Latest / The Diversity Career Podcast with Fexingo: Inclusion, Equity, and Underrepresented Professionals / How Pay Equity Audits Rebuild Trust
Transcript
- Lucas: Most people think a pay equity audit is just a spreadsheet exercise — run some numbers, find a gap, fix it, move on. But the reality is that these audits are often the first time leadership sees their own organization reflected in data they didn't generate. It’s uncomfortable, yes, but it’s also the only way to separate perception from reality. Luna: Exactly. And I think what surprises people most is that the gap isn't always about overt discrimination. Sometimes it's about historical compounding errors that started three years ago with one bad negotiation. Lucas: Right. Let's talk about a specific case from last quarter involving a mid-sized software company. They brought in an external consultant to do a regression analysis on compensation. The headline number was a thirteen percent adjustment needed for women in technical roles compared to their male counterparts. Thirteen percent sounds like bias, but when you drill into the variables, it turns out half of that was due to promotion delays, not base salary disparities. Luna: So the issue wasn't that they were paying less, it was that they weren't promoting them fast enough? That changes the whole conversation from 'fixing payroll' to 'fixing process.' Lucas: Precisely. And here is where it gets tricky. If you just raise salaries by thirteen percent across the board to match the average, you are essentially rewarding a broken system. You are paying people more because they waited longer for a promotion. That doesn't solve the equity problem; it just masks it with higher costs. Luna: It feels like a band-aid on a bullet wound. You have to fix the promotion criteria, or you're just subsidizing inefficiency. Lucas: But then you hit the legal wall. In many jurisdictions, once you identify a statistically significant disparity, you open yourself up to litigation if you don't have a legitimate business justification for the difference. So companies are terrified to do these audits because they might find evidence that makes them liable. Luna: That is such a perverse incentive. Basically, ignorance is legally safer than knowledge. So they keep doing nothing. Lucas: Until recently. There has been a shift in how courts view good faith efforts to correct disparities. If a company proactively identifies a gap and implements a remediation plan, even if it's imperfect, it often serves as a strong defense against claims of intentional discrimination. The trend is moving toward 'safe harbor' protections for transparent auditing. Luna: So the smart play is to audit, document the rationale, and fix it quickly before anyone else points it out? Lucas: Yes. And let's get specific about the 'rationale' part. A lot of companies make the mistake of attributing gaps to 'market rate adjustments.' But if your market data is sourced from competitors who also have diverse issues, you are just importing other people's biases into your own pay structure. Luna: That is huge. If everyone uses the same flawed survey data, the whole industry stays skewed. You need internal benchmarks, not just external ones. Lucas: Exactly. You have to build salary bands based on the value of the role, not the history of the person filling it. This is where the 'skills-based' hiring movement actually intersects with pay equity. If you hire based on skills and pay based on the skill set, you remove the noise of past salary history. Luna: Which brings us back to the candidate negotiation. If you ban asking for previous salary, you force both sides to agree on a number based on the job itself. It levels the playing field immediately. Lucas: It does. But there is a counter-argument that high performers will still demand more regardless of the baseline. And sometimes, ignoring prior salary means you miss out on negotiating down if someone is currently overpaid relative to the market. It's a delicate balance. Luna: I doubt that happens often enough to outweigh the structural damage of perpetuating low offers for marginalized groups. The risk seems asymmetric. Lucas: Agreed. Now, let's talk about intersectionality. Most initial audits look at gender or race in isolation. But the deepest gaps appear at the intersection. For example, Black women in finance often face a wider gap than white women or Black men. If you only adjust for gender, you leave the Black women behind. Luna: So you need multi-variable models. It requires much better data hygiene. You can't just tag someone as 'female' and call it a day. Lucas: You need robust demographic data collection. And this is where HR departments often struggle. They don't have the data because employees haven't self-identified, or the data is siloed in different systems. You can't analyze what you can't see. Luna: It feels like a technology problem as much as a cultural one. You need tools that can handle complex cross-referencing without violating privacy. Lucas: Absolutely. Anonymization is key. When you present these findings to the board, you don't show individual names. You show distributions. You show medians. You show the delta. It keeps the focus on systemic patterns rather than individual grievances. Luna: That protects the individuals while forcing the organization to confront the pattern. Smart framing. Lucas: One more thing on the remediation side. When you find a gap, how do you close it? Do you bring everyone up to the highest paid peer? Or do you freeze raises for the highest paid until the others catch up? Luna: Freezing raises is risky. It creates resentment among the highest earners. Bringing everyone up is expensive. Is there a middle ground? Lucas: There is. You create a 'red circle' policy for those who are already overpaid. Their salary stays flat, but they get non-monetary recognition or accelerated development opportunities. Meanwhile, you budget aggressively to bring the underpaid group up to parity over eighteen to twenty-four months. It spreads the cost and avoids immediate disruption. Luna: That is a practical roadmap. It acknowledges the budget constraints while committing to a timeline. It shows intent. Lucas: Intent matters. Because if you just say 'we'll get to it next year,' nobody believes you. Transparency forces accountability. When you publish the results internally, even partially, you signal that this is a priority. Luna: Do companies usually publish the results? Or is it mostly kept quiet? Lucas: Most keep it quiet initially. But the leaders who are winning now are sharing the 'before and after' metrics with their teams. They say, 'Here is where we were, here is our plan, here is where we are now.' It builds trust faster than any diversity training ever could. Luna: Because it proves they are willing to spend money and change processes. Actions speak louder than statements. Lucas: Exactly. And for the listener thinking about their own career, this is why you should ask about pay transparency policies during interviews. Not 'how much do I make?' but 'how does this company determine pay ranges?' That tells you everything you need to know about their maturity. Luna: That is a great tactical question. It shifts the power dynamic slightly by showing you understand how the sausage is made. Lucas: It does. And it filters out companies that aren't ready for that level of scrutiny. You want to work somewhere that wants to be audited. Luna: Or at least one that isn't afraid of the result. Lucas: Right. Before we go, I want to take a quick moment to address how we keep this network running. We produce these detailed, data-driven conversations every single day, and we don't run ads. That means our sustainability depends entirely on listeners who find value in this depth of analysis. Luna: If you've learned something concrete today about pay equity or career strategy, consider supporting us directly. It helps us keep the lights on and the research going. Lucas: You can support the show at buy me a coffee dot com slash fexingo. Even a small contribution makes a big difference in keeping this content free and independent. Luna: Thanks for listening, and thanks for caring about the quality of this conversation. Lucas: Back to the topic. One final thought on the future of auditing. As AI becomes more common in hiring and promotion decisions, we will see a new kind of audit emerge. Not just for pay, but for algorithmic bias. Luna: So the tools themselves will need to be audited. That adds another layer of complexity to the HR function. Lucas: It does. And it means that being a modern leader isn't just about managing people. It's about managing the systems that manage people. Which brings us to a broader question: if we automate the decision-making, who is responsible when the bias persists? Luna: The humans who built the model, obviously. But proving that is going to be the next big legal battleground. Lucas: It certainly will. And that is why understanding the basics of pay equity now is so critical. You are laying the groundwork for the next decade of corporate governance. Luna: So start asking the questions today, before the algorithms start making the answers for you. Lucas: Well said. We will leave it there for today.