Systemic Discrimination Among Large U.S. Employers is almost completely solved

[OfTheCross]

Black names don't hurt as much as they used to

We study the results of a massive nationwide correspondence experiment sending more than 83,000 fictitious applications with randomized characteristics to geographically dispersed jobs posted by 108 of the largest U.S. employers.

Distinctively Black names reduce the probability of employer contact by 2.1 percentage points, relative to distinctively White names.

The magnitude of this racial gap in contact rates differs substantially across firms, exhibiting a between-company standard deviation of 1.9 percentage points.

Despite an insignificant average gap in contact rates between male and female applicants, we find a between-company standard deviation in gender contact gaps of 2.7 percentage points, revealing that some firms favor male applicants while others favor women.

Discrimination exhibits little geographical dispersion, but two-digit industry explains roughly half of the cross-firm variation in both racial and gender contact gaps.

Contact gaps are highly concentrated in particular companies, with firms in the top quintile of racial discrimination responsible for nearly half of lost contacts to Black applicants in the experiment.

Controlling false discovery rates to the 5 percent level, 23 individual companies are found to discriminate against Black applicants. Our findings establish that systemic illegal discrimination is concentrated among a select set of large employers, many of which can be identified with high confidence using large-scale inference methods.

https://conference.nber.org/conf_papers/f156519.pdf

 

[Professor Emeritus]

To be clear, I think this sort of abstract bias is a pretty narrow type of discrimination and in no way should be taken as the sole or even major source of inequality. That being said, the worst of those companies should be put on blast. It looks like they're not disclosing which 21 companies were the clearcut discriminatory ones they mentioned.



In terms of individual name bias, I'm guessing that looking name-by-name might be a little misleading because with any one name there might be some random variation and the results are only significant when everything is totaled up. But that being said, names with specifically lowest callback rates: Ebony, Kenya, Lakeisha, Lakisha, Latisha, Latoya, Lawanda, Tameka, Tamika, Lamar, Maurice, Tyrone, and Geoffrey. Worst last names were Diggs and Jean.

So the worst luck would be someone named "Latoya Jean" or "Tyrone Diggs". But again, there might be some random variance there.


Best female names were "Heather", "Misty", "Kristen", "Laurie", and "Sarah". Best male names were "Adam" and "Brendan". The best last names were "Hostetler" and "Schroeder".

So you'd be best off being named "Adam Hostetler" or "Heather Schroeder".



Interestingly, it looks like most of the 20 Black female names they chose did worse than the 20 Black male names they chose, but the 20 White female names they chose did better than the 20 White male names they chose.