Implicit Biases & Evaluating Job Candidates (updated)

(Updated 11/7/13 to add link to Trix and Penska paper below. Thanks to Joanne Kamens for providing the link in the comments!)

(Note: this is the second in an occasional series* relating to job searches and hiring, though this one applies more broadly as well. The first post dealt with “illegal” questions, and appeared here.)

As much as we like to think we are all completely fair and unbiased, there is abundant evidence that we all have biases that influence how we think and act. These are known as “schemas”, and provide us with a framework for interacting with others. Schemas can be good – they allow us to more efficiently and rapidly process information – but they also can cause problems, in that sometimes our schemas lead us to treat people differently based on age, gender, sexual orientation, race, etc., in a way that we would not wish to.

We all do this – no one is immune to these implicit biases that influence how we act. This point was driven home to me clearly when I was a postdoc. I was a postdoc working on theoretical ecology projects, and viewed myself as someone who was strongly supportive of women in science. Yet, one day, I was at a seminar given by a woman who was presenting a lot of theory. I found myself wondering who she collaborated with on the mathy stuff. As soon as that thought popped into my head, I was shocked. How could I think that she needed to collaborate with someone in order to do theory? I was a woman doing a theory postdoc, for goodness’ sake. I was kind of horrified. But it fits in with what lots of studies have shown: everyone has schemas that affect how they perceive and treat other people.

What is the evidence? I’ll go into some of it here, because I think it’s important to cover. Then, in a later post, I’ll cover some related topics, including stereotype threat, and what we can do to try to overcome our biases.

A large set of evidence of how our schemas influence our evaluations of others comes from CV/resume studies. To summarize a few of these:

  • Race: One study (by Bertrand and Mullainathan) sent out resumes in response to help-wanted ads in Boston and Chicago. The study found that, in order to get a call back for an interview, applicants with typically black names (e.g., Jamal, Lakisha) had to send out 50% more resumes than did applicants with typically white names (e.g., Emily, Greg).
  • Gender: A study by Moss-Racusin et al. sent out applications for a lab manager position that had either a male or a female name. They found that the applications with male names were viewed as more competent and hireable, and were offered higher starting salaries.
  • Gender: Steinpreis et al. (pdf link) found that psychology professors (male and female) were more likely to hire someone named “Brian” as compared to someone named “Karen” for an assistant professor position.
  • Sexual orientation: A study by Tilcsik involved sending out resumes that were identical except that one indicated the applicant had been a treasurer in a gay student organization, whereas the other indicated that the applicant had been a treasurer in an environmental a progressive**** student organization. The “gay” applicant received 40% fewer call backs for interviews.
  • Parental status: Correll et al. (pdf link) found bias against mothers, but not against fathers. They sent out a pair of resumes of applicants with the same qualifications, but where one indicated parenthood and the other did not. Non-mothers received call backs twice as often as mothers did. There was no difference for fathers vs. non-fathers.

There are also plenty of studies that do not use the paired CV/resume approach. One well-known example is from Wennarås and Wold, which looked at the success of applicants to the Swedish Medical Research Council. They found that women had to be 2.5 times as productive to be viewed as equally competent. Similarly, a study by Ginther et al. looked at success rates for applicants to the US NIH for R01 awards. They found that “compared with NIH R01 applications from white investigators, applications from black investigators were 13.2 percentage points less likely to be awarded (P < .001), and those from Asian investigators were 3.9 percentage points less likely to be awarded (P < .001).”

These schemas also influence letters of recommendation that are written for applicants. Trix and Penska (pdf link) looked at over 300 letters of recommendation that had been written for successful applicants for faculty positions at medical schools. Among other things, they found letters for women tended to be shorter and tended to use more “grindstone” adjectives (e.g., “hard-working”, “conscientious”, “diligent”, etc.). A short letter can indicate there isn’t much positive to say, and tends to be viewed as a negative for that candidate. And while being hard-working or diligent is a good thing, as Trix and Penska say, “There is an insidious gender schema that associates effort with women, and ability with men in professional areas. According to this schema, women are hard-working because they must compensate for lack of ability (Valian, 1998: 170).” They also found that letters for men tended to repeat “standout” adjectives (e.g., “superb”, “outstanding”, “exceptional”) more often than do letters written for women.

Back to bias in terms of how we evaluate people: A survey of managers by McKinsey & Company found that “women are often evaluated for promotions primarily on performance, while men are often promoted on potential.”

Finally, success of men and women tends to be attributed to different things. A “citation classic” by Deaux and Emswiller (pdf link) found that success of men tends to be attributed to skill, while success of women tends to be attributed to luck. This particular study is older (from 1974), so hopefully some of these attitudes have shifted by now!

What can be done about this? I’ll cover this more in a future post. But, for now, I will just say that, when evaluating applications, I find it important to keep in mind that we all have these biases.

*By “occasional series”, I mean “your guess is as good as mine as to when the next post will appear.”

***Note: I first got interested in this literature as a postdoc, and have followed it as much as I can since then. But I was reminded of some of these studies, and found new ones, thanks to a really informative workshop run by the Strategies and Tactics for Recruiting to Increase Diversity and Excellence (STRIDE) committee at the University of Michigan.

****Updated 11/18/13 to correct mistake about control group. Thanks to Lirael for pointing this out!

27 thoughts on “Implicit Biases & Evaluating Job Candidates (updated)

  1. There are also the more trivial issues of font choice, formatting, word usage, and spelling. E.g., Comic Sans, unaligned CV columns, “data is” instances, and “ect cetera” [sic] can trigger subconscious doubt about the applicants overall qualifications. Oh, and apostrophe errors, like the one I left in previous sentence just to make sure people were paying attention.

  2. Thanks for the summary meg. It’s interesting to note the implication for ‘diversity hiring’. When I read the name for STRIDE (increasing diversity AND excellence) I thought to myself, if we select for diversity we may get complementarity gains for departmental performance but this will come at the cost of individual excellence (weaker candidates to get diversity). But, all the research above says that selecting for diversity will actually increase excellence by helping people find the true best candidate by offsetting our biases. Basically, diversity hiring is good for society but it sounds like it is also good for employers, helping them to not shoot themselves in the foot.

    • “But, all the research above says that selecting for diversity will actually increase excellence by helping people find the true best candidate by offsetting our biases. Basically, diversity hiring is good for society but it sounds like it is also good for employers, helping them to not shoot themselves in the foot.” Yes! Exactly. Committees aren’t being altruistic by considering these things — it’s good for them!

  3. Great summary of the few (we need more) studies on this area. I agree that awareness is the first and most important step in helping us deal with our biases. Years ago after reading the work on recommendation letters…I realized I DID IT TOO! Once I saw it, I stopped cold. Awareness helped me change my behavior dramatically.

    Here is the Trix and Psenka paper online:

    Here’s my blog on the topic which I gave as a keynote at a Pharma Women in Science Day

    • Thank you for the link! I just updated the post to add it in. I knew it had to be out there, but google was failing me!

  4. What about age bias? I find that over 50 early career researchers are not even considered for positions (ie don’t get interviewed).

    • Yes, age bias certainly could be an issue, though not one with which I personally happen to have encountered (perhaps Meg or others have).

      Can you clarify your own experience with age bias a bit John? You identify “not even considered” with “don’t get interviewed”. But for many faculty positions, only a small fraction of candidates get interviewed, making it difficult to infer age bias (or any sort of bias) solely from failure of an older candidate to get interviews. I’m guessing I must be slightly misunderstanding?

      • I have applied for jobs that eventually appointed far less experienced and qualified students, who in other ways are of the same general status as I am, bar age. Since I got my PhD in 2004 at 48, I have applied for around 200 academic positions around the world. I have had one phone interview.

      • Oh, and my CV is extensive. I have taught for 15 years, published four high index books, and many peer reviewed papers in leading journals. This is not about the failure of one candidate. Others have said similar things to me. After narrowing down the possible viable variables, age seems to be a leading factor, and I would love to see actual research done upon it. Of course, things are confounded by the GFC, and the corporatisation of education (which in fact may be one of the reasons for age bias in academe), but with around 10 years of working life left, I am unemployed doing adjunct teaching and living off my savings.

    • I would have to agree with John. I have a friend who, given his CV, the only way to explain his luck on the job market the last 3 years is if not age bias then at least a fear to touch anybody with a non-traditional trajectory.

    • I don’t know of any studies looking at this in academia, but I would bet that it exists. I suspect our schemas related to age probably differ based on the context. A 30-year-old assistant professor might struggle to be taken seriously by students, but a 60-year-old applying for a first faculty position probably would also encounter biases.

      Project Implicit is something that I really like and have found useful for evaluating my own biases. According to their website, their test related to age biases “often indicates that Americans have automatic preference for young over old.” So, that would support your experiences.

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  7. Just a quick note: You misread the Tilcsik study slightly. Tilcsik references another study where the control student organization was environmental, but in the Tilcsik paper, the control student organization is a progressive and socialist student group (which to me makes it even more interesting that Tilcsik found so much discrimination against the gay man resumes relative to the control resumes in conservative states, given how anathema the socialist label is in those states).

    • Hi Pleuni. I had forgotten about that study. I would need to reread it carefully before responding, and haven’t been able to find time for that. I thought someone else had already written a reply, but I can’t find that either. I’ll keep looking, though!

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