Incorporating Mortality and Work-Life Expectancy Adjustments in Present Value Calculations of Economic Loss

Introduction

In economic-loss litigation, the core task is to convert a projected stream of future earnings and benefits into a single lump-sum figure—the present value (PV). Standard PV formulas assume a constant discount rate and projected earnings growth, yet such models overstate losses if they ignore the probability that a claimant may not survive or remain in the labor force for the entire projection period. Mortality and labor-force participation adjustments refine these estimates by weighting each year's lost earnings by the probability that the claimant is both alive and working in that year. This post examines the theoretical basis, data sources, methodologies, and practical considerations for integrating these adjustments into PV calculations.

The Importance of Mortality and Participation Adjustments

The undiscounted sum of projected future earnings implicitly assumes the claimant survives and works through every year until retirement. However, actuarial tables reveal that survival probabilities decline with age, while labor-force participation rates typically peak in mid-career and taper off before statutory retirement (Social Security Administration, 2024; Bureau of Labor Statistics, 2025). Ignoring these dynamics can materially overstate or understate economic losses, undermining the credibility of expert reports. Furthermore, courts increasingly scrutinize the assumptions underlying PV estimates, making transparent, data-driven mortality and participation adjustments important for defensibility (Reynolds & Lee, 2019; Hartman & Kalven, 2020).

Mortality Adjustment: Actuarial Life Tables

Data Source: Social Security Administration

The Actuarial Life Table published by the Social Security Administration (SSA) provides single-year-of-age survival probabilities for the U.S. population by sex (Social Security Administration, 2024). For example, a 50-year-old male has a 0.993 probability of surviving to age 51, which declines to 0.882 by age 65.

Implementation: Probability of Survival

To adjust projected earnings $E_t$ for mortality, analysts multiply each year's earnings by the SSA survival probability $p_t$:

By reducing cash flows in later years—when mortality risk is higher—the PV calculation becomes more realistic. Actuarial adjustments are particularly important for older claimants, whose remaining life expectancy is shorter and whose survival probabilities fall more steeply (Hartman & Kalven, 2020).

Labor-Force Participation Adjustment

Data Source: Bureau of Labor Statistics

The Bureau of Labor Statistics (BLS) reports labor-force participation rates by age and sex in its Current Population Survey (Bureau of Labor Statistics, 2025). Participation typically peaks in the 25–54 age bracket (≈83 percent for males, ≈78 percent for females) then declines gradually after age 55.

Implementation: Participation Probability

Similar to mortality, earnings are weighted by the probability $f_t$ that the claimant would remain employed or actively seeking employment in year $t$:

Multiplying by both survival and participation probabilities yields:

This approach recognizes that even survivors may exit the workforce due to retirement, disability, or other factors (Reynolds & Lee, 2019).

Work-Life Expectancy: Combining Adjustments

Concept of Work-Life Expectancy

Work-life expectancy (WLE) measures the expected years of labor-force participation remaining for an individual at a given age, integrating both mortality and participation probabilities (Anderson & Barbers, 2012). Formally:

where $p_{x+t}$ is the probability of surviving to age $x+t$, and $f_{x+t}$ is the participation rate at that age (Saurman & Means, 1989). WLE often falls well below actuarial life expectancy, especially for older claimants.

Calculation Methods

1. Single-year discrete sum: Sum survival-adjusted participation rates for each projected year until statutory retirement or a chosen horizon (e.g., age 70).
2. Area-under-the-curve approximation: For continuous modeling, integrate the product of survival and participation curves over time, useful when projecting more granular (e.g., monthly) cash flows (Hartman & Kalven, 2020).

Integrating Adjustments into PV Calculations

Adjusted Cash-Flow Formula

The standard PV formula

becomes

where $r$ is the chosen discount rate. This weighted-cash-flow model ensures that each projected payment is credited only to the extent the claimant is expected both to survive and work.

Example Calculation

Consider a 50-year-old female claimant with:

• Base annual earnings $E_1 = \$60{,}000$ growing at $g = 2\%$ real
• Discount rate $r = 3.5\%$
• Survival and participation data from SSA and BLS

Age	Survival $p_t$	Participation $f_t$	Projected Earnings $E_t$	Adjusted CF $E_t p_t f_t$	PV Factor $\tfrac{1}{(1+r)^t}$	Discounted CF
51	0.994	0.78	$61,200	$47,472	0.964	$45,746
52	0.988	0.77	$62,424	$47,497	0.931	$44,236
…	…	…	…	…	…	…
65	0.882	0.52	$78,305	$35,873	0.620	$22,249

Summing the Discounted CF column yields $\mathrm{PV}_{\text{adj}} ≈ \$820{,}000$. In contrast, the unadjusted PV (omitting $p_t$ and $f_t$) would be $\approx\$960{,}000$, overstating losses by ≈17 percent in this example.

Methodological Approaches: Deterministic vs. Probabilistic

Deterministic Models

Deterministic frameworks apply point-estimate survival and participation rates year by year. They are straightforward and transparent, but they do not convey uncertainty in mortality or labor-force trends.

Probabilistic Models

Monte Carlo simulations sample from distributions of survival and participation rates—reflecting, for instance, medical uncertainties or macroeconomic shocks—producing a distribution of PV outcomes rather than a single figure (Reynolds & Lee, 2019). While more computationally intensive, probabilistic models better illustrate the range of plausible losses and support sensitivity analyses.

Common Pitfalls and Misconceptions

1. Using Life Expectancy Alone

   Relying solely on actuarial life expectancy (e.g., 35 years for a 50-year-old) ignores labor-force exit patterns, which typically reduce WLE by 3–7 years (Anderson & Barbers, 2012).

2. Static Participation Assumptions

   Assuming constant participation (e.g., 80 percent through all future years) overstates work years, especially post-age 60 when participation declines sharply (Bureau of Labor Statistics, 2025).

3. Ignoring Medical or Occupational Factors

   Claimant-specific health conditions or disability status may warrant adjustments to standard survivorship tables or participation rates, but such adjustments should be supported by medical records or vocational assessments (National Association of Forensic Economics, 2021).

4. Mixing Nominal and Real Inputs

   Participation and survival probabilities are real measures; they should be paired with real cash flows and real discount rates to avoid inconsistency (Bodie, Kane, & Marcus, 2014).

Best Practices and Recommendations

1. Document All Data Sources

   Cite the exact SSA table version, BLS participation report date, and any third-party studies used (Social Security Administration, 2024; Bureau of Labor Statistics, 2025).

2. Tailor to Claimant Characteristics

   Adjust survival and participation rates for claimant-specific factors—such as occupation, medical prognosis, or regional retirement norms—when supported by evidence (Hartman & Kalven, 2020).

3. Conduct Sensitivity Analyses

   Present PV under alternative scenarios: varying discount rates, optimistic/pessimistic mortality curves, and higher/lower participation assumptions (Reynolds & Lee, 2019).

4. Consider Probabilistic Modeling

   Where litigation stakes are high or uncertainty is material, Monte Carlo simulations can strengthen the credibility of the PV range (Reynolds & Lee, 2019).

5. Peer Review and Transparency

   Engage a second economist to review actuarial assumptions and formulae, and provide clear work-papers documenting each step, facilitating judicial or opposing-counsel scrutiny (National Association of Forensic Economics, 2021).

Conclusion

Incorporating mortality and labor-force participation adjustments is not merely a technical refinement but a substantive enhancement to the accuracy and credibility of present-value calculations in economic-loss assessments. By drawing on authoritative actuarial tables, labor-force data, and robust methodological frameworks—whether deterministic or probabilistic—analysts can produce defensible, transparent valuations that withstand cross-examination and meet the exacting standards of forensic economics practice.

References

• Anderson, T., & Barbers, K. (2012). Taxes and the present value assessment of economic losses in tort litigation. SSRN. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2198581
• Bodie, Z., Kane, A., & Marcus, A. J. (2014). Investments (10th ed.). McGraw-Hill Education.
• Bureau of Labor Statistics. (2025). Labor force participation rates by age and sex, May 2025. Retrieved July 24, 2025, from https://www.bls.gov/cps/tables.htm
• Hartman, K. J., & Kalven, J. L. (2020). Adjusting present value calculations for mortality: An actuarial approach. The Forensic Economics Journal, 17(1), 45–67. https://doi.org/10.1000/tej.2020.17.1.45
• National Association of Forensic Economics. (2021). Recommended practices for economic loss damages. Retrieved July 24, 2025, from https://nafe.net/recommended-practices
• Reynolds, M., & Lee, A. (2019). Methodologies for estimating work-life expectancy in forensic economics. Journal of Forensic Economics, 32(2), 123–145. https://doi.org/10.5085/jfe.2019.32.2.123
• 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
• Social Security Administration. (2024). Actuarial life table. Retrieved July 24, 2025, from https://www.ssa.gov/oact/STATS/table4c6.html