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When providing economic analysis and expert testimony for personal injury and wrongful death cases, clients often ask “How can a loss of earning capacity be calculated if the plaintiff has little to no historical work or earnings record?” In general, there are two categories that often occur in personal injury and wrongful death cases: (1) an adult with an established history in the workforce, and (2) a minor with no workforce history. While records documenting earnings will always be preferred, RPC has several methods for estimating the earning capacity of plaintiffs with little to no records. This blog explains how RPC consultants estimate earnings for these categories and identify the data sources used for these estimates.
Calculating Loss of Earning Capacity
Calculating a loss of earning capacity requires two main inputs: (1) the plaintiff’s pre-event earning capacity (i.e., what would the individual have made had the event that caused the alleged damages never occurred?), and (2) the plaintiff’s post-event earning capacity (i.e., what can the individual earn following the event?). Lost earning capacity is the individual’s post-event earning capacity less their pre-event earning capacity.
For an adult with a work history, the task of calculating pre-event earning capacity is straightforward when the plaintiff’s detailed earnings records are available. The single best predictor of an individual’s future wages is simply their past wages. Although the absence of detailed earnings records imposes challenges and introduces uncertainty, it does not prevent a pre-event earning capacity calculation. Decades of economic research demonstrate that other (and often more readily observable) characteristics determine an individual’s earning capacity, including their education, place of residence, gender, race, age, and other factors. When tax returns or other objective information on the plaintiff’s earnings are unavailable, RPC uses the available information about the plaintiff’s income-determining characteristics, peer-reviewed academic research, and published statistics to estimate pre-event earnings.
Data Sources
RPC primarily relies on two surveys to estimate wages and wage growth published by the Bureau of Labor Statistics (BLS), the Current Population Survey (CPS), and the Occupational Employment and Wage Statistics (OEWS). Both surveys are used for public policy planning and economic research. Each survey has different advantages and disadvantages, so which data source is most appropriate depends on the specific need.
The CPS is a monthly household survey and is known for producing the “jobs report” for labor force statistics that are released on the first Friday of every month.[1] The CPS contains demographic detail about non-farm employment statistics with both industry and geographic details. CPS demographic details include age, race, ethnicity, sex, education, veteran status, disability status, and whether the individual is native or foreign born. Because the CPS is a household survey, it can gather information about whether someone is unemployed or out of the labor force. Additionally, it collects information about employment such as full- or part-time status and union status. The CPS categorizes occupations into 526 categories.
The OEWS is a semi-annual business establishment survey generating employment and wage statistics for approximately 830 occupations with both industry and geographic details.[2] As OEWS surveys businesses, it does not collect information about workers that are unemployed or individuals that are out of the workforce entirely. Additionally, it will not include information about individuals who are self-employed. In general, OEWS’s greater number of occupations is one particular advantage when compared to the CPS. It also collects information about farmers, ranchers, and other agricultural managers, which the CPS does not.
The CPS and OEWS (and other surveys collected by the BLS and the United States Census Bureau) undergo constant revisions and methodological updates to ensure the most up-to-date and accurate estimates. Both agencies conduct validation studies to ensure consistent estimates between the surveys for overlapping information gathered in each survey.
When the Plaintiff is an Adult in the Workforce
In personal injury cases with limited or no earnings records, there is usually some information about the plaintiff’s type of work, past jobs, and job at the time of the event. With this information, RPC can use wage estimates from the OEWS to calculate a loss of earning capacity.
For example, consider a female plaintiff who most recently worked in 2025 in Houston, Texas, as a registered nurse. This job would fall into the Registered Nurse (RN) job category of the Standard Occupational Classification (SOC) system. According to the OEWS, median annual earnings for registered nurses in Houston, Texas, were $99,830 in 2025. For summary statistics such as median wages in a single year in a specific geographic area, OEWS is the best source for determining the most recent values.
To determine wage growth, RPC uses usual weekly earnings from the CPS by occupation (registered nurse) and sex. For female registered nurses, wages have grown 0.18% faster than overall inflation over the past decade. For year-over-year comparisons such as wage growth over time, the CPS is the best source for measuring wage changes by occupation.[3]
Information about where the plaintiff lives and works can have a substantial impact on the estimate and, when possible, it is best to incorporate these geographic factors into the calculation of earning capacity. Using the example of the registered nurse, a plaintiff working in Abeline, Texas, in 2025 would have estimated earnings of $80,240 (19% lower than a nurse in Houston). Earnings can vary greatly by location, and RPC takes these factors into consideration when estimating earnings.
In some cases, the plaintiff is self-employed and may have some information on revenue, but information on the expenses required to produce that revenue may be incomplete. For self-employed workers, earnings are calculated as revenue less expenses. When there is incomplete information on self-employment earnings it is appropriate to rely on statistics from OEWS to estimate earning capacity.
Minor Child With No Work History
When calculating the earning capacity of a minor child, there are no past earnings and nothing is known about the occupation in which they will (or would have) worked. There is no certainty about what education they would have ultimately attained absent the event – a significant determinant of any individual’s earning capacity. In these cases, RPC first determines the minor child’s probability of each educational attainment level using an empirical model developed by economists Lawrence M. Spizman and John Kane, published in the peer-reviewed Journal of Forensic Economics.[4][5]
The Spizman-Kane model uses several demographic characteristics about a child’s parents as inputs, including their education, household earnings, and family structure, and outputs the probabilities of eight possible educational outcomes: (1) less than a high school diploma, (2) GED, (3) a high-school diploma, (4) an associate’s degree, (5) a bachelor’s degree, (6) a master’s degree, (7) a PhD, and (8) a professional degree. The table below is an example of the model’s output.
| Educational Outcome | Cumulative Distribution Function | Probability |
| < High School Diploma | 6.6% | 6.6% |
| GED | 18.8% | 12.2% |
| High School Diploma | 69.2% | 50.4% |
| Associate’s Degree | 78.7% | 9.6% |
| Bachelor’s Degree | 95.8% | 17.0% |
| Master’s Degree | 99.3% | 3.6% |
| PhD | 99.6% | 0.2% |
| Professional Degree | 100.0% | 0.4% |
Notes. This example is based on a Hispanic male, age 10, living in an urban area. The mother has two years of college education. The father is a high school graduate. Both biological parents live in the household. The family is Catholic and their annual household income is $72,000.
The results of this model can be applied to calculate the expected value of the minor’s earning capacity by multiplying the probability of each educational attainment level by earnings for that education level. RPC relies on median earnings by age as reported in Full-Time Earnings in the United States data, a publication summarizing US Census data on earnings by education, gender, and race/ethnicity by age. This expected value calculation incorporates all probabilities and potential earnings of each of the eight educational outcomes.
The results of this model can also be used to determine the most likely educational attainment outcome for a minor. In the example above, the probability of the minor attaining a high school diploma is 69.2%, while the probability of him attaining an associate’s degree or more is 30.8%. Therefore, it is more likely than not his highest educational attainment level will be a high school diploma.
Key Takeaways for Estimating Earning Capacity with Limited Information
Overall, calculating earning capacity for plaintiffs without earnings records is possible using available surveys and economic models. Instances with limited earnings records—or even none at all—do not prevent economic analysis or expert testimony. Understanding when to use these methods helps ensure that economic assessments appropriately reflect the unique facts of each case.
[1] See https://www.bls.gov/cps/cps_over.htm for more details.
[2] See https://www.bls.gov/oes/oes_emp.htm for more details.
[3] See https://www.bls.gov/oes/oes_ques.htm# for more details.
[4] Lawrence M. Spizman and John Kane, Update of Educational Attainment Model for a Minor Child: Round 18 of NLSY (1997), Journal of Forensic Economics, Vol. 29, No. 1 (September 2020), pp. 131–141.
[5] Cahill, K.E. and Spizman, L.M., 2025. A Closer Look at Race, Ethnicity, and Gender Neutral Models. Journal of Forensic Economics, 32(1), pp.3-16.