Back to Details

algo-hr-compensation

Compare original and translation side by side

🇺🇸

Original

English
🇨🇳

Translation

Chinese

Compensation Benchmarking

薪酬基准分析

Overview

概述

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.
薪酬基准分析通过将内部薪资水平与外部市场数据对比，评估薪资竞争力。使用compa-ratio（实际薪资/市场中点值）和百分位定位方法，为薪资带宽设计、薪资调整及公平性分析提供依据。

When to Use

使用场景

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)
触发条件：
  • 评估当前薪资是否具备市场竞争力
  • 设计或更新薪资带宽与薪酬结构
  • 识别不同人口统计特征或岗位间的薪酬公平差距
不适用场景：
  • 基于个人绩效的薪资决策（请使用绩效管理工具）
  • 无市场数据可用时（至少需要调研基准数据）

Algorithm

算法

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.
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

阶段1：输入验证

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).
收集：内部薪酬数据（基本工资、奖金、股权）、市场调研数据（按岗位划分的P25、P50、P75分位值）、内部岗位与调研基准的匹配关系。 准入条件： 岗位匹配准确，调研数据为最新数据（不超过18个月）。

Phase 2: Core Algorithm

阶段2：核心算法

  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
  1. 根据岗位内容、职级和职责范围，将内部岗位与市场基准匹配
  2. 将调研数据更新至当前日期：应用市场变动预测率
  3. 计算每位员工的compa-ratio：实际基本工资 / 市场P50值
  4. 计算百分位定位：实际薪资在市场分布中的位置
  5. 分析：按部门、职级、任职年限、人口统计特征排查公平性差距

Phase 3: Verification

阶段3：验证

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.
检查：compa-ratio集中在0.85-1.15区间（正常范围）。标记异常值（<0.80为薪资偏低，>1.20为薪资偏高）。开展人口统计公平性测试。 准入条件： 分布合理，公平性分析完成。

Phase 4: Output

阶段4：输出

Return benchmarking results with band recommendations.
返回基准分析结果及薪资带宽建议。

Output Format

输出格式

json
{
  "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"}
}
json
{
  "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

示例

Sample I/O

输入输出示例

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.
输入： 50名工程师，市场P50=新台币180万，实际薪资范围新台币150万-210万 预期输出： 平均compa-ratio约为0.97，部分薪资低于带宽的员工会被标记为需调整。

Edge Cases

边缘案例

InputExpectedWhy
Hot market (tech boom)Market data rapidly outdatedApply higher aging factor
Remote work mixedLocation-adjusted bands neededSF vs Taipei market rates differ 2-3x
Small company, no survey matchUse broader industry proxiesImperfect but better than nothing
输入预期输出原因
热门市场（科技繁荣期）市场数据快速过时应用更高的更新系数
混合远程办公需要按地区调整带宽旧金山与台北的市场薪资相差2-3倍
小型公司，无匹配调研数据使用更广泛的行业代理数据虽不完美，但好过无数据可用

Gotchas

注意事项

  • 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).
  • 总薪酬： 仅对基本工资进行基准分析会忽略股权、奖金和福利。对比总薪酬才能获得准确的市场定位。
  • 调研数据滞后： 已发布的调研数据反映的是6-18个月前收集的信息。在快速变化的市场中，需将数据更新至当前日期。
  • 内部公平性vs外部竞争力： 与市场水平对齐可能导致内部不公平（新员工薪资高于老员工）。需平衡两者关系。
  • 地区差异： 远程办公使基于地点的薪资政策复杂化。需明确政策：按总部地点、员工所在地或混合模式支付薪资。
  • 薪酬公平法律风险： 无法解释的人口统计特征薪资差距会带来法律责任。需开展基于回归分析的公平性分析，控制合理因素（经验、绩效、职级）。

References

参考资料

  • For salary band design methodology, see 
    references/band-design.md
  • For pay equity regression analysis, see 
    references/pay-equity.md
  • 薪资带宽设计方法，请查看
    references/band-design.md
  • 薪酬公平回归分析，请查看
    references/pay-equity.md