California is enshrining the concept of intersectionality in how it classifies discrimination under its Equal Pay Act.
The state’s legislature passed a bill (CA SB 1137) Aug. 28 to clarify that California laws prohibiting discrimination also apply to intersectional claims of discrimination. Governor Gavin Newsom signed the bill into law Sept. 27 and it will take effect Jan. 1, 2025.
The California legislature described the concept of intersectionality as: “An analytical framework that sets forth that different forms of inequality operate together, exacerbate each other, and can result in amplified forms of prejudice and harm. The framework and term “intersectionality,” coined and popularized by legal scholar Professor Kimberlé Williams Crenshaw, captures the unique, interlocking forms of discrimination and harassment experienced by individuals in the workplace and throughout society.”
Through SB 1137, California’s Legislature affirms the decision of Lam v. University of Hawai’i (9th Cir. 1994) 40 F.3d 1551, where the Ninth Circuit found that when an individual claims multiple bases for discrimination or harassment, it may be necessary to establish whether the discrimination or harassment occurred on the basis of a combination of these factors, not just one protected characteristic alone.
It’s the latest indicator that jurisdictions will consider pay discrimination across multiple factors, which underscores the importance of conducting a true intersectional pay equity analysis.
The Legal Landscape
The workplace equity landscape is constantly evolving as more regulations come into force and court cases shape how those regulations will be applied. What is becoming increasingly clear is more jurisdictions are harping on pay discrimination across multiple protected classes.
In addition to California’s recent bill, the European Union and United Kingdom have indicated either via legislation or court rulings that it will consider pay discrimination across multiple class factors.
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The recently-elected Labour Party in the UK plans to expand required pay gap reporting to include race/ethnicity and disability. The UK government also provided guidance for how to report on race/ethnicity pay gaps, which highlights the need for intersectional analysis.
The EU Pay Transparency Directive also makes clear that discrimination will be considered across multiple protected factors, including sex, race/ethnicity, religion, disability, age, or sexual orientation.
In its original proposal, the European Commission noted: “A new definition aims at clarifying that, in the context of gender pay discrimination, such combination should be taken into account, thus removing any doubt that may exist in this regard under the existing legal framework. This will ensure that the courts or other competent authorities take due account of any situation of disadvantage arising from intersectional discrimination, in particular for substantive and procedural purposes, including to recognise the existence of discrimination, to decide on the appropriate comparator, to assess the proportionality, and to determine, where relevant, the level of compensation awarded or penalties imposed. One particular case of such intersectionality refers to the situation of migrant women who may risk multiple forms of discrimination based on their sex, racial or ethnic origin, or religion or belief.”
With this as context, it would behoove employers subject to any of these reporting requirements or with operations in any of these jurisdictions to conduct regression analyses that consider multiple protected classes.
Why Intersectionality Matters
Including multiple factors in your regression analysis will lead to more precise outcomes when you are looking to remediate pay disparities. As Trusaic Executive Vice President of Pay Equity and Total Rewards Strategies and Solutions Gail Greenfield lays out in her Pay Equity Deep Dive Series blog, many organizations examine race/ethnicity and gender separately in their pay equity analyses.
This approach is problematic for two reasons. First, as part of your pay equity analysis, you create a statistical model of pay for each of your Pay Analysis Groups (PAGs). Each model includes Wage Influencing Factors (WIFs), which are compensable factors that one would expect to influence employee pay. The regression weights associated with these WIFs are used to compute an employee’s neutral pay prediction.
If you run separate regression analyses to examine the relationship between gender and pay and between race/ethnicity and pay, you’ll end up with two sets of regression weights and two sets of predicted pay values. Predicted pay values are an important consideration in crafting a remediation strategy, so working with two (or more) sets of predicted pay values will be problematic to reconcile during remediation.
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Second, if both race/ethnicity and gender are related to pay, then excluding race/ethnicity from a regression analysis that examines the relationship between gender and pay will result in omitted variable bias. In the words of Statistics By Jim (a great resource!), “Omitted variable bias (OVB) occurs when a regression model excludes a relevant variable. The absence of these critical variables can skew the estimated relationships between variables in the model, potentially leading to erroneous interpretations. This bias can exaggerate, mask, or entirely flip the direction of the estimated relationship between an independent and dependent variable.”
What this means is that to estimate the relationship between gender and pay in the U.S., the regression analysis should include race/ethnicity as well, plus relevant WIFs. Otherwise, the estimate of the gender effect may incorrectly attribute variation in pay to gender that is actually due to race/ethnicity differences. Similarly, estimates of the effects of WIFs may incorrectly attribute additional variation in pay to WIFs that is actually due to race/ethnicity differences. If race/ethnicity plays no role in driving pay differences, its inclusion will not systematically distort gender or WIF effect measurements.
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The primary objective in conducting intersectional pay equity analyses is to mitigate risk by ensuring you are not blind to pay inequities that can lead to costly legal action. Beyond that, it provides a much clearer path toward identifying root causes of pay disparities at your organization and effectively remediating.
Achieve Authentic Pay Equity With Software
Trusaic’s PayParity® is the world’s premier software solution for conducting a pay equity analysis across your workforce at the intersection of gender, race/ethnicity, age, disability, and more in one statistical regression analysis. By offering an all-encompassing view of your workforce’s compensation, PayParity not only identifies pay inequities but also uncovers their root causes.
This invaluable insight empowers you to proactively address and prevent pay inequity, setting the gold standard for fairness. When you leverage Trusaic’s market-leading software solution and expert guidance, you can proceed in your pay equity journey with confidence.
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