Union Participation, Job Satisfaction, and Employee Turnover: An Event‐History Analysis of the Exit‐Voice Hypothesis
Bibliographic record
Abstract
This study examines the exit‐voice hypothesis by applying event‐history analysis to data from a sample of 674 unionized public school teachers from 405 schools. Union participation (i.e., voice) and job satisfaction had significant negative main effects on turnover. In contrast to the original formulation of the exit‐voice hypothesis, a test for interaction revealed that union participation had significant negative effects on quit behavior for members displaying both low and high satisfaction. The existing conclusions regarding the role of unions in reducing employee quits by providing voice mechanisms implicitly assume that union members actually use these mechanisms. Mechanisms such as attending union meetings and serving on a committee provide opportunities for members to express how they feel about their wages and working conditions. In this article we examine the influences of union participation and job satisfaction on individuals’ decisions to quit working for an organization using event‐history analysis. Unlike existing research, the present study focuses on member participation in a range of union activities (i.e., voting in union elections, attending union meetings, serving in a union office or on a committee, and seeking assistance from the union) to analyze the effect of union voice on employee quits. 1 We further investigate how union participation and job satisfaction may interact to influence employee turnover over time, controlling for demographic, job‐related, environmental, and contextual variables across 405 research sites.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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.
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".