Incorporating Geographic Wage and Cost‑of‑Living Differentials in Present Value Calculations of Economic Loss

Introduction

Economic‑loss assessments aim to convert a stream of projected future earnings into a single lump‑sum figure—the present value (PV). A foundational assumption is that projected earnings reflect the claimant's true earning capacity. Yet, wages vary substantially across regions due to differences in labor‑market conditions and local price levels. Failing to adjust for geographic wage differentials and cost‑of‑living disparities can misstate economic losses—either overstating or understating compensation. This article examines the data sources, methodologies, and best practices for integrating geographic adjustments into PV calculations, ensuring that economic‑loss valuations reflect the claimant's location‑specific economic reality.

Data Sources for Geographic Adjustments

Occupational Employment and Wage Statistics (OEWS)

The U.S. Bureau of Labor Statistics' OEWS program provides annual employment and wage estimates for over 800 occupations at the national, state, metropolitan, and nonmetropolitan levels (Bureau of Labor Statistics, 2025). For example, the May 2024 release reports that the national annual mean wage across all occupations was $67,920, whereas the Rhode Island mean was $60,870—10.3 percent below the national average (Bureau of Labor Statistics).

Regional Price Parities (RPPs)

The U.S. Bureau of Economic Analysis publishes Regional Price Parities measuring price‑level differences across states and metropolitan areas, expressed relative to the national average (100.0) (U.S. Bureau of Economic Analysis, 2025). In 2023, states with the highest overall RPPs included California (112.6) and New Jersey (108.9), while the lowest were Arkansas (86.5) and Mississippi (87.3) (Bureau of Economic Analysis). These parities capture variations in housing, goods, and services, and serve as location‑based cost‑of‑living indices.

Supplemental Cost‑of‑Living Indexes

Private indices—such as the MIT Living Wage Calculator—offer granular estimates of living‑cost requirements by county and family composition, supplementing RPP data for detailed adjustments (MIT, 2025).

Methodology for Geographic Wage Adjustments

Step 1: Base‑Year Earnings Selection

Select the claimant's most recent actual wage or an appropriate occupational median. For example, if a claimant worked as a production supervisor earning $75,000 nationally, use that figure as $E_1$ (Bureau of Labor Statistics, 2025).

Step 2: Regional Wage Differential

Compute a regional wage factor (RWF) by dividing the local occupational mean wage by the national mean wage:

Example (Rhode Island production supervisors):

Step 3: Cost‑of‑Living Adjustment

Derive a regional price factor (RPF) from RPPs:

For Rhode Island (RPP = 98.4):

Step 4: Adjusted Earnings Projection

Combine wage and price factors to obtain location‑adjusted earnings:

Using our example:

Step 5: Project Growth and Discount

Project $E_{t,\text{loc}}$ forward using a real wage‑growth rate $g$ (e.g., 2 percent). Then discount to present value at rate $r$ (e.g., risk‑free 3.5 percent):

Case Study: Rhode Island Claimant

Facts

• Age: 45
• Occupation: Production Supervisor
• National base wage ($E_1$): $75,000
• Local mean wage: $60,870 (Rhode Island) (Bureau of Labor Statistics)
• RPP (RI): 98.4 (FRED)
• Real wage growth ($g$): 2.0 percent
• Discount rate ($r$): 3.5 percent
• Projection horizon ($T$): 20 years

Calculation

1. $E_{1,\text{RI}} = 75{,}000 \times (60{,}870/67{,}920) \times 0.984 = 75{,}000 \times 0.897 \times 0.984 \approx \$66{,}300$.
2. Compute PV using the constant‑growth formula (Saurman & Means, 1989):
   $$\mathrm{PV} = E_{1,\text{RI}}\;\frac{1 + g}{r - g}\Bigl[1 - \bigl(\tfrac{1+g}{1+r}\bigr)^T\Bigr]$$
3. Substituting values:
   $$\mathrm{PV} \approx 66{,}300 \times \frac{1.02}{0.035 - 0.02} \Bigl[1 - (1.02/1.035)^{20}\Bigr] \approx \$1{,}065{,}000$$

Comparison

• Unadjusted PV (national wage, no RPP): $≈$1,205,000.
• Location‑adjusted PV: $≈$1,065,000.

Location adjustment reduces the PV by ∼11.5 percent, reflecting lower local wages and cost levels.

Common Pitfalls

1. Using a Single Factor

   Applying only a wage differential or only an RPP neglects the interplay of income capacity and living costs.

2. Mismatch of Index Types

   Combining nominal national wages with real‑adjusted local indices (or vice versa) introduces inconsistency; match nominal with nominal or real with real (Bodie, Kane, & Marcus, 2014).

3. Ignoring Intra‑State Variation

   Metropolitan areas (e.g., Providence) may exhibit different RPPs than the state average; use metro‑level data when available (U.S. Bureau of Economic Analysis, 2025).

4. Static vs. Dynamic Factors

   Local wages and price levels can evolve differently over time; when long horizons are used, consider projected changes in regional differentials (BEA, 2024).

Best Practices

• Document Sources and Dates:
  • OEWS retrieval date: April 2, 2025 (Bureau of Labor Statistics)
  • RPP data year: 2023 (Bureau of Economic Analysis)
• Tailor to Claimant's Location: Use the most granular RPP and wage data—state, metropolitan, or nonmetropolitan—consistent with the claimant's residence and worksite (Bureau of Labor Statistics, 2025).
• Perform Sensitivity Analyses: Vary RWF and RPF by ±5 percent to illustrate the range of PV outcomes.
• Review Jurisprudence: Some jurisdictions may disfavor location adjustments or require specific methodologies; align your approach with controlling case law (Brush, 2003).
• Peer Review: Engage a second economist to validate factor calculations and ensure consistency across nominal/real frameworks.

Conclusion

Incorporating geographic wage differentials and cost‑of‑living adjustments can be important for economic‑loss valuations. By leveraging BLS OEWS data to derive local wage factors and BEA RPPs for cost indices—and applying them systematically in PV models—practitioners can ensure that awards reflect the claimant's true economic circumstances.

References

• Bodie, Z., Kane, A., & Marcus, A. J. (2014). Investments (10th ed.). McGraw‑Hill Education.
• Brush, B. C. (2003). Risk, discounting, and the present value of future earnings. Journal of Forensic Economics, 16(3), 263–274. https://www.jstor.org/stable/42755953
• Bureau of Labor Statistics. (2025, April 2). Occupational employment and wages—May 2024 (OES news release). Retrieved July 25, 2025, from https://www.bls.gov/news.release/ocwage.htm (Bureau of Labor Statistics)
• Bureau of Labor Statistics. (2025). Occupational employment and wage statistics [Data set]. Retrieved July 25, 2025, from https://www.bls.gov/oes/ (Bureau of Labor Statistics)
• Saurman, D. S., & Means, T. S. (1989). Estimating earning capacity with constant earnings growth rates. Journal of Forensic Economics, 3(1), 51–60. https://doi.org/10.5085/0898-5510-3.1.51
• U.S. Bureau of Economic Analysis. (2025). Regional price parities by state and metro area, 2023 [Data set]. Retrieved July 25, 2025, from https://www.bea.gov/data/prices-inflation/regional-price-parities-state-and-metro-area (Bureau of Economic Analysis)
• MIT Living Wage Calculator. (2025). Living Wage Calculator: Rhode Island. Retrieved July 25, 2025, from https://livingwage.mit.edu/states/44