Methodology & Data Sourcing

1. National & Regional Baselines (BLS & BEA)

We rely on two foundational federal datasets to calculate domestic compensation benchmarks:

• Nominal Domestic Wages: Sourced from the Bureau of Labor Statistics (BLS) Occupational Employment and Wage Statistics (OEWS) survey, capturing cross-industry wage distributions across national, statewide, and metropolitan statistical areas.
• Purchasing Power Parity: Localized using the Bureau of Economic Analysis (BEA) Regional Price Parities (RPP)—including SARPP for statewide indexes and MARPP for metropolitan statistical areas.

2. Foreign Talent & H-1B Sponsor Data

Foreign labor market sponsorship metrics are derived from the U.S. Department of Labor (DOL) Labor Condition Applications (LCA):

• Filtering & Deduplication: Records are strictly filtered for cases marked CERTIFIED and designated as full-time positions (FULL_TIME_POSITION == 'Y'). Case numbers are deduplicated to eliminate amendment duplicates.
• Sample Size Suppression: To prevent statistical skewing in small sample pools, occupations, metros, or employers with fewer than 10 certified unique cases are suppressed from percentile distribution reporting.
• Employer Clustering: Standard corporate legal entity suffixes (e.g., LLC, INC, CORP, LLP) are normalized, and known subsidiaries are clustered into parent brand umbrellas (e.g., AWS to Amazon, Mindtree to LTIMindtree) for accurate organizational volume reporting.

3. H-1B Job Titles

Job-title salaries use the title the employer wrote on each LCA. Titles are cleaned by deterministic rules; no title is generated or rewritten by AI.

• Cleaning: requisition and HR job codes (e.g. JC50, REQ#123), location or employment-type noise (e.g. “Remote”, “- US”) and occupation names appended to the title are removed; abbreviations the employer typed are spelled out (Sr → Senior); a misspelled word is corrected only toward a spelling that appears in hundreds of other filings.
• Grouping: seniority words and level numbers are grouped, so “Senior Software Engineer II” is counted under Software Engineer. The exact titles in each group are listed on the title pages. Lead, Principal and Staff titles stay separate.
• SOC split: a title is always measured inside the SOC occupation it was filed under, because pay and prevailing-wage rules differ by SOC. Title pages show how one title is split across occupations.
• Minimum filings: 30 for a title nationally, 10 for a title within one SOC, 5 for an exact-title variant, SOC-split row or employer row.

4. Job Outlook, Skills & Related Occupations

• Job outlook: employment, projected change, average yearly openings, typical entry education, work experience and on-the-job training come unchanged from the BLS Employment Projections (Table 1.2). Openings include new jobs plus replacement of workers who retire or change occupations.
• Skills, knowledge, software, tasks, other job titles and related occupations come from the O*NET database. Skill and knowledge ratings are O*NET importance scores (1–5); ratings O*NET flags as unreliable are excluded.
• Code matching: each BLS occupation uses its O*NET profile with the same code. Where BLS reports a combined group (for example Buyers and Purchasing Agents, 13-1020), ratings are averaged across O*NET's detailed occupations in that group and the page says so.
• This site includes information from the O*NET Database by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA). Used under the CC BY 4.0 license. O*NET® is a trademark of USDOL/ETA. WageComparator has modified all or some of this information; USDOL/ETA has not approved, endorsed, or tested these modifications.

5. Inflation & Real Wage Growth Models

To accurately measure real wage growth beyond nominal increases, our analytical pipeline uses two distinct, cadence-matched inflation benchmarks derived from the BLS Consumer Price Index for All Urban Consumers (CPI-U, Series CUUR0000SA0):

Real year-over-year wage growth is computed using the standard Fisher adjustment formula: Real Growth % = [((1 + Nominal Growth / 100) / (1 + Inflation Rate / 100)) - 1] * 100