Implementing California Civil Code Section 3361: Ensuring Damages Estimates Comply with State Law
On July 30, 2019, the Governor of California signed SB41 into law. This law became effective January 1, 2020 as California Civil Code Section 3361, and requires that calculations of past, present and future lost earnings or impaired earning capacity which are based on sources such as the Current Population Survey, not be reduced based on race,ethnicity or gender. The following provides clarity on how forensic economist can comply with these requirements.
In the 2009 survey of forensic economists, over 92% of forensic economists surveyed used race and/or gender in using historical data to calculate lost earning capacity. Typically, such calculations involve the death or serious injury of a minor or young adult, who has yet to establish an earnings history. The U.S.Census Bureau, Current Population Survey, regularly publishes the average earnings of persons in the United States, based on, among other things, educational attainment. Within the educational attainment grouping from the survey is data divided into sub-groups based on age, race and gender. From the data sets, forensic economists have historically chosen the categories which they know fit the demographics of the injured or deceased person and apply that data set when computing the loss of earnings or earning capacity calculations.
However, the State of California, on July 30, 2019, passed a law that effectively all but eliminates this practice. Language from the legislative record explains the rational:
“(e) To determine projected lost earning potential, court experts typically rely on the Bureau of Labor Statistics’ Current Population Survey. The results are a reflection of gender pay gaps and workforce discrimination, and they fail to account for possible progress or individual achievement. (f) The consequence of this bias, i.e., to use averages that represent generations of discriminatory practices, is to perpetuate systemic inequalities. These practices disproportionately affect the measured damage calculations of women and minority individuals by depriving them of fair and equitable compensation. (g) Using race and gender-based tables automatically undervalues damages of women and minorities by thousands of dollars, including children who have not yet had the opportunity to work or identify career options. Specifically, these practices discriminate against children of color, who are more likely to be impacted by environmental hazards created by the industrial facilities and factories located in low- income communities. (h) ) Any generalized reduction of civil damages using statistical tables alone, based on a plaintiff’s membership in a protected class identified in Section 51 of the Civil Code, is counter to the public policy of the State of California.” The results of SB41 went into effect on January 1, 2020 in the form of California Civil Code Section 3361, which states: “3361. Estimations, measures, or calculations of past, present or future damages, lost earnings or impaired earning capacity resulting from personal injury or wrongful death shall not be reduced based on race, ethnicity or gender.”
How Does this Civil Code Section 3361 Affect the Forensic Economists? In micro economics, assumptions can be made in order to isolate the effects of changes in variables. There are often competing hypotheses and opinions regarding the correct way to approach an economic question. However, in civil litigation, when there is a conflict between economics and the law, the law must win. In other words, while an economist may believe that economic theory demands that “A” is the correct methodology, “B” must apply because the law dictates this conclusion. Forensic economics is simply applied economics based on the ‘rules of law’. The legal reference points for the forensic economist are jury instructions plus case law. In California, the prevailing jury instruction for calculating lost or impaired earnings capacity is CACI 3903D: CACI 3903D. Lost Earning Capacity (Economic Damages) To recover damages for the loss of the ability to earn money as a result of the injury, the claimant must prove: 1. That it is reasonably certain that the injury that the claimant sustained will cause [him/her] to earn less money in the future than [he/she] otherwise could have earned; and 2. The reasonable value of that loss to [him/her]. In determining the reasonable value of the loss, compare what is reasonably probable that the claimant could have earned without the injury to what [he/she] can still earn with the injury. Consider the career choices that the claimant would have had a reasonable probability of achieving. Also, it is not necessary that [he/she] have a work history.
The key word is “Capacity.” In case law (Hilliard v A.H. Robins Co. (1983) 148 Cal.App.3d 374, 401) the term capacity is defined as what a person “could” earn. Consequently, since a forensic economist must follow the law, and there is data that illustrates within each educational attainment level what the average highest earnings achieved is, the logical choice is to select, within each educational attainment data-set, the highest earnings number regardless of race, ethnicity or gender as the appropriate measure of “capacity.” It is important to note that this measure of earning capacity is “reasonable” since it is the average or median earnings number, not the most a person in that educational attainment group has ever earned. As an example of the difference in damage estimates when computing earnings or earning capacity, consider the following example: Assume, as a result of birth trauma, two unrelated children with identical birthdates who are injured to the point whereby neither child will have any employability. The male, Andy, is white and Ann, a female, is Hispanic. It is assumed that ‘but for’ their injuries, both would have earned a bachelor’s degree from college. In the pre-SB41 era, forensic economists would have applied the following methodology to determine the damages. According to the Current Population Survey, Table PINC-03, 2018 median earnings for an Hispanic female with a bachelor’s degree, age 25-64, were $49,156 per year. In contrast, White males like Andy, age 25-64, with a bachelor’s degree were $76,504. By choosing to limit earning capacity based on each person’s respective race and gender, Ann’s wage damages would be reduced by $27,348 per year (compared to Andy). Over a forty-year period, with no adjustments for inflation of wages, or zero reduction to present value, the difference between the two would be over $1,093,900. We note that while in this example we are using earnings data from the 25-64 age group, similar results would be produced by using any cohort of life-cycle earnings (e.g., 10-year deciles) data. As stated above, there are economists, some 92% reported in the survey above, who would measure Ann’s calculations based on the earnings of an Hispanic female (or simply female) and Andy’s calculations based on a White male. Civil Code section 3361 dictates that beginning January 1, 2020, this no longer applies. It should be noted both calculations would still be less than the earning capacity for an Asian male with a bachelor’s degree. What is Ahead for the Economist? The forensic economist must consider the origin of his/her task is the law as expressed in the CACI Jury Instruction 3903D, “Lost or Impaired Earning Capacity,” i.e., what is the earning potential of the claimant? Using “capacity” as the guiding principle, the first step is to determine what level of educational attainment to assume. The next step (within this educational attainment level) is a matter of choosing the race and gender combination which yields the “highest earnings”. According to the Current Population Survey, Table PINC-03, 2018 median earnings for an Asian male with a bachelor’s degree, age 25-64, was $82,046 per year. Therefore, the utilization of the Pre-SB41 methodology would have undervalued Ann’s losses by $32,890 per year (or $1,315,600 from ages 25-64) and Andy’s by $5,542 per year (or $221,680 from ages 25-64).
Note that the use of tables which only consider gender will run afoul of Civil Code 3361. With respect to the example above, according to Table PINC-03 for Female All Races, the median bachelor’s degree earnings for this group was $56,462. Similarly, the median earnings for Male All Races was $75,308, but both these figures are less than Asian Male, and are therefore reduced, based on gender and race. Use of “Both Sexes, All Races” tables present a similar problem. In 2018, the median bachelor’s degree earnings for this group was $65,410.00, or a deficit of $16,636 per year, resulting in a huge differential of $665,440 from ages 25-64. As a result of using the Capacity Selection Methodology, no racial, gender or ethnic group’s earning capacity will be reduced whatsoever. We can assume that as the data tables are updated from one year to the next, perhaps other race and gender combinations will yield the highest earnings levels. By applying the Capacity Selection Methodology, the standard of measuring wage losses would easily be determined with no bias for race or gender. Table #1 illustrates the median annual earnings for various levels of Educational Attainment that would be in compliance with section 3361 of the Code.
Table #1 SB41 Compliant Earning Capacity Table (2018*)
Median Annual Earnings Educational Attainment Age 25-64 High School Graduate (Including GED) $46,623 Some College (No Degree) $52,416 Associates Degree $59,049 Bachelor’s Degree $82,046 Master’s Degree $102,392 Professional Degree $150,031 Doctorate Degree $117,442 *U.S. Census Bureau, Current Population Survey. PINC-03. Educational Attainment – People 25 Years Old and Over, by Total Money Earnings in 2018, Work Experience in 2018, Age, Race, Hispanic Origin, and Sex; 25 to 64 Years, Worked Full-Time, Year-Round. Furthermore, when it comes to choosing the period of time over which to calculate the damages, i.e., the work-life, the economist must be cautious as well. Many economists use work-life expectancy tables which report the average number of years similarly situated persons remain in the work force. For instance, some forensic economists rely on “The Markov Process Model of Labor Force Activity: Extended Tables of Central Tendency, Shape, Percentile Points, and Bootstrap Standard Errors”1 as a source of measuring the length of work-life expectancy estimates. As an example of another potential problem, using work-life expectancy estimates from this study, assumes that a man and a woman are both injured in a motor vehicle accident. Both are age 40, college graduates, and employed at the time of the incident. But neither is able to return to gainful employment because of their injuries. If the forensic economist referred to the work-life expectancy tables in the study above, he/she would discover the mean work-life expectancy for the male individual would be 22.93 years; but for the female, only 21.78 years. In this example, relying on the work-life expectancy tales to estimate the damages period would compensate the injured female far less than the male simply because of her gender. Like the race and gender example above, this example, too, violates section 3361. The obvious solution is to apply the Capacity Selection Methodology and use the longest work-life data for both males and females. The authors of this paper believe that work-life expectancy tables are neither correct nor the appropriate metric to apply to the table of measuring earning capacity.
California Civil Code 3361 requires that lost earnings and earning capacity calculations in civil litigation not be reduced based on race, ethnicity or gender. Forensic economists can easily comply with this rule by utilizing the highest earnings in each appropriate educational attainment level, regardless of race, ethnicity or gender, as well as utilizing work-life assumptions that do not reduce damages based solely on gender.