When is More Better? The Effects of Racial Composition on Voluntary Turnover
Bibliographic record
Abstract
This study examines the relationship between racial composition and individual, voluntary turnover for minorities (i.e., Asians, blacks, and Hispanics) in a large organization. We present a critical test for two sets of contrasting predictions. The first draws on similarity attraction, social contact, and social identity theories to suggest that working with racially similar others enhances the work environment in terms of perceived career opportunities, mentoring relationships, and network ties, all of which would increase the likelihood of remaining in an organization. The contrasting predictions draw on group competition and group threat theories and propose that working with racially similar others might increase competition for resources and generate a backlash effect against minorities that would induce their turnover. We suggest the paradox that these two approaches might be compatible if the effect of demographic composition is nonlinear. Our data analyses show that individuals' likelihood of turnover decreases as the proportion of employees in a job from one's own race increases. Furthermore, this relationship is nonlinear: Members of minority groups with very small representation benefited more from the increased presence of their own race than minorities who already had a substantial presence. This finding suggests a potential backlash effect at higher minority proportions. Results also show that turnover decreases as the proportion of employees from one's own race increases in the level above an employee's job. Overall, these findings suggest that working with others of the same race reduces the likelihood of minority exits. Interestingly, the proportion of other minorities in a job has a marginally significant, negative effect on employees' voluntary turnover. Thus, increasing racial diversity from one's own race and other minorities appears to strengthen minority workforce retention.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".