Data is amazing! It can also be funny and sad. Take a recent data analysis of names on resumes and salaries put together by Adzuna who put together a tool that allows you to search your own first name and find your own value (ValueMyResume).
It turns out the amount of money you make based on your name is vastly different on average. Meaning, my average salary for “Tim” is around $103,723. Now, my friend “Jessica” Lee her first name gets her on average $50,571.
Seems weird, right?
My other friend, “Kris” Dunn, gets $0 because as it turns out, not too many dudes have the name “Kris” or “Kristian” with a “K”, but when I put in the traditional spelling of “Chris” he gets $98,257, while “Christopher” only gets $77,554.
From the data here are the top-paying Men and Women names:
So, immediately you see top women getting paid way less than top men names. Interestingly, you’ll also notice a bunch more “ethic” derived names on the men’s list. So, this is where data sometimes doesn’t give us the full story. The study found 16,000 unique names on 40,000 resumes. So, “Murat” might be the only “Murat” found and thus if he’s got a great job, his salary is going to be very high.
On the flip side, they might have had hundreds of “Kari’s” and now the overall salary is going to come down because of the extra data available.
Let’s look at the lowest paid Names on average:
Again, males on average make more on the low end than females on the low end. We don’t get specific data points to determine or occupations, so it’s hard to really make any true inferences about what this says.
Do the names on the low end have anything in common or different from the high end? The only real difference I can tell is really on the high-end males seem to have non-traditional American names. Names are derived from Arabic, Indian, and Chinese/Vietnamese. So, we could assume these males are most likely in STEM careers that are on the high end of the salary scale on average, based on college graduation programs correlated to names. You also see a few of these names on the high-end female names as well.
The other part of this data is based on “resume” data. Who has a resume? Usually professional, white-collar folks. So, overall you are going to see a higher overall pay rate on the high and low end than the national average.
You can go to the site and search on your own name and see where you fall. In terms of my own “high” salary average for “Tim”, I’m again going to assume there weren’t many “Tim’s” in the data set, so the ones they got had good jobs! Also, “Tim” is a very GenX name, meaning the Tim’s in the data set were probably late-career and in their prime earning years, so also more likely to have a higher than average salary.
Does this data show a larger problem in America when it comes to Pay Equity?
The hard part is that with only salary and name data you are going to make a gigantic jump to correlate pay equity issues based on those two data points. There are so many other factors at play that we don’t know from the resumes. Maybe the resumes that were gathered were heavy on males in the IT fields and the female resumes were heavy on non-technical careers. Maybe the male resumes had a higher overall average of experience and female resumes tended to be less experienced. Etc.
But, you can’t not see what you see. And what you see is what we already know to be true, on average, men make more than women, and pay equity is still a major issue that we must fix.