What Accounts for the Union Member Advantage in Voter Turnout? Evidence from the European Union, 2002-2008
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".