Incentivized Resume Rating is a new technique to study hiring bias
Rate resumes of hypothetical candidates created by researchers
Match with real job seekers based on recruiter preferences
Receive real opportunities in return for serving as characteristic bank for resumes
Based on our research…
There is no positive preference for female or minority candidates.
Even though… 90% of employers claim that increasing both gender and racial diversity factor into their hiring decisions.
In fact, in STEM fields, female and minority candidates need to outperform their white male counterparts.
Non-white males need to earn 0.27 GPA points more and white females need to earn 0.29 GPA points more than a white male to receive the same rating.
Firms should be cautious about unintentionally excluding candidates whose financial situations require paid work.
An internship is worth 0.21 GPA points. A work-for-money summer job is statistically the same as having nothing on the resume.
Female and minority candidates get less credit than their white male counterparts for the same prestigious internship experience.
White male candidates get the largest benefit from a prestigious internship, with a boost of 0.53 GPA points.
Firms may falsely believe female and minority candidates are harder to recruit due to other firms’ diversity preferences.
Firms believe female and minority candidates are less likely to accept their offers. But there is no positive preference for female or minority candidates in the data.
The research presented here was published as the lead article in the November 2019 issue of the AER, which you can access below.
What can our research do for your firm?
Audit internal hiring practices
IRR can help firms diagnose their hiring practices: hiring managers use the IRR tool, then results used for education
Identify areas for improvement
Can compare the preferences of firm leaders to those “on the ground” and find gaps between intention and practice
Partner with Wharton researchers
Opportunities to partner with Wharton on research to understand hiring decisions and firm preferences
Provide clean data for ML algorithms
Beware machine learning algorithms trained on historical data: IRR provides an opportunity to train on “clean data”
In The News
Op-Ed: It will take a lot more than diversity training to end racial bias in hiring
“Bias in résumé screening is just one part of the problem. Eliminating bias in employment will require rethinking every aspect of the hiring and promotion process. Our research shows that good intentions and pro-diversity goals aren’t enough.“
Uncovering Bias: A New Way to Study Hiring Can Help
“While testing this new method – incentivized resume rating — with companies recruiting Penn students, they uncovered evidence of how bias seeps into the hiring process of some of the world’s top firms, many of which have a stated commitment to diversity.“
Penn researchers offer new way to expose hiring bias
“Wanting diversity isn’t enough… You have to build practices that help you overcome powerful biases…. The employers who said, ‘Yes, we care about diversity, want diverse candidates’ were discriminating in multiple ways”
HRx Radio: Executive Conversations
“You want to get the real story of ‘Who do you really actually hire.’ And so the way that we did that is we said, go through and rate these fake resumes for us… when you read those fake resumes, our tool is going to use the machine learning algorithm to apply your preferences over these hypothetical resumes to a bank of hundreds of real candidates.”
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Incentivized Resume Rating
The Wharton School
Philadelphia, PA 19104