TRIGGERnometry - Harvard Professor： The Facts About Police Brutality

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00:00:46.080 Like Jersey Boys and Beautiful, the next musical mega hit is here, The Neil Diamond Musical, A Beautiful Noise.

00:00:52.820 April 28th through June 7th, 2026, The Princess of Wales Theatre.

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00:01:00.000 I thought I was going to be able to show that the police were biased very easily.

00:01:05.860 And then we gathered literally millions and millions of data points on lethal use of force.

00:01:12.020 What we found was zero racial differences.

00:01:14.220 And that is the part that made people really, really upset, really angry.

00:01:19.180 Lack of bias made people upset and angry.

00:01:21.300 Yes, yes, the world was turning upside down.

00:01:24.840 Yes, there were threats against me and my family.

00:01:28.220 Police are rationally responding to what they view as threats, right?

00:01:32.440 When I embedded myself in the Houston Police Department, I noticed that independent of the skin color,

00:01:37.020 the police officer was really scared when he pulled someone over.

00:01:39.940 I asked him why.

00:01:41.240 He says, man, this is Texas.

00:01:42.800 We assume everyone's got a gun.

00:01:44.440 Roland, it's awesome to have you on the show.

00:01:48.500 I've been tracking your work for a very long time.

00:01:51.680 The conversation you had with our good friend Barry Bice was one of the most interesting and funny things that I've ever seen.

00:01:57.220 As it turns out, you're a former stand-up, or you did stand-up for a while.

00:02:00.040 But before we get into your work and your opinions, there was one particular incident that obviously changed the course of your life quite profoundly.

00:02:08.560 You're this super promising economist.

00:02:10.900 You've done incredibly well from a very difficult background getting to that position.

00:02:15.720 You're very promising.

00:02:16.820 You do this one study about police brutality and police violence against black suspects in particular.

00:02:22.860 And then you're suddenly under police protection for over a month and all sorts of other crazy stuff happens.

00:02:31.600 Tell us about that.

00:02:32.500 What happened?

00:02:33.460 What happened in the study or what happened to me?

00:02:35.680 Both.

00:02:36.120 Both, yeah.

00:02:37.040 Well, in the study, it started back in 2014 when we all were kind of mesmerized by what was going on with Michael Brown, etc.

00:02:47.360 And there were protests happening around the country, and I wanted to do something, but protesting's not my thing.

00:02:54.120 I mean, other folks, go for it, but it's not my thing.

00:02:56.980 But I wanted to do something that I thought would help.

00:02:58.840 So I thought this was going to be the easiest thing I'd ever done.

00:03:01.500 So I went and tried to collect some data, and I thought I was going to be able to show that the police were biased very easily.

00:03:07.940 I grew up in the South, partly in Florida, partly in Texas, and I grew up not liking the police.

00:03:15.580 So I just thought this was literally the easiest thing I'd ever done.

00:03:19.660 And a colleague came to me and said, what are you working on these days?

00:03:23.480 Oh, I'm just about to write this great paper showing the police are biased.

00:03:27.020 And he says, interesting.

00:03:29.380 What do you think the police are maximizing?

00:03:33.000 What do you think their side of the story is?

00:03:34.780 What does maximizing mean, just for people who are listening?

00:03:38.440 Yeah, so what are they solving for?

00:03:39.200 When they go to work every day, what are their set of incentives?

00:03:43.300 What are they after?

00:03:44.500 What are they trying to achieve at work?

00:03:47.600 And it's something that I had not thought about at all.

00:03:50.940 It was embarrassing, right?

00:03:52.940 And so I made arrangements to figure out how to actually do ride-alongs with the police.

00:03:59.800 And, in fact, you guys should do ride-alongs.

00:04:02.140 You should be good at it.

00:04:02.660 We're planning to.

00:04:03.380 Yeah, you should do it.

00:04:04.220 And bring a weapon.

00:04:06.380 And it's, I don't recommend doing it in Camden.

00:04:10.820 That was the first city I had done it in.

00:04:12.360 Where is Camden, by the way?

00:04:13.340 Camden, New Jersey.

00:04:14.300 Okay.

00:04:14.520 It's, you know, a city that has at least had a lot of crime, and especially given it's relatively small.

00:04:23.920 It's close to Philadelphia.

00:04:24.720 And so here we are on the ride-alongs, and so here we are on the ride-alongs, and I'm embarrassed to tell you.

00:04:29.760 Like, after a few hours, I became the worst police officer you could ever imagine, right?

00:04:34.800 Everyone I saw looked like a criminal, right?

00:04:36.680 Like, I was going around, and the cops were like, you really should not be a cop.

00:04:39.720 Because I would go around, and I'd say, what's that guy doing, you know, dribbling that basketball?

00:04:43.880 Looks suspicious to me.

00:04:44.660 Let's pull him over.

00:04:45.360 What do you mean, let's pull him over?

00:04:46.260 Anyway, I did this, and then we gathered literally millions and millions of data points on police use of force.

00:04:56.420 And I, again, thought that this was going to show all sorts of bias.

00:05:00.280 What we found was that in the kind of non-lethal uses of force, so pushing someone up against a car or drawing your weapon on them but not arresting them or pushing them on the ground,

00:05:14.000 those types of uses of force, there are large racial differences.

00:05:17.820 There's real racial bias in those uses of force.

00:05:21.380 In fact, just here in New York, a black person during that time was 50% more likely to have force used on them in any given interaction with the police officers.

00:05:31.840 Crazy, right?

00:05:33.560 That was lower-level uses of force.

00:05:35.180 But then we also collected data from 16 different cities across the U.S. on lethal use of force, the kind of force used on Michael Brown and others.

00:05:43.780 And that's what the protesting really was about.

00:05:46.680 And in those uses of, in those situations, what we found was zero racial differences in police use of force.

00:05:53.380 And that is the part that made people really, really upset, really angry.

00:05:58.580 Lack of bias made people upset and angry.

00:06:01.180 Yeah.

00:06:01.980 And in fact, one of the, you know, I talked to a newspaper reporter before the paper was even public.

00:06:07.300 And he says, I wouldn't write about this.

00:06:10.260 The first part's obvious and the second part's wrong.

00:06:14.840 What do you do about that?

00:06:16.260 And so we released those results.

00:06:19.480 And look, I'm skipping over a lot of stuff here.

00:06:21.660 I mean, not only did we think we did a rigorous job in the actual analysis, but then we literally, when we got these very surprising results, hired new research assistants, had them do it all over again just to make sure that they were robust.

00:06:34.260 Right?

00:06:34.380 Like, I was a little worried about, not worried, but I wanted to be sensitive about putting results like this out into the world because they were also counter to my own beliefs.

00:06:44.840 But we did, and yeah, I'd say that, you know, for a while, I thought, yes, the world was turning upside down.

00:06:56.320 Yes, there were threats against me and my family.

00:06:59.080 But the interesting part was there were thousands and thousands of emails that came from places like Kansas and Colorado and everywhere across the U.S. who said, wow, thanks for actually using data to shed at least some light on this.

00:07:16.800 And I've got the following 30,000 questions, but at least you're bringing real analysis to this question because our cities are literally burning and no one's talking about what the actual facts are.

00:07:27.240 So thank you.

00:07:28.440 And I engaged with lots of people on that dimension.

00:07:31.940 But yes, I was very, very surprised at how upset, you know, fellow academics got.

00:07:41.520 You know, as I've said before, I was taken to the side and said, you're going to ruin your career.

00:07:45.920 And I naively thought we were in this to actually develop a set of facts.

00:07:52.300 I actually thought that's what tenure was for.

00:07:53.900 I didn't I later realized it was for drinking Chardonnay at 1030 in the morning.

00:07:58.300 But but I at that point, I thought tenure was for going after the truth, even if it was unpopular and that the university was going to protect you from whatever may come of you being a real social scientist or at least following the facts as you saw fit.

00:08:15.740 So, you know, it we produced this result.

00:08:20.920 I'm still very, very proud of the work we did.

00:08:25.180 But I highly underestimated the response.

00:08:29.780 Roland, there's something before we get into all of that.

00:08:32.280 You said that I think in the study and quote me if I'm wrong, black and Hispanic people are more likely to see to be pushed around by the police, suffer physical force, et cetera, et cetera.

00:08:47.960 Do we know why that is particularly?

00:08:50.060 Is it that people from those types of communities are more likely to see the police as a threat and therefore they're more likely to resist arrest?

00:08:57.480 Is it that the police go in more aggressively?

00:08:59.840 Do we know what is actually happening?

00:09:01.560 It's a great question.

00:09:03.060 We don't have a perfect answer on that, but we have some threads.

00:09:07.420 So one of the most interesting coefficients in that study to me is that when the police report that a suspect is fully compliant.

00:09:18.780 They were not arrested and no contraband was found.

00:09:23.260 Nothing went wrong in this traffic stop as reported by the police.

00:09:27.020 Blacks are 25 percent more likely to have force used on them, even in those interactions.

00:09:33.880 Wow.

00:09:34.020 And so, you know, yes, there's some police departments who have written to me and says, you know, you're saying use of force, but what you really mean is response to resistance.

00:09:44.440 And no, that's not what I mean.

00:09:45.980 I mean use of force here because even in situations where, again, I can't emphasize this enough.

00:09:52.340 The police report they are fully compliant, right?

00:09:55.060 I took a lot of flack for people saying, how dumb can you be?

00:09:57.440 You use police reports.

00:09:59.000 Well, stop and think for a second.

00:10:01.000 If the police are reporting it and they still, you can still find bias in that data.

00:10:06.760 You can really believe it.

00:10:08.100 See what I'm saying?

00:10:08.940 And so the police report them being fully compliant.

00:10:11.900 And yet there's still a large racial difference in the use of force.

00:10:15.420 So I don't know exactly why that is.

00:10:17.680 We gave, we did some very, a set of pretty rigorous bias tests.

00:10:23.600 So we actually think there is just, it's racial, part of it is racial discrimination, not all of it.

00:10:29.840 Some of it can be explained also by the behavior of civilians.

00:10:34.020 That also differs, but it can't explain all of it.

00:10:36.400 So I think there's enough kind of, not fault, but there's enough, both sides of that equation that you described, civilians acting differently, police potentially being biased.

00:10:49.640 We find evidence for both of those.

00:10:51.880 And now the key question is, what can we actually do about it, right?

00:10:56.520 And, you know, what we try to propose is a way to work with police departments to eliminate this type of bias, because I think it's at the root of a lot of the discontent in black neighborhoods between the police and civilians.

00:11:16.960 Look, if we admit that some of the racial differences in police use of force are due to bias, and then there's a controversial shooting, it's almost irrational for someone in the community not to believe that the shooting, before you saw this data, right?

00:11:33.860 You say, well, on the things I do know, I know there's discrimination.

00:11:38.540 On the thing I can't really observe, I wasn't at the shooting, probably was discrimination, right?

00:11:44.820 And so I think this is a point that we don't make enough, but if it's hard to negotiate with the police directly on shootings, right?

00:11:55.160 Hold your weapon, that's hard.

00:11:57.300 You know, their lives are at stake too, but on the lower level uses of force, right?

00:12:04.240 Every department I've talked to privately knows that there are things that they can do better on the lower level uses of force that would then garner community support so that we could actually have productive conversations when a controversial shooting happens.

00:12:21.420 Roland, sorry, Francis, I feel like we're getting into the meat of the conversation, which is great, but I want to take one quick detour before we carry on on that part, which is you said something that I think in the moment that we're sitting here is a very interesting conversation to at least explore, which is you said, protesting isn't for me.

00:12:39.220 Why?

00:12:41.020 There are many ways of making a difference, right?

00:12:43.880 And, you know, I don't begrudge anybody who wants to have free expression and to protest.

00:12:51.540 Great.

00:12:53.940 It's just an economic lingo.

00:12:56.620 It's not my comparative advantage.

00:12:58.280 Okay.

00:12:58.980 Right?

00:12:59.360 I am a data nerd, so when I see a problem, my first inclination is not to stand up and protest.

00:13:07.280 My first inclination is let me get a bunch of data and prove to people why this is, what's actually going on here.

00:13:12.900 And, you know, to make real social progress, maybe you need both.

00:13:17.260 And I'm sure some of the people who are out protesting are saying, well, numbers aren't my thing.

00:13:22.960 Great.

00:13:23.320 You do your thing and I'll do mine.

00:13:25.480 But it's not that I have anything against it in any way, shape or form.

00:13:28.820 It's not just for me.

00:13:30.520 And the other quick detour I wanted to take, and I'm posing this in a very kind of devil's advocate kind of way.

00:13:36.940 Me too.

00:13:38.000 My read of you is you're someone who cares about the truth, who's prepared to be the outlier, who's prepared to say the unpopular thing from various conversations that I've seen you have.

00:13:46.980 So how true is it that you did this study out of a desire to prove that the police are biased?

00:13:53.740 Was there a part of you that was kind of, I'm going to, like, find something out here that's going to be different to what everyone else is saying?

00:13:59.560 The exact opposite.

00:14:00.640 It was, wow, I'm going to finally find something people are going to like me.

00:14:05.580 Oh, boy, were you wrong.

00:14:07.240 Yeah, man.

00:14:09.560 Yeah.

00:14:10.140 No, it is not, I promise, I do not get up in the morning thinking, let me try to stir the Kool-Aid today, right?

00:14:17.060 Like, that is not my view.

00:14:19.060 On the other hand, you know, sorry, my fellow academics.

00:14:23.060 They don't, I'm not afraid of them, right?

00:14:25.020 Like, they don't, you know, they don't intimidate me in any way, shape or form.

00:14:28.500 And so I'm not scared to say what I believe is the truth.

00:14:34.760 But I'm not an instigator.

00:14:37.000 I'm not a, people, I hate when people call me controversialist.

00:14:39.980 I don't want to be, right?

00:14:41.580 I think people who stick to bullshit, even though it doesn't fit the facts, that's controversial.

00:14:46.820 People who follow the data wherever they lead, I mean, isn't that social science?

00:14:50.860 And so, no, I literally thought this is going to be the easiest paper I'd ever written because I'd just come off of paying kids to learn.

00:15:02.740 Oh, my gosh.

00:15:04.320 You know, now people pay kids to learn.

00:15:06.400 They're like, this is interesting.

00:15:07.440 But back in 2008, oh, this was horrible.

00:15:10.240 I mean, I was compared to the Tuskegee experiments, literally, right?

00:15:14.060 Can you imagine that?

00:15:15.380 Get up one day, you go to work, and there's someone with a picket, speaking about protesting, someone with a picket sign who said,

00:15:21.040 Roland Fryer is the worst thing for black people since the Tuskegee experiments.

00:15:24.660 What are they?

00:15:25.660 Because I don't know what they are.

00:15:26.640 Oh, okay.

00:15:27.360 Well, let me give you the dichotomy here.

00:15:29.500 They're paying kids, on the one hand, to do the things that will help them later in life, unknowingly injecting people with syphilis.

00:15:38.340 You do the math, right?

00:15:40.380 Crazy.

00:15:41.060 Like, just crazy.

00:15:42.700 Yeah.

00:15:43.240 Yeah, yeah.

00:15:43.940 No, that's what happened.

00:15:45.120 Why are you surprised?

00:15:46.440 Anytime anyone has the wrong opinion now, they're literally Hitler.

00:15:49.340 This is basically it.

00:15:50.260 Yeah.

00:15:50.800 It's the same thing.

00:15:51.620 It's crazy, right?

00:15:52.180 So I had just come off of that, and then we had done this work in Houston where we took the worst schools in Houston

00:15:56.800 and did all we could to reform them.

00:16:01.580 And so through that education journey, and we'd written some papers on charter schools that people didn't love,

00:16:09.020 I thought, this is my chance.

00:16:10.880 People are going to really, finally, like something that I do.

00:16:16.160 The thing that I found disappointing from the reaction and the inevitable fallout,

00:16:20.820 where it's inevitable now with the benefit of hindsight,

00:16:22.720 is your report, as we've touched on, raised some really interesting points,

00:16:28.420 which actually, if we look to the results dispassionately,

00:16:32.080 there are things that we all need to work on, particularly the police.

00:16:35.980 And actually, if we address them, we could make relations between the African-American community

00:16:42.280 and the police so much better.

00:16:45.120 And actually, we could have, as a result of that, a more cohesive society.

00:16:49.920 Look, where were you in 2016?

00:16:54.280 Keeping my head down, Mike.

00:16:55.640 That's where I was.

00:16:56.700 That guy's controversial.

00:16:59.920 Right.

00:17:00.580 Of course, yes.

00:17:02.260 A hundred percent.

00:17:03.420 But that's the reason I think there was so much pushback,

00:17:07.220 because it was coming from different directions.

00:17:09.100 Literally, the emails I was getting, the communication I was getting.

00:17:11.880 On the one hand, people thought, oh, my gosh, what are you doing?

00:17:15.680 This is not true.

00:17:16.560 There's, you know, police are out there murdering innocent black people.

00:17:22.280 And on the other hand, I had police telling me, what are you talking about?

00:17:25.080 We're not biased.

00:17:26.920 Right.

00:17:27.400 And so, and in fact, right, when we published this in the New York Times,

00:17:34.000 the folks that were interviewing me at the time,

00:17:37.920 they were more concerned with calling NYPD biased

00:17:40.560 than they were saying that shootings were unbiased at the time.

00:17:47.160 Very, very different now.

00:17:48.760 But at the time, that's where the nervousness was coming from when they published it.

00:17:52.760 The world has changed.

00:17:53.680 The world has changed.

00:17:55.880 He's apparently remained the same.

00:17:58.580 I'm consistent.

00:17:59.620 How much do you think the reaction to your report and the fallout

00:18:05.880 and the general narratives are to do with history,

00:18:09.500 the way that the police has behaved in the past,

00:18:11.280 if we think about things like Rodney King, for instance,

00:18:13.460 and all of those types of incidents?

00:18:15.520 It has a lot to do with that.

00:18:16.700 Again, the relationship between black communities and the police are broken

00:18:19.960 in most communities.

00:18:21.680 And that's why I believe, just like you just described,

00:18:25.500 working together on these lower level uses of force,

00:18:28.780 when the stakes are lower,

00:18:32.160 that is the place to really have reform.

00:18:36.240 It's hard when the police go to a scene,

00:18:40.060 there's weapons involved,

00:18:41.640 and you say, well, now is the time to compromise.

00:18:43.780 No, I'm not sure.

00:18:45.920 And so, yeah, that is the source.

00:18:49.500 And so there have been years and years,

00:18:52.980 decades of contentious relationships

00:18:58.800 between black people and the police.

00:19:00.280 So, you know, look, if you go to the black neighborhoods,

00:19:02.360 none of this surprised anybody.

00:19:05.180 And it has just come into the mainstream in the last few years.

00:19:09.560 But this has been boiling over for a while.

00:19:11.740 And again, the frustrating part to me is,

00:19:15.280 if we could all look clear-eyed at the data,

00:19:17.300 there are positive things we can do for change.

00:19:20.080 These are not marks on a chalkboard.

00:19:22.680 This isn't academic research anymore.

00:19:24.480 That's done.

00:19:25.320 The question is, how are we going to work together

00:19:27.740 to actually create better relationships

00:19:31.360 between communities and police

00:19:33.280 in a way that's productive,

00:19:34.800 in a way that we can actually

00:19:35.960 talk through some of the difficult issues?

00:19:39.320 It seems like the challenge there,

00:19:41.380 from what I understand,

00:19:42.460 is there's a kind of mutual bias going on

00:19:44.580 because from somebody who's a civilian,

00:19:48.500 as you say,

00:19:49.460 the perspective is they're being mistreated

00:19:52.040 because of their skin color and so on.

00:19:54.040 And from the police perspective,

00:19:55.180 from people that I've spoken to as well,

00:19:56.960 there's the thing they'll say is,

00:19:58.960 well, different groups of people

00:20:00.560 commit crimes at different rates.

00:20:02.400 So if I'm a black police officer,

00:20:04.880 white, it doesn't really matter.

00:20:05.720 If I turn up to the scene of a crime

00:20:07.000 and half the time it's a black perpetrator,

00:20:09.820 that's going to build in certain narratives

00:20:11.600 in my head.

00:20:12.240 So next time I pull someone over

00:20:13.480 and they are black,

00:20:15.420 how am I not going to be

00:20:16.840 a little bit more wary of that?

00:20:18.900 And then you've got that mutual bias kicking in.

00:20:21.100 People are mutually suspicious of each other.

00:20:23.200 How does that get untangled?

00:20:24.220 Well, I think we've got to look for better clues

00:20:25.860 than just black or white.

00:20:27.060 Right.

00:20:27.420 Right.

00:20:27.780 I mean, when I was in college,

00:20:29.140 we used to call it the usual suspects.

00:20:31.160 Like some crime would be committed

00:20:32.700 and they'd say,

00:20:33.900 well, the suspect is between 5'8 and 6'10.

00:20:38.000 He's only 150 and 300 pounds.

00:20:40.920 Black male, black hair and brown eyes.

00:20:43.460 And we're like, shit, none of us can go outside.

00:20:44.860 Right?

00:20:46.460 And so we need to do better than that.

00:20:49.320 I joked with Loretta Lynch once

00:20:54.380 that we need to have,

00:20:57.200 is there something,

00:20:57.800 can we have TSA pre?

00:20:59.100 Is there something I can do

00:21:00.360 that when the police pull me over,

00:21:02.400 they can look beyond just race, right?

00:21:06.340 Is it kind of,

00:21:07.100 maybe if I wear, you know,

00:21:08.160 salmon colored pants,

00:21:09.380 I mean, that's something

00:21:09.900 that not a lot of criminals do.

00:21:11.300 Right.

00:21:11.680 I don't want to do it either.

00:21:13.520 Might be worth being roughed up.

00:21:15.520 But you get the idea.

00:21:16.880 We have to figure out

00:21:18.100 other context clues beyond that, right?

00:21:20.900 But you're right.

00:21:21.620 The police are rationally responding

00:21:24.020 to what they view as threats, right?

00:21:26.140 When I embedded myself

00:21:27.800 in the Houston Police Department,

00:21:29.000 I noticed that independent of the skin color,

00:21:31.240 the police officer was really scared

00:21:33.020 when he pulled someone over.

00:21:34.180 I asked him why.

00:21:35.520 He says, man, this is Texas.

00:21:37.040 We assume everyone's got a gun.

00:21:39.100 Yeah.

00:21:39.520 Right?

00:21:39.940 And so there,

00:21:40.800 all of these layers of complexity are here.

00:21:43.620 I just want it to be

00:21:45.300 several layers deeper

00:21:47.820 than just saying,

00:21:49.180 look at that person of their race.

00:21:52.680 And, you know,

00:21:53.180 it's the other extreme

00:21:57.200 also frustrates a lot of us.

00:21:59.240 Have you ever been in a TSA pre-line

00:22:00.780 where they're patting down the 90-year-old?

00:22:02.660 And you're like, I don't know.

00:22:04.160 Could we get this line moving?

00:22:06.000 Right?

00:22:06.120 We start to stereotype

00:22:07.800 from the other direction.

00:22:08.940 So I just think we need better ways

00:22:11.440 of understanding who's a threat

00:22:13.740 and who's not.

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00:23:15.280 And so you presented your findings,

00:23:18.940 you presented your paper.

00:23:20.580 What happened then at that point?

00:23:24.480 You know, in the beginning,

00:23:28.000 I thought,

00:23:29.440 all this is going really well.

00:23:32.520 I did.

00:23:33.600 I got Interacted.

00:23:34.820 I'm not sure I've ever admitted it.

00:23:35.720 I really thought,

00:23:36.520 I said, this is like,

00:23:38.000 I really crushed this one.

00:23:39.800 I hit the spot.

00:23:40.480 We hit the spot, right?

00:23:42.720 Because, you know,

00:23:44.000 we have this place in economics

00:23:45.900 where we put our working papers

00:23:47.620 and one of the people

00:23:49.460 that worked there

00:23:50.200 sent me a note

00:23:51.800 and the first weekend

00:23:52.700 it was published

00:23:53.520 and says,

00:23:53.920 man, you just broke

00:23:54.580 all the download records.

00:23:55.920 You know,

00:23:56.760 this paper's hot.

00:23:58.280 And it was in the New York Times

00:23:59.580 and Upshot or what have you.

00:24:01.100 My emails were exploding.

00:24:03.560 President Obama at the time

00:24:04.780 invited me to the White House

00:24:05.860 for a very long meeting

00:24:06.800 about police use of force.

00:24:07.860 And I thought,

00:24:08.100 I am making a difference.

00:24:11.320 But as the narrative

00:24:12.920 started to change over time,

00:24:15.820 and yes, there was,

00:24:17.700 let me back up,

00:24:18.260 yes, there were threats

00:24:20.600 of physical violence

00:24:21.600 and we had police protection

00:24:23.220 and all that stuff.

00:24:24.340 And that was not good,

00:24:26.500 but I was focused on all

00:24:27.720 the good things

00:24:28.720 I thought could come out of this.

00:24:29.760 And, but over time,

00:24:32.840 it has become something where,

00:24:35.940 I don't know,

00:24:38.440 it's, it's, it's,

00:24:40.860 it's, people have used it

00:24:44.180 to kind of,

00:24:47.140 not just write me off,

00:24:48.240 but write off research

00:24:49.360 in this way,

00:24:50.620 kind of writ large.

00:24:52.020 And that has been very surprising.

00:24:53.360 You know,

00:24:55.520 it has

00:24:56.480 contributed to,

00:24:59.160 I think,

00:25:00.460 other social scientists

00:25:01.780 being less willing

00:25:03.860 to,

00:25:04.740 to follow the facts

00:25:06.300 wherever they leave.

00:25:07.420 And I think that is really,

00:25:09.640 if I'm right about that,

00:25:10.860 and I hope I'm not,

00:25:11.640 actually,

00:25:12.180 that's really,

00:25:13.360 really bad.

00:25:14.060 For sure.

00:25:14.940 You know,

00:25:15.200 I've got students

00:25:16.740 that,

00:25:17.660 you know,

00:25:18.160 I meet with

00:25:19.340 at Harvard Square

00:25:20.320 who say things like,

00:25:21.160 I can't say this in public.

00:25:22.700 And I'm like,

00:25:23.820 who are you?

00:25:24.820 Like,

00:25:25.040 this is just you and I

00:25:26.780 over a cup of coffee.

00:25:28.340 And people have become

00:25:29.340 so leery of it.

00:25:30.600 And,

00:25:30.980 and,

00:25:31.460 and the way that

00:25:34.380 the paper has been covered

00:25:36.020 by both sides,

00:25:37.220 right?

00:25:37.500 Both sides.

00:25:38.800 I,

00:25:39.460 I saw once

00:25:40.960 or read once

00:25:41.640 that

00:25:43.040 people were saying

00:25:44.740 football players

00:25:46.540 shouldn't kneel

00:25:47.240 at the national anthem

00:25:48.660 because

00:25:49.700 my paper demonstrates

00:25:51.160 they shouldn't kneel.

00:25:52.000 I thought,

00:25:52.360 my paper has nothing

00:25:53.160 to say about whether

00:25:53.900 or not they kneel

00:25:54.500 at the national anthem

00:25:55.220 or not.

00:25:57.420 And,

00:25:58.080 on the other hand,

00:25:59.700 I've seen

00:26:00.440 researchers find

00:26:01.760 the exact same results

00:26:02.860 but bury them,

00:26:04.240 either bury them

00:26:05.000 in appendix tables

00:26:05.840 or refuse to publish

00:26:06.840 them at all.

00:26:08.700 And so,

00:26:09.240 it has been

00:26:09.960 really quite chaotic

00:26:11.980 since then.

00:26:12.840 I would say that,

00:26:13.620 again,

00:26:13.840 the first month or two

00:26:14.800 I thought,

00:26:15.240 wow,

00:26:15.520 this is great.

00:26:16.240 I can't wait

00:26:16.660 to do the next thing.

00:26:17.500 but my life

00:26:19.500 really got turned

00:26:20.100 upside down

00:26:20.880 because of it.

00:26:22.860 And,

00:26:23.280 one last thing,

00:26:24.080 I know I'm rambling

00:26:24.580 but I don't want to say this.

00:26:25.540 Take your time.

00:26:26.440 And I want,

00:26:28.080 the most important thing is

00:26:30.020 I do it all over again.

00:26:32.720 A hundred percent.

00:26:35.320 And almost in the exact same way.

00:26:37.840 Not because I didn't make

00:26:39.520 any mistakes along the way

00:26:40.600 and all that,

00:26:41.180 but because

00:26:43.100 I just refuse

00:26:45.960 to lie

00:26:46.540 to people,

00:26:48.020 especially

00:26:48.500 the constituents

00:26:52.780 that I've been working

00:26:53.900 for my entire life.

00:26:54.920 I've been doing this

00:26:55.420 for 21 years,

00:26:56.660 80 plus hours a week.

00:26:59.540 It's hard enough

00:27:00.800 empirically

00:27:01.580 to figure out

00:27:02.160 what the truth is.

00:27:03.060 I'm not gonna

00:27:03.620 constrain myself

00:27:05.400 for only those truths

00:27:06.640 that are in a nice box

00:27:07.740 with a particular bow.

00:27:09.260 I can't do that.

00:27:09.960 This is after

00:27:11.320 we're trying

00:27:11.800 to solve problems,

00:27:13.180 right?

00:27:13.740 And,

00:27:14.940 yes,

00:27:15.200 my life was turned

00:27:16.160 upside down.

00:27:17.660 Who cares?

00:27:19.300 Right?

00:27:19.720 This is about

00:27:20.740 whether or not

00:27:21.580 we can actually

00:27:22.760 get police to change

00:27:25.020 and actually get

00:27:26.220 folks in the neighborhood

00:27:27.340 to understand

00:27:28.220 that,

00:27:29.640 yes,

00:27:29.840 there are some issues

00:27:30.820 but,

00:27:32.000 you know,

00:27:33.300 the biggest threat

00:27:34.020 in the world

00:27:34.480 is not their police officers.

00:27:36.720 And can we come together

00:27:38.120 to make the country better

00:27:40.360 as regards public safety?

00:27:42.960 For me,

00:27:43.320 that's what this is about.

00:27:44.420 So,

00:27:44.980 who cares what happened to me?

00:27:47.800 You know,

00:27:48.300 if I discover

00:27:49.340 a result like this tomorrow,

00:27:51.720 I'm gonna put it out

00:27:52.440 in the same exact way

00:27:53.480 and if I suffer

00:27:54.180 the same consequences,

00:27:55.360 so be it.

00:27:56.120 I respect that

00:27:57.000 and I get that.

00:27:58.220 The question I was gonna ask you is,

00:28:00.000 you mentioned

00:28:00.600 the narrative changing.

00:28:03.320 I can't imagine

00:28:04.000 what it's like.

00:28:04.660 You do a paper,

00:28:05.460 you get called in

00:28:06.040 by the president

00:28:06.780 for a lengthy conversation

00:28:09.140 about your research

00:28:10.360 and then,

00:28:12.240 quote-unquote,

00:28:12.740 the narrative changes.

00:28:14.080 I don't even know

00:28:14.700 what that means.

00:28:15.440 Like,

00:28:15.960 who changed it?

00:28:17.660 I don't know.

00:28:18.820 I don't know

00:28:19.540 who gets to change narratives.

00:28:21.080 It is mind-bending.

00:28:23.800 Truly.

00:28:24.620 I can imagine.

00:28:25.860 Or maybe I can't.

00:28:26.900 A few years ago,

00:28:27.780 you weren't allowed

00:28:28.240 to say a joke on campus

00:28:29.380 and now,

00:28:29.960 you can call

00:28:30.420 for the genocide

00:28:31.280 of whole populations

00:28:32.260 and that's free speech.

00:28:33.540 Exactly.

00:28:34.160 Progress.

00:28:40.180 And let me guess,

00:28:41.080 you were there all along.

00:28:42.200 Yeah.

00:28:42.860 I was consistent.

00:28:46.540 So,

00:28:47.140 I don't know

00:28:47.780 who decides that.

00:28:49.880 But who was coming,

00:28:50.900 I guess part of what

00:28:52.000 I'm asking is,

00:28:53.260 who was coming after you?

00:28:54.460 Was it black people

00:28:56.900 in the neighborhood

00:28:57.620 who were saying,

00:28:58.400 your research

00:28:59.180 is not sufficiently

00:29:00.280 reflective of our reality?

00:29:02.320 Was it white academics

00:29:04.160 who were like,

00:29:05.060 this is a toxic subject,

00:29:06.660 you've got the wrong conclusions?

00:29:08.460 Was it the media?

00:29:09.900 Was it all of those at once?

00:29:11.520 Like,

00:29:12.220 I get,

00:29:12.800 I'm genuinely asking,

00:29:13.700 like,

00:29:14.020 who gets to change

00:29:15.320 the narrative

00:29:15.880 from you're sitting

00:29:17.080 in the White House

00:29:17.900 to suddenly you're,

00:29:18.960 you're some kind

00:29:19.620 of demon monkey?

00:29:21.020 D,

00:29:21.300 all of the above.

00:29:22.680 Okay.

00:29:24.360 It was,

00:29:25.840 but I'd put

00:29:26.540 the least amount

00:29:27.280 of emphasis

00:29:27.700 on people

00:29:28.280 in the communities.

00:29:29.060 Okay.

00:29:30.840 You know,

00:29:31.980 I think people

00:29:33.060 in the communities

00:29:33.580 had questions

00:29:34.300 and,

00:29:34.620 and,

00:29:35.020 you know,

00:29:35.560 civil rights folks

00:29:36.280 in the communities

00:29:36.820 had questions,

00:29:38.280 which ranged from,

00:29:40.480 are you sure

00:29:42.300 that Houston's representative

00:29:43.400 of this or that,

00:29:44.340 which is a fair question.

00:29:45.540 Totally.

00:29:45.980 Bring that all day long.

00:29:47.040 Totally.

00:29:47.520 To,

00:29:48.840 did you have to put it out here?

00:29:49.920 We had them on the ropes,

00:29:50.820 brother.

00:29:51.160 You know?

00:29:51.320 I'm not going to say

00:29:54.920 who that was,

00:29:55.720 but I was like,

00:29:56.780 my bad.

00:29:57.280 I didn't,

00:29:57.500 I didn't know

00:29:57.920 you had them

00:29:58.320 on the ropes.

00:29:58.900 I did.

00:30:01.980 To,

00:30:02.680 you know,

00:30:03.260 academics who,

00:30:04.340 who,

00:30:04.640 yeah,

00:30:04.940 this is,

00:30:05.480 you know,

00:30:05.740 the paper was labeled

00:30:08.960 hate speech

00:30:09.600 on Twitter

00:30:10.060 at the time.

00:30:11.420 Right?

00:30:11.920 I'm not on social media,

00:30:13.020 but someone posted it

00:30:14.280 and the account

00:30:14.840 was suspended

00:30:15.380 for hate speech,

00:30:16.220 right?

00:30:16.940 That's ridiculous.

00:30:18.460 That's ridiculous.

00:30:20.740 To other academics

00:30:22.120 who are out

00:30:22.980 lying,

00:30:24.240 saying,

00:30:24.680 oh,

00:30:24.980 he's retracted

00:30:25.800 those results.

00:30:26.420 They aren't true.

00:30:28.160 Silliness,

00:30:28.940 right?

00:30:31.080 To

00:30:31.560 police officers

00:30:33.640 who,

00:30:34.480 some of which

00:30:34.940 who say,

00:30:35.300 yes,

00:30:35.520 you may have

00:30:36.720 a point here to,

00:30:37.540 no,

00:30:38.160 I don't think

00:30:39.000 there's any bias

00:30:39.860 in lower level

00:30:40.880 uses of force.

00:30:41.560 So again,

00:30:41.940 there was something

00:30:42.560 there for

00:30:44.260 everyone not to like.

00:30:47.260 And so maybe

00:30:48.280 it's true there

00:30:48.880 if it ended up

00:30:51.180 pissing off everyone.

00:30:52.840 So I don't know

00:30:54.120 your question

00:30:55.040 of who gets

00:30:55.560 to change the narrative,

00:30:56.400 but I will say

00:30:57.120 that different people

00:30:58.560 had different angles

00:30:59.420 on these results.

00:31:00.300 Yes.

00:31:00.600 Sure.

00:31:01.040 I guess what I'm

00:31:01.760 trying to get at is

00:31:02.920 that seems to me

00:31:04.880 like an extraordinary

00:31:06.020 thing to happen

00:31:07.140 where you go from

00:31:08.740 being celebrated

00:31:09.900 for this study

00:31:10.880 all the way

00:31:11.940 up to the White House

00:31:13.140 to then people,

00:31:14.980 something must have

00:31:16.000 happened in the

00:31:16.700 social consciousness

00:31:17.580 around that time

00:31:19.080 for people to just

00:31:19.780 see this issue

00:31:20.620 differently,

00:31:21.480 including one of the

00:31:22.480 things that strikes

00:31:23.120 me about it

00:31:23.660 is like,

00:31:24.760 why would people

00:31:25.860 be upset

00:31:26.640 about information

00:31:27.720 that's helpful

00:31:28.440 to taking

00:31:29.120 the conversation forward?

00:31:30.380 like that to me

00:31:31.740 is very strange.

00:31:32.780 Do you know

00:31:33.420 what I'm saying?

00:31:33.560 No, I do know

00:31:34.160 what you mean

00:31:34.440 and I've asked

00:31:35.300 people I respect

00:31:38.880 something similar,

00:31:39.920 not quite as elegant

00:31:40.600 as you just did,

00:31:41.420 but what their view

00:31:43.080 is, is look,

00:31:44.540 if you've got

00:31:45.440 your mind made up

00:31:46.380 that the police

00:31:48.040 are really

00:31:49.020 discriminatory,

00:31:50.380 then this is just

00:31:51.480 annoying to you,

00:31:52.540 right?

00:31:53.460 That because the

00:31:55.420 paper's not perfect,

00:31:57.280 right?

00:31:57.560 I don't have

00:31:58.520 every police

00:31:59.140 department.

00:31:59.960 I don't know

00:32:00.620 if Chicago's

00:32:01.300 police department

00:32:01.840 is discriminatory

00:32:03.040 in shootings

00:32:03.480 versus not.

00:32:04.100 I don't have

00:32:04.400 Chicago's data.

00:32:05.200 I'd love to.

00:32:07.020 I don't know

00:32:08.100 what's going on

00:32:08.920 in a lot of

00:32:09.300 police departments

00:32:09.880 and the data's

00:32:10.440 not perfect.

00:32:11.180 And so in their

00:32:11.760 view, I've put

00:32:13.500 something out there

00:32:14.380 irresponsibly

00:32:15.380 that shows

00:32:17.900 something that

00:32:18.400 they just know

00:32:19.160 can't be true.

00:32:20.880 And so it's

00:32:21.600 inconvenient for them

00:32:22.420 instead of

00:32:23.320 saying,

00:32:24.320 huh, how can

00:32:25.960 we work together

00:32:26.600 to collect more

00:32:27.620 data?

00:32:28.300 This is the best

00:32:28.900 evidence we have.

00:32:30.860 Let's collect more

00:32:31.900 data.

00:32:32.260 Let's see if it's

00:32:32.840 real robust.

00:32:33.500 That's how social

00:32:34.280 science typically

00:32:35.100 makes progress.

00:32:36.180 But in this case,

00:32:38.220 it's such a hot

00:32:40.080 topic, or at least

00:32:41.040 has been, that

00:32:42.440 people are just

00:32:43.400 jumping to

00:32:43.920 conclusions and

00:32:44.820 taking sides.

00:32:45.540 It's become much

00:32:46.260 more tribal than

00:32:48.000 social scientific.

00:32:50.000 And I'm not

00:32:51.040 equipped for that.

00:32:51.920 That's not what I

00:32:52.520 do for a living.

00:32:53.280 I don't join

00:32:53.880 tribes, unless the

00:32:56.400 nerds have one.

00:32:57.820 But you get the

00:32:59.020 idea.

00:32:59.460 So I don't, that's

00:33:00.600 what they're upset

00:33:01.280 about, I believe,

00:33:02.500 though I don't know.

00:33:03.520 It's about

00:33:04.120 narratives, isn't

00:33:04.900 it?

00:33:05.020 Every tribe,

00:33:06.000 they have their

00:33:06.600 narrative.

00:33:07.380 If you are, for

00:33:08.680 want of a better

00:33:09.200 term, conservative,

00:33:10.400 pro-police, you

00:33:11.660 have this narrative.

00:33:13.060 If you are a

00:33:13.980 liberal progressive,

00:33:14.880 you have this

00:33:15.540 narrative.

00:33:16.180 And when someone

00:33:16.900 who comes along

00:33:17.880 not only challenges

00:33:19.580 your narrative, but

00:33:20.460 also provides

00:33:21.300 evidence, which is

00:33:22.940 very strong,

00:33:24.520 borderline irrefutable,

00:33:26.000 then you're faced

00:33:28.020 with two options,

00:33:28.800 really.

00:33:29.360 You either change

00:33:31.360 the narrative, which

00:33:32.120 is incredibly painful

00:33:33.040 because that means

00:33:33.600 that you get

00:33:34.000 isolated from your

00:33:35.020 tribe, or you

00:33:37.100 defend your tribe,

00:33:37.980 which means you go

00:33:38.660 on the attack.

00:33:39.420 Yeah, and you

00:33:40.220 discredit the person

00:33:41.280 or the study or

00:33:42.020 what have you.

00:33:42.900 And I think part

00:33:44.320 of that's it.

00:33:44.940 This is a, there

00:33:47.260 is a political game

00:33:48.540 going on or a

00:33:49.720 political fight going

00:33:50.900 on, or a culture

00:33:52.020 war, whatever you

00:33:52.720 want to call it.

00:33:53.900 And frankly, for

00:33:55.500 both sides, this

00:33:56.160 was a little

00:33:56.520 inconvenient.

00:33:57.760 Right.

00:33:58.920 And, but, I

00:34:02.660 thought that's

00:34:03.120 what tenure was

00:34:03.740 for.

00:34:04.160 I thought that's

00:34:04.740 what we were

00:34:05.120 supposed to be

00:34:05.680 doing.

00:34:06.060 I'm pretty

00:34:06.400 thrilled by the

00:34:08.340 idea that you

00:34:10.720 can get data, you

00:34:12.080 can do the

00:34:13.140 analysis to the

00:34:13.800 best of your

00:34:14.200 ability, and you

00:34:14.740 can actually be

00:34:15.400 relevant, right?

00:34:16.780 Many of my

00:34:17.260 economist friends

00:34:17.880 are working on

00:34:18.340 the optimal cake

00:34:19.020 eating problem,

00:34:19.960 right?

00:34:20.200 That's not

00:34:21.080 what this is.

00:34:22.020 So, for me, it

00:34:22.860 was a real, truly,

00:34:24.220 a real opportunity

00:34:25.060 to actually feel

00:34:25.980 like you were

00:34:26.680 relevant in a

00:34:27.400 discussion that

00:34:27.980 was important.

00:34:28.480 I've never

00:34:28.840 written a paper

00:34:29.520 that spoke to

00:34:31.920 one of the

00:34:32.580 exact points that

00:34:34.300 people were

00:34:35.780 protesting in that

00:34:37.280 moment, right?

00:34:39.340 Usually, academics

00:34:40.160 are like, oh,

00:34:40.620 something happened

00:34:41.160 in the world,

00:34:41.800 seven years later,

00:34:42.680 I got it!

00:34:45.040 The world has

00:34:45.960 moved on, and

00:34:47.140 it becomes part of

00:34:47.920 history.

00:34:48.340 This, I got

00:34:49.980 really lucky, I

00:34:51.580 believe, that

00:34:52.420 this paper was

00:34:55.040 relevant in that

00:34:55.920 moment, unfortunately,

00:34:57.120 and continues to be

00:34:57.940 relevant, but I

00:35:00.200 was not expecting,

00:35:01.440 because I had not

00:35:01.860 seen that, I had not

00:35:02.920 had that experience

00:35:03.580 before, I was not

00:35:04.760 expecting what would

00:35:05.600 come.

00:35:06.580 It's also, and

00:35:07.900 maybe this is me

00:35:08.980 projecting a little

00:35:09.680 bit and push back if

00:35:10.580 you feel this is

00:35:11.220 incorrect, I imagine

00:35:13.120 it put a few noises

00:35:14.520 out of joint with

00:35:16.040 fellow academics,

00:35:16.920 you know, because a

00:35:17.920 lot of them toil

00:35:18.600 away, they produce

00:35:20.020 their papers, they

00:35:21.020 don't get, you

00:35:22.220 know, they're not

00:35:22.640 shared a lot, they're

00:35:23.860 not talked about a

00:35:24.860 lot, and then they

00:35:25.600 move on to the next

00:35:26.320 one.

00:35:26.740 Here you have this

00:35:27.680 young guy who comes

00:35:29.060 in, produces his

00:35:30.400 paper, gets to meet

00:35:32.020 with the president,

00:35:33.080 breaks the download

00:35:33.980 records, and all of

00:35:35.280 a sudden they're

00:35:35.640 going, him?

00:35:38.800 Why him?

00:35:39.400 Why not me?

00:35:40.400 Was that part of it,

00:35:41.340 do you think?

00:35:41.640 Yeah, that's

00:35:41.980 interesting.

00:35:43.300 I haven't thought

00:35:43.900 much about that it

00:35:44.660 could be, and I'd

00:35:45.540 also add to that,

00:35:46.520 that I don't, I

00:35:51.140 don't carry myself

00:35:51.960 as the typical

00:35:53.300 Harvard, maybe I

00:35:54.680 do now, the

00:35:55.120 sweater looks pretty

00:35:55.720 typical.

00:35:55.940 I mean, your

00:35:56.600 shirt is, my

00:35:58.820 wife bought me

00:35:59.480 this.

00:36:02.420 But, but in all

00:36:03.780 seriousness, you

00:36:04.360 know, when I was

00:36:04.740 younger in my

00:36:05.240 career, I'd wear

00:36:06.020 jerseys to class, and

00:36:07.360 you know, I was,

00:36:07.860 you know, just being

00:36:08.440 myself, right?

00:36:09.240 And so, and you

00:36:11.120 know, I'm a video

00:36:12.160 game aficionado, so I

00:36:14.140 had a video game

00:36:14.760 console in my

00:36:15.420 office, things like

00:36:16.200 that, that I think,

00:36:18.380 you know, they might

00:36:19.360 have not been, not

00:36:20.020 just saying, I can't

00:36:20.960 believe that, but

00:36:21.420 that guy, like the

00:36:22.880 guy who's playing

00:36:23.400 video games during

00:36:24.300 the day?

00:36:26.080 So some of that

00:36:27.140 could have been it.

00:36:27.680 There's, you know,

00:36:28.200 it's no secret, there's

00:36:29.300 a lot of jealousy in

00:36:30.780 the academy.

00:36:31.700 But I think it was,

00:36:32.740 it was more than

00:36:33.900 that.

00:36:34.140 I think it was

00:36:35.320 that people wanted

00:36:39.120 to be seen as being

00:36:40.320 on the right side of

00:36:41.380 this issue, back to

00:36:42.280 the tribes.

00:36:44.240 I mean, my own

00:36:45.080 department put out a

00:36:46.080 statement about police

00:36:46.900 use of force that was

00:36:47.880 completely opposite of

00:36:48.880 what the paper actually

00:36:50.600 showed, right?

00:36:52.040 That was one of the

00:36:52.580 hardest things to see.

00:36:54.080 I was like, it's right

00:36:55.300 here, I'm in the same

00:36:56.360 department, and their

00:36:57.860 statement started off,

00:36:59.040 we know how much bias

00:37:00.040 and race and

00:37:00.740 discrimination, I was

00:37:01.280 like, that's not

00:37:02.160 exactly what we found.

00:37:04.140 So not even my own

00:37:05.120 department could be

00:37:05.760 subtle in that way.

00:37:07.240 So I think people

00:37:08.160 were running scared.

00:37:11.340 There was a sense of

00:37:13.060 are you with the

00:37:14.420 good guys or the

00:37:15.140 not?

00:37:15.740 And so you couldn't

00:37:16.780 in 2017 or 18 in

00:37:19.100 an Ivy League

00:37:21.460 university stand up

00:37:22.580 and say, I don't

00:37:23.840 know, it seems

00:37:24.760 complicated, let's take

00:37:25.880 a look at the

00:37:26.300 numbers.

00:37:26.680 It was, nope,

00:37:27.420 clearly it's biased.

00:37:28.200 We've seen the

00:37:28.620 videos, we've seen

00:37:29.200 all, you know, 12,

00:37:30.620 15, 20 of them,

00:37:31.460 whatever the number

00:37:31.900 is, we've seen them

00:37:32.540 and that's bias.

00:37:33.100 And that's the, I

00:37:34.680 felt like that was

00:37:35.460 the knee-jerk reaction

00:37:36.320 that was happening.

00:37:37.360 I think the other

00:37:37.960 disappointing thing that

00:37:39.260 happened, in my opinion,

00:37:40.100 was that the standards

00:37:43.960 of evidence start to

00:37:44.880 change when you don't

00:37:45.500 like the result.

00:37:48.260 And so there were

00:37:50.360 people I really

00:37:50.840 respected, their

00:37:51.740 methods, their

00:37:53.140 analysis over the

00:37:54.040 years, followed their

00:37:55.220 work for literally

00:37:56.040 more than a decade,

00:37:57.340 who said things like,

00:38:00.040 well, I don't, this, I

00:38:01.260 don't like the way you've

00:38:02.060 done this paper.

00:38:02.740 And I was like, it's

00:38:03.280 the same as you do,

00:38:04.540 right?

00:38:05.460 So what has changed?

00:38:08.020 And so this cherry

00:38:09.460 picking of what's good

00:38:12.280 and what's bad, not

00:38:13.400 based on the

00:38:14.100 fundamentals, but based

00:38:15.920 on the output, that

00:38:17.800 is, that's the part

00:38:18.860 that's most concerning

00:38:19.680 to me.

00:38:20.280 You see what I'm

00:38:20.620 saying?

00:38:21.000 Yeah, yeah.

00:38:21.500 I see exactly what

00:38:21.660 you're saying.

00:38:22.120 I mean, what you're

00:38:23.300 talking about is you're

00:38:24.760 someone who's

00:38:25.360 interested in pursuing

00:38:26.220 the truth, who found

00:38:28.400 himself in a

00:38:29.040 politicized environment,

00:38:30.740 which, this will sound

00:38:33.100 perhaps like a strange

00:38:33.860 comparison, but as

00:38:34.600 someone who grew up in

00:38:35.260 the Soviet Union, that's

00:38:36.620 exactly the environment

00:38:38.100 that people operated

00:38:40.000 under, where there was

00:38:41.960 certain things, certain

00:38:42.940 areas.

00:38:43.320 If you research physics,

00:38:44.500 physics was non-political

00:38:45.920 until it became political

00:38:47.320 for whatever reason, and

00:38:48.440 suddenly your physics

00:38:49.820 paper would be

00:38:50.800 politicized, and people

00:38:52.160 would have opinions about

00:38:53.180 it, and they would

00:38:53.700 criticize it, not on

00:38:55.080 the merits, but because

00:38:56.320 it was the tribal

00:38:58.020 thing at that

00:38:58.840 particular moment in

00:38:59.740 time to do.

00:39:00.560 So I guess the

00:39:01.360 question I wanted to

00:39:02.100 ask you, we've had

00:39:02.760 obviously lots of

00:39:03.520 academics on the

00:39:04.280 show, and anyone

00:39:05.920 looking from the

00:39:06.740 outside into what we

00:39:08.820 see as the

00:39:09.380 manifestations of the

00:39:10.580 academic environment

00:39:11.840 now, people have

00:39:14.640 questions, and the

00:39:16.580 pursuit of truth seems

00:39:17.560 to be definitely, from

00:39:18.940 an outsider's

00:39:19.580 perspective, one of the

00:39:20.420 things that's really

00:39:20.920 taken a beating in

00:39:21.940 recent years.

00:39:22.780 Would you, on the

00:39:24.200 campuses around the

00:39:25.760 world, actually, the

00:39:26.580 Western world, would

00:39:27.320 you agree with that?

00:39:28.560 Do you think that's

00:39:29.040 true?

00:39:29.600 Certainly on, you

00:39:32.840 know, social topics and

00:39:35.540 sensitive subjects.

00:39:36.600 You know, I don't have a

00:39:38.500 sense that the math

00:39:39.240 guys are doing

00:39:39.860 functional theory any

00:39:40.840 differently than they

00:39:41.500 were before, but I do

00:39:43.620 believe that, you

00:39:46.200 know, research into

00:39:48.440 genetics, research into

00:39:50.120 gender identity or other

00:39:55.980 identities, research into

00:39:57.260 the police and the

00:39:58.220 education, I think those

00:39:59.460 things have been heavily

00:40:00.500 skewed, yes, I do

00:40:01.660 believe that, and it's

00:40:03.580 exactly as you

00:40:04.700 described.

00:40:05.660 I really, you know, I

00:40:06.960 showed up at Harvard at

00:40:08.280 age, I don't know, maybe

00:40:10.240 I was 26 or something, 25,

00:40:12.540 and the job I had before

00:40:16.440 that, I was like

00:40:16.940 McDonald's, I mean, I

00:40:17.760 don't know anything

00:40:18.540 better.

00:40:20.400 I really believed that, I

00:40:22.960 mean, we sit in these

00:40:23.680 seminars once a week, twice

00:40:25.680 a week, and someone

00:40:27.860 presents something, and

00:40:28.920 it's just crushing them

00:40:31.100 about exactly how you do

00:40:32.760 economics.

00:40:33.280 Let's get to the

00:40:33.920 fundamentals of what,

00:40:35.180 whether this paper is

00:40:36.040 correct or not.

00:40:37.520 I love that process,

00:40:39.380 because it didn't really

00:40:40.900 matter what the subject

00:40:42.420 was, the question is,

00:40:43.860 is this a valid research

00:40:45.800 design or not?

00:40:47.200 And those things get

00:40:48.380 spirited, and I did the

00:40:51.060 last three years of my

00:40:51.800 PhD at the University of

00:40:52.580 Chicago, which is where

00:40:53.480 they do it really in a

00:40:54.920 spirited way.

00:40:56.180 I mean, this is like, it

00:40:57.340 was a full contact sport.

00:40:58.640 I love the place, really

00:41:00.160 love the place.

00:41:00.800 I've given seminars there

00:41:01.740 that have spoiled it into

00:41:02.640 the middle of the night.

00:41:03.640 And so I fundamentally

00:41:06.120 believed that.

00:41:08.340 And so it literally

00:41:10.500 changed my whole worldview,

00:41:12.160 because I thought, oh,

00:41:14.580 crap, they were playing

00:41:17.120 a different, we're playing

00:41:18.060 different games here.

00:41:19.980 Right?

00:41:20.580 And I didn't know that.

00:41:22.640 And obviously it doesn't

00:41:24.200 apply everywhere.

00:41:25.340 There's, you know, there's

00:41:26.980 really, really great

00:41:28.500 academics everywhere, in

00:41:30.000 every place.

00:41:30.500 But I have been surprised

00:41:33.280 because when I came to

00:41:34.600 Harvard as a postdoc, I

00:41:37.300 went to the Harvard

00:41:38.180 Society Fellows.

00:41:41.820 Academics, I viewed

00:41:42.820 academics as the place

00:41:44.020 where smart, quirky people

00:41:47.980 went because they had no

00:41:50.040 political skills.

00:41:51.740 And so they weren't going

00:41:52.780 to go to corporations

00:41:53.500 because those were too

00:41:54.340 political.

00:41:55.280 But in a department, it was

00:41:58.560 really about meritocracy and

00:41:59.960 how smart you were and the

00:42:01.080 best ideas would rise to

00:42:02.060 the time.

00:42:04.000 I just don't think that's

00:42:05.040 true anymore.

00:42:06.120 I think that we're

00:42:08.000 selecting for people who

00:42:08.880 are much more political

00:42:09.760 and much more sophisticated

00:42:11.880 on that dimension because

00:42:13.400 the universities are, you

00:42:16.240 know, to be successful.

00:42:20.860 people, if you're a young

00:42:24.280 scholar, I do believe political

00:42:25.820 sophistication and savvy are

00:42:27.180 very important.

00:42:28.840 Just on that point, not to

00:42:30.540 personalize this too much,

00:42:31.800 Roland, but one of the people

00:42:32.900 involved in the campaign

00:42:34.660 against you was, I nearly

00:42:38.160 said the late Claudine

00:42:39.240 Gay.

00:42:42.020 And it seems to me, without,

00:42:44.100 you know, taking a particularly

00:42:45.560 strong stance either way on it,

00:42:46.920 that just that, her recent

00:42:48.920 departure kind of symbolized a

00:42:50.660 lot of the simmerings of the

00:42:51.980 stuff that we've been talking

00:42:53.680 about.

00:42:54.140 Now, I know you probably feel

00:42:56.180 quite strongly on a personal

00:42:57.420 level about her, but what did

00:42:59.080 you make of that whole saga,

00:43:00.760 including in relation to you,

00:43:02.340 but also just in terms of where

00:43:03.640 we are in the way that people

00:43:06.360 are appointed to positions within

00:43:08.000 academia, people are promoted

00:43:09.400 within academia, people are

00:43:10.980 required to have certain

00:43:12.880 opinions, people are treated

00:43:14.160 differently depending on their

00:43:15.320 background and ethnic background,

00:43:16.640 et cetera.

00:43:18.060 I found that whole situation

00:43:19.560 pretty sad.

00:43:23.960 It was just not a good day for

00:43:29.320 higher ed, for Harvard, or it

00:43:32.140 wasn't a day, weeks and weeks.

00:43:34.720 And so I just thought that

00:43:37.620 particularly because much of the

00:43:42.640 discussion was about her own

00:43:43.940 scholarship, which I know nothing

00:43:45.100 about, I've never read a paper by

00:43:46.380 Claudine Gay, but that people

00:43:51.420 seemingly around the world were

00:43:54.220 questioning the quality of that

00:43:55.740 at an institution like Harvard was

00:43:59.100 really hard.

00:44:00.280 You know, that was just really

00:44:03.820 sad.

00:44:05.220 So my personal involvement with the

00:44:11.180 administration aside, it's just, you

00:44:14.780 know, hard to watch every single

00:44:16.760 day people questioning the

00:44:18.680 integrity, the academic rigor of the

00:44:24.460 person who was supposed to be

00:44:25.600 leading the university.

00:44:26.760 And when you say it's hard, just to

00:44:28.620 be clear, you mean because it

00:44:32.380 possibly is the case?

00:44:34.640 Or do you mean because it's just

00:44:36.860 it kind of affects the reputation of

00:44:39.500 Harvard or why is it sad?

00:44:41.980 Both.

00:44:42.560 Because, you know, this is what we're

00:44:44.600 there for, rigor.

00:44:47.840 And, you know, I am a, I don't know a lot

00:44:51.040 about university administration.

00:44:52.280 I just don't, clearly.

00:44:53.420 Um, so, um, but, you know, I, I came to

00:45:01.140 the university when Larry Summers was

00:45:03.100 president and, um, I'll never forget.

00:45:08.600 Uh, this is not dodging your question.

00:45:10.940 This is giving context for your

00:45:12.000 question.

00:45:13.080 I got a call when I was, uh, a fourth

00:45:15.840 year graduate student and I never had a

00:45:17.880 call like this.

00:45:18.440 Maybe you, you're a fancy guy.

00:45:19.620 Maybe you've had one.

00:45:20.820 Uh, it's, you know, you pick up the

00:45:22.160 phone and he's, hello, hold for the

00:45:24.240 president of Harvard University.

00:45:26.520 Okay.

00:45:28.080 And so Larry gets on the phone.

00:45:29.420 We started talking and he tells me, if

00:45:32.760 you come to Harvard, because I had

00:45:34.020 offers at other places, we'll go to

00:45:36.840 lunch once a month and we'll talk about

00:45:38.300 your research.

00:45:40.080 What?

00:45:41.300 That would be unthinkable.

00:45:42.560 Now, I just couldn't imagine a

00:45:43.960 university president doing that.

00:45:45.120 Okay.

00:45:45.360 So he does it.

00:45:46.260 I don't believe him, but he says it.

00:45:49.140 So I get off.

00:45:49.940 I choose Harvard, go there.

00:45:52.160 Within four weeks, I get the call

00:45:53.740 off again.

00:45:54.740 I'd like to schedule lunch with you and

00:45:55.800 the president.

00:45:57.800 Okay.

00:45:58.600 So I go and we go to lunch and I had

00:46:01.000 just written a paper about how, um,

00:46:04.720 black students who go to historically

00:46:05.980 black colleges may pay, pay a wage

00:46:07.740 price.

00:46:09.780 And I was calling the paper at that

00:46:11.180 time, the price of identity.

00:46:12.960 Okay.

00:46:14.020 So he, he calls me in cause he wants to

00:46:16.200 discuss that paper.

00:46:16.940 And I go, Oh, I must be in trouble.

00:46:18.180 And he starts eating and he gets so

00:46:21.740 excited about the research research that

00:46:24.760 he's talking while he's eating and his

00:46:26.800 food is splattering all over him.

00:46:28.340 And in that moment, that's the first

00:46:29.560 time I ever really felt comfortable at

00:46:30.920 Harvard.

00:46:31.500 I thought this guy could be president.

00:46:33.080 He's a mess.

00:46:34.840 If he can be president, then there's a

00:46:36.400 chance because in that moment, it felt

00:46:38.400 all about ideas.

00:46:39.640 Right.

00:46:40.020 Yeah.

00:46:40.760 Right.

00:46:41.040 He was sloppily eating.

00:46:44.060 I probably was using the wrong fork, but

00:46:46.180 we were talking about statistical

00:46:47.740 identification on a particular paper

00:46:49.360 that he thought was interesting.

00:46:51.880 And so that's what I pride myself of in

00:46:54.560 administration.

00:46:55.460 I want someone who's been through it to

00:46:57.420 help guide you through.

00:46:59.560 It would have been nice to have, um,

00:47:02.520 Larry in 2016, when I was navigating this

00:47:05.340 stuff with Washington and with the paper

00:47:07.240 to provide guidance.

00:47:08.920 Right.

00:47:09.040 I, my budget, make sure I'm, that's

00:47:12.360 silly.

00:47:12.680 Anyone can do that.

00:47:13.620 So I, I, um, the reason it was hard is

00:47:16.640 because my preference and the way I

00:47:18.740 started at Harvard was to have a scholar,

00:47:21.000 a true scholar president.

00:47:23.200 And, uh, again, I don't know about, uh,

00:47:27.240 former president, gay scholar.

00:47:29.200 I don't, I've never written a paper,

00:47:30.240 read a paper.

00:47:30.760 Maybe they're phenomenal.

00:47:31.600 I don't know.

00:47:32.440 But to see the media attacking them,

00:47:35.340 those papers and questioning scholarship

00:47:37.100 and questioning what it meant to be at

00:47:38.640 Harvard university as someone who's been

00:47:40.700 there for 20 years, that part was hard.

00:47:43.120 Sure.

00:47:43.460 I understand the experience you described.

00:47:45.600 It sounds like the way universities should

00:47:48.240 operate, the pursuit of truth, the discovery.

00:47:51.140 I'm going to sound like a wide eyed romantic

00:47:53.880 here, but that's kind of what I thought

00:47:55.420 academia was for.

00:47:57.500 That's what I thought.

00:47:58.020 Me too.

00:47:58.260 You know, I, I remember giving, uh, I had an idea once

00:48:03.000 about, uh, segregation and how to measure it differently

00:48:06.180 because the old measures of segregation have been around

00:48:08.240 for decades and decades, but could we use our new

00:48:11.700 understanding of network theory to find a kind of

00:48:15.000 network-based model of segregation, right?

00:48:17.320 It's, it will sound weird, but it's the truth.

00:48:19.160 I was flying into Chicago and if you fly into Chicago at

00:48:21.580 night, the whole city is massive, but it looks like

00:48:24.360 this grid and the lights kind of make the edges of that

00:48:28.180 grid.

00:48:28.500 You can kind of see a matrix.

00:48:30.140 And I looked out the window on the plane and I thought,

00:48:32.080 geez, that's how we should think about segregation.

00:48:34.400 Cause I knew Chicago and I knew basically the, uh, black

00:48:38.540 people lived in this part of the grid and the Polish people.

00:48:40.640 And so, um, I eventually wrote that paper and then gave it

00:48:47.220 in, in one of the flagship seminars, uh, the Gary Becker

00:48:51.180 ran at Chicago and Gary Becker's got a Nobel prize.

00:48:53.980 He's a phenomenal, he was a phenomenal, one of my just

00:48:57.060 absolute heroes in the profession.

00:48:59.660 And that seminar went for an hour and a half.

00:49:02.580 Then it spilled over at, uh, to the Q club, uh, where I'm

00:49:07.480 sitting there with multiple Nobel prize winners around

00:49:10.620 the table and assistant professors and other people and

00:49:13.000 me, um, as a, an assistant professor or a postdoc.

00:49:17.380 And I just have this image in my head of literally Gary

00:49:21.080 Becker, this famous economist with a chicken wing hanging out

00:49:24.180 of his, out of his mouth and pointing to a piece of, uh, a

00:49:29.080 napkin where he was going to scribble some notes while eating

00:49:31.480 the chicken wing about this paper.

00:49:33.560 That's, we can both romanticize, but those are the ways I grew

00:49:36.980 up in academia.

00:49:37.720 So to now have it be a question of, can we say that?

00:49:42.060 Can we say this?

00:49:42.780 Can we do that?

00:49:43.420 Can we, we're not the world's spokespeople, right?

00:49:46.800 In the same way comedians aren't political ambassadors.

00:49:50.840 I mean, some think they are, right?

00:49:52.360 I'm going to be honest with you.

00:49:53.820 But we are supposed to be the, the, the ground bearers of truth

00:49:57.460 and then let everyone else figure out the politics.

00:50:00.840 We are supposed to just inject truth into the world.

00:50:03.660 Maybe that's naive.

00:50:04.380 Maybe I'm stupid, et cetera.

00:50:05.720 But that is the way I grew up.

00:50:07.820 That's what Glenn Lowry and Gary Becker and, and some of the

00:50:12.100 greats, they, that's what they, that's what they taught me.

00:50:14.460 And we have, we're way, way, way different from that.

00:50:17.080 Roland, you know, when you were talking about getting, uh,

00:50:21.160 your research published, breaking download records, et cetera,

00:50:24.060 et cetera.

00:50:25.260 I, my, my thought to myself is in a post George Floyd world,

00:50:30.420 there's part of me that thinks you wouldn't even get published now.

00:50:34.520 I don't, I think there would be some academic institutions,

00:50:37.580 maybe not all of them, but certainly some of them were like,

00:50:40.220 we can't publish this.

00:50:41.580 Yeah.

00:50:42.100 We, we, what are you trying to do?

00:50:44.560 Yeah.

00:50:45.520 You might be right.

00:50:46.800 Um, I, I, I, um, one of the projects I'm working on right now

00:50:51.440 is to understand academic freedom.

00:50:52.960 So I have, um, I'm looking into all the published papers, uh,

00:50:57.700 in the last two decades and I'm using AI to help me understand,

00:51:02.740 uh, the political slant of the results.

00:51:06.100 Would these results be considered centrist or conservative or liberal?

00:51:10.240 And what I'm showing, I'm not sure I'm right just yet,

00:51:14.000 but the preliminary results are showing are exactly what you just

00:51:16.920 described, which are, uh, there are far fewer published results

00:51:21.640 that are both center and, and right after, um,

00:51:26.560 leading up to and after 2020.

00:51:28.340 Now the question is, is it because the journals are not publishing them,

00:51:31.980 which is, what's your inclination or people aren't writing them?

00:51:34.380 I can't tell the difference.

00:51:35.480 Maybe progressivism is just true.

00:51:37.760 There was a paper, you know, there was a, there was a, uh, uh,

00:51:41.480 an article about that in the, in the Harvard student newspaper.

00:51:45.680 They said, what do we need diversity for of thought

00:51:48.100 when progressivism is just the right.

00:51:51.640 It's true, I don't, I don't, yeah.

00:51:54.100 Why research?

00:51:55.100 We know, yeah, we know the answer to everything.

00:51:57.220 Exactly, precisely.

00:51:58.560 But, and that is terrifying because they, as, as is most commonly said,

00:52:05.320 as is said all the time, the greatest form of censorship

00:52:08.160 is self-censorship.

00:52:09.280 Yeah.

00:52:09.820 So if you've got this idea and you think,

00:52:11.800 this could be a dynamite paper and then go,

00:52:15.040 but I can't write it because it won't get passed.

00:52:18.780 I've got the wrong results.

00:52:19.760 Yeah, because it will get the wrong results

00:52:21.520 or what you most likely think is self-preservation.

00:52:25.080 Yes.

00:52:25.380 What is this going to expose me to?

00:52:26.900 Because not everybody's like you temperamentally.

00:52:29.420 Then what we've got is essentially a,

00:52:33.540 a system that isn't fit for purpose.

00:52:36.740 A hundred percent.

00:52:37.340 And you're going to be making policies

00:52:38.960 based on the research that comes out,

00:52:40.780 which is only going to be one way

00:52:42.420 because no one wanted to show the other.

00:52:44.720 So that's, that's what I'm,

00:52:45.700 that's fundamentally what I,

00:52:47.220 what I'm losing sleep at night about.

00:52:48.820 That's exactly it.

00:52:50.220 So I guess the question is,

00:52:51.980 what do we do about this?

00:52:53.420 How can we challenge this?

00:52:54.780 How can we make a better academy?

00:52:58.480 Well, I don't know the answer to that

00:53:01.180 because it's very difficult

00:53:03.040 because you have to,

00:53:05.640 I used to think,

00:53:07.400 well, let's have grant money

00:53:09.820 for controversial ideas.

00:53:11.700 That's not going to work

00:53:12.480 because unless people can get tenure

00:53:16.600 and raise amongst the ranks

00:53:18.360 in their profession

00:53:19.180 by doing what they know is right.

00:53:24.200 And I'm skeptical

00:53:25.420 that we can change that part.

00:53:27.020 Unless you change

00:53:27.980 that overall set of incentives,

00:53:29.680 well, you know,

00:53:30.240 all the rest of the stuff

00:53:31.280 is window dressing.

00:53:32.240 That makes sense to me.

00:53:33.220 So until you make truth seeking

00:53:35.020 a high status thing

00:53:35.940 to do within academia,

00:53:37.940 you can't win.

00:53:39.160 You can't win.

00:53:39.640 But that's the purpose of academia.

00:53:41.940 So how, how, how do,

00:53:43.460 I mean, there's got to be a way

00:53:44.600 to make it,

00:53:45.840 to coin a phrase,

00:53:46.900 to make academia great again.

00:53:49.200 But there's got to be a way, right?

00:53:52.180 I'm trying to think of the,

00:53:53.520 it's not MAG.

00:53:58.020 Yeah, I think, you know,

00:53:59.720 all it takes is a couple of universities

00:54:01.200 to show us the way.

00:54:02.540 So I think, you know,

00:54:03.700 obviously that's what University of Austin

00:54:05.240 is trying to do.

00:54:05.980 You know, there are universities

00:54:09.260 like University of Chicago

00:54:10.420 who has tried to keep it that way.

00:54:12.420 There's, I just talked to the,

00:54:14.980 you know, general counsel

00:54:15.820 of a couple of other universities

00:54:16.940 who asked me the exact same question.

00:54:18.420 How do we do this?

00:54:19.500 And the issue is,

00:54:20.560 even if you do it

00:54:21.320 within your own university,

00:54:22.680 if that person doesn't get tenure there,

00:54:25.280 they fall potentially.

00:54:29.060 You know, imagine I had a research program

00:54:30.900 that said, you know what?

00:54:33.380 A lot of people have been looking

00:54:35.180 into government programs

00:54:37.680 as a way of helping black Americans.

00:54:41.220 I'm going to look into

00:54:42.340 how families can do a better job.

00:54:47.560 I'm not saying that's a good idea or not,

00:54:48.920 but just imagine someone said that.

00:54:50.960 And they decided on,

00:54:51.980 they went on a very rigorous research course

00:54:53.720 over five or eight years

00:54:55.300 on that side of the equation.

00:55:00.040 And even if their university supported that

00:55:03.540 and said, this is great,

00:55:04.560 but it just is beneath the line,

00:55:06.820 where are you going to go

00:55:07.520 if that doesn't work out?

00:55:09.400 Right?

00:55:09.600 There might be a much steeper fall

00:55:12.680 because other universities

00:55:13.920 might not like that line of inquiry.

00:55:16.380 If that's true, again,

00:55:18.260 the incentives for a person to do that

00:55:20.160 are very small.

00:55:22.320 And so you're not,

00:55:23.380 what am I saying with all this?

00:55:24.600 You not only need to have to fix

00:55:25.820 one university or two,

00:55:27.200 you have to fix a lot.

00:55:29.380 There's got to be a way,

00:55:31.120 there needs to be a way of saying,

00:55:32.820 if the research design

00:55:35.220 and the methods are rigorous enough,

00:55:37.000 we don't care what the answer actually is.

00:55:39.880 If the data are high quality

00:55:41.640 and the research design is solid,

00:55:45.260 we don't care if the solution points

00:55:47.460 right or left or in the middle.

00:55:49.280 And if there were a way

00:55:50.240 to provide insurance for those people,

00:55:53.580 then we could solve the problem.

00:55:56.760 But right now,

00:55:57.620 what I am hearing

00:55:59.280 from young scholars,

00:56:03.440 from graduate students,

00:56:04.600 is that they are scared

00:56:06.360 to do these things.

00:56:08.480 Right?

00:56:09.140 I've had people literally ask me,

00:56:11.100 what do you do

00:56:12.000 when you come up with something

00:56:13.140 that's taboo?

00:56:15.400 Right?

00:56:17.080 I've got these questions,

00:56:18.460 but I'm not sure I can answer them.

00:56:21.080 Do you think I can?

00:56:23.020 I, of course, encourage them.

00:56:24.800 But I also know

00:56:25.880 that there are potential costs to this.

00:56:28.500 And so,

00:56:29.440 we've got to find a way

00:56:31.860 to provide insurance.

00:56:33.000 Isn't that crazy?

00:56:33.940 To provide insurance

00:56:34.840 for telling the truth.

00:56:36.000 Roland, yeah.

00:56:39.220 Truth insurance, man.

00:56:41.640 I mean,

00:56:42.180 I don't think a lot of people

00:56:43.480 would give you that insurance,

00:56:44.740 I'm going to be honest with you.

00:56:45.700 A lot of the company's insurance...

00:56:46.780 The premiums on that, baby,

00:56:48.000 are going to be...

00:56:48.700 It's skyrocketing.

00:56:49.500 It's a new idea for Geico.

00:56:51.600 There's...

00:56:52.440 It seems to me

00:56:53.540 when I talk to academics

00:56:54.540 or we talk to academics

00:56:55.740 and we interview them,

00:56:57.480 they broadly fall into two camps.

00:56:59.340 There's two people...

00:57:00.700 There's the camp that says

00:57:01.920 the academy can be saved.

00:57:04.840 There's the other camp

00:57:05.880 which says

00:57:06.540 the academy is beyond saving.

00:57:08.600 We need to build new academies.

00:57:10.600 I realize that's essentially

00:57:12.680 a binary choice

00:57:13.740 I'm presenting you with.

00:57:14.840 Are you in one camp or another

00:57:17.120 or is there a third way?

00:57:19.860 I don't think you left

00:57:20.780 any room for a third way.

00:57:23.120 I'll tell you a third way, actually,

00:57:24.640 just to add some nuance to this.

00:57:26.100 So I guess

00:57:26.400 what we were posing

00:57:27.700 is like Ralston College

00:57:29.000 in Savannah, Georgia.

00:57:29.820 They're building a new thing

00:57:30.820 from the ground up.

00:57:31.480 Yes.

00:57:32.000 University of Austin, similar.

00:57:33.960 There are other people

00:57:34.620 who work within institutions.

00:57:35.840 I was in Grand Junction

00:57:38.320 in Colorado

00:57:38.880 and they have

00:57:40.840 a relatively small college there

00:57:42.920 and they don't do either.

00:57:45.720 They're not like massive,

00:57:49.080 obsessed with DEI

00:57:50.460 or anything like that,

00:57:51.060 but they're also not

00:57:51.940 aggressively against any of it.

00:57:53.680 They just do things

00:57:54.800 their own way.

00:57:55.840 Yeah.

00:57:56.200 Maybe that's the third way.

00:57:57.680 Yeah, I consider that

00:57:58.400 the first way,

00:57:58.980 which is reforming

00:57:59.700 the new institutions

00:58:00.540 we already have.

00:58:01.860 Right?

00:58:02.420 So I think there's a question

00:58:03.800 of, you know,

00:58:05.040 as if this were a house,

00:58:07.080 do we refurbish what we have

00:58:09.720 or do we start

00:58:10.640 with new construction?

00:58:12.360 And, you know,

00:58:13.320 maybe the market's

00:58:14.260 going to tell us

00:58:14.720 a little bit of both

00:58:15.380 and what the optimal mix is,

00:58:16.560 I don't know.

00:58:17.380 My inclination is

00:58:18.540 to try to refurbish

00:58:19.960 what we have,

00:58:20.700 although I'm very supportive

00:58:21.960 of what University of Austin

00:58:23.100 is doing, trying to do.

00:58:26.520 And here's a,

00:58:28.020 but here is a third way.

00:58:31.580 The people who are doing

00:58:32.900 the research

00:58:33.640 could just care less

00:58:35.280 about what other people think.

00:58:36.360 every big movement,

00:58:39.520 people had to have

00:58:40.480 sacrifice.

00:58:42.920 So either you believe in truth

00:58:45.420 and you're willing

00:58:46.780 to pay some personal cost

00:58:48.540 or you don't.

00:58:51.680 Right?

00:58:52.000 If this were,

00:58:53.100 if we were arguing about,

00:58:54.960 you know,

00:58:58.580 some social issue,

00:59:01.000 we would say,

00:59:02.280 well, yes,

00:59:02.820 we can have this policy

00:59:03.560 or that policy

00:59:04.100 or they could just work harder.

00:59:05.180 Right?

00:59:06.660 And so in this case,

00:59:08.040 why are we letting

00:59:08.920 the scholars off the hook?

00:59:11.640 So maybe that's

00:59:12.640 the third choice

00:59:13.420 is you can go

00:59:15.000 into this realizing

00:59:15.960 maybe I won't win

00:59:18.460 all the awards

00:59:19.280 that I could

00:59:21.080 if I,

00:59:21.640 if I made the results

00:59:23.120 seem more coherent

00:59:24.780 to the day's politics.

00:59:26.520 Maybe that's okay.

00:59:27.700 And maybe if there's

00:59:28.520 enough of those people

00:59:30.100 that that becomes

00:59:31.560 a new thing

00:59:32.100 to actually tell the truth.

00:59:33.420 Right?

00:59:33.620 Someone said

00:59:35.060 the other day,

00:59:36.040 I got a note saying,

00:59:37.400 I really liked

00:59:38.040 your interview on XYZ.

00:59:41.340 Authenticity is in.

00:59:42.300 And I thought,

00:59:42.640 what does that even mean?

00:59:44.360 Right?

00:59:45.420 I can tell you

00:59:46.340 exactly what it means.

00:59:47.260 We are in the authenticity moment.

00:59:48.900 This is why new media

00:59:50.140 is taking off

00:59:50.780 the way it is

00:59:51.320 because,

00:59:51.960 you know,

00:59:52.820 if you think about

00:59:53.280 what TV is,

00:59:54.020 it's a bunch of fake people

00:59:55.020 having a fake conversation

00:59:56.280 in a fake room.

00:59:57.380 This is the exact opposite.

00:59:58.800 Right?

00:59:58.980 We're just talking straight.

01:00:00.720 And I think

01:00:01.220 this is the moment

01:00:02.140 for authenticity.

01:00:03.360 The flaw in your argument...

01:00:05.000 Why can't the academics

01:00:06.000 be authentic?

01:00:06.220 Well,

01:00:06.400 this is the flaw

01:00:07.080 in your argument,

01:00:07.700 right?

01:00:07.920 Because I agree

01:00:09.400 with you 100%

01:00:10.680 about people

01:00:11.340 just need to man up

01:00:12.340 and tell the truth

01:00:13.360 and pursue the truth

01:00:14.500 and do the work

01:00:15.360 that they're actually

01:00:15.860 supposed to do.

01:00:16.760 On the other hand,

01:00:17.380 you're an economist

01:00:17.920 and you know

01:00:18.500 that human beings

01:00:19.200 respond to incentives

01:00:20.060 first and foremost.

01:00:21.420 And if it's not

01:00:21.940 a high status thing to do,

01:00:23.220 if it's not a money making

01:00:24.320 thing to do,

01:00:25.140 if it's not a blah, blah, blah.

01:00:26.220 And look at universities.

01:00:27.360 I mean,

01:00:27.500 I mentioned DEI.

01:00:28.440 They're not even hiring

01:00:31.060 or recruiting people

01:00:32.400 based on merit.

01:00:34.040 So if you have

01:00:34.820 an institution

01:00:35.300 that operates

01:00:36.180 in that way,

01:00:36.700 why would the people

01:00:37.640 in it pursue the truth

01:00:38.700 at any cost?

01:00:39.420 I mean,

01:00:39.720 that's not reflective

01:00:41.960 of how people work.

01:00:43.080 Let me push back.

01:00:44.560 This is fun.

01:00:47.280 Well,

01:00:47.700 incentives matter,

01:00:48.580 but what economists

01:00:49.700 really believe

01:00:50.200 is that people

01:00:50.520 maximize utility.

01:00:52.100 So then the question is,

01:00:53.280 why isn't it

01:00:53.920 that the academics

01:00:54.640 care enough

01:00:55.400 about pursuing the truth?

01:00:57.200 Right?

01:00:59.180 Yes,

01:00:59.620 incentives on the margin

01:01:00.620 matter here or there.

01:01:01.580 And yes,

01:01:01.920 I agree the incentives

01:01:02.720 are pointing

01:01:03.040 in the opposite direction.

01:01:05.560 But if you look

01:01:06.720 at some of the things

01:01:08.960 that people are,

01:01:09.720 how wild,

01:01:10.480 this is where we started.

01:01:11.520 Why are things,

01:01:12.080 who gets to change

01:01:12.820 the narrative?

01:01:13.400 Why do things swing

01:01:14.300 wildly one way

01:01:15.620 and wildly the other way?

01:01:17.700 Maybe,

01:01:18.960 you called it man up,

01:01:20.660 I'm going to call it,

01:01:21.540 maybe people ought

01:01:22.160 to have a North Star.

01:01:23.600 Sure.

01:01:23.880 that maybe we've lost

01:01:26.500 that compass,

01:01:28.840 that driving force

01:01:30.860 that said,

01:01:31.500 it doesn't matter

01:01:33.980 if I find

01:01:35.680 that this group

01:01:38.460 is to blame

01:01:39.080 for something,

01:01:40.240 I'm going to say it.

01:01:41.020 If I find another group

01:01:41.920 is to blame for that

01:01:42.720 or that's what

01:01:43.100 the empirical evidence says,

01:01:44.160 I'm going to say it.

01:01:45.620 Right?

01:01:45.840 And then,

01:01:46.240 yes,

01:01:46.540 let's get the universities

01:01:47.500 to back you up,

01:01:48.420 to provide support

01:01:50.020 for that.

01:01:50.660 Can we go this far?

01:01:52.780 Encourage that.

01:01:54.980 Right?

01:01:55.240 That's what I had

01:01:56.360 when I first got to Harvard.

01:01:58.020 I had a president

01:01:59.340 who not only said,

01:02:03.300 this is okay,

01:02:04.300 but actually encouraged

01:02:06.380 the work.

01:02:07.760 Right?

01:02:08.500 And so,

01:02:09.420 I think encouragement

01:02:11.080 within the university

01:02:12.720 could go a long way,

01:02:14.360 but I also believe

01:02:15.760 that,

01:02:16.960 you know,

01:02:19.080 maybe we should be

01:02:19.940 selecting different people

01:02:20.860 to go into the academy.

01:02:21.740 Do you think

01:02:23.360 it's...

01:02:24.360 I love it.

01:02:28.220 Is that a wrap?

01:02:29.260 I mean,

01:02:30.180 it works for me.

01:02:31.600 France has got

01:02:32.200 more and more questions.

01:02:33.040 One more question,

01:02:33.740 which is,

01:02:34.600 how much do you think

01:02:35.940 this is,

01:02:37.740 and I've seen it

01:02:38.560 because I used to work

01:02:39.740 in teaching,

01:02:42.720 and people being afraid

01:02:45.400 of their own students,

01:02:47.540 how big of a problem

01:02:49.060 is that?

01:02:49.480 again,

01:02:54.560 I don't know.

01:02:58.820 And I'm not even sure

01:02:59.900 people are afraid

01:03:00.820 of their own students.

01:03:01.780 Maybe they are.

01:03:03.640 It could be that

01:03:05.140 the incentives

01:03:06.240 to do the right thing

01:03:10.040 when it comes to grading

01:03:11.500 or putting pressure

01:03:12.760 on students

01:03:15.100 to study

01:03:16.560 for this or that

01:03:17.280 are just not there.

01:03:20.420 And,

01:03:21.020 you know,

01:03:24.040 earlier in my career,

01:03:27.080 I was a hardliner

01:03:30.460 on,

01:03:31.780 I said,

01:03:32.700 the papers

01:03:34.180 would do at five o'clock.

01:03:35.980 Five o'clock,

01:03:36.600 I don't take them.

01:03:38.080 And I didn't say

01:03:38.780 you had to turn it in five.

01:03:39.520 You could turn it in

01:03:40.020 three weeks ago

01:03:40.500 if you wanted to,

01:03:41.220 but five o'clock

01:03:41.840 is the deadline.

01:03:43.760 I'm softer

01:03:45.660 on that now.

01:03:46.560 Not because I'm

01:03:48.060 scared of the students,

01:03:49.600 but because

01:03:50.980 it's five o'clock.

01:03:52.580 Do I want the headache

01:03:53.500 that this is going to cause?

01:03:55.100 Right?

01:03:55.360 Because the incentives

01:03:56.180 are very different.

01:03:58.400 So I think,

01:03:58.720 I think distinguishing

01:03:59.540 between those two

01:04:00.300 is really important.

01:04:01.120 I,

01:04:01.420 you know,

01:04:01.840 I don't,

01:04:02.740 I think that,

01:04:04.940 I think we actually

01:04:06.940 oftentimes

01:04:08.300 can bend over backwards

01:04:10.480 so much

01:04:10.940 that you actually

01:04:11.420 do the students

01:04:12.260 a disservice.

01:04:13.840 Let me give you

01:04:14.540 a tiny,

01:04:15.140 tiny example

01:04:15.780 that means nothing.

01:04:17.800 You teach a course

01:04:18.800 and you say,

01:04:19.420 hey,

01:04:19.680 you should read

01:04:20.460 Gary Becker's dissertation

01:04:22.940 on the economics

01:04:23.620 of discrimination

01:04:24.280 published in 1957.

01:04:25.480 See you next class,

01:04:26.640 Peter.

01:04:28.240 Professor,

01:04:28.760 could you provide

01:04:29.420 a link to that?

01:04:30.580 Could you send us

01:04:31.340 around a link?

01:04:33.400 No,

01:04:33.740 just go find it.

01:04:35.800 But that we are,

01:04:36.680 they're used,

01:04:37.280 students are now

01:04:37.820 becoming used to

01:04:38.600 a level of customer

01:04:39.380 service

01:04:39.880 that is such

01:04:41.380 that,

01:04:42.100 you know,

01:04:43.400 everything is just

01:04:44.060 really laid out

01:04:44.960 for them

01:04:45.380 very,

01:04:45.860 very carefully

01:04:46.380 and it's more

01:04:48.220 like problem

01:04:48.760 set solving

01:04:49.380 than deep thinking.

01:04:50.660 You have a paper,

01:04:51.480 how exactly,

01:04:52.040 how many words

01:04:52.480 should it be?

01:04:52.840 What exactly should,

01:04:53.720 it's like,

01:04:54.440 why don't you decide

01:04:55.520 those,

01:04:56.360 the answers

01:04:56.840 to those questions?

01:04:58.200 So I,

01:04:58.720 you know,

01:04:58.960 part of the reason

01:04:59.460 I don't provide

01:05:00.160 links for papers

01:05:00.940 is not because

01:05:01.480 I'm being lazy

01:05:02.140 but because

01:05:02.800 when I was a student,

01:05:03.860 when you went

01:05:04.220 to search for the paper,

01:05:05.380 you actually found

01:05:05.840 three or four

01:05:06.380 more around there

01:05:07.260 were actually interesting

01:05:07.940 and I pulled

01:05:08.340 those off the shelf too.

01:05:10.000 And so I actually think

01:05:10.720 by not providing

01:05:12.240 every single thing

01:05:13.400 they need

01:05:13.820 for their

01:05:14.180 customer service

01:05:15.520 needs,

01:05:16.680 you actually provide,

01:05:17.540 sometimes can provide

01:05:18.300 a better education.

01:05:19.860 Roland,

01:05:20.120 it's been an absolute

01:05:20.760 pleasure having you on.

01:05:21.660 We're going to ask you

01:05:22.200 a bunch of questions

01:05:23.040 from our supporters

01:05:23.840 in a second

01:05:24.380 but we always wrap up

01:05:25.680 with the same question

01:05:26.460 which is,

01:05:27.200 what's the one thing

01:05:27.720 we're not talking about

01:05:28.780 as a society

01:05:29.500 that you think

01:05:30.040 we should be?

01:05:30.500 I think that

01:05:39.560 for me

01:05:42.840 that question

01:05:46.240 boils down to

01:05:47.300 we basically know

01:05:51.000 there are

01:05:52.120 three or five things

01:05:54.540 that could fundamentally

01:05:56.500 change

01:05:57.340 racial inequality

01:05:58.560 in America.

01:06:00.220 We know it,

01:06:01.080 we know what to do.

01:06:02.700 Not to solve

01:06:04.080 but fundamentally

01:06:05.560 change racial inequality

01:06:06.620 in America.

01:06:07.540 Why don't we have

01:06:08.720 the political courage

01:06:09.660 to do it?

01:06:10.160 What other things?

01:06:13.820 Education's huge,

01:06:15.160 number one.

01:06:16.240 So when I did

01:06:16.960 some work on mobility

01:06:18.620 trying to understand

01:06:19.560 for people,

01:06:20.900 you know,

01:06:21.040 you have a set of people

01:06:21.840 who were born

01:06:22.940 into poverty,

01:06:24.020 some got out,

01:06:24.660 some didn't.

01:06:25.120 what are the differences

01:06:26.840 between them?

01:06:27.580 Number one,

01:06:29.720 resoundingly education.

01:06:31.500 So we know

01:06:33.100 how to

01:06:34.560 create better schools

01:06:36.400 for kids.

01:06:36.860 We've got exemplars

01:06:37.620 all over the country

01:06:38.460 and yet politics

01:06:39.740 gets in the way

01:06:40.460 of making those reforms.

01:06:42.720 That'd be number one.

01:06:43.840 Why?

01:06:44.340 Why do we let that happen

01:06:45.620 as a country?

01:06:47.120 Right?

01:06:47.400 We've got places

01:06:48.240 in our country

01:06:48.900 like,

01:06:49.840 you know,

01:06:50.580 D.C. public schools

01:06:51.500 where

01:06:53.760 less than 10%

01:06:55.540 of kids are,

01:06:56.260 black kids are proficient

01:06:57.140 in reading math

01:06:57.980 and

01:06:59.860 we just go on

01:07:02.360 like nothing happened.

01:07:03.720 We're talking

01:07:04.300 about other stuff

01:07:04.840 and so for me,

01:07:08.080 and we fundamentally

01:07:08.900 know how that's

01:07:09.940 impacting their mobility

01:07:10.840 and

01:07:12.840 if it is

01:07:14.220 extraordinarily

01:07:15.520 frustrating

01:07:16.280 to be

01:07:16.680 in a social

01:07:17.760 scientist's shoes

01:07:18.580 and to

01:07:19.580 see,

01:07:20.740 hey,

01:07:21.280 we actually

01:07:21.860 know

01:07:22.560 some things

01:07:23.600 that could

01:07:23.860 fundamentally,

01:07:24.800 I'm not talking

01:07:25.220 about at the margin,

01:07:26.120 fundamentally make

01:07:27.220 their lives better

01:07:28.060 and yet

01:07:29.180 the adults

01:07:30.500 can't get their

01:07:31.180 shit together

01:07:31.780 to help the

01:07:32.680 kids that they

01:07:33.340 say they care

01:07:33.840 so much about.

01:07:35.100 So as a society,

01:07:36.680 why do we

01:07:37.660 continue to let

01:07:38.780 that happen?

01:07:40.380 Roland,

01:07:40.660 what are the things

01:07:41.320 in that particular,

01:07:42.280 I want to go into

01:07:42.880 the other things,

01:07:43.600 but on education,

01:07:45.160 what are the

01:07:45.680 maybe not simple,

01:07:47.200 but the obvious

01:07:48.880 answers to

01:07:49.740 dealing with that

01:07:50.700 issue?

01:07:51.840 You know,

01:07:52.280 at a high level,

01:07:55.200 you know,

01:07:56.060 I'm going to show

01:07:56.400 you where we got

01:07:56.920 it from now

01:07:57.280 to exactly what

01:07:57.840 they are.

01:07:58.580 So we took

01:07:59.620 a couple years

01:08:00.160 and went and,

01:08:00.880 you know,

01:08:01.120 charter schools

01:08:01.880 on average

01:08:02.620 are no better

01:08:03.020 than public

01:08:03.820 schools in America,

01:08:05.060 but there's some

01:08:06.500 that are amazing

01:08:07.320 and there's some

01:08:08.460 that are pretty

01:08:09.380 awful.

01:08:09.880 Okay.

01:08:10.340 Okay.

01:08:10.960 And so what we

01:08:11.800 did was we went

01:08:12.480 in and tried to

01:08:13.040 understand what

01:08:13.760 makes them good

01:08:15.020 and what makes

01:08:15.380 them awful,

01:08:16.060 right?

01:08:16.280 And what we

01:08:16.700 found,

01:08:17.060 where there

01:08:17.460 were five

01:08:17.940 factors that

01:08:18.620 explained 50%

01:08:19.780 of the variance

01:08:20.400 and what makes

01:08:21.060 some schools

01:08:21.540 great and other

01:08:22.020 schools not so

01:08:22.620 great.

01:08:22.980 And those five

01:08:23.800 factors are the

01:08:25.120 amount of time

01:08:25.720 you spend in

01:08:26.220 school,

01:08:26.880 right?

01:08:27.220 So that's,

01:08:27.860 that's one

01:08:28.360 factor.

01:08:28.780 I call it the

01:08:29.200 basic physics

01:08:29.820 of education.

01:08:30.400 If you're behind

01:08:31.140 and they're ahead,

01:08:32.000 you need to put

01:08:32.480 in more time

01:08:33.220 or ask them,

01:08:33.900 please stop

01:08:34.280 working so hard.

01:08:35.820 Number two,

01:08:36.560 number two

01:08:38.000 was,

01:08:40.020 you know,

01:08:41.180 how schools

01:08:42.100 use data to

01:08:42.880 drive instruction.

01:08:44.240 So,

01:08:45.040 you know,

01:08:46.240 when I got

01:08:47.460 started,

01:08:48.380 data was a

01:08:50.000 real asset

01:08:50.460 in schools.

01:08:50.960 Now it's

01:08:51.220 almost like a

01:08:51.640 liability.

01:08:52.200 There's so much

01:08:52.860 of it.

01:08:53.140 No one knows

01:08:53.580 how to process

01:08:54.040 it.

01:08:54.220 What do you

01:08:54.560 do?

01:08:55.080 The schools

01:08:55.580 that are

01:08:55.780 effective,

01:08:56.220 when they see

01:08:57.320 that they're

01:08:57.620 students,

01:08:58.040 they take

01:08:59.220 assessments

01:08:59.680 throughout the

01:09:00.160 year,

01:09:00.300 when they see

01:09:00.820 the students

01:09:01.260 aren't getting

01:09:01.940 what they're

01:09:02.260 supposed to

01:09:02.760 in classes,

01:09:03.280 they regroup,

01:09:04.820 they figure it

01:09:05.340 out,

01:09:05.580 they come up

01:09:05.940 with a different

01:09:06.320 strategy,

01:09:06.760 and they go

01:09:07.320 back to work.

01:09:08.140 So data-driven

01:09:08.760 instruction.

01:09:09.220 The third

01:09:10.420 is

01:09:12.140 small group

01:09:13.580 tutoring.

01:09:14.160 It is

01:09:14.480 high-dosage

01:09:15.300 tutoring.

01:09:15.780 If you had

01:09:16.200 to do one

01:09:16.820 thing in

01:09:17.260 education,

01:09:18.040 in my

01:09:18.380 opinion,

01:09:18.760 it'd be

01:09:18.960 high-dosage

01:09:19.540 tutoring.

01:09:19.900 You can

01:09:20.100 get incredible

01:09:20.940 gains when

01:09:21.620 you've got a

01:09:22.000 kid around

01:09:22.380 a half-moon

01:09:23.120 table,

01:09:24.240 two kids,

01:09:24.880 one tutor,

01:09:25.900 and with

01:09:26.460 very direct

01:09:27.300 instruction.

01:09:29.240 The fourth

01:09:30.140 is,

01:09:30.840 you know,

01:09:31.340 human capital.

01:09:33.000 Who are

01:09:33.560 the teachers

01:09:34.100 teaching them

01:09:35.400 and how often

01:09:36.160 you give them

01:09:36.660 feedback,

01:09:37.560 right?

01:09:37.740 So the

01:09:38.040 bad schools

01:09:38.680 give you

01:09:39.280 feedback once

01:09:39.900 a year.

01:09:40.360 They come

01:09:40.720 in in the

01:09:41.480 spring,

01:09:42.040 you don't

01:09:42.540 teach very

01:09:42.960 well,

01:09:43.760 most of the

01:09:44.220 kids fail,

01:09:44.740 and they

01:09:44.840 say,

01:09:45.080 see you

01:09:45.380 again in

01:09:45.620 the fall.

01:09:46.980 Schools

01:09:47.420 that are

01:09:47.660 very effective,

01:09:48.620 very often,

01:09:49.980 small bits

01:09:50.460 of feedback

01:09:50.940 to ensure

01:09:51.700 that teachers

01:09:53.280 have the

01:09:53.840 support they

01:09:54.360 need to

01:09:54.780 get better.

01:09:55.820 And the

01:09:56.060 last one

01:09:56.620 is very,

01:09:57.340 very important

01:09:57.780 to me.

01:09:58.920 All of them

01:09:59.520 are important,

01:09:59.920 but this one

01:10:00.300 speaks to me

01:10:00.940 personally,

01:10:01.920 is cultured

01:10:03.760 expectations.

01:10:05.240 Probably one

01:10:05.940 of the most

01:10:07.340 eloquent

01:10:08.960 political

01:10:09.580 statements

01:10:09.920 I've heard

01:10:10.240 in my

01:10:10.480 lifetime

01:10:10.880 was the

01:10:11.660 soft bigotry

01:10:12.260 of low

01:10:12.540 expectations.

01:10:13.960 And I

01:10:14.860 believe,

01:10:15.700 and the data

01:10:16.180 support it,

01:10:16.840 that kids

01:10:17.260 live up or

01:10:17.960 down to

01:10:18.320 your expectations.

01:10:19.500 Like the

01:10:19.840 schools that

01:10:20.280 are effective

01:10:20.800 understand that

01:10:21.600 if you go

01:10:21.900 into an

01:10:22.200 inner city

01:10:22.560 you're dealing

01:10:23.040 with very

01:10:25.500 high rates

01:10:26.140 of single

01:10:26.720 female-headed

01:10:27.220 households,

01:10:28.540 poverty,

01:10:29.560 et cetera.

01:10:30.240 They don't

01:10:30.840 use that as

01:10:31.480 an excuse

01:10:32.040 not to educate

01:10:32.860 them.

01:10:33.280 They use that

01:10:33.960 as an excuse

01:10:35.120 to be more

01:10:35.660 efficient inside

01:10:36.640 the school

01:10:37.220 day to

01:10:37.900 say,

01:10:38.340 hey,

01:10:38.640 we only

01:10:39.120 have seven

01:10:39.660 hours with

01:10:40.160 this kid.

01:10:40.700 We've got to

01:10:41.120 make up for

01:10:41.700 single-female

01:10:42.620 headed household

01:10:43.220 poverty,

01:10:43.960 et cetera,

01:10:44.200 so we need

01:10:44.700 to get to

01:10:45.080 work.

01:10:45.360 We need

01:10:45.580 to use

01:10:45.840 data smartly.

01:10:46.560 I need

01:10:46.740 to be able

01:10:47.000 to give

01:10:47.200 you feedback

01:10:48.180 on your

01:10:48.480 teaching,

01:10:49.200 et cetera.

01:10:49.900 So those

01:10:50.680 five factors,

01:10:51.360 we took

01:10:51.720 those,

01:10:52.140 we put

01:10:52.440 them in

01:10:52.780 a randomized

01:10:53.220 experiment in

01:10:53.960 Houston,

01:10:54.260 Texas,

01:10:54.600 in regular

01:10:55.000 old public

01:10:55.460 schools,

01:10:56.140 and we

01:10:56.400 showed that

01:10:57.780 in three

01:10:59.500 years you

01:11:00.040 could close

01:11:00.460 the racial

01:11:00.820 achievement gap

01:11:01.460 in man,

01:11:02.260 and five

01:11:03.220 years you

01:11:03.600 could do it

01:11:03.940 in reading.

01:11:04.460 So you

01:11:04.780 can do

01:11:05.460 this stuff.

01:11:06.160 And this

01:11:06.600 is just

01:11:06.860 little old

01:11:07.540 nerdy me.

01:11:08.160 This is not

01:11:08.760 these phenomenal

01:11:11.040 superstars like

01:11:11.940 Jeff Canada

01:11:12.500 who runs

01:11:13.900 the Harlem

01:11:14.180 Children's

01:11:14.500 Zone here

01:11:14.760 in New York.

01:11:15.240 Of course

01:11:15.560 he's amazing,

01:11:16.180 but he's

01:11:16.460 done the

01:11:16.780 same thing.

01:11:17.620 So you

01:11:17.980 can do

01:11:18.380 this at

01:11:19.000 scale.

01:11:19.540 We've

01:11:19.680 done it

01:11:19.920 in Colorado,

01:11:20.840 we've

01:11:21.000 done it

01:11:21.240 in Houston,

01:11:21.840 we've

01:11:21.980 done it

01:11:22.160 in other

01:11:22.400 places.

01:11:23.660 And so

01:11:24.040 it's very

01:11:24.640 frustrating

01:11:25.540 that even

01:11:26.520 just in

01:11:26.880 the simple

01:11:27.300 slice of

01:11:27.760 education,

01:11:28.900 we don't

01:11:31.560 see policy

01:11:32.420 going towards

01:11:33.300 the things

01:11:33.800 that we

01:11:34.180 know

01:11:34.520 actually

01:11:35.240 can be

01:11:36.280 effective.

01:11:36.940 And so

01:11:37.320 the answer

01:11:38.580 to your

01:11:38.840 question for

01:11:39.360 me is

01:11:39.840 how can

01:11:41.220 we as

01:11:41.640 a society

01:11:42.320 both sit

01:11:43.160 around and

01:11:43.940 lament the

01:11:44.720 lack of

01:11:45.120 progress and

01:11:46.260 also not

01:11:47.540 be willing to

01:11:48.640 do whatever

01:11:49.120 it takes to

01:11:50.580 solve these

01:11:51.320 things in our

01:11:51.860 generation?

01:11:52.860 Okay.

01:11:53.280 I want to

01:11:53.900 dig more

01:11:54.300 though,

01:11:54.540 even though

01:11:54.840 it's the

01:11:55.080 last question.

01:11:55.580 You said

01:11:55.820 education,

01:11:56.500 there's

01:11:56.700 others though,

01:11:57.320 right?

01:11:57.820 Sure.

01:11:58.580 We need to

01:11:59.400 really figure

01:11:59.920 out how do

01:12:00.900 we increase

01:12:02.740 the non-cognitive

01:12:04.320 skills like

01:12:05.960 resilience and

01:12:08.000 grit, those

01:12:09.820 things.

01:12:10.300 Really, those

01:12:10.880 were the, in my

01:12:11.480 mobility thing,

01:12:12.140 those were the

01:12:12.600 next ones.

01:12:14.040 We also have a

01:12:15.560 sense of, you

01:12:16.900 know, how to do

01:12:18.400 health care in a

01:12:19.280 more efficient and

01:12:20.180 better way.

01:12:21.040 I mean, I'll come

01:12:22.160 back on the show,

01:12:22.820 health care is a

01:12:23.340 whole different

01:12:23.760 topic, but you

01:12:24.600 get the point.

01:12:25.860 Health care,

01:12:27.020 criminal justice,

01:12:27.740 education, and

01:12:30.720 non-cognitive

01:12:31.760 skills, which is

01:12:32.420 included in

01:12:32.840 education.

01:12:33.800 We have, the

01:12:34.980 social science is

01:12:36.020 not perfect by

01:12:36.780 any means, but

01:12:38.620 we know enough

01:12:39.860 to make

01:12:40.360 fundamental

01:12:41.120 progress and

01:12:42.060 we just can't

01:12:42.800 get past

01:12:43.220 ourselves.

01:12:43.740 And that is

01:12:45.400 extremely, extremely

01:12:46.880 frustrating.

01:12:47.760 Well, Roland, we

01:12:48.460 would be delighted

01:12:49.440 to have you back

01:12:50.100 on to talk about

01:12:50.860 some of those

01:12:51.280 things next time.

01:12:51.980 But for now,

01:12:52.440 follow us over

01:12:53.180 to Locals, where

01:12:53.940 we continue the

01:12:54.580 conversation with

01:12:55.300 your questions.

01:12:57.740 Do you think

01:12:58.360 the narrative of

01:12:59.020 systemic racism

01:12:59.920 towards African

01:13:00.740 Americans by

01:13:01.460 governments and

01:13:02.000 law enforcement

01:13:02.620 ever can ever be

01:13:03.780 changed?

01:13:04.380 Or has the media

01:13:05.220 now perpetuated

01:13:06.120 this narrative to

01:13:07.440 the point it

01:13:08.040 almost can't be

01:13:08.780 challenged in the

01:13:09.600 mainstream?

TRIGGERnometry - June 16, 2024

Harvard Professor： The Facts About Police Brutality - Roland Fryer

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