Methodology • • 10 min read

Forecasting Real Wage Growth Over Multi‑Decade Horizons: Data Sources and Methodologies

A comprehensive examination of methodologies for projecting real wage growth in economic loss calculations, balancing historical trends, productivity relationships, and demographic shifts.

By Christopher T. Skerritt, CRC, MBA

Introduction

Real wage growth—the increase in purchasing power over time—is an important variable in forensic economic calculations. A seemingly small difference in assumed growth rates compounds dramatically over multi-decade horizons. For a 30-year-old claimant with 35 years of lost earnings, the difference between 1% and 2% real wage growth can alter present value calculations by 20–30%. Yet forecasting wage growth decades into the future presents profound challenges: productivity trends shift, demographic transitions reshape labor markets, and technological disruptions create structural breaks. This article examines data sources, forecasting methodologies, and practices for projecting real wage growth in forensic economic contexts.

1. Understanding Real Wage Growth

1.1 Definitions and Components

Real wage growth represents the change in wages adjusted for inflation:

$$g_r = g_n - \pi$$

Where:

1.2 Economic Drivers

Real wages grow through several mechanisms:

  1. Labor productivity gains: Output per hour worked increases through technology, capital deepening, and process improvements
  2. Human capital accumulation: Education and experience enhance worker skills
  3. Labor market dynamics: Supply-demand imbalances affect wage bargaining
  4. Institutional factors: Minimum wage laws, unionization, and labor regulations

2. Historical Evidence and Trends

2.1 Long-Term U.S. Real Wage Growth

Period Annual Real Wage Growth Key Characteristics
1947–1973 2.8% Post-war boom, strong productivity
1973–1995 0.2% Productivity slowdown, oil shocks
1995–2007 1.8% IT revolution, productivity revival
2007–2019 0.8% Financial crisis recovery, weak productivity
2019–2024 1.4% Pandemic disruption, tight labor markets
1947–2024 Average 1.4% Full post-war period

Source: Bureau of Labor Statistics, Real Earnings Series; Economic Policy Institute (2025)

2.2 The Productivity-Wage Relationship

Economic theory suggests wages should track productivity over the long run. However, this relationship has weakened:

Productivity vs. Wage Growth (1973–2024)

  • Labor productivity growth: 1.8% annually
  • Median real wage growth: 0.5% annually
  • Gap: 1.3 percentage points

This divergence reflects changing income distribution, with productivity gains increasingly captured by capital rather than labor (Bivens & Mishel, 2015).

3. Data Sources for Wage Growth Analysis

3.1 Government Sources

  1. Bureau of Labor Statistics (BLS)
    • Employment Cost Index (ECI): Quarterly wage and benefit changes
    • Current Employment Statistics: Average hourly earnings
    • Occupational Employment and Wage Statistics: Occupation-specific data
  2. Social Security Administration (SSA)
    • Average Wage Index (AWI): National average wages since 1951
    • Trustees Report: Long-term wage growth projections
  3. Congressional Budget Office (CBO)
    • Long-term economic projections including real wage growth
    • Typically projects 1.2–1.5% real wage growth over 30 years
  4. Federal Reserve Economic Data (FRED)
    • Real median weekly earnings (LEU0252881600A)
    • Productivity and costs data

3.2 Academic and Private Sources

4. Forecasting Methodologies

4.1 Historical Average Approach

The simplest method uses long-term historical averages:

$$\hat{g}_r = \frac{1}{T}\sum_{t=1}^{T} g_{r,t}$$

Advantages: Simple, transparent, defensible
Disadvantages: Ignores structural changes, sensitive to period selection

Period Sensitivity

  • 1947–2024 average: 1.4%
  • 1973–2024 average: 0.9%
  • 2000–2024 average: 0.7%

Choice of historical period dramatically affects the forecast.

4.2 Productivity-Based Models

Link wage growth to productivity projections:

$$g_w = \alpha \cdot g_p + \beta$$

Where:

4.3 Demographic-Adjusted Models

Account for workforce composition changes:

  1. Age structure: Aging workforce affects aggregate wage growth
  2. Education levels: Rising educational attainment boosts wages
  3. Industry mix: Shift from manufacturing to services impacts growth

4.4 Econometric Forecasting

Time-series models incorporating multiple variables:

$$g_{w,t} = \phi_0 + \sum_{i=1}^{p}\phi_i g_{w,t-i} + \sum_{j=1}^{q}\theta_j X_{j,t} + \epsilon_t$$

Where $X_j$ includes productivity, unemployment, inflation expectations, etc.

4.5 Scenario-Based Approaches

Develop multiple scenarios reflecting different economic futures:

Scenario Real Wage Growth Assumptions
Pessimistic 0.5% Continued productivity stagnation, high inequality
Baseline 1.2% Moderate productivity gains, stable institutions
Optimistic 2.0% Technology breakthrough, inclusive growth policies

5. Official Projections and Benchmarks

5.1 Social Security Trustees

The 2025 Trustees Report projects (intermediate assumptions):

5.2 Congressional Budget Office

CBO's 2025 long-term projections:

5.3 Federal Reserve

While the Fed doesn't publish explicit wage forecasts, their long-run projections imply:

6. Occupation and Industry Considerations

6.1 Differential Growth Rates

Not all occupations experience uniform wage growth:

Occupation Category 10-Year Real Growth Relative to Average
Computer/Mathematical 2.3% +1.1%
Healthcare Practitioners 1.8% +0.6%
All Occupations 1.2% Baseline
Production 0.7% -0.5%
Food Service 0.4% -0.8%

6.2 Skill-Biased Technological Change

Technology adoption affects occupations differently:

7. Best Practices for Forensic Applications

7.1 Selection Criteria

  1. Time horizon alignment: Longer horizons favor conservative estimates
  2. Occupation specificity: Use occupation-specific data when available
  3. Geographic adjustments: Regional variations may persist
  4. Age-earnings profiles: Younger workers typically see higher growth

7.2 Documentation Requirements

7.3 Common Pitfalls to Avoid

  1. Recency bias: Over-weighting recent trends
  2. Neglecting structural breaks: Assuming past relationships continue
  3. Ignoring composition effects: Workforce changes affect aggregates
  4. Double-counting inflation: Mixing real and nominal rates

8. Sensitivity Analysis Framework

Impact of Wage Growth Assumptions

For a 30-year projection with 3.5% discount rate:

  • 0.5% real growth: PV factor = 23.2
  • 1.0% real growth: PV factor = 25.3 (+9%)
  • 1.5% real growth: PV factor = 27.7 (+19%)
  • 2.0% real growth: PV factor = 30.3 (+31%)

Each 0.5% increment changes present value by approximately 10%.

Conclusion

Forecasting real wage growth requires balancing historical evidence, economic theory, and future uncertainty. While post-war average growth of 1.4% provides a reference point, structural changes in the economy—from technology adoption to demographic transitions—complicate simple extrapolation. Forensic economists should ground projections in official benchmarks (SSA's 1.14%, CBO's 1.2–1.3%) while acknowledging occupation-specific variations and conducting thorough sensitivity analyses. By combining multiple methodologies and clearly documenting assumptions, practitioners can provide courts with robust, defensible wage growth projections that appropriately reflect both central tendencies and inherent uncertainties in multi-decade economic forecasting.

References

About the Author

Christopher T. Skerritt, CRC, MBA is a forensic economist and certified rehabilitation counselor with over 20 years of experience in economic damage analysis. He provides expert testimony in personal injury, wrongful death, and employment litigation matters throughout New England.

Contact: (203) 605-2814 | chris@skerritteconomics.com

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