Fallacy of the Gender Wage Gap

Is the gender wage gap as simple as some media outlets report it to be? PayScale's data paints a more complicated picture.

On Equal Pay DayGeekWire recently wrote about a study ranking Seattle as the worst metro for the gender wage gap. According to the National Partnership for Women & Families, women in Seattle earn $0.73 to the man’s dollar. This is worse than the national gender wage gap of $0.77 to the dollar. But the story may not be this simple – PayScale’s data paints a more nuanced picture.

In the study cited by Geekwire,  the metrics are calculated from Census data. These numbers simply compare the average earnings of men and women overall. In other words, they don’t take into account job choice, industry, experience, education, or any other factor that contributes to one’s pay.

Here at PayScale, we’ve examined the gender wage gap in depth and shown that job choice plays a huge role in these numbers.  Simply put, high paying fields, such as IT and engineering, are dominated by men, while women are more likely to hold jobs in low paying fields such as education and nursing.

We collect data on over 250 compensable factors in our more than 35 million profiles. With this large database, we can examine the gender wage gap utilizing a true apples-to-apples comparison. In other words, we can provide one of the strongest answers to the question, “If a man and woman are doing the exact same job with the exact same qualifications, responsibilities, and employer type, is the man still paid more than the woman?”

The short answer is no. The long answer is, for a subset of jobs, a wage gap does in fact exist, even when we control for all measureable factors. However, these jobs, which are primarily high level executive and director positions, are the overwhelming minority.

As far as Seattle, our data shows the wage gap across full-time men and women is $0.69 – not far from that quoted by the Census. However, once we control the sample for job choice, industry, and other compensable factors the wage gap shrinks to $0.97, which is within errors of parity.

One reason such a stark difference exists between the genders before defining controls for the sample is that the prevalent industry in the Seattle metro is tech, and thus Seattle has a larger than average proportion of software developers and other tech workers. These are positions that are paid well and dominated by men.

For example, our data shows Software Development Engineers (SDE) to be 90% male/ 10% female. Our data also shows men and women in these roles in Seattle earn median pay of $89,400 and $79,000 respectively – a wage gap of $0.88. However, this calculation is simply comparing men and women with the same job title in the same metro area. It still doesn’t account for differences in experience, background or responsibilities. If we start with the typical characteristics of a male Software Development Engineer, then adjust the characteristics of the typical women with this job to match those of the average man, then the female pay increases to $86,700 – a wage gap of only $0.97.

 

So what does this all mean? Our data shows that details matter when discussing the gender wage gap, and thus conclusions drawn from broad compensation data points are erroneous. Instead of focusing on the difference in pay between the average woman and average man, discussions should instead be focused on how and why men still dominate the highest-paying job types and industries.

 

 

Gender Pay Gap Infographic

In discussing the gender wage gap, and thus conclusions drawn from broad compensation data points are erroneous. Instead of focusing on the difference in pay between the average woman and average man, discussions should instead be focused on how and why men still dominate the highest-paying job types and industries.

 

1 Comment

  1. 1 Benny Profane 11 Apr

    You appear to have never read a single argument put forth regarding the gender pay gap. A big part of the issue are the elements you are controlling for - job choice, level of seniority etc. If you control for these of course the gap goes away. Have you heard of vertical and horizontal segregation? They are the two fundamental concepts driving the gender pay gap and you have effectively removed them from your analysis.

    There are several easily accessible reports which go into this in greater detail so I won't bother, but your reductive approach is rather disappointing given the useful information usually provided by payscale.com.

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