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Record W2077751895 · doi:10.7202/1028110ar

What Accounts for the Union Member Advantage in Voter Turnout? Evidence from the European Union, 2002-2008

2015· article· en· W2077751895 on OpenAlexaffvenue
Alex Bryson, Rafael Gómez, Tobias Kretschmer, Paul Willman

Bibliographic record

VenueRelations industrielles · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsUniversity of Toronto
FundersEconomic and Social Research CouncilNorges Forskningsråd
KeywordsVotingBallotTurnoutWageIncentiveCorporate governanceEconomicsLabour economicsMonopolyPolitical scienceDemographic economicsPolitical economyMicroeconomicsPoliticsLawFinance

Abstract

fetched live from OpenAlex

Across countries, union membership and voter turnout are highly correlated. In unadjusted terms, union members maintain a roughly 0.10 to 0.12 point gap in voting propensity over non-members. We motivate empirically and propose a model—with three causal channels—that explains this correlation and then empirically tests for the contribution of each channel to the overall union voting gap. The first channel by which union members are more likely to vote is through the so-called "monopoly-face" of unionism (i.e., unionization increases wages for members and higher incomes are a significant positive determinant of voting). The second is the so-called "social custom" model of unionism, which argues that union co-worker peer pressure creates incentives to vote amongst members for the purpose of having cast a ballot or being seen at the voting poll. The third and final channel is based on the "voice-face" of unionism whereby employees who are (or have been) exposed to the formalities of collective bargaining and union representation at the workplace are also more likely to increase their attachment to structures of democratic governance in society as well. We test to see how much of the raw "union voting premium" is accounted for by these three competing channels, using contemporary data from 29 European countries. We find that all three channels are at work, with voice the dominant effect (half of the overall gap attributed to this channel) and the other two (monopoly and social custom), each accounting for approximately one-fourth of the overall union voting gap.

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 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.482
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.075
GPT teacher head0.324
Teacher spread0.249 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations19
Published2015
Admission routes2
Has abstractyes

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