Assessing the loss of “human capital;” the reduced worklife expectancy and lifetime loss of earning capacity
Conventional wisdom would suggest that an individual with a mild traumatic brain injury (TBI) who has returned to work, perhaps at the same or even greater compensation, has suffered no lifetime loss of earning capacity. This is a misperception. The issue before the courts, loss of earning capacity, addresses “lifetime compensation” which includes both earnings and worklife expectancy as elements. Even if an individual has returned to work earning more money than ever before, this individual with a mild traumatic brain injury has suffered a loss of earning capacity due to the likely decline in his or her worklife. The losses in these cases can range from several hundreds of thousands of dollars to millions of dollars.
In addressing the loss of earning capacity, it is necessary to measure the reduction in human capital. Economists define human capital as the acquisition of knowledge, skill and understanding as a result of education, training and experience that allows an individual to sell his or her services in the labor market in exchange for compensation. When an individual suffers from a mild traumatic brain injury or non-severe cognitive disability, the individual’s human capital is normally reduced. The precursors to human capital are intelligence and physical ability. It is easy to imagine how a cognitive disability can impact both of these precursors to human capital. Data emanating from the U.S. Census Bureau’s American Community Survey (ACS) tells us that, on average, cognitive limitations reduce both earnings and worklife expectancies for workers across all levels of educational attainment.
The American Community Survey
The U.S. Census Bureau’s American Community Survey (ACS), the largest annual survey in the United States, provides a large variety of statistics on numerous characteristics for the nation. Numerous researchers have utilized ACS data for a wide variety of purposes. The information provided by ACS concerning individuals with disabilities is considered the “gold standard” by most researchers in examining the earnings and employment levels for persons with a disability. The Disability Statistics Rehabilitation Research and Training Center for Economic Research on Employment Policy for Persons with Disabilities publishes an annual disability compendium of disability data from the ACS (Houtenville, Andrew J., and Tony Ruiz. 2012 Annual Compendium of Disability Statistics. Durham, NH: University of New Hampshire, Institute on Disability, 2012.).
In cases involving cognitive disability, we utilize the 2009-2013 ACS data where a respondent to the survey answers the question: “Because of a physical, mental, or emotional condition does this person have serious difficulty concentrating, remembering, or making decisions?” When a person meets the ACS definition of cognitive disability, his or her human capital is typically reduced. The government data are clear about persons with a cognitive disability: 1) when working year-round, full-time, they earn less, on average, than their counterparts without a cognitive disability, and 2) they typically work less over their lifetime than their counterparts without a cognitive disability. The data also allow us to consider severe cognitive disabilities; a cognitive disability is considered severe when problems with self-care or going outside the home are also reported. For the purposes of this article, non-severe cognitive disabilities are discussed such as those resulting from mild traumatic brain injury.
The ACS contains multiple levels of educational attainment that can be utilized in computing both average earnings figures (13 classifications to be exact) and worklife expectancy values. For sake of clarity in presentation, we present only three levels of educational attainment (high school graduate, baccalaureate degree, and professional degree). Individuals with a professional degree are those who received any degree with a “D” that is not a Ph.D. (such as: J.D., M.D., D.D.S., etc.).
Table 1 provides the average earnings figures for both non-disabled and non-severely cognitively disabled individuals. These earnings figures are statistical averages for full-time year-round workers and are stated in 2015 dollars. As is apparent, cognitive disability significantly reduces earnings for males across various levels of educational attainment. When the level of educational attainment increases, it is not surprising that the disability decrement increases as well. This may be because with higher levels of education, we use our mind more at the job (remembering, concentrating, making decisions, etc.) and cognitive disability has a greater impact. These figures all pertain to non-severe disabilities. The disability effect would be much greater if the data pertained to a severe disability.
As shown in Table 1, females typically earn 60 percent to 80 percent of what males earn depending on their level of educational attainment. The reason for this is twofold: the effects of the composition of the labor force being male-dominated several decades ago and gender discrimination. Since females worked less several decades ago, older female workers in the sample are biasing the numbers downward. The good news is that the gap in wages is closing. It may be preferable to utilize “All Person Dollars” for the earnings of all workers of a particular level of educational attainment when a statistical earnings figure is used as a proxy for a female’s pre-injury and/or post-injury earning capacity.
An individual’s worklife expectancy is the “how long” of lifetime earnings. It tells us the number of years of future earnings that should be considered when estimating a loss. A worklife expectancy could be assumed (to Social Security or retirement age) but that is usually inaccurate. Instead, worklife expectancy can be statistically measured. Worklife expectancy, when statistically measured, is an average that combines the probabilities of life, participation in the labor force, and employment rates. It adjusts for periods when an individual may be out of the labor force. It is driven by variables such as age, gender, education, and disability status.
Table 2 provides worklife expectancy values for persons with no disability and persons with a non-severe cognitive disability as defined by the ACS. The worklife expectancy values take into account periods of unemployment and life events where an individual may not be in the labor force or employed. Worklife expectancy tends to increase with higher levels of education. Individuals with a non-severe cognitive disability experience a substantial decline in worklife expectancy as shown in Table 2.
The statistically average female has a lower worklife expectancy than the statistically average male. It is important to consider a female’s pattern of employment to determine whether a female exhibits a pattern of work most like that of the average female or average male. Otherwise, it would be unfair to reduce a female’s worklife expectancy by 4 to 5 years simply because she is female when she exhibits a work pattern most like that of a typical male.
Loss of future earning capacity
In estimating the loss of future earning capacity, an analyst can choose to use the individual’s actual pre-injury and post-injury earnings, or a proxy such as the statistical average earnings for an educational group as is presented in Table 1. However, in some cases an individual has earned the same dollar figure both pre-injury and post-injury. In this case, an analyst can account for the probable diminution of earnings over time by assuming the injured individual’s pre-injury earning capacity is 24.6 percent greater than his current post-injury earnings. This is because the difference between the earning capacity of a male with no disability and one with a non-severe cognitive disability is 24.6 percent. Another way to analyze this case is to utilize the same dollar figure for both the pre-injury and post-injury earnings figures. In this case, the expert believes both the pre-injury and post-injury earnings figures have demonstrated a sufficient number of years and are a reasonable representation of the individual’s capacity to earn money. There is still a substantial loss of earning capacity due to the reduction in worklife expectancy.
Table 3 presents the loss of future earning capacity for 35-year-old persons with cognitive disability under scenarios that consider both the worklife and earnings reduction and in scenarios that consider only the worklife reduction. If we take for instance, a 35-year-old male with a professional degree who has suffered a mild traumatic brain injury and is non-severely cognitively disabled, the loss of future earning capacity can be calculated. First, let’s consider that his earnings both pre-injury and post-injury are best represented by the statistical average for 35-year-old males with a professional degree: $183,626 pre-injury and $108,188 post-injury as in Table 1. Next, his worklife expectancy is calculated as 32.1 years pre-injury and 16.9 years post-injury as in Table 2. His loss of lifetime expected earnings is calculated by finding the difference between multiplying his pre-injury earning capacity by his pre-injury worklife expectancy and multiplying his post-injury earning capacity by his post-injury worklife expectancy. This is completed as follows: Loss = ($183,626 X 32.1 years) – ($108,188 X 16.9 years) = $4,066,017. All of the losses that consider both worklife reduction and earnings reduction are calculated in this manner.
Now let us suppose that this same male earns the same dollar figure post-injury as he does pre-injury, and as a result the expert concludes that his post-injury and pre-injury earning capacity figures can reasonably be represented as the same dollar value. In this case, the loss results from considering the reduction in worklife expectancy. His loss of lifetime expected earnings is calculated by finding the difference between his pre-injury worklife expectancy and post-injury worklife expectancy, and then multiplying this figure by his earning capacity that is held constant in both the pre-injury and post-injury scenarios.
This is completed as follows: Loss = (32.1 years – 16.9 years) X ($183,626) = $2,791,115. All of the losses that consider only worklife reduction are calculated in this manner. It is important to note that fringe benefits should also be considered, and for sake of simplicity have not been presented in this article. In addition, if the analyst utilizes either a net negative or a net positive discount rate, these calculations become more complicated when the loss is discounted to present value. The net neutral discount (total offset) approach is the one best supported by historical data and the approach assumed by these loss calculations.
Table 3 clearly shows that substantial losses result when only the probable reduction in worklife is considered. Clearly, an individual that goes back to work earning the same money as before injury suffers a loss of future earning capacity.
Case Study: Knitowski v. Gundy
A recent appellate court decision, Knitowski v. Gundy and State of New Jersey and John Glover, provides a good framework from which to think about earning capacity loss (Knitowski v. Gundy and State of New Jersey and John Glover, 2011 N. J. Super. Unpub. LEXIS 2797 (N.J. Super. 2011)). While traveling on the highway, Mr. Knitowski was forced to swerve to avoid another vehicle. As a result of swerving, his vehicle collided with a tractor trailer. Mr. Knitowski was flown to a hospital and was diagnosed with multiple facial fractures, traumatic brain injury and post-traumatic seizures.
The medical doctor opined to the permanency of his cognitive impairments. He earned around $800,000 in his last full year of employment prior to the injury. After the injury he earned $1.2 million, $1.3 million and $1.4 million in the three subsequent years. Even though he is now earning more, he still suffered an earning capacity loss as a result of the injury.
The attorney in this case went after the probable reduction in Mr. Knitowski’s worklife expectancy. His future worklife was likely reduced as a result of the permanent cognitive impairment. There is an interaction effect of disability and the aging process. Disabled individuals tend to retire sooner and become less productive in their work causing them to lose their current employment sooner than their non-disabled counterparts. The jury awarded future economic loss damages of $3.6 million in this case based on the reduction of worklife expectancy. The appellate court affirmed the trial court’s decision allowing the expert’s testimony based on reduced worklife expectancy for disabled individuals emanating from ACS data. The judge concluded that the plaintiff’s reduced worklife expectancy was based upon an accepted methodology and reliable data (the same source as that presented in this article).
The probable reduction in worklife expectancy for individuals with mild traumatic brain injury leads to a substantial loss of future earning capacity even when the individual returns to work earning the same or greater amount of money than prior to injury. This conclusion is supported by the best available disability data emanating from the U.S. Census Bureau’s American Community Survey.
Joseph T. Crouse, Ph.D., MBA, MA, CPA, CRC is a Vocational Economic Analyst with Vocational Economics, Inc. based in the San Francisco, CA office of the firm. He functions as an expert witness and consultant in cases involving personal injury, wrongful death, and wrongful termination. He received his Ph.D. in economics from University of Nevada, Reno, and is both a certified rehabilitation counselor and a certified public accountant. He may be reached at 415-367-9600 or via email to email@example.com.
2022 by the author.
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