Predicting future wages

Predicting future compensation shouldn’t rely on guesswork, outdated surveys, or static spreadsheets. To forecast competitive pay, modern compensation models pair granular baseline figures with dynamic economic adjustments.
Our predictive salary model combines two gold-standard datasets from the U.S. Bureau of Labor Statistics (BLS): the Occupational Employment and Wage Statistics (OEWS) and the Employment Cost Index (ECI).
1. The Foundation: BLS OEWS Baseline Data
Every predictive model needs a solid foundation. We start with the BLS OEWS (formerly OES) dataset, which provides comprehensive, localized, and industry-specific wage estimations.
10,000+ Job Titles: Detailed coverage across over 800 specific occupational codes.
Geographic Granularity: Micro-level wage distribution capturing national, state, and metropolitan statistical areas (MSAs).
Percentile Distribution: Percentile breakdowns (10th, 25th, 50th, 75th, and 90th) to account for seniority, experience, and regional variation.
While the OEWS dataset provides exceptional regional depth, its annual release cadence creates a natural lag. Relying solely on historical baseline figures leaves a gap in dynamic market conditions.
2. The Engine: Inflation & Projections via the ECI
To bridge the gap between historical baseline reports and future target dates, our model integrates the BLS Employment Cost Index (ECI).
Unlike flat inflation markers like the Consumer Price Index (CPI), the ECI measures pure changes in labor costs across specific industries and occupational groups, isolated from employment shifts:
Quarterly Velocity: Captures real-time wage growth and labor trends across private and public sectors.
Industry & Sector Specificity: Applies wage adjustments matched directly to specific industry classifications rather than a flat macro rate.
Compound Projections: Escalating historical OEWS base rates forward through compound quarterly ECI trends allows our model to project pay rates into future target years with high accuracy with recruiting data to measure demand and supply.
Why This Matters for HR and Compensation Leaders
Combining baseline regional pay with dynamic labor index adjustments yields a predictive framework designed for practical decision-making:
Validate Job Offers: Assess market-rate competitiveness across different states, metropolitan areas, and industries with confidence.
Multi-Year Budget Planning: Model future headcount costs using macro-level wage trends and modeling fringe and wrap rates per industry.
Reduce Reliance on Stale Surveys: Eliminate static yearly survey gaps with dynamic updates driven by federal labor statistics. Benchmark your current salaries and combine with other surveys