Pay equity audit challenges

6 min readPublished May 02, 2022Updated May 06, 2022

Caveat: I am not a lawyer and this post is not legal advice. The information provided is general in nature. Please consult your legal counsel for more information.

In one of my recent roles as an Engineering Manager, I advocated for and successfully conducted a pay equity audit. My team was small and we didn't have an HR or legal department. We solved some unique challenges to complete our audit and stay committed to our inclusion goals.

In this post, I outline the challenges we encountered as a small tech company self-conducting an audit and how we solved them. For more information about what a pay equity audit is, please refer to my previous post.

1. Leadership buy-in

I got the idea for a pay equity audit in early 2020. My team had spent the previous year adjusting our titles, creating career paths, and standardizing our salary bands (which we eventually published publicly). After all that work, I wanted to make sure we weren't overlooking any existing pay discrepancies.

Like all big company initiatives, leadership buy-in is critical for successful pay audits.

We were a small team (30-ish people) and did not have any HR employees. This meant that it was challenging to get the leadership team's buy-in that this was a good idea. Not because anyone was against equity! But because we didn't have any in-house professionals with this kind of experience. And because we didn't have anyone who knew if we would be opening ourselves up to legal liability.

Many states now have safe harbor laws for companies self-conducting pay equity audits. This limits legal liability. And proactively remediating discrepancies mitigates future risk.

Before moving forward, we consulted with our legal counsel. We shared our methodology with them and our goals for the audit.

To address the challenge of who would do the work, we sat down and planned out the audit timeline. We assigned tasks for each step of the audit. This meant myself and another manager taking on a lot of the organization tasks. And we leaned heavily on our office administrator for the administrative tasks. She was the real hero of this initiative.

2. No prior experience

No one in our company had ever participated in a pay equity audit before. We didn't have a previous process to iterate on.

The two managers tasked with organizing the audit (myself and a coworker) did a lot of research on how to conduct an audit of this kind. We created a methodology for us to follow before we started anything. By outlining the entire process step-by-step, we were able to scaffold our deliverables to make it easier for everyone involved.

We also hired a data scientist who would act as an impartial party for the data analysis. We didn't have any experience with complex data analysis and wanted the audit to be unbiased. By hiring him early in the process, we were able to lean on his expertise when creating the methodology, particularly around data collection.

3. Title changes

In the year before our audit, we adjusted our career paths and role titles. We had changed the names of the titles and introduced a new mid-level role for both our designers and our engineers. Because we needed data over time, there wouldn't be an easy 1-to-1 mapping for titles and salaries.

To address this, we included a historical title mapping in our methodology document to provide our data scientist. For the titles that were renamed, this was straightforward. But for the mid-level titles that were split into two new titles, we used a base salary threshold to determine the new title mapping.

4. Dirty data

A big part of any pay equity audit is collecting data. You can rely on your HR systems or on self-reporting.

For our audit, we used a hybrid approach. We used data from our HR systems for historical data and gave current employees the option to self-report or use their data from our HR systems.

Much of the data you need to conduct a comprehensive audit is not captured in HR software. And if it is, it may not be up-to-date or of the quality that you want for your analysis.

Gender is one common example. Many HR systems reduce gender to "Male" and "Female". Not only is this an inaccurate representation of gender (male and female refer to biological sex), but it also fails to represent the full gender spectrum.

To manage the incomplete data, we created a demographic survey to allow employees to opt in and self-identify their demographic information. This also allowed us to include more demographic groups than any of our HR systems collected. Because of the manual nature of the data collection, we allowed extra time in our timeline for our team to complete the survey.

5. Little data

Even after we had collected all the data that we wanted to analyze, we had a very small dataset. Our team was around 30 people. When we removed data points that didn't fit the constraints of our analysis (opted-out or no comparable job role to compare with), we were left with around 20 data points for analyzing job level and promotion data.

This is where hiring a data scientist was especially helpful. He built models and ran standard regression analyses on those demographic groups where we had enough data points and variability. For those groups where we didn't, he used cohort analysis. Because many of the cohorts were quite small, we relied on main effects and weren't able to look at how interactions affected the outcome.

Despite all his work to do qualitative analysis where quantitative was impossible, there were still some demographic groups where we didn't have a large enough sample size to analyze. Unfortunately, that is the reality of doing any kind of data analysis with a small dataset.

6. Vocabulary

We collected data around several demographic groups, including:

  • Gender identity

  • Gender

  • Race/ethnicity/cultural background

  • Sexual orientation

  • Health or disability status

  • Age

  • Caretaker responsibilities

These categories and the labels within them aren't widely understood by everyone. And often, there isn't universal agreement about the definition and connotation of a term, especially when applied to one’s identity.

To achieve shared understanding and reduce confusion, we defined all the terms we used in the audit. We used terms preferred by the groups which they described (but language isn't perfect and no demographic group is a monolith).

We included our acting definitions in every presentation we gave to the team and on the demographic survey itself.


Despite encountering some unique challenges organizing the first pay equity audit for a small company, we also experienced some wins in the process.

We were a small team and had a demonstrated history of investing in inclusion and transparency. Because of this, we were able to earn the team's trust in the audit process relatively easily.

For large companies and/or companies that have HR departments conducting the audit, this might not be the case. HR exists to protect company interests. There can be a lot of justified hesitancy to self-identify as part of a marginalized group and share that with your company. If you are considering a pay equity audit, pay special attention to building the trust of your team before starting this process.

A successful, informative pay equity audit is possible no matter your company's specific challenges. Large or small, with or without HR, experienced or not. Proper planning and proactive problem-solving are key.

Well-Rounded Dev

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