Gender and race differences on incentivized personality measures
Bibliographic record
Abstract
Introduction Employment screening based on personalities gives applicants incentives to misrepresent themselves. Studies of group differences on personality measures primarily examine differences on measures taken without incentives for misrepresentation. Incentives may matter for group differences for at least two reasons. First, groups with different unincentivized means have different scope to distort their responses—differences in “opportunity-to-fake.” Second, groups may differ in their notions of what constitutes a desirable personality. Methods We use a within-subject laboratory experiment to examine group differences on Big Five measures. Subjects first responded without incentives. A week later, subjects viewed a job ad and were informed that bonuses would be paid to subjects best fitting the hiring criteria. The treatments varied the information in the ad about desired personality traits. Results Controlling for opportunity-to-fake, we find evidence of racial but not gender differences in faking. Incentives attenuate gender differences on unincentivized personality measures but lead to racial differences where no differences exist on unincentivized measures. In every instance where a gap emerged on an incentivized measure where none existed on the unincentivized measure, the minority group would be disadvantaged were hiring based on the measure. We assess whether protected groups would be adversely impacted from selection on incentivized measures using the realized group differences in the experiment and the Equal Employment Opportunity Commission's “four-fifth's” rule. We find no evidence that women would be adversely affected by selection on incentivized personality measures, but racial minorities would be adversely impacted in the majority of trait-treatment comparisons. Discussion Given the prevalence of personality testing in employment screening, more research is needed on how the incentives for response distortion present in hiring influence racial differences on personality measures and whether any such differences influence hiring outcomes.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".