What happens when the EU Pay Transparency Directive forces companies to report gender pay gaps by worker category, and the math starts flagging problems that may not exist? Labor economist Brian Levine and people analytics expert Helen Friedman of Merit Analytics explain how organizations are getting ahead of EU reporting requirements, why narrow worker categories can backfire under a fixed 5 percent threshold, and why job grades are often the more defensible way to group equal value work. The conversation also covers why pay equity is only one part of workplace equity, how career path choices and promotion decisions create long-term pay gaps, where AI actually helps in pay equity analysis, and how to find the root causes behind pay gaps that keep coming back year after year. Chapters
09:23 – Where pay equity and transparency work stands today
10:51 – Getting ahead of EU directive gender pay gap reporting
13:26 – How to define equal value work and worker categories
19:45 – Will the 5 percent threshold create false flags
23:31 – Why the raw pay gap misses the bigger problem
24:08 – Selection and self-selection in career paths
29:51 – Using data to make fairer promotion decisions
32:10 – What AI can and cannot do for pay equity analysis
36:42 – Finding the root causes of recurring pay gaps
43:16 – Better HR data and evidence-based decisions
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