Why You Need Multivariate Regression for True Pay Equity
There’s a good chance you are not familiar with multivariate regression analysis and have no idea what it means. While it sounds like complex statistical jargon, it’s actually a practical tool that answers a simple question: “Are we paying people fairly when all relevant factors are considered?” Based on a few reader questions, I thought it would be good to break this down and show you why it’s the only reliable way to truly understand pay equity in your organization.
Our reports show that we don’t have a pay gap
That’s wonderful! But I hope you haven’t used a simple average report to determine the (gender) pay gap. While it is a starting point, it’s far from sufficient under the Directive. Simple averages mask the complex factors that legitimately influence compensation. The Directive requires demonstrating equal pay for work of equal value, which necessitates a more sophisticated approach.
Simple averages or medians (e.g. in Excel) often mask underlying inequities. When you account for variables like education, experience, tenure, job level, and performance ratings simultaneously, you might discover pay differences that weren’t initially apparent. A proper multivariate regression analysis helps you:
- Identify which factors legitimately influence compensation in your organization
- Spot unexplained pay differences that could indicate bias
- Quantify the actual gap when comparing truly comparable positions
- Create more targeted remediation plans if issues are found
Even companies with seemingly balanced overall numbers should invest in this deeper analysis. The Directive expects organizations to demonstrate not just equal pay on paper, but equal pay for work of equal value, something that requires sophisticated statistical approaches.
What specific insights can multivariate regression provide that simpler methods miss?
Multivariate regression is uniquely powerful because it can:
- Identify the specific impact of each factor on compensation. For example, quantifying exactly how much value your organization places on each year of experience
- Reveal interactions between variables, such as when education is valued differently across departments
- Isolate unexplained pay differences that may indicate bias or discrimination
- Provide statistical confidence levels for your findings
- Identify which employee groups might need targeted adjustments
This level of insight is invaluable not just for compliance, but for creating truly fair compensation systems that attract and retain top talent.
Multivariate regression sounds complicated. Do we really need it for our mid-sized company?
It’s true that the complexity of your analysis should scale with your organization, but even mid-sized companies can benefit substantially from regression approaches. As your workforce grows beyond a handful of employees, the number of variables influencing pay decisions multiplies exponentially. An Excel sheet or simple report will not be enough to properly account for all variables. In fact, I would not recommend it for any company larger than 25-30 employees.
Remember that the Directive isn’t just about checking a box: the purpose is to create meaningful transparency that builds trust. Regression analysis provides defensible, data-driven insights that simple ratios simply cannot deliver. There are many solutions that make this type of analysis accessible even without dedicated data scientists on staff. An added advantage of these solutions is that you can track pay transparency over time. Some even have functionality that allows you to plot new hires in such a way that you don’t change the pay gap.
When should we NOT use multivariate regression analysis?
While multivariate regression is powerful, it’s not appropriate in every situation:
- When you have very small sample sizes (typically fewer than 30 employees in a comparison group), regression results may not be statistically reliable
- If your organization lacks sufficient data on legitimate pay factors (education, experience, etc.), you might inadvertently create misleading models
- For highly specialized roles where each position is unique and true “comparable work” doesn’t exist
- When using the analysis as a substitute for addressing known structural inequities (like occupational segregation)
- If you lack the expertise to interpret results correctly and could draw false conclusions
In these cases, you should consider alternative approaches like matched-pair analyses or qualitative job evaluations. You could also work with external specialists who can help design appropriate methodologies for your specific context.
How should we communicate regression analysis findings to employees?
Don’t feel the need to overwhelm your employees with statistics. That’s not necessary and the Directive does not require it. In fact, it doesn’t say anything about the format of publication. I’d recommend that you do the following when you communicating your findings:
- Explain which factors were included in your analysis and why
- Share aggregate results rather than individual comparisons
- Use visual representations where possible
- Describe any action plans for addressing identified issues
- Create opportunities for questions and discussion
Your goal is to build trust through appropriate transparency, not to create confusion with overly technical explanations.