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Unions without rents: the curious economics of faculty unions

2004· article· en· W2165960553 on OpenAlexaffvenueabout
Arthur J. Hosios, Aloysius Siow

Bibliographic record

VenueCanadian Journal of Economics/Revue canadienne d économique · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEarningsEconomic rentPolitical scienceWageHumanitiesRedistribution (election)EconomicsLabour economicsArtAccountingMicroeconomics

Abstract

fetched live from OpenAlex

Abstract. We show that Canadian faculty unions have had no effect on university revenues, only a small positive effect on earnings, a negative effect on research output, and lead to earnings redistributions across disciplines and ranks. We argue that faculty unions have a negligible positive (and often negative) effect on average faculty wages, because a union that promotes the median faculty member's welfare negotiates wage redistributions and more onerous teaching conditions, despite the negative impact these changes have on research output. Average wages may rise, but only because non-unionized universities trade off wages against the non-pecuniary benefits from research and teaching. JEL classification: I20, J51, J54

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)0.003
metaresearch head score (Gemma)0.013
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.980
Threshold uncertainty score0.389

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0090.016
Scholarly communication0.0070.006
Open science0.0010.004
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0290.001

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 head0.181
GPT teacher head0.285
Teacher spread0.104 · 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

Citations9
Published2004
Admission routes3
Has abstractyes

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