\"algo-hr-compensation\"

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

1. Match internal jobs to market benchmarks by content, level, and scope
2. Age survey data to current date: apply projected market movement rate
3. Compute compa-ratio per employee: actual base / market P50
4. Compute percentile positioning: where does actual pay fall in market distribution
5. 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