EXTRA: Roland Fryer Refuses to Lie to Black America (Update)

0:00:06 Hey, there, it’s Steven Dubner, and today we’ve got a bonus episode for you.
0:00:12 It is an update of a 2022 interview we did with Roland Friar, a much acclaimed and frequently
0:00:15 controversial economist at Harvard.
0:00:18 When we spoke, Friar had recently returned from a two-year suspension, which you will
0:00:20 hear about in the episode.
0:00:25 The person who suspended him was Claudine Gay, who at the time was the Dean of Harvard’s
0:00:27 Faculty of Arts and Sciences.
0:00:32 Gay went on to become president of Harvard, but then she famously resigned amidst plagiarism
0:00:38 charges and criticism of Harvard’s response to anti-Semitic demonstrations.
0:00:42 The reason we thought you might like to hear this episode now is because it follows naturally
0:00:47 from the two-part series we just published on the Rooney Rule, that is the National Football
0:00:51 League policy that was designed to increase diversity among coaches, and the Rooney Rule
0:00:56 has since been adopted by many firms and institutions outside of sports.
0:01:02 Roland Friar, who is black, has his own thoughts about how firms and institutions have handled
0:01:05 diversity hiring, and you’ll hear about that, too.
0:01:08 We have updated facts and figures as necessary.
0:01:13 As always, thanks for listening.
0:01:19 In 2005, I wrote a piece for the New York Times magazine called “Toured a Unified Theory
0:01:21 of Black America.”
0:01:27 It was a profile of a young Harvard economist named Roland Friar, whose journey to Harvard
0:01:31 was beyond surprising, beyond unpredictable.
0:01:37 Given his background, it may have seemed impossible, and yet there he was.
0:01:43 A lot of things happened to get Friar into the upper echelons of academia, and even more
0:01:47 has happened since, much of it controversial.
0:01:50 How does Friar describe his research agenda today?
0:02:05 So Roland, it feels like most public discussions about race these days, at least the ones that
0:02:13 I read in academia, in journalism, and elsewhere, do treat blackness as essentially a handicap.
0:02:17 What are the costs to that perception?
0:02:21 I mean, how much time you got?
0:02:24 We’ve got plenty of time.
0:02:30 Today, on Freakin’omics Radio, a conversation with Roland Friar about his research on policing.
0:02:37 I had a five-hour meeting with Obama and other folks, and we got zero done.
0:02:38 On education?
0:02:42 The thing that drives me nuts is that this woman is doing everything that she thinks
0:02:43 is right.
0:02:46 We’ll get his take on corporate diversity programs.
0:02:49 It made me sick in my stomach, man.
0:02:55 And we’ll hear about Friar’s personal controversy, including a two-year suspension by Harvard.
0:02:58 I broke a lot of glass early on in my career, and I don’t think that was helpful.
0:03:03 To be fair, Roland Friar is still breaking glass.
0:03:05 All that on Freakin’omics Radio right now.
0:03:23 This is Freakin’omics Radio, the podcast that explores the hidden side of everything,
0:03:35 with your host Stephen Dubner.
0:03:40 In 2007, at age 30, Roland Friar became the youngest African-American to receive tenure
0:03:41 at Harvard.
0:03:46 He would go on to win a MacArthur Fellowship, the so-called Genius Grant, as well as the
0:03:51 John Bates Clark Medal, one of the top prizes in academic economics.
0:03:56 He got his own research lab at Harvard to study the achievement gap between black and
0:03:57 white kids.
0:03:59 He even made it onto late-night TV.
0:04:02 Please welcome Roland Friar!
0:04:07 He has some controversial areas you study here.
0:04:15 You say that blacks are the worst-performing ethnic group in our school system, but that
0:04:18 is a pretty racist thing for you to say.
0:04:34 A lot of Friar’s research pushed boundaries and crossed ideological lines.
0:04:41 My life’s work is about making those communities better, and so whatever cost there is, frankly,
0:04:46 to me of telling what I think is the data-driven truth about these issues, whether it’s health
0:04:50 or police or education, I’m going to do it!
0:04:56 In 2019, Friar was suspended by Harvard over allegations that he had engaged in “unwelcome
0:05:01 conduct of a sexual nature” and charges that he had violated some of Harvard’s finance
0:05:02 rules.
0:05:07 In a letter he sent to his dean, Friar wrote, “I apologize for the insensitive and inappropriate
0:05:09 comments that led to my suspension.
0:05:14 I didn’t appreciate the inherent power dynamics in my interactions which led me to act in
0:05:20 ways that I now realize were deeply inappropriate for someone in my position.”
0:05:24 In 2021, Friar returned to teaching and research.
0:05:28 Today he is permitted to advise students again, but his research lab remains shut down.
0:05:34 We’ll hear more about his suspension later in today’s conversation, but let’s start
0:05:36 with Friar’s origin story.
0:05:41 When I wrote that Times magazine piece years ago, he and I spent time together in New York
0:05:46 and Boston, and we visited his grandmother and father in Central Florida.
0:05:52 We went to the small city outside Dallas, where Friar spent his unproductive teens,
0:05:57 and we went to Oklahoma to visit his mother from whom he had long been estranged.
0:06:03 When we spoke recently, I asked Friar to summarize his unlikely path to becoming an Ivy League
0:06:05 economics professor.
0:06:09 I was born in Daytona Beach, Florida, oddly enough, and this will tell a lot of the story,
0:06:15 brought home from the hospital with my grandmother and lived the first few years of life in Florida.
0:06:19 My mother kind of disappeared out of my life very, very early.
0:06:20 My father was still there.
0:06:25 He was kind of a copy salesman, and my uncles were there.
0:06:30 I grew up in this really ironic, strange, I don’t know quite what to do about it, environment
0:06:36 in which my grandmother was a teacher, and my great aunt, my grandmother’s mother’s sister,
0:06:38 was also a teacher.
0:06:44 The other sister ran one of the largest distributors of crack cocaine in Central Florida.
0:06:47 High achievers in different realms, we’ll call it.
0:06:53 She was the CEO of a street pharmaceutical company, and yet we would chat with each other,
0:06:57 and there was no judgment and love of each other, and so I grew up in this strange world
0:07:02 where my grandmother viewed it as hard work and get ahead, though she thought discrimination
0:07:04 was omnipresent in the world.
0:07:08 And my great aunt, who was, for lack of better words, an entrepreneur, who did lots of illegal
0:07:13 stuff to get ahead, and I spent, I’d say, four months out of the year, even when my father
0:07:15 and I moved to Texas.
0:07:18 Early on, my father and I had a reasonable relationship.
0:07:23 It deteriorated over the years, and by the time I was a teenager, I was just angry, man.
0:07:25 I was really angry at the world.
0:07:30 Pretty good at sports, but just an angry kid, and I wasn’t trying hard at school at all.
0:07:34 They used to pass out books in school, and I’d say, “You just keep them, I’ll lose them.”
0:07:38 My father did a liquor homework, managed to eke out of high school with a GPA of, I don’t
0:07:43 know, two point something, but had really high test scores, oddly.
0:07:47 And got involved in a bunch of little petty crimes when I was 15, like was driving a car
0:07:52 without a license, stealing stuff out of stores, forged my birth certificate so I could work
0:07:53 in McDonald’s.
0:07:57 My father eventually lost his house, and he ended up going to prison and all that kind
0:07:58 of stuff.
0:08:03 I didn’t know my mother, and I just felt really, really angry and alone in the world.
0:08:11 I made my way to a college, not a great one, but a good enough one, and it’s there that
0:08:13 I took principles of microeconomics course.
0:08:17 It was 8 a.m. He had office hours at 7 a.m.
0:08:20 I know now that was a joke, but back then, I thought it was pretty convenient because
0:08:22 I’m an early morning guy.
0:08:27 And I was there before every class at 7 a.m., and we started arguing about welfare and economic
0:08:32 policy and how to use food stamps, what the restrictions should be, and really fell in
0:08:33 love with it.
0:08:40 But let me just say, I have been on an absolute quest for the last 25 years to catch up because
0:08:45 I spent the first 20 goofing off and being angry at the world.
0:08:49 I was just meeting with a guy who’s a really accomplished businessman, and he came from
0:08:52 the golf course, and he says, “Well, why do you work so hard?
0:08:53 What are you doing?
0:08:54 It’s time to enjoy life.”
0:08:58 I said to him, “I gave you a 20-year head start, man, and a better family,” and all
0:08:59 that stuff.
0:09:01 So every time you play golf, I catch up a little bit.
0:09:03 Every time you take the night off, I catch up, right?
0:09:06 And that’s been my attitude, and you know that for years.
0:09:11 I remember when I was spending time with you, you were a relatively young professor at Harvard,
0:09:14 and there was that car in the parking lot.
0:09:20 This car was outside early every morning, late at night, and you were like, “Damn.
0:09:24 He is out working me,” and you were pissed off, and then you later found out they were
0:09:26 on vacation, just parked there.
0:09:28 It was like a silver one, right?
0:09:31 I remember that, and I was worried about that, but that’s my attitude.
0:09:37 I’m in a big hurry to catch up, and my upbringing, it really has formed my view of how to do
0:09:38 this.
0:09:42 Number one, data first, obviously, but this is why I don’t have any politics in this
0:09:49 stuff because I have seen that, yes, there are things that need to be changed in black
0:09:56 communities, but I also see the effects of discrimination, whether perceived or real,
0:09:57 on these communities.
0:10:00 The only thing I care about is making them better.
0:10:01 That’s it.
0:10:05 I really don’t care otherwise.
0:10:11 Friar is probably best known for a 2016 research paper called An Empirical Analysis of Racial
0:10:14 Differences in Police Use of Force.
0:10:20 It incorporated a variety of data sets, including a federal survey on interactions with police
0:10:26 and the data on police shootings from 10 police departments around the country, including Houston,
0:10:29 Jacksonville, and Los Angeles County.
0:10:30 What did he find?
0:10:36 On average, in any given stop, black people are 50% more likely to have force used on
0:10:37 them than white people.
0:10:38 Okay.
0:10:41 That’s on average not controlling for anything.
0:10:42 That’s exactly it.
0:10:43 That’s just in the raw data.
0:10:49 Now, if we control for lots of other things where they are in terms of the city, we had
0:10:54 millions in data points, so we could really be very strict on the controls.
0:11:00 If we do that, then the difference decreases substantially, but this number I find the
0:11:05 most compelling in this entire research is that we looked at the cases in which the police
0:11:12 officers themselves said the civilian was perfectly compliant, didn’t have contraband,
0:11:14 was not arrested.
0:11:19 Even in those instances, blacks were roughly 20% more likely to have force used on them
0:11:20 than whites.
0:11:25 So the conclusion that one would almost be forced to draw from that is that police are
0:11:27 on average racist against blacks?
0:11:30 No, I’m not going to say racist, but there’s discrimination going on.
0:11:31 Okay.
0:11:32 Noted.
0:11:36 So that’s the part of your research about what’s called the non-lethal use of force,
0:11:42 and then there’s lethal use of force, which essentially means a police shooting, correct?
0:11:43 Yes.
0:11:44 Yes.
0:11:45 Whether it’s fatal or not?
0:11:46 Exactly.
0:11:47 So what’d you find there?
0:11:50 What we found there was no racial differences whatsoever in lethal uses of force.
0:11:52 Roland, that can’t be right.
0:11:55 Roland, my friend.
0:11:56 That cannot be right.
0:11:58 I read the newspapers, Roland.
0:12:00 Yeah, I know.
0:12:02 Well, it surprised me as well.
0:12:06 We had 10, 15 cities by the time we finished that research.
0:12:11 In no city was it true because the thing is, if you read the Washington Post or the Guardian,
0:12:19 they’ll say things like the fraction of black people who were unarmed and were shot at by
0:12:23 police is higher than the fraction of white people who were unarmed and shot at by the
0:12:24 police.
0:12:28 But that’s just not the right way to do those statistics.
0:12:29 Because why?
0:12:30 Because they’re not accounting.
0:12:35 That’s comparing apples with cars, and I’d like to compare apples with apples.
0:12:36 I don’t understand.
0:12:37 Why is that not a relevant variable?
0:12:40 It’s not that it’s not a relevant variable, it’s incomplete.
0:12:44 There are a lot more things about a police interaction than whether or not the person
0:12:46 eventually has a weapon.
0:12:53 Before you got into this big data analysis, as I understand it, you also felt the need
0:12:58 or at least the intellectual curiosity to understand the day-to-day life of a police
0:12:59 officer a little bit better.
0:13:05 Let me back up slightly before that even, which is, we all saw what was going on in
0:13:11 these videos, and the Walter Scott video was the one that got me off the couch, so to speak.
0:13:12 I just couldn’t take it.
0:13:13 I was furious.
0:13:17 He was the guy that was running away after a traffic stop, is that right?
0:13:19 Yeah, running is generous.
0:13:24 It was shuffling across an abandoned field, and somehow because I think it was in South
0:13:29 Carolina, the picture of it reminded me of Daytona Beach, and there was something about
0:13:34 that that said to me, “There but for the grace of God, go I,” and it just woke me up.
0:13:41 I was upset, and I went to my colleagues in the economics department, and I was all fired
0:13:45 up about this in the hallways, and they weren’t as fired up as I was, but I was pretty fired
0:13:47 up about it.
0:13:51 One of them, Andre Schleifer, who is a dear friend of mine, he said to me, “I don’t know
0:13:52 really.
0:13:54 Do you even know what police do?”
0:13:55 It was like a gut punch, man.
0:13:56 I didn’t know.
0:13:57 The truth is, I was biased.
0:13:58 I don’t like police.
0:13:59 I don’t like them now.
0:14:02 I was driving down the highway yesterday.
0:14:03 Police put his lights on.
0:14:05 I started remembering stuff I did in seventh grade.
0:14:06 I mean, it was scary.
0:14:09 Luckily, it wasn’t for me, but anyway.
0:14:15 So I decided, maybe I should go embed myself in police departments, and so I did just that.
0:14:21 I went to Camden and did a couple double shifts in Camden, riding around, went to Houston
0:14:26 and did that, and even did some simulation and de-escalation training with another police
0:14:27 department.
0:14:32 The truth is, I didn’t like who I became riding around in a police car after three to four
0:14:33 hours.
0:14:39 If you ride around looking for bad guys, lo and behold, you see bad guys.
0:14:41 I was the worst police officer you can imagine.
0:14:47 They kept saying, “Rolling, it’s not illegal to dribble a basketball,” I’m not sure.
0:14:52 What was interesting though, in all seriousness, was in Camden, I met police officers who walked
0:14:53 a beat.
0:15:00 What happens there is that when you see someone who might look dangerous or at least uncertain
0:15:05 or random to me, because they know the person, he’s like that on Thursdays.
0:15:10 It was really interesting for me to see them say that, because most police officers see
0:15:11 you at your worst.
0:15:15 But if you’re hanging out in the communities, it gives you a denominator for which to understand
0:15:17 the other behavior.
0:15:22 So were these ride-alongs before you actually started analyzing the police data?
0:15:24 Didn’t have any data yet.
0:15:27 Part of the ride-alongs were to try to understand what data existed, to really understand from
0:15:31 the police the types of data they collected, the types of data they wanted to collect,
0:15:35 and also to get their sense of what was going on in these interactions.
0:15:37 What were we missing in the videos?
0:15:38 We had only seen 12.
0:15:43 We had all made these huge conclusions because we had seen 12 horrific videos.
0:15:45 But police stops happened thousands of times a day.
0:15:51 Look, 80% of the police shootings in our data came from a 911 call, not someone pulling
0:15:57 someone over and escalating, which is how we typically see it on TV, but a 911 call where
0:16:01 the police show up, the person has a weapon, there are multiple witnesses and a shooting
0:16:02 happens.
0:16:06 And if that’s 80% of the data, it’s not that surprising that there are no racial differences,
0:16:07 right?
0:16:11 Whenever we talked about lethal use force, they would become very earnest and say things
0:16:16 like discharging your weapon is a life-changing event.
0:16:22 I heard that in city after city, discharging your weapon, sir, is a life-changing event.
0:16:28 Not one police officer told me roughing up a black kid in an alley is a life-changing
0:16:29 event.
0:16:32 So these are categorically different in their frame?
0:16:33 Categorically different.
0:16:38 And the incentives or disincentives as it were are not the same.
0:16:42 Because once you discharge your weapon, then what happens?
0:16:46 There’s a real investigation that happens, independent of if many people saw it and they
0:16:48 think it was fully justified.
0:16:54 But we don’t put the same scrutiny on how we treat young black men civilians, right?
0:16:58 So how did these ride-alongs change the shape of your understanding of this job?
0:17:02 It gave me a better sense of how complicated the job is, man.
0:17:04 It’s really complicated, okay?
0:17:07 And you realize that the job is really difficult.
0:17:08 Okay.
0:17:11 And let’s go back then to your findings on lethal use of force.
0:17:15 On lethal use of force, we found no racial differences in those.
0:17:18 And it caused, you know, some alarm among people.
0:17:19 What do you mean by alarm?
0:17:22 Was there alarm even before you published the research?
0:17:23 Oh, man.
0:17:27 I mean, I had colleagues pull me to the side and say, “You’re crazy.
0:17:28 Don’t publish this.
0:17:30 You’re going to ruin your career.”
0:17:31 Because why?
0:17:32 I don’t know.
0:17:33 They didn’t give a full example.
0:17:38 Ruin your career because here you are a black economist who’s saying what exactly?
0:17:40 You know, that’s the difference between you and I, Steve.
0:17:41 When someone said, “Man, that’s going to ruin your career.”
0:17:46 I don’t go, “Exactly how, bud?”
0:17:50 But they came to me and said, “Look, after a seminar, here’s what you do.
0:17:53 You take the lower level uses of force, publish that.
0:17:54 Don’t publish the other.”
0:17:58 And I said to this person, “If the results were that there were racial differences in
0:18:02 lethal use of force that looked like discrimination, do you think I should publish them then?”
0:18:05 And they said, “Yeah, because then it would fit with the first part.”
0:18:08 And I said, “Well then, you just insured I’m going to publish it all because I’m not
0:18:11 going to hide a result because you don’t like it.”
0:18:16 I have heard you refer to other researchers who analyze police bias and police behavior
0:18:18 as cowards.
0:18:23 Is this what you’re talking about, that scholars are withholding either evidence or emphasis
0:18:26 at least on the lack of racial bias in police shootings?
0:18:28 Yes, that’s exactly what I mean.
0:18:33 Because you can look at the papers and people have similar findings, but it’s an appendix
0:18:35 table 157.
0:18:36 And they bury it.
0:18:38 And here’s the difference, man.
0:18:43 You’re one of the rare people who have actually been to the communities where I grew up.
0:18:46 And they haven’t changed much since I was a kid.
0:18:50 My life’s work is about making those communities better.
0:18:53 And I refuse to lie to them, right?
0:18:59 I just refuse because the folks in those communities, they know when they’re being lied to.
0:19:04 And we think we’re making all this progress because we now capitalize the letter B in black.
0:19:07 No one in that neighborhood gives a crap about that.
0:19:08 They don’t.
0:19:13 These are real issues, and I’m not going to bury the truth that can actually help folks
0:19:16 just because people are going to be upset about it, right?
0:19:21 Like the thing about lower level uses of force is that actually, for me, provides some optimism
0:19:27 because that’s a place that with the police, we can dig in and try to actually make real
0:19:29 progress together.
0:19:34 If folks would just stay true to the data instead of trying to make it be what they want
0:19:37 it to be, we’d all be in a better place.
0:19:42 I’ve heard you use the word dignity before in discussing this or the absence of dignity.
0:19:44 Can you talk about how that works?
0:19:50 Really important when I wrote this, someone I respect shot me a text and said, “I don’t
0:19:55 see this as a big problem because this is about black lives matter and not about being
0:19:57 roughed up.”
0:20:03 And that really upset me because as a person who’s been roughed up by police, it is a very
0:20:05 stressful, stressful interaction.
0:20:07 It is not a good interaction I have.
0:20:11 So I wrote back to him, “Black dignity matters as well.”
0:20:15 If you talk to kids in the communities that I care about, when you ask them about their
0:20:19 prospects and how fair the world is and how much effort matters, things like that, one
0:20:22 of the things they will mention is police.
0:20:28 How can it be that effort matters so much and there’s this true meritocracy if I can’t
0:20:30 get a home safe for my own police?
0:20:33 And so I think it erodes trust in the American dream.
0:20:36 It erodes trust in American institutions.
0:20:41 And it’s something that we could spend some good time really working on, right?
0:20:47 Because there’s a place where we’re not hardly at all collecting data to hold police accountable.
0:20:48 We don’t have an incentive scheme.
0:20:54 It feels like that is ripe for reform and if it were me, that’s where I would start.
0:21:00 Seven days after that paper came out, I had a five-hour meeting with Obama and activists
0:21:01 and other folks.
0:21:03 Al Sharpton was there.
0:21:06 And Al Sharpton and I probably don’t agree on hardly anything, but there’s one thing
0:21:07 we agreed on.
0:21:13 He said, “Look, if we weren’t being harassed daily, then we’d be willing to listen on
0:21:15 some of these shootings that weren’t clear.”
0:21:17 That’s a profound point.
0:21:22 If we could take away the discrimination that we know exists, then there’d be room for
0:21:24 common ground on these other things.
0:21:26 So Roland, what happened next?
0:21:31 Did the president or the Justice Department issue a new manual on best policing practices
0:21:35 as determined by Roland Fryer, for instance?
0:21:37 Not even close.
0:21:38 Not even close.
0:21:42 But we did have some follow-up meetings with other senior White House officials.
0:21:49 And the hope was to try to get the head of the FBI at that time, Comey, involved because
0:21:53 they serve for much longer, typically, than administrations.
0:21:57 And to start doing these things where we can potentially look at tying federal resources
0:22:00 to police, collecting the right data, et cetera.
0:22:01 And just none of it happened.
0:22:03 I was failed.
0:22:04 Because why?
0:22:05 Because that’s just how it works in government.
0:22:06 Is that the easiest answer?
0:22:07 I don’t know.
0:22:08 Maybe I suck.
0:22:09 I don’t know.
0:22:15 I really don’t know, but it was extraordinarily frustrating to me because this is something
0:22:20 that matters so much and it felt like there was a wedge to really make progress.
0:22:24 After the break, Roland Fryer and the push for corporate diversity.
0:22:28 I said, why aren’t they doing what we know works?
0:22:29 I’m Stephen Dubner.
0:22:31 This is Freakin’omics Radio.
0:22:41 We’ll be right back.
0:22:46 In 2019, the economist Roland Fryer started a firm called EO Ventures.
0:22:48 The EO stands for Equal Opportunity.
0:22:53 Then he got a two-year suspension from Harvard and had time on his hands.
0:22:57 He worked 100 hours a week to get EO Ventures off the ground.
0:23:03 We really want to invest in companies that move the levers that we know are important
0:23:06 for increasing economic mobility.
0:23:11 Most companies in their portfolio were founded by women and people of color, people who traditionally
0:23:14 don’t have the best access to venture capital.
0:23:17 I mean, as you know, Stephen, I did fight the good fight.
0:23:21 I wrote a bunch of papers for academic journals, like seven people read them.
0:23:22 You’re one of them.
0:23:23 Thank you.
0:23:28 I spent pretty good time with folks in Washington and other state governments, but I just had
0:23:29 no impact whatsoever.
0:23:30 I didn’t get anything done.
0:23:34 At some point, I had to look myself in the mirror and go, would Milton Friedman go around
0:23:37 all these foundations and beg them to do the right thing?
0:23:38 No.
0:23:42 We would try to use the market forces to increase opportunities.
0:23:44 When I was a kid, people told me capitalism was the problem.
0:23:47 What I’m trying to say here is it’s going to be part of the answer.
0:23:53 One of your portfolio companies I see is called Intus Care, which is described as a data-driven
0:23:54 elder care company.
0:23:59 Can you tell me about that firm and how that fits into this notion of investing in a business
0:24:01 that increases opportunity?
0:24:02 Yeah.
0:24:07 This is an interesting one because two of the co-founders are two young black men from
0:24:09 Brown University.
0:24:14 They’re former athletes, they marched into my office, these in-shape young black gentlemen
0:24:21 with this passionate plea for elder care, and it was like, what the heck is going on?
0:24:27 It’s an interesting field because they are using data in really sophisticated and interesting
0:24:32 ways to essentially, not to sound like buzzwords, but to risk group people.
0:24:36 What they’re saying is to actually increase the quality of care.
0:24:39 Maybe I should check on Steven three times a day because he’s in a higher risk group
0:24:40 and other people one time a day.
0:24:44 That would have been great when trying to care for my grandmother at the final stages
0:24:48 of her life because I never could tell from Boston whether or not she was getting the
0:24:52 right care, whether or not someone was stopping by to check on her.
0:24:57 There are big differences by population in terms of how that care goes based on how much
0:24:58 income you have.
0:25:03 There’s another company that I guess is a portfolio company in your venture capital
0:25:05 firm, but you are also involved in this company.
0:25:10 This is called Sigma squared, which is described as science-driven diversity.
0:25:13 Can you just give me the top line description on that?
0:25:22 Sigma squared really is trying to help companies, universities, not for profits, increase diversity,
0:25:27 but do it in a way that frankly makes sense that is data first.
0:25:30 That isn’t just hand waving, let’s say.
0:25:35 The hand waving is, that’s why I started it because after George Floyd, I saw what a lot
0:25:39 of corporations did in terms of the, I would call it value signaling, and it made me sick
0:25:41 my stomach, man.
0:25:46 On the other hand, I went to a friend of mine and I said, “Why aren’t they doing what we
0:25:47 know works in this area?
0:25:49 Theorem’s proven.
0:25:50 There are simulations that have been run.
0:25:52 Why aren’t they using that?”
0:25:57 They rolled their eyes at me and said, “Because most CEOs and people in HR are not reading
0:25:59 papers and economics from the 1990s.”
0:26:00 I said, “Oh.”
0:26:04 We wanted to make it easy for them, so we essentially created software that does the
0:26:06 analytics for them.
0:26:11 It seems as though just about every kind of institution these days, corporations and
0:26:17 government departments and universities and so on, have embarked on diversity, equity,
0:26:20 and inclusion, or DEI programs.
0:26:25 This was certainly boosted by the outrage around the murder of George Floyd in 2020.
0:26:32 McKinsey Analysis found that Fortune 1000 companies committed to $66 billion in 2020
0:26:35 to spending on racial equity initiatives.
0:26:37 How well do you think that money’s being spent?
0:26:42 I don’t know, but I would say the jury’s still out, and that’s being very gracious.
0:26:45 You recently published a piece, Roland, in Fortune magazine.
0:26:51 It was headlined, “It’s time for data-first diversity, equity, and inclusion.”
0:26:53 Here’s one sentence I found particularly interesting.
0:26:59 The average impact, you write, of corporate DEI training is zero, and some evidence suggests
0:27:04 that the impact can become negative if the training is mandated.
0:27:11 Uh-oh, that doesn’t sound like a very good return on $66 billion or whatever these firms
0:27:12 are spending.
0:27:16 Walk me through that, and then I want to know when it’s failing, why it’s failing, and what
0:27:17 you want to do differently.
0:27:21 Yeah, this is not my research, but there have been lots of others who worked on this.
0:27:27 Professor Princeton Betsy Pollock has a recent paper, and she reports that the impact of those
0:27:28 trainings is zero.
0:27:34 Frank Dobbin at Harvard and Sociology Department has also written things that show that not
0:27:39 only is the impact zero, but he has shown data from companies that when it was mandated,
0:27:45 the actual share, the percentage, of minority managers goes down.
0:27:50 So I’m looking at something that I think is from the Dobbin research done with Alexandra
0:27:54 Kalev in the Harvard Business Review.
0:27:59 One of the reasons they write that it doesn’t work when it’s mandated, and I just found
0:28:07 this surprising, but in retrospect maybe not, was that when it’s mandated, managers don’t
0:28:11 like it because they, quote, “resist strong arming.”
0:28:14 Can you just talk about how it actually works, how it plays out?
0:28:16 It varies a lot, company by company.
0:28:22 Some will put folks through mandatory trainings, some people will just add it as a resource
0:28:24 for those who want to use it.
0:28:32 Others will do things like mask information on resumes of applicants because they think
0:28:35 that is the right thing to do.
0:28:39 All of these things, even when they have really, really great intentions, scare the heck out
0:28:40 of me.
0:28:41 Because why?
0:28:45 Because they are the equivalent of giving antibiotics no matter what happens when someone
0:28:46 comes into the doctor’s office, right?
0:28:50 So you come in with a broken leg, antibiotics, you come in, you got an earache, antibiotic,
0:28:55 and what happens inevitably is that, let’s suppose that antibiotics works in 10% of the
0:29:00 cases, then those 10% go, “I told you antibiotics worked, maybe I need a different dosage.
0:29:03 I can’t quite get this broken ankle to feel better.”
0:29:08 And so what we really need to do, in my opinion, and this should be controversial, I do this
0:29:13 in every other research project I’m in and corporations do it in every other aspect of
0:29:16 their business, is to start with the data.
0:29:18 Where do we actually have issues?
0:29:24 And I mean, deeper than, “We don’t have enough black people in the engineering department.
0:29:25 We know you don’t, okay?
0:29:26 We don’t have to look.”
0:29:27 Right?
0:29:28 Like most companies, that’s true.
0:29:34 And so the question becomes, “Can I get a full picture of what’s going on in my company?”
0:29:38 And what type of bias is producing those disparities?
0:29:41 Social scientists tend to group disparities in different parts.
0:29:47 Maybe it’s good old fashioned bigotry, but bucket number two could be information.
0:29:52 Maybe you don’t have perfect information when you hire or promote or assign work.
0:29:56 And in those cases, maybe you rely on your stereotypes accidentally or on purpose.
0:30:01 The third one is what I loosely call structural bias.
0:30:08 And that is you’re doing something that on its surface seems absolutely fine, but unknowingly,
0:30:11 it’s got a disparate impact on one group or the other.
0:30:17 I was talking to a company a couple of months ago and they had some supply issues.
0:30:20 They couldn’t get enough people to apply to their internships.
0:30:21 Okay?
0:30:22 And I said, “Well, how do you find people for your internships?”
0:30:24 They said, “Oh, Roland, it’s really random.”
0:30:26 I said, “What do you mean random?”
0:30:30 “Well, we all just, you know, look at our alma maters and we kind of select any random
0:30:32 kid from our alma mater who wants to work here.”
0:30:34 And I said, “Well, where’d you go to school?”
0:30:36 He said, “Yeshiva University.”
0:30:40 And that’s fantastic, but he might not get the diversity.
0:30:44 That is not a historically black school, as far as I understand, correct?
0:30:45 Not historically.
0:30:46 And again, it’s unwittingly.
0:30:52 In my experience, the vast majority of the disparities that we see in companies are being
0:30:54 produced by buckets two and three.
0:30:59 But you need to know that because imagine it is an information problem, okay?
0:31:03 The last thing you want to do is hide information on the resume.
0:31:07 And so that’s my antibiotics versus ankle pain issue, which is you have to take the
0:31:11 data, diagnose, and then figure out solutions that actually work.
0:31:12 We’re doing it all wrong.
0:31:18 So this magic software viewers sounds to me from the outside a little bit like science
0:31:24 fiction in that you have some kind of magic X-ray wand that you can wave across a firm
0:31:27 and you can glean what is in people’s minds and hearts.
0:31:31 But I assume this is real science.
0:31:34 Persuade me that it’s real and tell me how it actually works.
0:31:36 It is not at all science fiction.
0:31:40 It’s going to be embarrassingly simple when I tell you how it works.
0:31:46 What happens is we integrate into their HR data where they may have data from their applicant
0:31:48 tracking systems, et cetera.
0:31:55 The first thing we try to do is understand what the real disparities truly are because
0:32:00 average disparities are very different than once you actually account for apples and apples,
0:32:01 right?
0:32:03 Let me give you an example from a case study.
0:32:10 So a hospital network reached out to us and said we have a 33% difference in wages.
0:32:13 So women earn 33% less in our hospital.
0:32:15 We’ve done all the training we can do implicit bias.
0:32:17 We’ve done everything.
0:32:18 And so what can we do?
0:32:25 We got their data and that 33% was true on average, but it’s a hospital network.
0:32:30 You can’t compare doctors’ salaries with nurses’ salaries and things like that.
0:32:34 And so once you’re actually accounted for some basic demographics to compare apples
0:32:38 with apples, that 33% went down to 8.9%.
0:32:44 Now that wouldn’t surprise anyone who’s familiar with the research of your Harvard colleague
0:32:51 Claudia Golden, who’s been writing about the gender pay gap and how we get it wrong.
0:32:55 But still 8. something percent is not zero.
0:32:57 So what did you want to do next?
0:32:58 Oh, and that’s important.
0:33:00 It’s not zero, but it’s not 33%.
0:33:04 So you’d be surprised, or maybe you wouldn’t, that this emboldened the COO.
0:33:07 The COO was a woman who said 8.9%.
0:33:09 I can do something with 33.
0:33:11 I don’t know what to do.
0:33:14 That’s step one, understanding what the true disparities are.
0:33:21 And for this particular network of hospitals, once you accounted for overtime hours, that’s
0:33:23 what explained the 8.9%.
0:33:28 And so we had to figure out why is it that women weren’t working as many hours?
0:33:33 Was it because they didn’t demand as many hours, or was there a structural barrier to
0:33:35 them getting the hours they wanted?
0:33:37 Like family care, perhaps.
0:33:38 There you go.
0:33:43 And so what happened is very simple, they had shifts from 7 to 7.
0:33:46 That’s just the way they’ve always done it, 7 to 7.
0:33:50 And when they changed the shifts to 10 to 10, then this disparity went away.
0:33:51 Walk me through that.
0:33:54 7 to 7, 7 a.m. to 7 p.m. that is?
0:33:58 Yeah, that was the shift for the nurses, for the hospital network.
0:34:02 And the issue for this particular network of hospitals, not all, obviously, but this
0:34:08 particular one, that many of the female employers who were working those shifts told the administration,
0:34:11 it’s really hard to find childcare in the morning.
0:34:14 But if I can get my kids to school and come to work, it’s easier to find in the evenings.
0:34:18 And so therefore, if you shift the schedule back, we can work as many hours because we
0:34:19 want to work hours.
0:34:20 They were demanding more.
0:34:27 The C-suite of this hospital network had put themselves in pretzels trying to understand
0:34:30 why these big 33% disparities were going on.
0:34:33 Turns out it was actually 8.9%.
0:34:37 Turns out if you made a scheduling change, even that was dramatically reduced.
0:34:43 And so by using data in a matter of a few weeks, it completely transformed how they thought
0:34:46 about the disparities in their organization.
0:34:48 That example is incredibly compelling.
0:34:54 It also strikes me as very different from a lot of what we read about as DEI awareness
0:34:56 and training and hiring.
0:35:01 Because the example you gave was so empirical, it was so concrete, it was so actionable and
0:35:02 so on.
0:35:04 I hate to be cynical about it.
0:35:10 And I’m sure that the cases that become public that we read about are just a small fraction
0:35:11 of them.
0:35:13 But they make a really big impression.
0:35:17 I remember there was a story about Wells Fargo, which had done all kinds of illegal junk
0:35:19 over the previous several years.
0:35:27 But in 2020, they pledged to increase diversity and they took up this policy that all hires
0:35:29 over a certain salary level.
0:35:34 I think it was $100,000 would include at least one interview with a candidate who was female
0:35:36 or a person of color.
0:35:40 It was kind of the corporate version of what’s called the Rooney Rule in the NFL.
0:35:44 It was later revealed, and this was just a couple of years ago, that Wells Fargo had
0:35:51 conducted fake interviews with women and minorities after the job had already been promised to
0:35:54 someone else, which I’m assuming is often a white man.
0:35:59 So when you read that kind of story, you say, “Oh gosh, $66 billion worth of hand-waving
0:36:00 and window dressing.
0:36:05 I’m sure it’s not that bad, but can you talk to me about how bad it actually is?”
0:36:07 Well, I read the same story as you do.
0:36:10 I just approach it from a different way.
0:36:16 Some training and those programs, they can work, but when targeted to the problem, this
0:36:18 is not controversial.
0:36:19 This is not a magic wand.
0:36:25 This is, let’s start with the data, and if the data lead us to a particular empirical
0:36:30 issue that we think the Rooney Rule can solve, let’s do it.
0:36:34 Let’s actually do it, and it’s not fake do it, but let’s actually do it.
0:36:40 If the data lead us to a place where we think training is really important, so be it.
0:36:43 But the issue, I believe, is people are desperate to make a difference.
0:36:47 They are desperate to show that they are an ally.
0:36:51 They are desperate to show that they are on the right side of history here.
0:36:57 So they’re in a big scramble to just do something, and I think that is dangerous because over
0:37:04 and over again, we have examples that even if they’re earnest attempts, guessing hurts
0:37:07 the people a lot of times that we’re trying to help.
0:37:10 And DEI were scared to make mistakes.
0:37:14 And so I want to introduce, lo and behold, I’m an economist, a little bit of experimentation,
0:37:19 a little bit of trying things, data-driven to try to really make progress.
0:37:24 This approach is about getting 2% better every day, not about checking a box so that you
0:37:28 can appear on the nightly news and say, “We did this great thing and move on.”
0:37:34 So let me ask you this, Roland, you’ve said in the past that your research on police bias
0:37:41 hasn’t changed policing, and that your research in education hasn’t really changed education.
0:37:46 I’m guessing your work on DEI programs probably isn’t going to change DEI also?
0:37:51 It’s doing amazing, third time’s a charm.
0:37:57 No, the truth is, I don’t know what impact it has had, but it’s not enough for me because
0:38:00 I’m committed to what we’re doing, but I don’t want a lot of myself either about the
0:38:02 impact we’re making.
0:38:09 The reason that we started EO Ventures was because founders have real power.
0:38:12 The schools in my grandmother’s neighborhood in Florida are all still bad, but everybody’s
0:38:14 got an iPhone.
0:38:18 These founders, these technology companies have really changed the way, obviously, changed
0:38:20 the way we live and work.
0:38:26 Can we use that same power to make real changes in the neighborhoods that I’ve been concentrating
0:38:29 on my whole career and in companies and things like that?
0:38:30 I really believe that’s true.
0:38:37 And so I am 100% behind the strategy of using market forces to close racial gaps.
0:38:38 Hey, that’s pretty good.
0:38:43 I got to put that on a t-shirt.
0:38:49 Coming up, after the break, more Roland Friarisms that ought to go on a t-shirt.
0:38:50 Shalom, brother.
0:38:55 And what happened when the New York City schools made Friar their chief equity officer?
0:38:57 Well, kids have a lot of cell phones in schools.
0:38:59 Do I get credit for that?
0:39:00 This is Freakonomics Radio.
0:39:01 I’m Stephen Dubner.
0:39:11 We’ll be right back.
0:39:12 A quick reintroduction.
0:39:15 This is who we are speaking with today.
0:39:20 I’m Roland Friar and I am a professor of economics at Harvard University and the managing partner
0:39:21 at EO Ventures.
0:39:27 And then there’s another project you’re involved in called the Reconstruction Education Project,
0:39:31 which offers what one person calls an unapologetically black education.
0:39:34 And this is not instead of school, right?
0:39:36 It’s online classes on top of school.
0:39:38 Yeah, this is supplemental.
0:39:41 But you know, it’s Hebrew school for black kids, right?
0:39:42 It is.
0:39:43 Shalom, brother.
0:39:47 Shalom to you too.
0:39:51 My co-founder and CEO on that is Kaya Henderson, who is like an American hero, right?
0:39:56 She did amazing work in DC public schools and with other organizations.
0:40:02 And Kaya’s got a real vision that black communities should support and nurture and take over how
0:40:06 we tell history and how we get a chance to write our own story.
0:40:12 She wants kids to see themselves differently in life and in school, again, very similar
0:40:16 to what lots of other groups do, whether it’s a Saturday school for folks who are Korean
0:40:19 or Hebrew school, that’s what Reconstruction is.
0:40:21 And they’re doing really, really quite well.
0:40:26 You know, what’s interesting is I hear you just describe Kaya and that project.
0:40:32 It seems that the public discourse about race treats blackness as a problem.
0:40:38 And it seems that you not only never thought like that, but think quite the opposite.
0:40:42 And that feels manifest in something like this Reconstruction project.
0:40:44 Can you talk about that for a moment?
0:40:45 And maybe I’m wrong.
0:40:46 Tell me if I’m wrong, Roland.
0:40:47 No, man.
0:40:49 That’s a great point, Stephen.
0:40:50 You are exactly right.
0:40:55 And you met years ago, my grandmother, who was really close to me.
0:40:59 And we had our issues, but love, love, love that woman.
0:41:05 And one of the things that she did when I was a kid was anytime we saw a white person mess
0:41:10 up anything, it could be accidentally tripping over a curb.
0:41:12 It could be that they weren’t quite dancing on beat.
0:41:16 It could be that they gave the wrong change at the grocery store.
0:41:21 She would look at me, right in the eyes, and then she would roll hers and she would say,
0:41:24 “Hmm, that’s that superior race.”
0:41:28 And she just made me always feel like I was so lucky to be black.
0:41:33 And so I’ve never, ever, for one second, thought of blackness as a problem.
0:41:34 And you’re right.
0:41:39 Many people are seeing that, or at least implicitly and sometimes explicitly, saying that in public
0:41:45 discourse, we as a community can take over what we teach our kids, what we signal to
0:41:51 them, and reconstruction is a celebration of black culture.
0:41:53 It’s a celebration of black love.
0:41:55 It’s a celebration of black excellence.
0:42:01 So Roland, you are not what I think of at least as a behavioral economist, but you certainly
0:42:08 know enough about behavioral ideas like anchoring and framing to understand how strong those
0:42:13 concepts are for many people and how they can set expectations.
0:42:19 It’s huge because it implicitly puts a cap on what kids think they can achieve, right?
0:42:28 Like I really thought I could be anything, and I didn’t understand that we grew up without
0:42:32 money until I got to Harvard and someone says, “Wow, you grew up without money.”
0:42:33 I didn’t understand.
0:42:35 We didn’t summer in places.
0:42:38 And so I think it’s really important.
0:42:41 You mentioned framing and anchoring.
0:42:45 Let’s add another one to that, which economists don’t know a whole lot about, which is identity,
0:42:46 right?
0:42:50 Two of my favorite economists, George Akerlof and Rachel Cranton, somewhere around 2000,
0:42:54 wrote a paper on the economics of identity, and they start this paper with this beautiful
0:43:00 sentence that identity is one of the most important choices that an individual can make.
0:43:01 That’s what I think this is about.
0:43:02 Who am I?
0:43:03 Who are my people?
0:43:08 You know, when I was in school in Texas, it was like, well, there was slavery, and then
0:43:11 there was Jim Crow, and then there’s you, right?
0:43:17 Let’s take a test, whereas we didn’t learn about the rich history that all of us come
0:43:18 from.
0:43:26 And other cultures have institutions or organizations that help them understand their history and
0:43:33 their role in promoting their own culture and how they situate positively in that history.
0:43:35 And we don’t do a good job of that.
0:43:39 I’m really influenced by that question, Stephen, because I just returned a couple of weeks
0:43:45 ago from Israel, and I called Kaya from Israel, and I said, wow, right?
0:43:53 This is an example of some of the things we were trying to do in terms of a shared understanding
0:43:59 of our history, the ups and the downs, and building upon that history to make a generation
0:44:03 of super kids who, like me, felt like they could do anything.
0:44:10 Way back in 2008, you went on the Colbert Report, and you said that the achievement
0:44:14 gap in this country is our biggest civil rights concern.
0:44:18 That statement has some echoes, to me at least, of Du Bois saying the problem of the 20th
0:44:21 century is the problem of the color line.
0:44:22 Yes.
0:44:25 15 years later, how do you think about that statement of yours?
0:44:28 The achievement gap is the biggest civil rights concern.
0:44:29 You think that’s still the case?
0:44:30 Yeah, absolutely.
0:44:33 The issue is development, and that’s what I meant.
0:44:35 It’s development, period the end.
0:44:39 Yes, of course there’s discrimination in the world.
0:44:45 Of course there is, but if you just look at the data, the vast majority of the disparities
0:44:48 are driven by differences in development.
0:44:51 This is opera pro of everything we’ve been talking about.
0:44:54 Folks have now decided it’s not the achievement gap anymore because that’s offensive.
0:44:55 It’s the opportunity gap.
0:44:56 Give me a break.
0:44:57 You think that’s helping the people in the neighborhood?
0:44:58 Oh, thanks.
0:44:59 Appreciate that.
0:45:01 I can really go now.
0:45:06 But yes, that’s it.
0:45:12 If we can solve that problem, many of the other things that are social ills, there’s
0:45:16 no magic bullet here, but they will get a lot better.
0:45:21 When I wrote about defunding the police, I was like, why don’t we invest in kids and
0:45:28 they’ll have productive jobs and the police can defund themselves?
0:45:35 Years ago, you were something called the chief equity officer in the New York City schools.
0:45:37 Tell me how that went and what you learned from it.
0:45:40 I was fresh out of graduate school, wanting to make a difference.
0:45:45 I called up PS70 in the Bronx, which was off of Colgate Avenue in the South Bronx, close
0:45:48 to where my uncle used to live.
0:45:50 They wanted to do an incentive program.
0:45:52 I love the idea of an incentive program.
0:45:54 It fit everything I knew as a young economist.
0:45:59 They would take tests and they would literally email me the results and I’d order pizza.
0:46:00 That’s how it started.
0:46:03 Meaning the kids who did well got pizza or cash later, right?
0:46:04 Absolutely.
0:46:05 No cash pizza, man.
0:46:06 They got pizza.
0:46:07 Well, didn’t you try some cash later?
0:46:08 No.
0:46:09 I tried a whole lot of cash later.
0:46:10 Yes.
0:46:16 That was the start of it, one school, and then that blossomed to 14 and then to 140 in New
0:46:22 York City and half of the school district in Washington, D.C. and schools around the
0:46:23 country.
0:46:28 Yes, one of our first things as the chief equity officer in the New York City Department
0:46:34 of Education was to try innovative programs and rigorously evaluate them, okay?
0:46:36 Data first.
0:46:41 Really simple stuff here, like collect data, analyze it, be honest about the answer, don’t
0:46:45 bury the results when you don’t like them, and then have a real strategy about how to
0:46:48 execute on ideas that are promising.
0:46:50 That’s all we were trying to do in the New York City DOE.
0:46:53 That’s all I’ve tried to deal with the police and all I’m trying to do now with corporate
0:46:55 DEI.
0:46:58 In that case, we ran incentive programs, some of them worked, some of them didn’t.
0:47:02 But then the cool thing that we tried to do, Stephen, that we never quite got off the ground
0:47:08 was I wanted to rebrand education for kids and communities.
0:47:11 We had this campaign called School Is Money.
0:47:16 One of my ideas was, let’s get a rare shoe that LeBron James will wear, and the only
0:47:22 way to get that shoe is to make good grades in New York City public schools.
0:47:27 All the adults, they were like, “Oh, this is great, Roland, this is so smart,” right?
0:47:30 We focused group this with kids, and in 37 seconds, a kid looked at me and said, “Man,
0:47:36 I want those air nerds.”
0:47:37 That was done.
0:47:40 But we tried all sorts of things like that, so we tried an initiative called the Million
0:47:44 Program where we gave kids cell phones and then we texted them things throughout the
0:47:50 day to try to give them messages to change the culture around trying hard in school.
0:47:54 We would say stuff like, “Your average life expectancy is 72 years, that’s a long time
0:48:01 to be broke,” or give them the fraction of millionaires who also have a high school or
0:48:06 college degree, things like that, to try to get them to rebrand education.
0:48:11 This was all a part of that job of chief equity officer, and again, some things really
0:48:12 worked and some things didn’t.
0:48:13 But that’s the whole point.
0:48:17 It was kind of an R&D unit within the New York City Department of Education, and kudos
0:48:21 to Joe Klein and Mike Bloomberg for letting a 28-year-old kid come and try new things
0:48:24 that would be kind of unheard of now.
0:48:29 Are any of those programs, or ones like them, still in existence, either in New York or
0:48:30 elsewhere?
0:48:32 Well, kids have a lot of cell phones in schools.
0:48:33 Do I get credit for that?
0:48:34 Yes.
0:48:36 I think we’ll give you all the credit for that, yeah.
0:48:37 Thank you.
0:48:38 Thank you, Verizon.
0:48:42 The incentive programs going on across the country, they’re just not well-publicized
0:48:46 because they’re still controversial, oddly enough.
0:48:50 Controversial because of the idea that learning should be driven intrinsically, right, by
0:48:52 the love of learning versus rewards?
0:48:54 Yes, absolutely, yes.
0:48:55 What do you think of that idea?
0:48:56 I agree with that.
0:49:00 I also think that there should be peace in the Middle East, and I think global warming
0:49:01 should stop.
0:49:02 Okay.
0:49:03 Anything else?
0:49:07 But no, seriously, I took it the opposite direction, right?
0:49:09 They thought you’re going to destroy the love of learning.
0:49:11 I thought this is a way to cultivate it.
0:49:14 Do you think you were mostly right then or now?
0:49:15 I think both of us were wrong.
0:49:20 I think it had no effect because we measured, lo and behold, there’s that data again.
0:49:25 We measured using their measures, pre- and post-love of learning, and the coefficient
0:49:27 was positive, but it wasn’t statistically significant.
0:49:32 So we had no real effect or big effect on love of learning either way.
0:49:34 What we did have an effect on is test scores went up.
0:49:36 And is that not enough?
0:49:38 To me, that’s fantastic, right?
0:49:41 You can get test score gains, and you don’t change the love of learning.
0:49:43 That to me is a policy worth doing.
0:49:44 Never mind it.
0:49:46 It’s really inexpensive relative to other reforms, right?
0:49:51 This isn’t changing the number of kids in a classroom, which is really expensive, but
0:49:55 this is something where you can actually increase student achievement at a relatively low price.
0:50:02 Most education reformers, even the most idealistic, also care about test scores, and you are offering
0:50:07 here a route to a relatively inexpensive intervention that raises them.
0:50:13 I would think, therefore, that incentive programs in schools would be everywhere and widely
0:50:14 publicized.
0:50:15 Why are they not?
0:50:20 Because there’s real pushback on incentive programs in schools by lots of folks.
0:50:25 It’s not just the progressives who believe that we should do it for the love of learning.
0:50:29 I mean, I published this piece in the Wall Street Journal a couple months ago about using
0:50:31 incentives to battle COVID loss.
0:50:33 I got it from both sides there again.
0:50:35 Some people said you’re going to destroy the love of learning.
0:50:38 The other says, “Here you go again wanting another welfare program.”
0:50:42 It’s just a controversial thing, and I think that schools are doing it in secret.
0:50:44 I think they’re doing it without cash.
0:50:47 They’re doing it with school t-shirts and pizza parties and things like that.
0:50:51 There’s lots of charter schools and public schools who are doing this that I know about.
0:50:54 It’s just not as publicized as you might think.
0:50:59 I want to read a little section of an article that recently appeared in The New York Post.
0:51:03 This was an opinion piece called, “A New York City School Diploma Isn’t Worth the Paper
0:51:04 It’s Written On.”
0:51:06 It’s by Wai Wai Chin.
0:51:08 She’s a fellow at the Manhattan Institute.
0:51:13 She’s also the founding president of the Chinese American Citizens Alliance of Greater New York.
0:51:15 Here’s the opening of this piece in the post.
0:51:20 When renowned economist and education innovator Roland Fryer served at the School Board of
0:51:27 Massachusetts and attended a public meeting to close a failing school, a black mother walked
0:51:30 up to him and told him that the school was a good school.
0:51:32 “No, ma’am,” said Fryer, who was also black.
0:51:33 “It’s not.”
0:51:36 The mother pulled out her child’s report card from her purse.
0:51:37 It was all A’s.
0:51:40 The mother insisted, “This is a good school.”
0:51:45 Fryer had to tell her, “Ma’am, they have lied to you.”
0:51:49 That story sounds as though it must have come from you originally, so first of all, is that
0:51:50 a true story?
0:51:52 It is a true story, yes.
0:51:56 What does that even mean that the school lied to parents that the A’s that their kids were
0:51:58 getting weren’t real A’s?
0:52:03 The issue is this is one of the toughest things I did serve on the School Board of Massachusetts.
0:52:04 It was an amazing experience.
0:52:08 To see it from that angle, I took it very seriously.
0:52:12 The hardest thing I did during my tenure there was to have those meetings where busloads of
0:52:13 parents came.
0:52:19 It was really hard for me to watch parents fight to keep open a school where the literacy
0:52:23 rates and the test scores were so low.
0:52:28 What I meant by, they have lied to you, is that there’s no way a kid can get all A’s
0:52:34 and still perform so poorly on these exams that are measuring basic skills.
0:52:39 The expectations must be very low in that school, and so that’s what I was reacting
0:52:40 to.
0:52:41 It was sad, man.
0:52:43 The thing that drives me nuts, sorry I’m going to get fired up about this, is that this woman
0:52:47 is doing everything that she thinks is right.
0:52:51 Part of it is on us dumb researchers who keeps telling stuff in standard deviation units.
0:52:55 Just don’t understand things in standard deviation units, folks.
0:53:02 We are obfuscating the truth by providing this in a way that not all parents can digest
0:53:05 in the way they should.
0:53:06 That’s what I was seeing.
0:53:09 This woman looked at the school, saw a great report card, so she thought it was a good
0:53:11 school, but her kids were not being served there.
0:53:16 Is this what’s behind the motivation of a lot of schools to get rid of standardized
0:53:17 tests then?
0:53:19 I don’t know if it’s to get rid of them.
0:53:21 It may even be to make them easier.
0:53:25 I really do believe that kids will live up or down to your expectations.
0:53:28 That if we really held those things high, and this is not just role and talking when
0:53:34 we did our work on effective schools, one of the five tenants that makes the school
0:53:36 effective is high expectations.
0:53:41 When you don’t have that, then there is this gap between what a kid thinks that they’re
0:53:45 doing and their report card potentially, and actual skills.
0:53:50 You mentioned that high expectations are one of five behaviors or policies that make good
0:53:51 schools good.
0:53:57 This was in a study you did of charter schools, but as I understand it, this would apply universally.
0:53:59 Can you list the other four?
0:54:00 Sure.
0:54:03 More time in school, so basic physics of education.
0:54:04 That’s pretty simple.
0:54:06 Then there’s using data to drive instruction.
0:54:11 It wasn’t just that you use data, because nearly every school now has some form of data.
0:54:15 What was different about the schools that were effective is that they had a real plan.
0:54:21 If we take this assessment and 50% of the kids pass, then we pause and we reteach.
0:54:22 We don’t just keep going.
0:54:26 Number three was small group instruction, or what my grandmother would call it, good old
0:54:27 passion tutoring.
0:54:32 If you tutored kids in groups of six or less for four or more days per week, then your
0:54:35 test scores were a lot higher than human capital.
0:54:41 How you select, retain, and develop teachers, and particular teacher feedback, really, really
0:54:42 important.
0:54:46 The last one was, as I said before, a culture of high expectations.
0:54:50 They all understood that they were dealing with high poverty rates.
0:54:55 They all understood that they were dealing with, unfortunately, many single family households,
0:54:59 et cetera, but they didn’t use as an excuse not to teach.
0:55:03 Those five factors explained 50% of the variance and what made some charter schools good and
0:55:06 others not so good.
0:55:07 Let me ask you this.
0:55:14 If you look back at your life and career, how would you think about measuring the costs
0:55:19 and benefits of your being black, both professionally and personally?
0:55:24 Man, that’s a great question.
0:55:33 Never really think about it, except for, you know, I’m a lot cooler than my colleagues.
0:55:35 Honestly, I don’t really think about it.
0:55:41 Look, let’s be honest, it has open doors, it has closed windows, right?
0:55:45 I have been given tremendous opportunities, and I try to take advantage of them.
0:55:48 I don’t have any to spare, put it that way.
0:55:50 I think it’s helped me some.
0:55:51 It’s hurt me some.
0:55:52 What the net effect is?
0:55:53 Don’t know, don’t care.
0:55:57 Do you think race played some significant role in what happened with you at Harvard and
0:56:00 the suspension or no?
0:56:07 You know, I think that probably, but not in the ways that you might think, I also believe
0:56:12 that, you know, being an asshole probably didn’t help, right?
0:56:14 I’m no angel here either.
0:56:21 Early in my career, for example, I really tried not to have any money that I raised
0:56:25 privately go to overhead for the university.
0:56:28 I was trying to give it to poor communities, and that’s probably not the best way to build
0:56:30 teamwork.
0:56:34 And there was a point at which, like, folks inside the university didn’t want to be associated
0:56:38 with my incentives research, because it was too controversial.
0:56:42 So did I push the boundaries trying to do things for the kids in the neighborhoods I
0:56:43 served?
0:56:44 Yes.
0:56:47 Did I spend more time off campus, because I thought that the kids in these neighborhoods
0:56:49 needed my time more than the kids on campus?
0:56:51 Yes, I did.
0:56:54 And did that rub people the wrong way, and did that come across?
0:56:56 That’s all I’m sure it did.
0:56:57 Do you regret it?
0:57:01 Do you feel like that’s just who you are and you’ve learned from it and you’re in a different
0:57:02 mode now?
0:57:09 Of course, of course, I regret it and have apologized for it, and the thing that I worked
0:57:16 really hard on while I was off campus was trying to be authentically me, but not have
0:57:21 anything that could be even perceived as offensive to someone else, and that’s hard.
0:57:24 But I took it very seriously, and I’m back better.
0:57:25 I’m back stronger.
0:57:31 I used to—it would have been nothing for me to have on Thanksgiving, to have 20 students
0:57:34 over my house for Thanksgiving dinner, cook a bunch, they fall asleep on the couch, we
0:57:35 play video games.
0:57:37 I don’t do that anymore.
0:57:43 So I would say that there’s clear delineation from my living room to work, and I think that’s
0:57:44 a good thing.
0:57:47 I was worried that was not going to be—I thought, “How could I do really cutting-edge
0:57:48 burger?”
0:57:49 But I was wrong about that.
0:57:55 I can still be—me, you and I have mixed it up the whole time here—I’m still rolling,
0:57:56 I still care about these issues.
0:58:02 I refuse to not tell the truth, but I’m at a different and, dare I say, more mature
0:58:04 part of life.
0:58:07 I broke a lot of glass early on in my career that had nothing to do with that, but all
0:58:12 to do with me being impatient as hell and wanting to help and trying to do the right
0:58:16 thing, and I don’t think that was helpful.
0:58:18 Thanks to Roland Fryer for this conversation.
0:58:23 We covered a lot of ground, and even though I thought I knew his work pretty well, I learned
0:58:24 an awful lot.
0:58:25 I hope you did too.
0:58:29 We checked back in recently with Fryer to get an update on his various projects.
0:58:36 He told us that his early-stage investing fund EO Ventures raised $100 million in 2022,
0:58:42 and Sigma Squared, his data-driven diversity consulting firm, raised $10 million in Series
0:58:43 A funding.
0:58:47 It is now leveraging its analytics platform beyond enterprise HR.
0:58:52 Police departments around the country have started using Sigma Squared’s toolkit as
0:58:56 an early warning sign to flag bias in police practices.
0:59:00 Fryer is also now a Wall Street Journal contributor, and his first piece there caught my eye,
0:59:05 so he and I have decided to turn that into a future Freakonomics Radio episode, so keep
0:59:07 your ears open for that.
0:59:11 We will be back soon right here with our regular weekly episode.
0:59:15 Until then, take care of yourself, and if you can, someone else too.
0:59:18 Freakonomics Radio is produced by Stitcher and Renbud Radio.
0:59:24 You can find our entire archive on any podcast app, also at Freakonomics.com, where we publish
0:59:25 transcripts and show notes.
0:59:28 This episode was produced by Alina Cullman.
0:59:33 Our staff also includes Augusta Chapman, Dalvin Abouaji, Eleanor Osborn, Ellen Frankman,
0:59:37 Elsa Hernandez, Gabriel Roth, Greg Rippen, Jasmine Klinger, Jeremy Johnston, John Snarrs,
0:59:42 Julie Canford, Lyric Bowditch, Morgan Levy, Neil Caruth, Rebecca Lee Douglas, Sarah Lilly,
0:59:44 Teo Jacobs, and Zac Lipinski.
0:59:48 Our theme song is Mr. Fortune, by the Hitchhikers, our composer is Luis Guerra.
0:59:52 As always, thank you for listening.
0:59:56 My favorite quote of all of our things when we were going around, you said, “Wow, man,
1:00:00 you’re really in shape for whatever it was at that point, 80,” and he said, “What’s
1:00:01 your secret?”
1:00:02 “Oh, man, eat well.
1:00:03 I don’t eat any pork.”
1:00:05 And I definitely don’t eat no pork.
1:00:08 He said, “Well, what are that in your beans there, sir?”
1:00:09 “Ham.”
1:00:24 The Freakin’omics Radio Network, the hidden side of everything.
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