It's been just over three years since President Obama signed the Ledbetter Fair Pay Act into law, and my RSS feed reader has been overflowing this past week with posts about the anniversary of this piece of legislation.
One of the headlines that caught my attention was as follows:
This article, along with most others on the issue, is still citing to the "77 Cents Statistic", which is an inappropriate comparison of male-to-female earnings. It expresses the raw difference between average male and female earnings, and doesn't account for differences in occupation, industry, union status, hours worked, compensation expectations and willingness to engage in negotiations, the cash-benefits tradeoff, the role of personal choices, etc. We know from empirical studies that these factors all play a role in determining compensation levels, irrespective of gender (or any other protected characteristic).
Aside from being an inappropriate earnings comparison, the "77 Cents Statistic" - and much of the discussion surrounding it - is used to advance the idea that the gender pay gap is attributable to gender discrimination.
Let's think about this. Consider the image above. You may think it's sexist and downplays the role of women's contributions in the workforce. But it sheds light on one of the most fundamental market truths there is: businesses exist to make money. We've all heard stories about outsourcing - shifting manufacturing and production to China, shifting customer service departments and call centers to India and the Philippines. Why do businesses do this? Because the cost of labor in China, India and the Philippines is cheaper than in the US. Cheaper labor costs mean higher profit margins, and higher profit margins are good for businesses.
We can apply this same idea of outsourcing to the gender pay gap / gender discrimination discussion. If women truly were "just like men, only cheaper", why would businesses hire men at all? Seeking to maximize profits, they would always express a preference for cheaper labor by employing women. Businesses would only hire men - and pay higher wages - when the supply of feminine labor had been totally exhausted. If women truly were like men, only cheaper, we would expect to see full employment for women in all industries and occupations, coupled with higher rates of unemployment and underemployment for men.
This isn't what we see when we look at (un)employment rates by gender, or what we see when we walk onto a shop floor, conference room, or employee gathering. Why? Because women are not just like men.
Men aren't even just like men. We don't bat an eye at the idea that a man working as a surgeon or a nuclear engineer earns more than a man working at a McJob. It's what we expect - surgeons and nuclear engineers have years of education that prepare them for their high-stress work environments dealing with life-and-death decisions every day. A wrong decision could be fatal - either for an individual or for an entire community. While getting a hamburger when you ordered a cheeseburger may dampen your afternoon, it's not going to kill you.
Why should we be surprised, then, that a man working as a surgeon or nuclear engineer earns more than a woman working as a sales manager or a software programmer?
Nearly 50 years after the Equal Pay Act and 3 years after Ledbetter, we're still talking about the overall pay gap - which is the wrong metric - because we fail to recognize that women aren't just like men. And based on my own personal observations, most women don't want to be "just like men."
Stephanie R. Thomas is an economic and statistical consultant specializing in EEO issues and employment litigation risk management. Since 1999, she's been working with businesses and government agencies providing expert analysis. Stephanie's articles on examining compensation systems for internal equity have appeared in professional journals and she has appeared on NPR to discuss the gender wage gap. Stephanie is the founder of Thomas Econometrics and is the host of The Proactive Employer Podcast. Follow her on Twitter at ProactiveStats.