wage setting. We allow the presence of unions to affect nonunion wages both through the typically discussed channel of nonunion firms emulating union wages in order to fend off the threat of unionisation and through a bargaining channel in which nonunion workers use the presence of union jobs as part of their outside option. We specify these channels in a search and bargaining model that includes union formation and, in our most complete model, the possibility of nonunion firm responses to the threat of unionisation. Our results indicate an important role played by union wage spillovers in lowering wages over the 1980-2010 period. We find de-unionisation can account for 38% of the decline in the mean hourly wage between 1980 and 2010, with two-thirds of that effect being due to spillovers. Both the traditional threat and bargaining channels are operational, with the bargaining channel being more important.

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)0.006
metaresearch head score (Gemma)0.019
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 score0.021
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0020.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0210.002

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 head0.045
GPT teacher head0.391
Teacher spread0.345 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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