[authors: Thomas Munson and Katherine Boukidis]

An Introduction to Civil Code 3361

Signed into California State law in July of 2019, California Civil Code Section 3361, also known as Senate Bill 41 (or SB 41), states:

“Estimations, measures, or calculations of past, present, or future damages for lost earnings or impaired earning capacity resulting from personal injury or wrongful death shall not be reduced based on race, ethnicity, or gender”.

Although seemingly straightforward, the wording of this addition to the Civil Code presents a number of challenges for forensic economists in the State of California.

• How do I incorporate this into my report?
• What does it mean to “reduce” damages?
• What are we reducing from?
• How do I interpret this new Civil Code so that I may properly abide by the statute and calculate damages?

Forensic economists rely on statistical data, such as life expectancy, work life expectancy, and earnings data, etc., that is often inherently based on race, ethnicity, and gender. Based on gender alone, we see statistical differences in the number of years lived, salaries earned, and number of years worked, among other differences. These differences compound when looking at data among different racial cohorts.

This white paper will discuss the potential impact and some of the potential interpretations of California Civil Code Section 3361 currently being discussed in the field of forensic economics.

Goals and Challenges

California Civil Code Section 3361 (CA 3361) was introduced to the State Senate as Senate Bill 41 (SB 41) with the intention of promoting “fairer” judgments for groups of people that have historically suffered from discrimination. SB 41 specifically points to women and minority individuals whose judgments in civil suits may be undervalued due to historical statistical data that reflects gender gaps and workforce discrimination. SB 41 provides the example that women in America typically earn lower wages than men, so any damages awarded to a woman for loss of earnings would be lower than the award calculated for a man.

SB 41 touts the State of California as being a “pioneer in civil rights, leading the way in prohibiting discrimination on the basis of race, ethnicity, gender, and other protected categories.” However, CA 3361 does not seem written in the level of detail and precision most forensic economists would have liked to see.

As most forensic economists in California consider at least gender in their calculations, there is pressure to understand and adhere to CA 3361. Unfortunately, phrases like “shall not be reduced” are vague and require a legal interpretation to understand exactly what was intended in CA 3361. The wording of the Civil Code section is vague enough so that it’s potentially not applied consistently across reports.

Some Interpretations

There are many interpretations of CA 3361 as the topic is still debated among forensic economists in California and nationwide. Even today, the topic is debated among forensic economics in meetings of the National Association of Forensic Economics and the American Economic Association.

One such interpretation by proponents of CA 3361 is that in the calculation of a female’s loss of earnings or earning capacity, the economist should rely on statistical data for males with respect to work-life expectancy or earnings data, when applicable. Males typically work longer and earn higher salaries than females, so the use of this data for a female’s damages calculation is viewed as more equitable.

Opponents of this interpretation would argue this approach is unfair to the defense as they should not be liable for higher, inflated awards for gender discrimination that exists in statistical data that the defense did not cause.

A middle-ground approach to CA 3361 is to run calculations using both male and female statistical data and let the court decide how to apply the Civil Code. Alternatively, relying upon statistical data that looks at the entire population may be the most neutral approach to the issue. For example, an economist could calculate losses based upon the median earnings of the population as a whole, ignoring differences between male and female. However, data for the total population still contains the same allegedly discriminatory statistics that led to CA 3361, resulting in lower damages for females than proponents of CA 3361 would expect.

Unfortunately, without more specific instructions or clarification on CA 3361, there will continue to be multiple interpretations of the Civil Code and how best to comply.

Economic Implications: Work-Life Expectancy

To better highlight the potential impact CA 3361 could have on an actual loss calculation, we have prepared an analysis of work-life expectancy utilizing data from a commonly cited source among forensic economists, The Markov Model of Labor Force Activity 2012-17: Extended Tables of Central Tendency, Shape, Percentile Points, and Bootstrap Standard Errors.

Work-life Expectancy (WLE) is a measure of the average number of working years a person has remaining based on their age, gender, level of education, and labor force activity. On the basis of gender alone, there are significant variances in WLE.

We have prepared a comparison of the average remaining number of years that males and females of the same age and education have left to work, or WLE (Figure 1). This table demonstrates how one single source of data could cause differences in the loss calculations between male and female plaintiffs. The example below assumes the plaintiff in question was active in the labor force and 30 years of age. There is no consideration for race or ethnicity in the underlying data.

^{Figure 1}

The data is presented in graph form below (Figure 2).

^{Figure 2}

At every education level chosen, males have a higher WLE than females. All else being equal, damages awarded to a male for loss of earnings or earning capacity calculated to their statistical WLE would be higher for a male plaintiff than a female plaintiff.

Economic Implications: Earnings Data

Differences also exist among statistical studies of males and females. One such source of earnings data is the US Census Bureau, Current Population Survey Tables for Personal Income (aka PINC tables). PINC tables provide mean and median levels of earnings for males, females, and the population as a whole divided by age groups, education levels, race, and work activity.

As an example, below is a chart of median earnings for males and females of all races, working full-time year-round, at different levels of education, for age groups 25-34 and 35-44, according to 2020 tables for personal income, PINC-03 (Figure 3).

^{Figure 3}

The data is presented in graph form below (Figure 4).

^{Figure 4}

In 2019, males generally had a higher median level of earnings than females at each of the chosen education levels, assuming the only difference is gender.

Again, we see a source of statistical data differentiating between males and females that, if used in an economic analysis, would create a divergence in the total economic damages for a male and female plaintiff.

Economic Implications: Lifetime Earnings

To further illustrate the point that gender differences in statistical data can lead to differences in damage awards, we have prepared an example calculation of loss of earnings capacity for a hypothetical minor plaintiff in a personal injury case utilizing the WLE and earnings data discussed above.

We assume the following:

• A male and female born on the same date;
• Bachelor’s degrees as their highest level of education;
• Workforce entry at age 22 following graduation;
• Annual earnings at the statistical median level for their respective age groups according to US Census Bureau, Current Population Survey Tables for Personal Income; and
• Statistical work-life expectancies per The Markov Model of Labor Force Activity 2012-17: Extended Tables of Central Tendency, Shape, Percentile Points, and Bootstrap Standard Errors.

Race and ethnicity are not taken into consideration. For purposes of this analysis, we assumed a date of birth and incident for both plaintiffs of January 1, 2014, with future damages discounted to present value at a net discount rate of 1.5% with a present value date of January 1, 2022, also known as the date of valuation (DOV). The DOV is the date to which future earnings are discounted to present value.

The only differences in our example are the plaintiffs’ gender and the utilization of male vs. female WLE and earnings data per the sources above. Note that median earnings are not available for ages 18-24 in the PINC tables, so mean earnings were used in its place.

A summary of assumptions and calculation totals is presented below (Figure 5).

^{Figure 5}

Given just the difference of 4 years in WLE of males and females age 22 and the differences in statistical median earnings, the total present value future loss of earnings for females is over 30% less than that of males.

This relationship is generally true across all ages and education levels, though not as significant as that of an injured minor plaintiff as in the example above. Differences in WLE and earnings between genders tend to be smaller as the plaintiff –in question is typically of an older age.

Above, we show that males with a BA degree earn significantly higher over their working life than a female with the same degree. To further support the issues that CA 3361 is trying to solve, we ran calculations across all education levels and found that males always out earn females (Figure 6). One of the reasons for this is because men have a historically longer WLE, which means they are in the workforce earning money for longer (Figure 7).

^{Figure 6}

^{Figure 7}

Conclusions and the Future of CA Civil Code 3361

While California might be “a pioneer in civil rights, leading the way in prohibiting discrimination on the basis of race, ethnicity, gender, and other protected categories” as claimed in CA Senate Bill 41, other states could soon follow in California’s footsteps and introduce similar provisions in their statutes. To date, legislation similar to CA 3361 has also been introduced in the Senate and Washington, DC, though their outcomes remain to be seen.

Understanding and properly interpreting CA 3361 is an important goal for litigators and economists who want to be prepared for the future.

Inherent biases and differences in the statistical data used by forensic economists tend to favor male plaintiffs when it comes to total damages as males statistically work longer and earn higher wages, as demonstrated in the examples above. But should defendants shoulder the consequences of this statistical bias, or can the forensic economist find a middle ground that is fair to all plaintiffs, independent of gender, race, and ethnicity?

Acknowledgments

We would like to thank Thomas Munson and Katherine Boukidis for providing insight and expertise that greatly assisted this research.

References and Sources

1. California Senate Bill No. 41, approved by Governor July 30, 2019, filed with Secretary of State July 30, 2019
2. Gary R. Skoog, James E. Ciecka and Kurt Krueger, "The Markov Model of Labor Force Activity 2012-17: Extended Tables of Central Tendency, Shape, Percentile Points, and Bootstrap Standard Errors," Journal of Forensic Economics, Vol. XXVIII, No. 1-2, (September 2019)
3. US Census Bureau, Current Population Survey Tables for Personal Income, PINC-03
4. District of Columbia B828 -Stormiyah Denson-Jackson Race and Gender Economic Damages Equality Temporary Amendment Act of 2020
5. Fair Calculations in Civil Damages Act of 2016, S. 3489, 114^th Congress (2016)
6. Fair Calculations in Civil Damages Act of 2016, H.R. 6417, 114^th Congress (2016)