Lead With Inclusion

Let’s read between the lines of diversity data

April 3, 2024

Today marks AANHPI Equal Pay Day, marking how long Asian Americans, Native Hawaiians, and Pacific Islanders would need to work to earn as much as white, non-Hispanic men.

April 3rd, you might think, that doesn’t sound too bad.

Hold on, though. What if you zoom in on the data? What if you disaggregate it? What if you ask, what kind of work is considered? Are there disparities among different groups of AANHPI women?

Be an Inclusive Leader:

I’m actually not here to discuss pay equity (I tackled that here) but to discuss data.

Specifically, how data showing “good” progress on diversity, equity and inclusion can mask underlying issues. Consider a workforce with a higher-than-average representation of women and nonbinary folks, BIPOC, and LGBTQ+ individuals. This seems positive, right?

But if you take a closer look, what does the underlying data reveal about who’s on performance improvement plans, or the demographics of individuals laid off or furloughed? How is compensation distributed among different groups? Who is being promoted, and is there a clear path to promotion that works the same way for every employee?

Or, to use AANHPI pay equity as an example: US Census data shows that Asian American, Native Hawaiian, and Pacific Islander women working full time earn $0.93 cents for every dollar made by a white, non-Hispanic man. Again, that doesn’t sound too bad. Only seven cents to go!

Yet a closer look reveals that if you include part-time, part-year, and other workers, earnings-on-the-dollar drop to $0.80. And if you disaggregate the data and look at specific communities (AANHPI is an enormously diverse group), Burmese women make $0.50 cents to the dollar, while Indian women make $1.22:

List of "How Much AANHPI women working full time, year round lose to the wage gap"

Lead with Inclusion:

I cannot stress enough: diversity is in the data.

I go into detail about how to accurately analyze diversity data in chapter 6 of my book, “UNBIAS: Addressing Unconscious Bias at Work”. Order the book for yourself or your team here.

Have you taken more than a superficial look at your company’s data? I encourage you to do so. Start by considering these dimensions of diversity:

  • Geography: I once observed a company that boasted “excellent” diversity. However, a closer look at the numbers by location showed a stark reality: Black employees primarily worked out of Inglewood, Asian employees in Koreatown, and white employees in Pacific Palisades. Despite all being in Los Angeles, neighborhood data shed light on the actual diversity situation.
  • Family Status: Explore how compensation varies not only between women and men but also among mothers and fathers.
  • Department: Does a particular department receive more resources than others because its head has a closer relationship with the CEO, or simply because he is male?

My call to action for you is to look deeper. What story is your data telling you about the unexpected places unconscious bias is alive and well? How might you be contributing to the biases identified above?

What revelations have you discovered upon examining your data more closely? I’ve shared insights from my experiences above, and I’m eager to hear about yours. Please share your ah-hah moments in the comments.

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