In today’s evolving workplace, organizations are increasingly held accountable for ensuring fair and equitable pay practices. Amid several high-profile pay discrimination settlements over the past few months, it’s prudent for enterprises to ensure they are able to identify and quickly remediate pay inequities.
Without appropriate controls — specifically a pay equity analysis software solution — in place, pay inequities can remain hidden and expand over time. As more pay transparency legislation goes into effect across the globe, the chances of a company’s pay practices being scrutinized, and followed by litigation, increases significantly.
Whether you are conducting proactive or compliance-driven pay equity analyses, you should do so across gender, race, age, disability, and more in one comprehensive analysis. This enables you to address pay disparities that could be occurring across multiple protected characteristics.
True Intersectional Pay Equity Analysis
The pay equity landscape is constantly evolving as more regulations come into force and court cases shape how those regulations will be applied. California, for example, recently included an intersectionality protection into its equal pay law. This affirmed the decision of Lam v. University of Hawai’i (9th Cir. 1994) 40 F.3d 1551, in which 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.
What is becoming increasingly clear is that more jurisdictions are emphasizing 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.
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.
Thus, while it’s important to identify and remediate pay inequities across gender and race, doing so via separate regression models is problematic and could lead to unresolved and persistent pay inequities ripe for litigation.
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 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.
Level Up Your Pay Equity Knowledge
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.
How Trusaic Can Help
Trusaic’s PayParity® solution is uniquely equipped to support global enterprises conduct true intersectional pay equity analyses. Our platform enables businesses to:
- Conduct comprehensive pay equity analyses across multiple dimensions of diversity.
- Identify and address disparities with precision and confidence.
- Stay compliant with global regulations while fostering a culture of equity.
By partnering with Trusaic, organizations gain access to cutting-edge tools and expert guidance to navigate the complexities of a true intersectional analysis.
An important part of your due diligence in selecting a pay equity software provider is ensuring that the solution is backed by robust methodology and a foundation of compliance.
Achieve Global Pay Equity With Software
Trusaic’s pay equity software solution analyzes compensation and benefits data directly from your HRIS system to uncover pay inequities across gender, race/ethnicity, disability, age, and more.
And when you choose PayParity, you have access to our innovative R.O.S.A. tool. Other pay equity solutions use a standard regression analysis, which doesn’t guarantee complete resolution and leaves you stuck in a frustrating cycle of “Whack a Mole” with your remediation approach. Our solution is able to run hundreds of analyses in parallel. This enables you to address inequities where they will have the most impact on pay equity while optimizing your remediation budget.
With Trusaic, you can proceed with confidence in conducting true intersectional pay equity analyses, knowing you are working toward achieving pay equity with the backing of a robust methodology that is accurate and explainable to comply with fast changing regulations.
