Dynamic Employment Litigation Trends in California

American law and employment litigation comprise dynamic, ever-evolving developments and trends. Recognising the variety of developments throughout the 50 states of the USA, California stands out as the most dynamic and far-reaching in its workplace protections and in the corresponding employment-related litigation. California is extraordinary in its dynamism both in employment law protections and in employment litigation. This article highlights key trends related to California employment law and litigation in a practical, innovative fashion, providing insights for moving forward in what is one of the world’s largest gross national incomes and most diverse populations.

California jury considerations and jury verdicts: high stakes in the California courts

Jury trials are on the decline nationwide. While trial frequency may be declining, for those cases that do reach trial, plaintiff win rates have increased. Even more disheartening for employers, the percentage of large trial awards (5 million or more) has also increased. This is the California experience.

California is the most challenging venue in the nation for jury trials, securing special recognition on the American Tort Reform Foundation (ATRF)’s Annual “Judicial Hellholes” list. The ATRF characterises California as the “plaintiffs’ bar’s laboratory for finding new ways to expand liability”. 

With the increase in number of filed claims, jury verdicts in employment cases have continued to skyrocket in recent months and years. There is no sign that they are levelling off.

California has experienced runaway verdicts in recent years, particularly in the Los Angeles Superior Court. In June 2024, a Los Angeles jury awarded a plaintiff nearly USD1 billion in damages for workplace sexual assault. The defendant, billionaire Alkiviades David, was hit with a staggering USD900 million verdict in favour of his former employee, who filed suit against him in 2020 alleging years of sexual assault, battery and harassment. The plaintiff was hired as a “brand ambassador” at one of David’s companies. She alleged she was subjected to sexual harassment, sexual assault and rape during the course of her employment. The jury awarded the former employee USD100 million in compensatory damages and USD800 million in punitive damages in what is one of the largest verdicts in a sexual assault case in history.

In November 2023, a jury delivered a verdict of USD14.17 million consisting of USD1.17 million in past and future lost earnings and USD13 million in emotional distress damages in a wrongful termination and gender discrimination case. The plaintiff was a former branch manager of a bank who alleged she was fired because she took medical leave to care for her ill husband. The jury found the plaintiff was fired because she took medical leave; the jury determined she was the victim of gender discrimination and that the bank had failed to take reasonable steps to prevent it. The bank claimed plaintiff was fired for using her position and power to abuse her subordinate employees, including putting her hands on one of those employees on at least three occasions.

In December 2023, a jury delivered a massive USD41.5 million verdict in a whistle-blower retaliation case. The verdict included USD2.5 million in past and future lost earnings, USD9 million in emotional distress damages, and USD30 million in punitive damages. The plaintiff worked as a nurse in a neonatal intensive care unit and alleged that she was fired after she raised concerns over patient safety.

In June 2022, a Los Angeles jury awarded USD464 million to two plaintiffs who alleged they were retaliated against for making complaints about sexual and racial harassment in the workplace. One plaintiff brought complaints to management about the alleged sexual harassment of two female employees. His claim asserted he was then constructively discharged. The other plaintiff made anonymous complaints to the internal ethics hotline about racial and sexual harassment of himself and other co-workers. After a two-month trial, the jury awarded one plaintiff USD2 million in compensatory damages and USD40 million in punitive damages, and the other plaintiff USD22.4 million in compensatory damages and USD400 million in punitive damages.

A December 2021 jury verdict from Los Angeles Superior Court awarded USD5.4 million in compensatory damages and USD150 million in punitive damages to a discharged insurance company executive who alleged discrimination and retaliation. The judge reduced the verdict to USD18.95 million in punitive damages but the total verdict still topped USD20 million.

Increasingly employers are looking to enter into arbitration agreements with employees and prospective employees. This trend is likely to continue in light of the runaway verdicts plaguing California’s court system. Enforceable arbitration agreements remain a safeguard for employers against catastrophic verdicts like these – catastrophes that are occurring with ever-greater frequency in the trial courts of California.

Complex California wage and hour class action litigation

The plaintiffs’ bar continues to utilise class action litigation to reap large damages from employers. That increase has not been lacking in wage and hour litigation, especially in California. Both collective actions under the federal Fair Labor Standards Act (FLSA) and class actions pursuing California law are powerful tools for employees litigating wage and hour claims.

Employees may bring federal wage and hour claims under the FLSA. A plaintiff suing on FLSA claims may seek certification of a collective action of “similarly situated” employees, who “opt in” to the lawsuit after certification is granted. Employees may also bring claims against an employer for violating California state wage and hour laws. In contrast to the FLSA collective action, Federal Rule of Civil Procedure 23 and equivalent state class action rules allow a plaintiff to pursue a class action if certain prerequisites are met, including:

• the numerosity of class members, the presence of common questions of fact or law, the typicality of the representative members’ claims in comparison to the class, and the adequacy of class counsel; and
• usually, the predominance of common questions of fact and law, and the superiority of the class action to other methods of adjudication.

Other plaintiffs do not “opt in” to a Rule 23 class action – instead, they “opt out” after receiving notice of the litigation.

Within both collective action and class action litigation, plaintiffs’ attorneys commonly use statistics to avoid issues of individual proof and to establish common liability at the class certification stage. The use of statistics in this context refers to the surveying of employee experiences – including job requirements, activities performed throughout the workday, wage and payment details, hours spent working, and management practices – and the analysis of the results of those surveys.

Setting the stage: Dukes, Duran, and Tyson Foods

As class litigation and the use of statistics have increased during the past 15 years, seminal cases from the US Supreme Court and the California Supreme Court have guided parties and the lower courts on the uses and limitations of statistics in class litigation.

In 2011, in Wal-Mart Stores, Inc v Dukes (“Dukes”), the US Supreme Court reversed certification of a nationwide class of 1.5 million female employees who alleged sex discrimination. The proposed method of analysing class claims, approved of by the Ninth Circuit, included depositions of a sample to determine liability and extrapolation of damages: “A sample set of the class members would be selected, as to whom liability for sex discrimination and the back pay owing as a result would be determined in depositions supervised by a master. The percentage of claims determined to be valid would then be applied to the entire remaining class, and the number of (presumptively) valid claims thus derived would be multiplied by the average back pay award in the sample set to arrive at the entire class recovery –without further individualised proceedings.”

Writing for the court, Justice Scalia “disapprove[d] that novel project”, emphasising that “a class cannot be certified on the premise that Wal-Mart will not be entitled to litigate its statutory defenses to individual claims”.  Following Dukes, courts around the country used the decision to enforce narrowed applications of statistics in class litigation.

Three years after Dukes, in 2014, the California Supreme Court issued its Duran v US Bank decision (“Duran”), undermining plaintiffs’ use of statistics in class litigation. Duran involved wage and hour claims brought as a class action under California’s unfair competition law. The plaintiffs claimed US Bank misclassified 260 loan officers as exempt from overtime payments. Interestingly, the plaintiffs in Duran employed the same expert as in Dukes and attempted to use statistical sampling beyond certification to prove class-wide liability.

The California trial court permitted the plaintiffs to prove liability and damages on behalf of the entire 260-member class using a small sample of 19 class members and two named class representatives. Even more problematic, the trial court refused to allow US Bank to present testimony of employees who claimed they spent more than 50% of their time on exempt duties. Based on the testimony of the sample group alone, the trial court determined US Bank misclassified every class member. The lower court then approved damages based on a calculation derived from the sample group, leading to a USD15 million award with interest.

The California Supreme Court reversed and ordered the class decertified. According to the court, the statistical method caused a “manifest” injustice to US Bank and was “profoundly flawed”. Duran advised that “[t]he sample relied upon [to prove liability or damages in wage and hour litigation] must be representative and the results obtained must be sufficiently reliable to satisfy concerns of fundamental fairness”. The court provided three reasons why the sampling employed by the plaintiffs did not meet this criteria, as follows.

• A sample size of 19 class members and two named representatives was too small relative to the variability of the class members. As explained by the court, “[i]t is impossible to determine an appropriate sample size without first learning about the variability in the population”. Variability – or the differences that exist in the total population – can be determined by an expert using existing data, timesheets, other personnel records, or surveys. Ultimately, it is important to remember that sample size cannot be random; it must be based on the population’s distribution.
• The statistics were plagued by non-response bias and selection bias. Non-response bias occurs where individuals who receive the survey but fail to answer differ in significant ways from those who participate. Selection bias occurs where individuals are selected by the survey administrator to be included or excluded from the survey. These biases cause the results to be unreliable. It is best to ensure participants are truly randomly selected (eg, by a computer) and that an expert analyses the data to ensure that non-respondents do not differ meaningfully from those who do respond.
• The plaintiffs’ statistical model was plagued by a high margin of error, as is common with small sample sizes. Such a high margin of error renders the results unreliable. As Duran noted, “the court must determine [with the help of experts] that a chosen sample size is statistically appropriate and capable of producing valid results within a reasonable margin of error”. Only then will the court meet its burden of ensuring that the proposed methodology will produce reliable results. To avoid an erroneously high margin of error, it is again important to ensure that the statistical model is appropriately developed with a proper sample size.

Following Duran, litigants and the lower California courts used these factors – sample size, non-response bias and selection bias, and margin of error – to evaluate the representativity of proposed statistical models and to distinguish proposed models from the “trial by formula” that Dukes rejected.

Two years after Duran, in 2016, the US Supreme Court in Tyson Foods, Inc v Bouaphakeo (“Tyson Foods”) affirmed certification of a class of employees who alleged that Tyson’s failure to pay them for donning and doffing protective gear violated the FLSA. In doing so, the court permitted the plaintiffs to use representative statistical evidence to establish the number of individual hours each employee worked, so as “to fill an evidentiary gap created by the employer’s failure to keep adequate records”. In finding that the use of a sample was an appropriate method of proving class-wide liability, the US Supreme Court noted that “one way” to establish the sample was permissible was “by showing that each class member could have relied on that sample to establish liability if [they] had brought an individual action”.

In Tyson Foods, individual employees could rely on the sample owing to Tyson’s failure to keep adequate records. Importantly, the court noted that – although “[r]epresentative evidence that is statistically inadequate or based on implausible assumptions could not lead to a fair or accurate estimate of the uncompensated hours an employee has worked” – Tyson did not raise any challenge to the plaintiffs’ experts’ methodology under Daubert (ie, using the above-mentioned factors to undermine the reliability of the statistical model).

At bottom, Tyson Foods held: “Whether a representative sample may be used to establish class-wide liability will depend on the purpose for which the sample is being introduced and on the underlying cause of action. In FLSA actions, inferring the hours an employee has worked from a study such as [the plaintiff’s expert’s study] has been permitted by the [c]ourt so long as the study is otherwise admissible.” Where the employer fails to maintain adequate records of how much overtime each employee worked, Tyson Foods determined plaintiffs are permitted to establish class-wide liability on wage and hour claims through representative evidence.

Application by the California courts

Against this backdrop, the California lower courts have issued numerous decisions analysing litigants’ use of statistics in wage and hour litigation, providing factors for parties to consider at the certification and de-certification stages.

Denial of class certification

Following the Dukes and Duran decisions, several federal district courts in California rejected plaintiffs’ statistical methodologies at the class certification stage. By way of example, in 2014, the plaintiffs in Sirko v IBM Corp sought to certify a Rule 23 class of exempt IT employees who were allegedly misclassified and denied overtime by IBM. In an effort to gain class certification, the plaintiffs concocted a 47-question survey concerning putative class members’ work duties. The Central District of California rejected the survey, determining it “lack[ed] basic indicators of reliability”.  Specifically, the survey:

• was devised and administered by plaintiffs’ counsel, not a statistician or expert;
• included some questions that required non-binary answers, rather than a simple “yes” or “no”, which are not easily quantifiable through statistics; and
• likely included biased results, given that respondents were provided a cover letter that noted their potential ability to recover damages in the class action.

Likewise, in 2020, the Northern District of California in Santos v UPS (“Santos”) rejected the plaintiffs’ use of nine declarations out of more than 2,000 putative class members, finding the nine handpicked examples likely suffered from selection bias. The Santos court cited Duran, noting that putative classes may rely on statistical sampling from a qualified expert to show evidence of a consistently applied policy, but the “degree of consistency” required to certify a class is likely to depend on the circumstances. The court emphasised that statistical samples cannot be too variable and thus a court may conduct a preliminary assessment to determine the level of variability.

The California Court of Appeal similarly denied class certification in McCleery v Allstate (“McCleery”) in 2019, after finding that the plaintiffs’ trial plan was inadequate and unfair. There, the plaintiffs relied on an expert’s declaration that liability could be determined and damages calculated class-wide through statistical analysis of results obtained from an anonymous, double-blind survey of a sampling of class members. Citing Duran, the McCleery court found that the survey did not necessarily fail as a scientific measurement procedure, but that it failed as a trial plan because it failed to enable the plaintiffs to establish defendants’ liability on a class-wide basis.

First, the expert’s survey did not ask key questions essential to establishing liability. Additionally, anonymising responses from survey participants unfairly insulated the survey from any meaningful examination. Although the plaintiffs intended to answer the ultimate question of class-wide liability solely using expert testimony regarding the survey responses, the court found that the testimony was based on multiple layers of hearsay that the defendants could never challenge. The court held that the defendants had the right to defend against the plaintiffs’ claims by impeaching the evidence supporting them, but the proposed procedure utilising only the anonymous survey forestalled the exercise of that right.

Class certification granted

While the foregoing decisions – among many others – invoked Dukes and Duran to deny class certification, other courts post-Tyson Foods have become increasingly more receptive to class certification, even in the face of less statistically sound representative evidence.

Shortly after the Tyson Foods decision, in 2016, the Ninth Circuit affirmed certification of a California wage and hour class in Vaquero v Ashley Furniture Indus, Inc. There, the defendants relied on Dukes to argue that the use of representative evidence would inevitably change the substantive rights of the parties by preventing defendants from individually cross-examining and challenging each class member’s claims. The court disagreed: “[The] defendants’ reliance on Dukes, in this regard, is misplaced. As the [c]ourt made clear in Tyson Foods: ‘[Dukes] does not stand for the broad proposition that a representative sample is an impermissible means of establishing class-wide liability.’”

Noting Tyson Foods expressly permitted the use of representative evidence to establish class-wide liability, the court found the lower court’s grant of class certification did not expand the plaintiff’s or the class’ substantive rights. Instead, the court determined that the defendants could challenge the viability of the representative evidence at a later stage, but class certification was appropriate.

In 2019, the Northern District of California denied a motion to decertify a wage and hour class in DeLuca v Farmers Ins Exchange (“DeLuca”). There, the plaintiffs sought unpaid overtime wages for themselves and a group of current and former employees. The case covered a total of 78 individuals. The plaintiffs’ trial plan proposed using two groups of testifying opt-in plaintiffs from the same sample of 20 trial witnesses. The defendant complained that no explanation was provided regarding the methodology behind the sample, other than that class counsel selected witnesses to represent a range of geographic areas and levels of experience, showing concern that the plan was based on non-random, cherry-picked testimony of only named or opt-in plaintiffs (ignoring the 40 absent class members). The defendant relied on Tyson Foods to argue that “‘[r]epresentative evidence that is statistically inadequate or based on implausible assumptions could not lead to a fair or accurate estimate of the uncompensated hours an employee has worked’”. The defendant further expressed concerns that the sample size had not been determined using a statistical approach by first selecting a desired confidence level.

However, the court held that Tyson Foods “does not require [p]laintiffs to apply statistical principles to ensure representativity”. It explained that Tyson Foods “did not discuss expert statistical studies because they are the only way a plaintiff may prove [their] claim by representative evidence… but because those plaintiffs offered such a study”. The court emphasised: “[T]he standard is just and reasonable inference and not mathematical certainty and trial can proceed on a representative basis… Furthermore, there is no rigid requirement that the number of [p]laintiffs and absent class members who testify must meet the margin of error threshold set forth under statistical principles.” Although the defendant raised concerns about sample size and selection bias, the court held that “the law does not require [p]laintiff’s proposed sample to meet a particular statistically significant threshold or be designed to generate results within a certain confidence level and margin of error” – instead, the results simply need to be representative.

Practical considerations for employers

While a court’s receptiveness to the use of statistics to either certify or decertify a class action will depend greatly on the size of the class, the claims at issue, and the statistical methodologies proposed, these recent decisions provide helpful insight to employers who are confronted with statistical models in wage and hour litigation. Employers who are confronted with proposed statistical models from plaintiffs should still consider the Duran factors in determining whether to combat the proposed models:

• sample size used;
• presence of non-response bias;
• presence of selection bias;
• potential for large margin of error;
• whether the model was created by counsel or a non-expert;
• whether the model calls for non-binary responses; and
• whether the survey will be or was provided with a cover letter that describes the potential for class-wide payouts.

Following Tyson Foods (and as emphasised in DeLuca), any arguments regarding the impropriety of a sample should consider the availability of other evidence upon which class members could rely (especially in the absence of a record-keeping failure) and should be rooted in representativity.

In summary, California and national wage and hour litigation continues to threaten employers. As the use of class actions to pursue these claims continues, so does the use of statistics by plaintiffs to establish the appropriateness of a class model for both liability and damages. Employers need to understand how they can combat unreliable statistical models that may lead to erroneously large damages awards. Keeping in mind the courts’ lessons about combating plaintiffs’ statistical models will go far in evading class liability and damages in wage and hour litigation.

Conclusion

As the trends analysed illustrate, California is a trendsetter in its dynamic employment litigation. Addressing these matters requires a thoughtful, strategic approach. The most effective strategy blends an understanding of the law and the litigation dynamics with the human dimension of respect, fulfilment, and promise. California’s public policy protections present remarkably far-reaching implications and nuances for even the most sophisticated employers.