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Use when running pay equity audits — covers method, privilege, axes to examine, remediation, and counsel involvement.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
| name | pay-equity-analysis |
| description | Use when running pay equity audits — covers method, privilege, axes to examine, remediation, and counsel involvement. |
Pay equity work has serious legal implications. Conduct pay equity analyses under attorney-client privilege when remediation will follow. Engage qualified employment counsel before running the analysis, not after. The agent flags; counsel decides.
Disparate findings without privilege can be discoverable in litigation. Privileged analyses can be remediated discreetly.
The evidence on unexamined pay distributions is consistent: gaps emerge and grow without intentional examination. Hiring biases compound (salary history, negotiation premium, manager pressure). Performance rating biases affect comp adjustments. Time at level biases delay promotion-driven raises.
Regular pay equity analysis is the audit; it doesn't fix the underlying mechanisms — but it surfaces them.
Define comparable groups: similar level, function, geography, tenure
Variables to include:
Run regression: comp ~ tenure + level + experience + performance + geo + demographic
Examine demographic coefficients:
Investigate before assuming: legitimate explanations include role composition, recent hire timing, performance rating differences. But: those explanations may themselves contain bias (rating bias, promotion bias).
For statistical reliability and confidentiality: n ≥ 5–8 per cell. Intersectional cuts often run into cell-size limits — surface in aggregate or qualitatively when statistics aren't reliable.
When unexplained gaps exist:
Pay equity is one piece. Also examine:
A pay equity audit that ignores promotion equity catches only half the picture.
After a properly-run analysis:
For larger companies (1000+): in-house people analytics + counsel partnership For mid-size (100–1000): vendor (Syndio, Trusaic, similar) + counsel review For small (<100): often too small for statistically meaningful regression; use comparator review with counsel
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