asgard-ai-platform Compensation Benchmarking
Overview
Compensation benchmarking compares internal pay levels against external market data to assess competitiveness. Uses compa-ratio (actual pay / market midpoint) and percentile positioning. Informs salary band design, pay adjustments, and equity analysis.
When to Use
Trigger conditions:
- Evaluating whether current salaries are competitive with the market
- Designing or updating salary bands and pay structures
- Identifying pay equity gaps across demographics or roles
When NOT to use:
- For individual performance-based pay decisions (use performance management)
- When no market data is available (need at least survey benchmarks)
Algorithm
IRON LAW: Benchmarking Is Only Valid With COMPARABLE Jobs
Matching by job TITLE alone is unreliable — "Senior Engineer" means
vastly different things at different companies. Match by: job content
(duties, scope), level (IC vs manager, experience band), industry,
geography, and company size. Poor job matching produces misleading
market rates.
Phase 1: Input Validation
Collect: internal compensation data (base, bonus, equity), market survey data (P25, P50, P75 by role), job matching between internal roles and survey benchmarks. Gate: Jobs properly matched, survey data current (< 18 months).
Phase 2: Core Algorithm
- Match internal jobs to market benchmarks by content, level, and scope
- Age survey data to current date: apply projected market movement rate
- Compute compa-ratio per employee: actual base / market P50
- Compute percentile positioning: where does actual pay fall in market distribution
- Analyze: by department, level, tenure, demographics for equity gaps
Phase 3: Verification
Check: compa-ratios cluster around 0.85-1.15 (normal range). Flag outliers (< 0.80 underpaid, > 1.20 overpaid). Test demographic equity. Gate: Distribution reasonable, equity analysis completed.
Phase 4: Output
Return benchmarking results with band recommendations.
Output Format
{
"summary": {"avg_compa_ratio": 0.97, "below_band_pct": 12, "above_band_pct": 8},
"by_role": [{"role": "Software Engineer", "market_p50": 1800000, "avg_actual": 1750000, "compa_ratio": 0.97}],
"equity_flags": [{"dimension": "gender", "gap_pct": 3.2, "statistically_significant": true}],
"metadata": {"employees": 500, "survey_source": "Mercer", "survey_date": "2025-H2"}
}
Examples
Sample I/O
Input: 50 engineers, market P50=NT$1.8M, actual range NT$1.5M-2.1M Expected: Avg compa-ratio ~0.97, some below-band employees flagged for adjustment.
Edge Cases
| Input | Expected | Why |
|---|---|---|
| Hot market (tech boom) | Market data rapidly outdated | Apply higher aging factor |
| Remote work mixed | Location-adjusted bands needed | SF vs Taipei market rates differ 2-3x |
| Small company, no survey match | Use broader industry proxies | Imperfect but better than nothing |
Gotchas
- Total compensation: Base salary benchmarking alone misses equity, bonuses, and benefits. Compare total comp for accurate positioning.
- Survey data lag: Published surveys reflect data collected 6-18 months ago. In fast-moving markets, age the data forward.
- Internal equity vs external competitiveness: Aligning with market may create internal inequities (new hire paid more than tenured employee). Balance both.
- Geographic differentials: Remote work complicates location-based pay. Define a clear policy: pay by HQ location, employee location, or hybrid.
- Pay equity legal risk: Unexplained demographic pay gaps expose legal liability. Conduct regression-based equity analysis controlling for legitimate factors (experience, performance, level).
References
- For salary band design methodology, see
references/band-design.md - For pay equity regression analysis, see
references/pay-equity.md