From Freakonomics. Excerpt:
"if discrimination can be costly for firms, what sort of benefits are created by diversity?
CALDER-WANG: This is a good question.
Sophie Calder-Wang is an economist at the Wharton School at the University of Pennsylvania. She and two co-authors — Paul Gompers and Kevin Huang — recently published a study examining this question.
CALDER-WANG: The particular setting is an M.B.A. program at Harvard Business School. From 2013 to 2016, they ran a course which asked students to form small teams of entrepreneurial startups. What is curious is that the first time they launched this class, instead of asking people to form their own teams, the course administrator had this idea of, “Why don’t we just sort people into teams using a computer algorithm so that we can make sure every team have a balanced ratio of men to women, of minorities and whatnot?” In 2014, 2015, they scrapped the computer algorithm and said, “People, you can form your own teams by your own choice.”
DUBNER: Thank you, H.B.S., for setting up a beautiful experiment for me, right? I mean, it is very close to the real world, even though it is a class and not an actual startup. It’s as close as you could get, really, yeah?
CALDER-WANG: Yeah. Obviously there are some differences. But, they actually get graded by a panel of judges, which is comprised of their own section leader, their faculty advisor, and a number of industry judges who are actually practitioners in V.C. and entrepreneurship. So the grades should give you some resemblance of what actually matters in these fund-raising rounds in startups.
DUBNER: And what sort of startup ideas are they coming up with?
CALDER-WANG: Folks come up with businesses that are pretty similar to a typical early-stage startup, but less fleshed-out.
DUBNER: So Uber for X, Y, and Z, let’s say.
CALDER-WANG: Yeah, exactly. Airbnb for dogs.
What Calder-Wang would be measuring, then, was the performance of groups that were assembled by the algorithm versus groups where the members chose themselves.
The Harvard Business School data that she analyzed covered four years of students.CALDER-WANG: This was basically 1,000 students each year. So, 4,000 students in total. Each team is about five to six people. So you wind up with something like 1,000 teams, which in the realm of firm-level outcomes is a decent sample. I know in the world of big data, 1,000 is tiny.
DUBNER: But in the world of most academic experiments, this is gigantic.
CALDER-WANG: Yeah, exactly.
DUBNER: How do you control for intelligence and talent and connections?
CALDER-WANG: We control, for lack of a better word, the ranking of their undergraduate institutions. We control for whether they have worked in the startup sector before. And to the extent that you think a certain demographic group is just disproportionately more prepared than other groups, we actually can also control for just the fraction of, say, white students.
DUBNER: What about GMAT scores? Are you including that?
CALDER-WANG: If you can help me convince the administration to share with us the GMAT scores — we’re working on that because we want to make sure the errors are not biased.
The student population was a relatively diverse mix of men and women from various ethnic and racial backgrounds. Calder-Wang found that when students could choose collaborators for themselves, the groups were significantly less diverse than when the algorithm created intentionally diverse groups. Given how human beings work, you probably don’t find this surprising. So how did the organically chosen groups perform, compared to the randomly assigned groups — or what Calder-Wang calls the “forced diversity” groups?
CALDER-WANG: What we find in a randomly assigned cohort, one standard deviation increase in diversity leads to about 15 percent degradation in their performance, whereas in the organic formation teams, one standard deviation increase in diversity is only about three to five percent degradation in performance.
DUBNER: So we’re talking about a three to five times difference.
CALDER-WANG: Yes.
In other words, when people were allowed to choose on their own:
CALDER-WANG: When people were allowed to choose on their own, diverse teams performed just fine. The problem lies when you are forced to work together in the diverse team. And that’s why I’ve manufactured this word “forced diversity,” as opposed to organic diversity. We always have this notion that diversity might lead to better performances and we were actually fairly annoyed because we found the coefficient to be negative.
DUBNER: What are we to make of this finding? I mean, what are the mechanisms by which those teams do worse?
CALDER-WANG: What we are finding is when people are matched with teams that are the same in terms of both gender and ethnicity, these teams do much better than teams that mismatch on both dimensions, or on either dimensions, actually. It’s not like one subgroup is the biggest culprit, as far as we can tell. Now, I’m sure communication is a component to it, but at that point, I’m also guessing.
DUBNER: I have to say, when I read your research and then I read, let’s say, The Wall Street Journal talking about how American firms in particular are moving toward what I guess I would now call, thanks to you and your research, “forced diversity” — let me just read to you here: “An analysis released in June found that nearly 75 percent of new independent directors at companies in the S&P 500 are women or belong to a racial or ethnic minority.” So that’s a massive jump. “Furthermore, Nasdaq has recently required that listed companies need to meet certain minimum targets for the gender and ethnic diversity of their boards or explain in writing why they aren’t doing so.” Now, I think most people would look at that movement and say, “Well, it’s about time,” right? “Too much business has been too white and male for too long at the exclusion of other groups who want to be there.” But when I read your paper, I think, “Uh-oh, things might not turn out the way people are hoping they turn out.” And maybe all we’re getting here is window dressing that’s going to lead to bad results that’s going to have a backlash. So, what do you predict?
CALDER-WANG: This paper is not meant to bash any sort of mandated diversity policies, but just to highlight one potential negative consequence. We clearly have inequality in outcome. But in an attempt to address the inequality of outcome, I think we are a little bit lazy to think about what is the cause that actually lead to the inequality of the outcome. So these board policies that required increased representation from women or from underrepresented minorities is an attempt to change the outcome without being very thoughtful in understanding the cause and to actually remove the cause. I would like to spend more time to think about not necessarily how to achieve equality of outcome by manipulating the outcome, but rather to unearth the path towards us achieving some form of equality or opportunity and to find out what changes in the existing institutions and the framework can be done to achieve that."
"Diversity and Performance in Entrepreneurial Teams
55 Pages Posted: 21 Aug 2021 Last revised: 27 Oct 2021
There are 2 versions of this paper
Date Written: October 25, 2021
Abstract
We study the role of diversity on the performance of entrepreneurial teams by exploiting a unique experimental setting of over 3,000 MBA students who participated in a business course to build startups. First, we quantify the strong selection based upon shared attributes when students are allowed to choose teammates. Team formation based upon shared endowed demographic characteristics such as gender, race, and ethnicity is stronger than team formation based upon acquired characteristics such as education and industry background. Second, when team memberships are randomly assigned, greater racial/ethnic diversity leads to significantly worse performance. Interestingly, the negative performance effect of diversity is partially alleviated in cohorts where teams are formed voluntarily. Finally, we find that teams with more female members performed substantially better when their faculty section leader was female. These findings suggest that policy interventions targeting greater diversity should consider match-specific qualities in forming teams to prevent the potential negative impact of diversity. Our results on vertical diversity suggest that capital allocators could also play an important role in the mentoring and advising of minority entrepreneurs.
Keywords: Diversity, Gender, Minorities, Entrepreneurship
JEL Classification: J1, J15, J16, M12, M13, M14"
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