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Speaker 1: Hello everyone, and welcome to Talk Nerdy. Today is Monday,

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April twentieth, twenty twenty six, and I'm the host of

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the show, Doctor Kara Santa Maria. And as always, before

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we dive into this week's episode, I want to thank

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those of you who make Talk Nerdy possible. Remember, we

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use Patreon for our kind of supportive structure. It's the

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NPR PBS model here in the US where I want

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the show to be free and it will always be free.

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And even though I sell like some pre roll kind

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of programmatic ads, that's a nothing burger. It really doesn't

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pay the way that host red ads used to do.

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So we really do rely on the support of folks

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through Patreon. Individuals from all over pledge their support on

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an episodic basis as little as I don't know, fifty

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cents dollar and some much more. So I want to

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thank those of you who pledge much and much more,

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because you really are like the lifeblood of the show.

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You keep it going and you make it so that

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I can do all my other jobs and still make

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the show because I've got to pay my incredible team

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to keep this going. So I want to start with

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Chuck Blell, David J. E. Smith, Daniel Lang, Mary Neva,

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Will DeFraine, David Compton, Brian Holden, Gabo, Jay Ulrika Hagman,

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Pasqually Gilatti, and Joe Wilkinson, and of course so many more.

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Thank you, thank you, thank you. All Right, so what

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do I have for you this week? I have the

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opportunity to speak with doctor Jennifer Doliac. She is the

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executive vice president of Criminal Justice at Arnold Ventures, which

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is a philanthropy focused on evidence based policy. And before

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moving into that space, she was an economic professor at

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both Texas A and M University and the University of Virginia.

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She's written for a bunch of different outlets and well

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we'll talk about her academic training in a bit, but

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she has a new book out. It's called The Science

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of Second Chances, A Revolution in Criminal Justice. So, without

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any further ado, here she is doctor Jennifer Doliac. Well, Jen,

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thank you so much for joining us today.

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Speaker 2: Hi, thanks so much for having me.

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Speaker 1: I'm excited to talk about your new book, The Science

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of Second Chances, A Revolution in Criminal Justice. And for

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folks who have been listening to the show for a while,

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maybe not the whole time. Because this show has been

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on air now for what like fourteen no, I think

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longer sixteen years. I don't even know. I can't track.

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It started as a pretty hard science show. I was

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mostly covering like physics, biology, chemistry, some environmental science. As

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I went back to school, finished my PhD, became a

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clinical psychologist, and moved more into like social justice areas,

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the show has definitely shifted in a lot of ways.

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We still cover a lot of you know, kind of

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hard science, but we've more and more been talking about

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social justice issues. And lately, yeah, the criminal justice system

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I guess we can call it a system as a

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whole has become just increasingly interesting to me because it

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is so fundamentally tied to so many aspects of our society.

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So I'm just I'm I'm really excited to talk to

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you about what's working, what's not working, how we as

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a society are impacted, how change or reform is difficult,

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but what you know, could be on the horizon. So

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before we even get into all of that, I just

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want to learn a little bit more about you. So

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you are trained as an economist, right.

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Speaker 2: That's right? Yes? And so I yeah, so I went

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to graduate school for economics, and I really, you know,

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I knew that I loved the intuition and the toolkit

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that economics provides. But it really is a toolkit. It's

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it's a way of thinking about human behavior in terms

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of how people respond to incentives. But also economists have

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really focused on developing an empirical toolkit that distinguishes correlation

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from causation. So of all the social scientists out there,

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were the most obsessed with questions, with causal questions and

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causal inference. And so we use randomized trials like you

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like the lab experiments that that you'll see in the

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hard sciences, but out in the field of course, but

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we also use natural experiments. So these are situation in

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the real world that sort people into treatment and control

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groups as if at random. So something that approximates that

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ideal lab experiment that we wish we could run, but

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that of course is a little bit messier because we're

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dealing with human beings out in the real world. And

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so those types of natural experiments are really the bread

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and butter of a researcher like myself, and that has

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really provided a cool and new opportunities to learn a

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lot about what works in the criminal justice space and

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how to make people's lives better.

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Speaker 1: Yeah, you know, it's interesting. I think when a lot

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of people think of economics they think simply of money,

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or when they think of the economy, they think of

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the money system. But what you're describing in a lot

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of ways sounds like kind of economics as a field

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of inquiry really is almost like a form of applied

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psychology or applied sociology, sort of human behavior, how humans

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make decisions, how they grapple with risk, how they grapple

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with as you mentioned, incentives, uncertainty, and then how that

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affects these larger maybe like political or social systems.

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Speaker 2: Is that right, Yeah, that's right. I mean, I you know,

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people sometimes give economists a hard time for encroaching on

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other people's territory. We are certainly prone to doing that,

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and I think in general most economists do think that

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almost any topic is fair game. Economics is everywhere, you know,

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people are responding to incentives all over the place. But

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also that empirical tool kit I mentioned is really useful

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in all kinds of settings, and it helps us really

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quantify what the costs and benefits of different interventions are

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different programs, different policies, and that's really useful for policy makers.

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So if we want to know how to make people healthier,

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make our healthcare system work better, if we want to

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know how to make our schools better, if we want

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to know how to make our prisons better, any of

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these spaces, this empirical toolkit is really useful for helping

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to crunch the numbers and get the biggest bang for

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our buck. And so, as I said, economists are the

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most obsessed with doing those kinds of calculations, and so

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we tend to be useful in many kinds of conversations.

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Speaker 1: And so I'm curious. You know, you studied at Stanford,

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and then you were a professor at both A and

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M and the University of Virginia before moving into a

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sort of like philanthropic policy space when you were a

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graduate student, and then when you moved on into your

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academic appointments, what were some because it does sound like

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like ECON as a whole is like such a broad field,

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what were some of your kind of personal areas of

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interest and inquiry?

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Speaker 2: Yeah, when I first went to graduate scho we, I

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think I assumed I would study education or the economics

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of education or higher education, probably because that's where I'd

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spent most of my life, right like, that's you know,

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that's the system I knew best. I'd been a student

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for a really long time, and so it really wasn't

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until it was later in my graduate training where I

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and all of my colleagues were hunting around for these

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natural experiments that we could apply our new toolkit to.

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And I read a newspaper article about law enforcement DNA databases,

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and the point of the article was that, you know,

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it seems really arbitrary that in some states, all you know,

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states are determining the rules about which groups of people

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convicted or arrested for certain crimes are required to provide

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DNA to the database, and so the rules differ from

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state to state, and that sets up situations where you

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could have identical people convicted of the exact same crime,

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you know, one state apart, and one since in the

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database and the other one isn't. And the point of

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the article is, isn't this silly, you know, And isn't

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this bad policy? And it might be bad policy, and

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it might be silly, but it's great for research. And

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so that's exactly the kind of setup that where it

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does feel arbitrary or even random that you know, identical

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people have different treatments, and so that sets up that

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natural experiment that someone like me is looking for. And

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so I started looking into, well, what do we know

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about the impacts of these law enforcement databases. They're used

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across the US, they're used around the world, and we

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really knew nothing. We knew nothing about the impact of

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this extremely common tool, and so that became my dissertation.

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And then I graduated in twenty twelve and was coming

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out of my PhD at a time when there were

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a few more senior economists who are really trying to

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develop a cohort of more junior scholars to focus on

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this topic crime and the criminal justice system, because economics

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really hadn't focused on this much previously. Economic or education

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and health and development economics and all these other topics

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had been had been common in ECON for a long time,

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but for various reasons, economists hadn't thought much about crime,

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and so we were way behind other fields like sociology

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and criminology in that sense, and so it was a

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little bit risky for you know, a junior scholar to say, sure,

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I'll embark and define myself, you know, in terms of

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this field where you know, when it's doing this and

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trying to build a career. Academia can be very traditional

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in many ways, and so that's a risky paths to

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go down. But there are just so many interesting and

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important questions in the public safety and criminal justice spaces,

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and the vast majority of them are questions where the

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toolkit that I have to be able to answer cause

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questions about what works and what doesn't and if something works,

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how much does it work, how much does it move

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the needle? Those questions are really crucial for for safety

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in communities, for the directions of people's lives. And we

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really know so much less than we should given how

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important those questions are. And so you know, here I am, uh,

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you know, over a decade later, still still fascinated by crime.

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And and so that's where I've focused my attention.

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Speaker 1: Yeah, and so you've you've written for several outlets. But

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is this your first full length book, that's right.

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Speaker 2: Yeah, So economics isn't really a book field, it's a

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it's a journal article field. And so so this is

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my first book and and it's written for a broad audience,

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and there's no there's no professional incentive to write for

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other academics in this this format, and and I and

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you know, my goal here was really to write something

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that could communicate all of the ideas that are swirling

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around in academic circles to that audience of you know,

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interested citizens, policymakers, practitioners who want more information about how

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to help put people on a better path, who are

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who were involved in the criminal justice system, how to

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reduce recidivism and reoffending after people are convicted or spend

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time in prison for a crime. And my sense was

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really that and still is that the cable news conversation

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about crime and criminal justice is really divorced from from

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the evidence and from what scholars talk about in the

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academy and the types of interventions that we're excited about

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and that we're seeing or effective. I think in the

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cable news space you have the you know, this traditional

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argument between kind of the far left saying we need

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to defund police and defund prison and just you know,

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invest in social services and that's how we reduce crime,

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and the far right is saying the only way to

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reduce crime is to just lock people up at their

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first the first sign of moral weakness, and you know,

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some people are just bad people and there's nothing you

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can do. And I think the middle ninety percent of

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the country looks at those those two proposals and they're like,

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those are our options, you know, and it's just really

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unsatisfying and someone like me coming into the policy space,

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as you said, I joined Arnold Ventures about two and

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a half years ago, which is a philanthropy that focuses

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on evidence based policy. So now I spend a lot

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more time in the policy space and talking to lawmakers

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and practitioners. And the conversations that my colleagues were having

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that I'm hearing from lawmakers, they're just so completely different

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from anything we talked about in the research space. And

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so I was really able to come into this space

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with fresh eyes and much less wed to the traditional

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that these conversations were had, and it just seemed like

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an opportunity and still does. I mean, I think this

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is most the most exciting piece of the work. That

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there there are so many things we know do work,

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and we know how to learn more. We have a

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sense that there there probably are more solutions out there

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we just need to find them, and so it makes

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me very optimistic about what's possible in this policy space,

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and my interactions with lawmakers continue to impress me and

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continue to you know, support that optimism or they just

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genuinely don't know and they don't know any researchers, and

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so being able to connect these two worlds has tremendous upside.

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Speaker 1: You know, I'm so excited about this conversation as somebody

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who identifies as you know, like a scientific skeptic, a

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science space or evidence space thinker. When I work in

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another podcast, The Skeptics Guide to the Universe, where that's

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basically what we do is we debunk suit pseudoscience and

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we kind of teach critical thinking skills and neuropsychological humility,

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understanding cognitive biases, all these different things. I'm curious, sort

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of just at the tip, I guess of the of

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the iceberg here, could you give us an example of

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maybe an obvious situation in which there's something that's just

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like well known to be evidence based when it comes

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to criminal justice policy in research or academic circles, but

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like within the political the public, you know, the media arena,

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Like the conversations are just super divorced, like you mentioned,

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like is there just an area or a specific talking

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point where you're like, why does this persist? Like we

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have evidence that it fully flies in the face of this,

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Like could you give us an example of that.

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Speaker 2: Yeah, a bunch of examples are coming to mind. One

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that I spend a lot of time talking about now

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because I do think it could really move the needle

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if we can change the conversation is how we deter crime? Right,

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And so you know, I'm an economist. I think people

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respond to incentives. I think that, you know, the way

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to change behavior is to change the cost and benefits

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that people respond to. And so to get people to

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commit less crime, you either have to reduce the benefits

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of committing crime. That's hard to do, or you can

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increase the cost of committing crime. And so that's where

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the criminal justice system and law enforcement come in. And

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there are two ways to increase the perceived cost or

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the expected costs of committing crime for someone who's considering

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doing so. One is to increase the punishment that they

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would receive if they are caught, and the other is

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to increase the probability that they are caught. So you know,

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there's some chance that you're caught, and then if you

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are caught, conditional on being caught, you'll face some punishment.

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But if you're not caught, then you don't face any punishment.

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And so it's a probability. You multiply the two together,

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and that's how you get the expected cost of committing crime.

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And for many decades in the United States, and you know,

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the US is better than this at this than any

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other country, we have really really focused on increasing sentences.

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So really tried that that lever that rule. If we

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wanted to ter crime, let's just make punishment harsher and longer.

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Speaker 1: And so we had these like mandatory minimums, and we

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had all of these policies for a long time that

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were like really punitive.

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Speaker 2: Yeah yeah, and still I mean, I think you know,

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even you know, the most recent during COVID, we had

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increases in homicides and shootings and that scared everybody. And

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so I think for understandable reasons, lawmakers are saying, we

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have to we have to crack down on this, we

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have to figure out how to prevent crime. We'll just

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make sentences longer. And so you see, you know, all

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of these sentence enhancements and truth and sentencing bills and

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all these different types of policies that are really aimed

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at locking people up for longer. This is the way

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we're going to reduce crime. And but what we know

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from lots and lots of research is that the thing

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that changes behavior is not the long sentences. Although it's

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perfectly reasonable idea, in practice, it turns out that most

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people who are at risk of committing crime, the people

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were trying to deter they're simply not thinking that far ahead,

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and so changing a punishment from one year to two years,

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to five years to ten years in prison, none of

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that is registering if they're not thinking past this week

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or this month. And so what does matter, though, is

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increasing the probability of facing consequences. And so the swiftest

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uncertainty of punishment is how it's often often phrased. And

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so if you can increase the likelihood that people get

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caught and face any consequences, that really moves the needle.

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If you have consistent, reliable consequences that people know they're

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going to face, and then it doesn't really matter how

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long the person sentences. It's just increasing the likelihood that

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they get caught, and that really matters because in the

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current in the United States, the likelihood that you get

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caught for crime is really low, and I think most

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people are really surprised by how low clearance rates is.

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How we typically measure this. This is basically the share

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of reported crimes that results in an arrest. So clearance

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rates in the US hover between fifty and sixty percent

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for homicide. So if you commit a homicide, it's basically

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a coin flip whether you get arrested or not.

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Speaker 1: And that's just getting arrested, not convicted, just.

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Speaker 2: Getting arrest exactly. So some of those people not being convicted, right,

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So it's a coin flip even getting arrested for other

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But that's the best case scenario any other crime, less

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serious crimes. When you think about still felonies, think about

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the theft or larceny, burglary, the probability of getting caught

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or the clearance rate is about ten to fifteen percent.

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Speaker 1: Oh god, And what about like I wouldn't say equally, well,

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I don't even want to put a moral judgment on this,

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but like severe crime like rape, isn't the clearance rate.

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Speaker 2: Like oh my, Goshel abysmal because it is because even

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those crimes, you have the double whammy of people aren't

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reporting and then the share of the report. So all

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we can see really is the share of reported crimes

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that result in an arrest. And so of course if

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people or communities don't trust that the police are actually

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going to do anything, then they're going to stop reporting

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the crime. So this is a well known issue in

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gender based violence, like rape. It's also an issue for

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like bike theft, right, Like I have a ton of

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friends who had their bike stolen, and you know, after

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they've had their first bike stone, they report to the police.

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They don't do anything when their second bike stole on

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they don't even bomber, right, And so there's a lot

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of especially the low level crimes, you don't even bother

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reporting to the police because you figure they're not gonna

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it's not gonna be a priority they you know. And

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in defense of the police, like they're triaging, they're trying

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to figure out, you know, they're trying to solve the homicides.

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But this does mean that we've set up a system

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where the vast majority of our criminal justice funding is

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going to keeping people in prison for a really long time,

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and if we invested even a small share of those resources,

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shifted those resources and that attention to helping police solve

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more crimes faster, we would get a huge bang for

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our buck and a huge reduction in crime. And again,

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this is something like everyone in the academy knows. Researchers

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know this. We've known this for a long time. The

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evidence keeps getting better and better, but we've known this,

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this basic concept for a long time. But we talked

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to lawmakers about it and their minds are just blown

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right and you can see it makes sense to you

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once you explain it, and they're like, oh my gosh,

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we've been doing this all backwards. It's like, yes, we've

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been doing it all backwards. So that's a their until issue.

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Speaker 1: Yeah, there are incentives, i think, to continuing to do

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it backwards for a lot of simply because of the

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way that our economic system functions in this country. It

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does seem to be lower hanging fruit to get people

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into the prison pipeline and keep them there as opposed

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to changing or improving policing. Because we've tried to change

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and improve policing a lot, and we've tried a lot

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of things that, as you mentioned, don't really work. We

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put we funnel a lot of money into policing, but

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it doesn't seem to as you're saying, you know, have

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the the outcome, like like we know what we want

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to get out of it. Right, as you mentioned, less

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kind of crime would be deferred better if we spent

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less of our let's say, resources on keeping people in

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prison longer and more of our resources on ensuring that

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people who commit crime go to prison. But in practice

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is that easy to do?

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Speaker 2: Yeah, I mean I agree. You know, we do spend

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a lot of money on policing. We've tried lots of

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things in policing, but my sense is that we really

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haven't focused on this aspect of policing. So there's tons

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of evidence at this point that hiring more police, putting

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more police on the streets, that does reduce crime, and

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that is primarily through deterrence. If you see a cop

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standing on a street corner, you're not going to steal

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the car right in front of him, right, you know

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you're going to get caught. That's like that basic concept

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and action increase the probability of getting caught, then you'll

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reduce crime. But so most attention the policing world has

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really gone into just patrol, like having officers out on

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patrol to deter crime in that way, and there's been

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much less attention and experimentation and learning around how to

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solve crimes after they've happened. And so, for instance, it

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turns out and this is something like I've been learning

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a lot along with my team about all of this

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as we try to think about how can we help

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states and cities do better on this dimension? Because you're right,

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there is a way in which, you know, for lawmakers

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who care about this, it's really straightforward, just right into

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statute that sentences for this crime are now longer than

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they were before. Right, that's a really it just.

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Speaker 1: Feels like cheaper and easier to do. So they're like,

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let's do the easy thing.

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Speaker 2: Yeah, and so but it's not effective. And so it's like, okay,

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so how can we help lawmakers do do things better here?

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And so so there are our bills that many states

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are considering where that would provide funding to law enforcement

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specifically focused on interventions that would reduce that would that

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would solve more crimes. So you can use the money

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for forensic analysis or DNA analysis. You can use the

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money for uh licensed play readers or new tech you

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can hire, you can hire more detectives, say, But something

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else that was I found really surprising and that has

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motivated some some work that we're we're reding, is that

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it turns out most detectives in the country don't really

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get any training as detectives. They people tend to get

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promoted to be a detective based on seniority or because

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they seem to have good people skills or something like that,

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but then they just are expected to learn on the job.

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And so, you know, this is a really important crime

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or a really important job, and so wouldn't it be

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useful if they were trained in some way with the

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skills they need to be able to do that job well.

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And so we're partnering. We being Arnald Ventures is partnering

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with PERF, the Police Executive Research Forum to develop a

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training curriculum for detectives. And so basically, you know, could

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we just create a detective school essentially, and then we're

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going to roll it out as a randomized trial so

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that we can learn what the impact is. But there's

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just a lot of low hanging fruit like that out there.

435
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Crime labs are super backlogged. We don't really prioritiz highs

436
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evidence going through those crime labs in any sort of

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useful way. There's just so much we could be doing

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better that I think we just haven't. We haven't really

439
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focused on it in the past, and so we haven't

440
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learned what works.

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Speaker 1: And I'm curious, just from a sort of evidence based perspective.

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One of the things that you mentioned about deterring crime is,

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you know, increasing the incentive not to commit a crime

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or decreasing the incentive to commit a crime. And I

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can't help but think about like recidivism as another knock

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against these like long prison sentences, because I'm curious, if

447
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you've looked at the data here, do people who end

448
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up staying in prison for a very long time, are

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they more or less likely to repeat My guess is

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that they're not getting a lot of rehabilitation and they're

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just languishing, and so when they get out, they're having

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a really hard time re entering into society and there's

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less incentive not to commit crimes. Then, But do we

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know what the data show?

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Speaker 2: Yeah, So there has been a bunch of research using

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natural experiments where you know, I'd love to know what

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the effect of going to prison is on someone, right,

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And so the ideal experiment is you randomly send some

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people to prison and others not, and obviously which.

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Speaker 1: Would be horrifically unethical laws you mentioned, but you're right,

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you mentioned that there are places in the country where

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like the well like, yeah, the laws are different.

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Speaker 2: Yep, exactly. So this is a place where we use

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these natural experiments. So either sentencing guidelines where people are

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just above or below the threshold that puts them into

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a different category, or there's a policy change, or something

467
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that's very common is that cases are randomly assigned to

468
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different judges, and different judgestural have different inclinations. Naturally, some

469
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are more lenient and some are more harsh. And so

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if you get lucky and you get a more lenient

471
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judge who's less likely to send you to prison or

472
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who sentences you to a lower sentence, we can compare

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you to someone who got unlucky and got the harsher

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judge and still got a longer sentence. And so through

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all of that there's a general I think the the

476
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interesting result that's coming out of this literature is that

477
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the sentence like we can see in the data that

478
00:28:32,680 --> 00:28:35,319
you know, if you are convicted and go to prison,

479
00:28:36,000 --> 00:28:38,200
your employment rates when you get out are much lower

480
00:28:38,240 --> 00:28:40,480
than they were before. They were already low, but they

481
00:28:40,680 --> 00:28:45,200
you're much lower. And we know the recidivism rates are

482
00:28:45,200 --> 00:28:48,240
really high, and so we can see that, you know,

483
00:28:48,720 --> 00:28:51,720
at the very least, we're not doing a very good

484
00:28:51,799 --> 00:28:55,759
job of using prison effectively as an intervention point. But

485
00:28:55,799 --> 00:28:58,160
what's interesting coming out of the research is that most

486
00:28:58,200 --> 00:29:02,400
of that impact on employment in particular, seems to be

487
00:29:02,440 --> 00:29:06,160
coming from the conviction having a criminal record itself, having

488
00:29:06,200 --> 00:29:10,240
something on your redtle, rather than the incorpration spell. Now,

489
00:29:10,240 --> 00:29:14,559
the incarceration spell is I really think of as an

490
00:29:14,640 --> 00:29:18,519
intervention point that we've got you, you know, in a

491
00:29:18,599 --> 00:29:22,960
place for some period of time, often a long period

492
00:29:22,960 --> 00:29:25,960
of time, where you have nothing else to do. Wouldn't

493
00:29:26,000 --> 00:29:28,960
that be a great opportunity to make lots of education

494
00:29:29,319 --> 00:29:34,640
available to mental health treatments, lots of you know, job training,

495
00:29:34,720 --> 00:29:36,160
whatever it is that would help.

496
00:29:36,319 --> 00:29:38,680
Speaker 1: Yeah, but we know that doesn't happen, I mean only

497
00:29:38,720 --> 00:29:40,079
in very ideal situation.

498
00:29:42,160 --> 00:29:44,440
Speaker 2: So I think a lot of prisons they have it,

499
00:29:44,480 --> 00:29:48,279
and I think the challenges we don't really have great

500
00:29:48,400 --> 00:29:52,440
data on which programs are effective and which ones aren't

501
00:29:52,960 --> 00:29:57,240
because the people who because the programs tend to be

502
00:29:57,920 --> 00:30:01,400
held out for people based on good behavior, or you

503
00:30:01,440 --> 00:30:05,000
select into them based on your motivation or your interest,

504
00:30:05,400 --> 00:30:06,759
and so then it's really hard to tell what the

505
00:30:06,839 --> 00:30:11,079
value add of certain programs is. So we're just starting

506
00:30:11,079 --> 00:30:13,799
to get better data on like exactly which programs should

507
00:30:13,839 --> 00:30:17,960
we have people taking, And does you know a college

508
00:30:18,039 --> 00:30:21,160
degree actually help or does the criminal record itself is

509
00:30:21,160 --> 00:30:23,319
that such a barrier that it doesn't matter what your

510
00:30:23,400 --> 00:30:27,119
education is, and so are there are a lot of

511
00:30:27,160 --> 00:30:31,039
open questions about you know, which types of programs are

512
00:30:32,319 --> 00:30:35,839
actually good investments in terms of money, In terms of

513
00:30:35,880 --> 00:30:40,160
time and scarce resources, prisons tend to be really understaffed,

514
00:30:40,240 --> 00:30:42,039
so often these programs are the first thing that are

515
00:30:42,039 --> 00:30:45,599
cut when they're really worried about safety and and don't

516
00:30:45,599 --> 00:30:49,880
have enough officers on staff. And so so this is

517
00:30:49,920 --> 00:30:51,839
all to say, like this is I think there's a

518
00:30:51,880 --> 00:30:54,920
general perception that you know, there's Norway that does this

519
00:30:55,039 --> 00:30:58,279
really well and has this really rehabilitative model, and that

520
00:30:58,400 --> 00:31:01,000
US prisons have none of this, and that's not true.

521
00:31:01,039 --> 00:31:03,279
They do. I mean prisons across the country do have

522
00:31:03,319 --> 00:31:07,200
a lot of programming. It's just we could be doing

523
00:31:07,319 --> 00:31:10,720
so much better. And there is a lot of variation

524
00:31:10,799 --> 00:31:15,440
across different facilities, of course, and across different states, and

525
00:31:16,720 --> 00:31:20,440
you know, the labor shortages are a huge issue. So

526
00:31:20,599 --> 00:31:23,559
this is all to say, I think, you know, we

527
00:31:24,000 --> 00:31:25,920
you know, we could increase the probability of getting caught,

528
00:31:25,960 --> 00:31:29,079
But for the people who do wind up incarcerated, then

529
00:31:29,519 --> 00:31:32,519
we a we know that long sentences are not having

530
00:31:32,519 --> 00:31:34,720
the deterrent effect on future crime that we would like.

531
00:31:35,160 --> 00:31:37,440
We also know that locking people up for long periods

532
00:31:37,480 --> 00:31:40,880
of time doesn't really have much of what we call

533
00:31:40,960 --> 00:31:43,720
an incapacitation effect. Like the other reason to lock people

534
00:31:43,759 --> 00:31:46,200
up they're an active public safety threat is you want

535
00:31:46,240 --> 00:31:48,400
to take them out. To take them out, there will

536
00:31:48,440 --> 00:31:50,720
always be a role for jail in prison for some people,

537
00:31:50,880 --> 00:31:53,200
right where there's some people where we're just going to

538
00:31:53,200 --> 00:31:55,000
have to lock them up. But it turns out most

539
00:31:55,000 --> 00:31:58,319
people who commit crime age out of crime really quickly,

540
00:31:58,599 --> 00:32:01,200
Like people do a lot of dumb, reckless, impulsive stuff

541
00:32:01,240 --> 00:32:04,599
when they're teenagers and young adults, and then they grow up.

542
00:32:05,079 --> 00:32:08,440
And of course some people there are exceptions out there

543
00:32:08,480 --> 00:32:11,519
who will continue to be violent into their you know,

544
00:32:11,599 --> 00:32:14,880
adult years. But those exceptions really do prove the rule

545
00:32:15,119 --> 00:32:19,599
that most people just do this stuff when they're teens

546
00:32:19,599 --> 00:32:21,240
and young adults and then they grow up. And so

547
00:32:21,400 --> 00:32:24,799
locking someone if someone's committed a crime when they're sixteen,

548
00:32:24,920 --> 00:32:30,279
eighteen nineteen, locking them up for twenty thirty years, you know,

549
00:32:30,359 --> 00:32:34,400
by the time they're forty or fifty years old, they're

550
00:32:34,480 --> 00:32:37,119
definitely not a public safety threat anymore. At that point,

551
00:32:37,160 --> 00:32:40,640
we're not getting any public safety for that money and

552
00:32:40,680 --> 00:32:44,720
for that time and network out of the and so

553
00:32:44,720 --> 00:32:47,079
so Yeah, I think there are a lot of opportunities

554
00:32:47,079 --> 00:32:51,480
to use prison in a more cost effective way, in

555
00:32:51,519 --> 00:32:57,000
a way that is actually you know, putting people on

556
00:32:57,079 --> 00:33:00,839
a better path, rather than just using it as a

557
00:33:00,839 --> 00:33:04,160
holding pen. Right, that's a really expensive holding pa if

558
00:33:04,160 --> 00:33:06,440
that's all we can get out of it, But it

559
00:33:06,480 --> 00:33:09,200
is an intervention point for people who are clearly struggling

560
00:33:09,240 --> 00:33:11,200
in their lives. Right, people don't wind up in prison

561
00:33:11,200 --> 00:33:13,960
because everything's going well for them, and so it is

562
00:33:14,000 --> 00:33:19,200
a chance to really help people level up and leave

563
00:33:19,319 --> 00:33:21,880
better than they came in. And I think it's just

564
00:33:21,960 --> 00:33:25,599
a really missed opportunity that we don't take that, don't

565
00:33:25,680 --> 00:33:27,200
use that opportunity as much as we can.

566
00:33:28,519 --> 00:33:30,599
Speaker 1: That makes sense, you know. I think when when we

567
00:33:30,640 --> 00:33:34,400
think about some of the more progressive or liberal or

568
00:33:34,480 --> 00:33:40,079
left wing reform, you know, rally cries. One of the

569
00:33:40,119 --> 00:33:43,920
things that we talk about a lot is the sort

570
00:33:43,960 --> 00:33:46,480
of you know, the new Jim Crow of it all,

571
00:33:46,720 --> 00:33:53,559
the deeply racial components of our criminal justice system, and

572
00:33:53,759 --> 00:33:58,359
the I think the fair amount of evidence that exists

573
00:33:58,400 --> 00:34:02,079
out there that, whether we're talking talking about policing or

574
00:34:02,160 --> 00:34:09,480
later about convictions and the prison system itself, that individuals

575
00:34:09,599 --> 00:34:14,880
who come from poor backgrounds, individuals who are black or brown,

576
00:34:15,159 --> 00:34:19,639
individuals from you know, certain neighborhoods, they do tend to

577
00:34:20,159 --> 00:34:25,920
experience harsher treatment on the streets and also harsher punishments

578
00:34:25,960 --> 00:34:30,599
for similar crimes. And I'm curious, you know, how much

579
00:34:30,679 --> 00:34:37,239
of your work dives into the social justice and kind

580
00:34:37,239 --> 00:34:41,679
of the racial components of our criminal justice system.

581
00:34:42,400 --> 00:34:44,679
Speaker 2: I have studied that a bit myself. There are definitely

582
00:34:44,760 --> 00:34:47,079
other researchers who spend a lot of time on it.

583
00:34:47,800 --> 00:34:53,400
My view, so, you know, one thing about economists and

584
00:34:53,440 --> 00:34:56,039
the economics tool get is I very much view my

585
00:34:56,159 --> 00:35:01,360
role as saying, you know, you tell me what problem

586
00:35:01,440 --> 00:35:03,559
you're trying to solve. You tell me what your goal

587
00:35:03,679 --> 00:35:07,000
is and what your constructs are, and I will help

588
00:35:07,039 --> 00:35:13,199
you optimize and optimize the allocation of your scarce resources

589
00:35:13,199 --> 00:35:16,639
to meet your goal. So, for some policymakers, the only

590
00:35:16,679 --> 00:35:19,960
thing they care about is reducing crime rates, and so, okay,

591
00:35:20,039 --> 00:35:21,920
let's figure out how to reduce crime rates. For all

592
00:35:21,920 --> 00:35:23,920
the other policy makers, the only thing they care about

593
00:35:24,000 --> 00:35:28,400
is racial disparities. Let's reduce racial disparities. You know, others

594
00:35:28,400 --> 00:35:30,800
will say both of those things matter, right, the harms

595
00:35:30,800 --> 00:35:34,159
of the system matter, and public safety matters. I think

596
00:35:34,199 --> 00:35:37,400
frankly that's most. That's the most law enforcement, I would hope.

597
00:35:37,480 --> 00:35:39,639
Speaker 1: So, yeah, we can do both.

598
00:35:39,679 --> 00:35:42,400
Speaker 2: Both matter, and we should be talking about both and

599
00:35:43,079 --> 00:35:44,880
so and then it's just a matter of like, well,

600
00:35:45,039 --> 00:35:48,039
how are we waiting these two things? And is there

601
00:35:48,079 --> 00:35:51,599
a way? You know? I think I think this is

602
00:35:51,639 --> 00:35:55,079
another place where the general perception is there's this like

603
00:35:57,559 --> 00:36:01,400
we're in a zero sum situation where we can either

604
00:36:02,119 --> 00:36:06,159
have more public safety or we can have less harm

605
00:36:06,199 --> 00:36:12,840
from the system and less racial injustice. And we're just arguing,

606
00:36:12,960 --> 00:36:18,079
like the left and right arguing about where on that

607
00:36:18,079 --> 00:36:23,280
that like super efficient frontier, we are landing, but you

608
00:36:23,320 --> 00:36:28,440
can't things. And the reality is there is so much

609
00:36:28,480 --> 00:36:34,360
inefficiency throughout the system that we can have less crime

610
00:36:35,000 --> 00:36:39,840
and less punishment and spend less money and reduce racial disparities,

611
00:36:39,880 --> 00:36:41,559
and we can do all of these things and make

612
00:36:41,599 --> 00:36:47,599
everyone better off because we're just we're just so far

613
00:36:47,960 --> 00:36:53,280
from an optimal efficient system. And so what I wind

614
00:36:53,320 --> 00:36:57,760
up thinking a lot about is more directly to your question,

615
00:36:58,000 --> 00:37:00,480
is is what I find to be the more interesting

616
00:37:00,559 --> 00:37:04,119
questions are less about like how do we simply minimize

617
00:37:04,400 --> 00:37:11,079
disparities or what the disparities are, but how we can

618
00:37:11,159 --> 00:37:17,039
actually like what interventions are actually effective at reaching those goals. Right,

619
00:37:17,079 --> 00:37:20,199
So I think often people just sort of look descriptively

620
00:37:20,239 --> 00:37:26,599
at data and see that you know, this court is

621
00:37:26,639 --> 00:37:31,519
incarcerating black defendants at much higher rates, and that seems

622
00:37:31,559 --> 00:37:34,400
really unfair and kind of like just like normatively saying

623
00:37:34,800 --> 00:37:38,840
this is an unfair outcome. And there's certainly a place

624
00:37:39,079 --> 00:37:44,880
for describing the problems with the system. As you said,

625
00:37:44,880 --> 00:37:46,440
I think a lot of people now are familiar with

626
00:37:46,480 --> 00:37:48,800
the new Jim Crow Like. I think we have a

627
00:37:48,840 --> 00:37:50,760
sense at this point of like what the problems with

628
00:37:50,800 --> 00:37:53,559
the system are. The question now is how to fix

629
00:37:53,599 --> 00:37:57,119
it right? Like what do we do now? Like what's next?

630
00:37:57,920 --> 00:38:00,760
And that's where I think things get really interesting because

631
00:38:00,760 --> 00:38:04,719
a lot of our good ideas aren't going to work right,

632
00:38:04,840 --> 00:38:07,000
a lot of the things that might be obvious what

633
00:38:07,039 --> 00:38:10,079
the problem is. And even if we all agree on

634
00:38:10,119 --> 00:38:14,039
what the problem is, that doesn't mean like our best idea.

635
00:38:14,039 --> 00:38:15,920
If we all, you know, put our heads together and

636
00:38:15,960 --> 00:38:18,519
think about our top our you know, five good ideas,

637
00:38:18,519 --> 00:38:20,320
and we narrow it down to the one best idea

638
00:38:20,320 --> 00:38:23,800
we have, I would put good money on that idea,

639
00:38:23,960 --> 00:38:26,000
not knowing what it is that it's not going to

640
00:38:26,079 --> 00:38:29,159
work because most things we try don't work. These are

641
00:38:29,199 --> 00:38:32,960
really complicated problems. Human behavior surprises us, and so we

642
00:38:33,119 --> 00:38:35,280
have to be out there trying things, but do it

643
00:38:35,320 --> 00:38:37,960
in a do it with a lot of humility, so

644
00:38:38,000 --> 00:38:41,360
that we don't become web to solutions before we know

645
00:38:41,360 --> 00:38:42,119
if they're effective.

646
00:38:42,719 --> 00:38:46,280
Speaker 1: Yeah, and I think I consider myself a really progressive thinker,

647
00:38:46,400 --> 00:38:49,840
and I do hear these calls. Like you mentioned before

648
00:38:49,880 --> 00:38:53,119
at the beginning of the show, I'm definitely not a

649
00:38:53,199 --> 00:38:56,719
tough on crime. We need like stronger and Harsher. You know,

650
00:38:57,039 --> 00:39:00,840
like approaches. I'm definitely probably tend to towards the left.

651
00:39:00,920 --> 00:39:03,679
But when I hear the rallying cry of just like

652
00:39:03,880 --> 00:39:08,159
fully defund the police, there's something in it that doesn't

653
00:39:08,400 --> 00:39:12,079
feel like I imagine a world where we do have

654
00:39:12,480 --> 00:39:16,239
incredible social systems, right, Yes, if in the United States

655
00:39:16,360 --> 00:39:20,119
people didn't have medical debt or educational debt, and everybody

656
00:39:20,159 --> 00:39:23,599
could afford a home, and you know, there were just

657
00:39:23,719 --> 00:39:28,039
like more opportunities for individuals to exist within a middle class,

658
00:39:28,199 --> 00:39:31,000
and there was no redlining, and there was no racial violence,

659
00:39:31,039 --> 00:39:32,519
and there was no you know, obviously, if there was

660
00:39:32,519 --> 00:39:36,000
a utopian world, there would be significantly less crime. I

661
00:39:36,000 --> 00:39:38,320
think we can all agree on that. But the problem

662
00:39:38,360 --> 00:39:42,079
is there would still be some crime. And when when

663
00:39:42,119 --> 00:39:46,559
there's a threat to public safety, how do we make

664
00:39:46,599 --> 00:39:50,039
sure that everybody is safe? And so I think, you know,

665
00:39:50,360 --> 00:39:53,920
defunding the police maybe is an extreme cry. Maybe the

666
00:39:53,920 --> 00:39:57,559
people who say defund really just mean dramatically reduce the

667
00:39:57,559 --> 00:40:01,880
funding or alligate it towards other places. But you know

668
00:40:02,079 --> 00:40:04,920
that really I guess brings us to that question and

669
00:40:05,280 --> 00:40:07,480
sort of like where you're going, because I think that

670
00:40:07,719 --> 00:40:10,679
is on the far left at least that is their

671
00:40:10,719 --> 00:40:13,440
best idea that they've come up with, right, is, let's

672
00:40:13,440 --> 00:40:16,480
look at all the data. The police are getting so

673
00:40:16,639 --> 00:40:20,280
much of our money, and they're committing so many atrocities,

674
00:40:20,840 --> 00:40:24,119
and nothing feels better, Everything feels worse. What would happen

675
00:40:24,199 --> 00:40:26,519
if we had a dramatic change and we burned down

676
00:40:26,559 --> 00:40:29,119
the system? But it sounds like what you're saying is

677
00:40:29,280 --> 00:40:33,599
that's probably not the best approach to fixing all the

678
00:40:33,599 --> 00:40:36,199
broken things that are in front of us, or.

679
00:40:36,199 --> 00:40:38,840
Speaker 2: Even just I mean, I think my my perspective is

680
00:40:38,880 --> 00:40:42,320
basically just a very pragmatic one that even if that

681
00:40:42,440 --> 00:40:44,760
would work, And so, I mean, sounds great, Yeah, we

682
00:40:44,800 --> 00:40:47,199
found a way to eliminate poverty. I do think that we.

683
00:40:47,400 --> 00:40:50,000
Speaker 1: Exactly right, Like I think that would.

684
00:40:49,880 --> 00:40:53,159
Speaker 2: Ave But but the but my question is like how

685
00:40:53,159 --> 00:40:53,840
do we do that?

686
00:40:54,320 --> 00:40:54,480
Speaker 1: Right?

687
00:40:54,559 --> 00:40:57,039
Speaker 2: Like what is your people? What is like word step

688
00:40:57,559 --> 00:41:00,320
what step B? Right? Like how do we get from

689
00:41:00,440 --> 00:41:02,920
you know, how do we get there? And and there

690
00:41:02,960 --> 00:41:07,119
isn't an actual plan for how we get to that utopia?

691
00:41:07,719 --> 00:41:09,840
And so yeah, this idea, I mean, part of part

692
00:41:09,840 --> 00:41:11,519
of the other reason I really wrote the book is

693
00:41:11,559 --> 00:41:14,239
I think the other the other idea floating around in

694
00:41:14,320 --> 00:41:19,239
the cable news ecosystem, is that the only way to

695
00:41:20,519 --> 00:41:24,119
make meaningful progress on these these many problems we see

696
00:41:24,119 --> 00:41:25,760
in the world and in the in the public safety

697
00:41:25,760 --> 00:41:29,800
space specifically, the only possible solution that's going to be

698
00:41:29,840 --> 00:41:32,679
that's going to have any impact is major structural reform.

699
00:41:33,360 --> 00:41:37,920
It is to burn down and and I you know,

700
00:41:37,960 --> 00:41:43,280
we just we have so much evidence of small incremental changes,

701
00:41:43,679 --> 00:41:46,400
things that are implemented in states and cities right now

702
00:41:46,559 --> 00:41:50,159
that could easily be implemented elsewhere that have really meaningful

703
00:41:50,199 --> 00:41:54,440
impacts on behavior and on trajectories and people's you know,

704
00:41:55,000 --> 00:41:58,599
uh future criminal behavior, on their employment outcomes, on all

705
00:41:58,679 --> 00:42:03,239
kinds of good things that don't require big structural reform.

706
00:42:03,679 --> 00:42:07,320
I also think big structural reform frankly, just isn't going

707
00:42:07,320 --> 00:42:10,599
to happen. Like we're not going to burn it all right, right, And.

708
00:42:10,559 --> 00:42:12,440
Speaker 1: It's too ideal.

709
00:42:12,679 --> 00:42:16,360
Speaker 2: Yeah, yeah, it's just it's like, I appreciate the idealism,

710
00:42:16,440 --> 00:42:20,400
but it's I ya, the pragmatist in me is like, Okay,

711
00:42:20,440 --> 00:42:22,639
so are there are people who need our help right now?

712
00:42:22,679 --> 00:42:24,800
So what are we doing for them? And I do

713
00:42:24,880 --> 00:42:28,039
think there's a way in which those calls for structure reform,

714
00:42:28,800 --> 00:42:33,199
really do let the perfect be the enemy of the

715
00:42:33,199 --> 00:42:38,280
good and keep us from making meaningful change that we

716
00:42:38,320 --> 00:42:41,679
could make today where we have evidence that would move

717
00:42:41,719 --> 00:42:46,719
the needle substantially today. And and I think, you know,

718
00:42:47,320 --> 00:42:49,199
saying well, none of that's going to work until we

719
00:42:49,400 --> 00:42:53,159
until we have structural reform is just an excuse sometimes

720
00:42:53,159 --> 00:42:57,079
to just not do anything. And I become very frustrated

721
00:42:57,119 --> 00:42:57,320
with that.

722
00:42:58,360 --> 00:43:00,960
Speaker 1: Yeah, and so so let's talk about some of those meaningful,

723
00:43:02,440 --> 00:43:09,079
like incremental evidence based, you know, maybe seemingly small changes

724
00:43:09,119 --> 00:43:12,519
that we could be making that actually would have a

725
00:43:12,719 --> 00:43:16,800
massive outcome. I mean, you did kind of touch on one,

726
00:43:16,840 --> 00:43:20,840
like if there are more just like the presence of

727
00:43:20,880 --> 00:43:23,400
more police, they don't even have to be doing anything right,

728
00:43:23,559 --> 00:43:27,119
just like knowing that there are more public safety officers

729
00:43:27,199 --> 00:43:31,000
and let's think of them that way at present helps

730
00:43:31,039 --> 00:43:33,639
neighborhoods feel safer and it does deter people. We've all

731
00:43:33,679 --> 00:43:35,400
been driving down the road and you see a cop

732
00:43:35,440 --> 00:43:38,239
and you slow down, right, Like, that's it works. We

733
00:43:38,280 --> 00:43:41,280
know it works. So there's one thing we know, not

734
00:43:41,360 --> 00:43:42,800
keeping people in prison longer.

735
00:43:43,280 --> 00:43:46,920
Speaker 2: But yeah, and so along the same along the same

736
00:43:46,920 --> 00:43:49,960
lines as releasing increasing the probability of getting caught. There's

737
00:43:50,000 --> 00:43:53,199
evidence on the impacts from some of my own research

738
00:43:53,480 --> 00:43:56,320
on the impacts of adding people to DNA databases that

739
00:43:56,400 --> 00:44:00,400
it reduces future recidivism by over forty percent.

740
00:44:01,199 --> 00:44:03,760
Speaker 1: So like if you know that if you commit a

741
00:44:03,840 --> 00:44:06,840
murder or a rape and there now your DNA is

742
00:44:06,880 --> 00:44:09,039
in the system, like that, you will get caught. So

743
00:44:09,119 --> 00:44:10,760
probably we don't do it again.

744
00:44:10,880 --> 00:44:13,199
Speaker 2: And not just for murder rates, for all kinds of

745
00:44:13,280 --> 00:44:16,280
like burglary, like lots of lots of lots of offenses

746
00:44:16,320 --> 00:44:19,320
and so because the technologies evolved so much now, so

747
00:44:19,519 --> 00:44:23,280
just increasing the probability that people get caught really does

748
00:44:23,440 --> 00:44:25,920
change it. It's like that's the wake up call, that's

749
00:44:25,960 --> 00:44:29,079
the nudge, you know, they need to say, Okay, I

750
00:44:29,159 --> 00:44:31,320
better go get a job, I better go, like I

751
00:44:31,360 --> 00:44:33,360
better stop hanging out with those friends. They're always getting

752
00:44:33,360 --> 00:44:36,239
me into trouble, you know, and really it's a time

753
00:44:36,239 --> 00:44:38,679
to clean up their act. And the effect is most

754
00:44:39,119 --> 00:44:43,000
is biggest for younger defendants and younger, younger people who

755
00:44:43,039 --> 00:44:44,880
are charged with crimes. And so I think that's also

756
00:44:44,960 --> 00:44:48,480
really informative, Like the earlier we can get people into

757
00:44:48,519 --> 00:44:51,360
these or have this type of intervention, whether it's a

758
00:44:51,400 --> 00:44:54,039
DNA database or something else. People who are younger have

759
00:44:54,119 --> 00:44:57,400
more opportunity to course correct, right, they can, they can.

760
00:44:57,679 --> 00:45:00,000
It's still possible for them to take a different pace

761
00:45:00,079 --> 00:45:03,280
half yeah, yeah, yeah, it's just harder when you when

762
00:45:03,320 --> 00:45:05,519
you get older. So that's one is just increase the

763
00:45:05,519 --> 00:45:07,599
problemate of getting caught like that. That has a big

764
00:45:07,639 --> 00:45:10,519
detern effect which not only reduces crime, it actually it

765
00:45:11,039 --> 00:45:13,599
improves the lives of the people who who are no

766
00:45:13,639 --> 00:45:17,039
longer committing crime, right like they are, they're doing better things.

767
00:45:17,800 --> 00:45:19,960
Speaker 1: And that's something I think that's really important not to

768
00:45:20,119 --> 00:45:22,119
not to interject and like, you know, hold that thought.

769
00:45:22,199 --> 00:45:23,800
I want to know the next the next thing. But

770
00:45:24,840 --> 00:45:27,960
one of the things that that frustrates me is something

771
00:45:27,960 --> 00:45:30,440
you mentioned earlier and then you also mentioned kind of

772
00:45:30,440 --> 00:45:32,599
in passing here when you were like, you know, they're

773
00:45:32,639 --> 00:45:35,000
you know, do something different, do something better, like get

774
00:45:35,039 --> 00:45:37,920
the job. But our criminal justice system is built in

775
00:45:37,960 --> 00:45:40,800
such a way that people who have committed felony crimes

776
00:45:40,840 --> 00:45:42,480
have a harder time getting jobs.

777
00:45:42,960 --> 00:45:43,159
Speaker 2: Yeah.

778
00:45:43,199 --> 00:45:45,639
Speaker 1: So wouldn't that be like a massive policy change that

779
00:45:45,679 --> 00:45:49,000
would have sweeping downstream effects.

780
00:45:49,559 --> 00:45:51,840
Speaker 2: Yeah, So this is a place that I've I've spent

781
00:45:51,880 --> 00:45:54,559
a lot of time and a lot of my own research,

782
00:45:54,559 --> 00:45:56,920
and I find fascinating. So this is this is this

783
00:45:57,000 --> 00:46:00,280
policy space is a great example of one where solving

784
00:46:00,320 --> 00:46:03,679
this problem is much easier said than done. So it

785
00:46:03,760 --> 00:46:07,440
is a huge like I think completely agree that figuring

786
00:46:07,440 --> 00:46:10,400
out how to help people with criminal records get jobs,

787
00:46:10,440 --> 00:46:13,039
like find employment, get get onto a more stable path,

788
00:46:14,280 --> 00:46:17,119
this is super important. It's really difficult to imagine someone

789
00:46:18,119 --> 00:46:22,280
living a stable, crime free life without stable without study employment, right,

790
00:46:22,320 --> 00:46:25,639
like you need a job. And we also have lots

791
00:46:25,679 --> 00:46:29,679
of evidence that employers routinely discriminate against people with criminal records.

792
00:46:29,760 --> 00:46:32,360
There's something about that record that really worries them. They

793
00:46:32,360 --> 00:46:33,199
don't want to hire them.

794
00:46:34,079 --> 00:46:37,480
Speaker 1: Well, and we're like legally required to ensure that that

795
00:46:37,519 --> 00:46:41,199
record is disclosed, right right.

796
00:46:41,039 --> 00:46:45,360
Speaker 2: Well yeah, I mean obviously that's changing in certain changing

797
00:46:45,559 --> 00:46:50,719
changing policies, but I think so the approach here to

798
00:46:50,719 --> 00:46:54,480
to solve this problem has been to go after that

799
00:46:54,480 --> 00:46:58,239
that issue, that the availability of the information. Right, So,

800
00:46:58,360 --> 00:47:01,199
if we see that employers are discriminating against people with

801
00:47:01,239 --> 00:47:05,199
criminal records, we don't want them to. We wish they didn't, right,

802
00:47:05,239 --> 00:47:06,719
it would be better for all of us who care

803
00:47:06,760 --> 00:47:09,800
about that record. So let's pass a law that says

804
00:47:09,840 --> 00:47:13,920
they can't ask anymore. And so that's banned.

805
00:47:13,960 --> 00:47:14,360
Speaker 1: The box.

806
00:47:15,079 --> 00:47:17,800
Speaker 2: So there's traditionally a box on a job application that

807
00:47:17,840 --> 00:47:20,760
you check if you says, you know, have you ever

808
00:47:20,800 --> 00:47:23,480
been convicted of a crime or do a felony conviction? And

809
00:47:23,519 --> 00:47:27,360
if you check that box anecdotally, employers just throw your

810
00:47:27,400 --> 00:47:29,679
application in the trash. They don't even give you a chance, right,

811
00:47:30,119 --> 00:47:32,400
And that's really unfair, and that's really unfair, especially for

812
00:47:32,440 --> 00:47:35,800
people who have done the work, have you know, turned

813
00:47:35,840 --> 00:47:38,599
their lives around and really are serious and would be

814
00:47:38,639 --> 00:47:41,880
good employees. That also, frankly, is bad for the employer

815
00:47:41,960 --> 00:47:45,480
if they're missing out on good matches. Right. Anyone who

816
00:47:45,480 --> 00:47:49,800
talks to small business owners knows like everyone's desperate for good,

817
00:47:49,960 --> 00:47:52,280
reliable workers or in a show up on time every

818
00:47:52,400 --> 00:47:54,639
day and they want them to start yesterday, right. And

819
00:47:54,679 --> 00:47:58,519
so the fact that there are good potential employees out

820
00:47:58,559 --> 00:48:02,239
there and employers aren't able to see them or identify them,

821
00:48:02,559 --> 00:48:05,440
that's a market failure. In my that's like for economists,

822
00:48:05,480 --> 00:48:07,639
that's like a blinking red light. That's a market failure. Right,

823
00:48:07,639 --> 00:48:10,599
that's bad for everybody, and so there's something going on

824
00:48:10,679 --> 00:48:14,960
here that's problematic. But anyway, so ban the box says,

825
00:48:15,039 --> 00:48:18,000
let's take that box off the application. The idea will

826
00:48:18,000 --> 00:48:19,639
be people with criminal records can get their foot in

827
00:48:19,679 --> 00:48:22,239
the door, build or poor with you know, the employer,

828
00:48:22,679 --> 00:48:24,679
and by the time that they employer does do a

829
00:48:24,679 --> 00:48:27,239
background check at the end of the process, hopefully we

830
00:48:27,320 --> 00:48:29,239
don't care about the record anymore, or they'll have more

831
00:48:29,280 --> 00:48:31,280
context for it, and then the person will be more

832
00:48:31,320 --> 00:48:34,559
likely to get a job. I heard about this policy,

833
00:48:34,639 --> 00:48:37,840
and I'd spend a lot of time studying discrimination and

834
00:48:38,119 --> 00:48:41,840
think about incentives. And I immediately and a lot of

835
00:48:41,840 --> 00:48:45,159
my ECON colleagues immediately were worried this policy would backfire

836
00:48:45,880 --> 00:48:49,760
because if employers are worried about a criminal record for

837
00:48:49,760 --> 00:48:53,280
some reason, and we're not doing anything to change their

838
00:48:53,360 --> 00:48:58,800
underlying incentives about that record, but now we tell them

839
00:48:58,840 --> 00:49:02,360
they can't ask anymore, I would expect them to use

840
00:49:02,360 --> 00:49:07,840
whatever remaining information they have to try to guess. And

841
00:49:07,880 --> 00:49:09,440
then what we wind up and then so there's a

842
00:49:09,440 --> 00:49:12,360
bunch of research now showing that winds up happening is

843
00:49:12,400 --> 00:49:16,639
that it just increases racial discrimination. It essentially broadens the

844
00:49:16,639 --> 00:49:22,440
discrimination from people with criminal records to like all young

845
00:49:22,480 --> 00:49:23,159
black men.

846
00:49:24,440 --> 00:49:26,239
Speaker 1: Like, let's just assume that you're a criminals.

847
00:49:26,639 --> 00:49:31,440
Speaker 2: Let's assume and so so it's backfired and so you know,

848
00:49:31,480 --> 00:49:35,079
and there's there's evidence coming out that that clean slate

849
00:49:35,159 --> 00:49:37,440
which seals records, because then people are like, well, let's

850
00:49:37,440 --> 00:49:39,360
the problem is the background check Let's let's take the

851
00:49:39,360 --> 00:49:44,320
background check out, which wasn't the core issue. But but

852
00:49:44,440 --> 00:49:48,639
clean slate is we don't have evidence on unintended consequences yet,

853
00:49:49,760 --> 00:49:51,280
or maybe I might have seen one paper, but there's

854
00:49:51,320 --> 00:49:54,239
definitely solid evidence that it's not increasing employment. It's just

855
00:49:54,280 --> 00:49:56,320
not working. And be on the box. It didn't work

856
00:49:56,360 --> 00:49:59,800
increase employment for those with records either. And so so

857
00:50:00,159 --> 00:50:01,840
I take at this point is like, look, this is

858
00:50:01,840 --> 00:50:04,840
a really important problem to solve. The first things we

859
00:50:04,880 --> 00:50:08,880
tried didn't work, but we need to keep trying. So

860
00:50:09,000 --> 00:50:13,000
like what's next. And my view where I'm really optimistic

861
00:50:13,079 --> 00:50:18,119
is in thinking about, you know, take the employers and say, okay,

862
00:50:18,159 --> 00:50:20,840
there's something about the criminal record they're worried about. Let's

863
00:50:20,880 --> 00:50:24,400
figure out what they're worried about and directly address those concerns.

864
00:50:24,920 --> 00:50:28,719
And so one example is you know, if what they're

865
00:50:28,760 --> 00:50:31,199
worried about is that the person isn't going to be

866
00:50:31,239 --> 00:50:33,239
reliable and they're not going to show up on time

867
00:50:33,280 --> 00:50:35,599
every day, Well, how can we provide more information that

868
00:50:35,719 --> 00:50:38,719
actually they would right to help make these matches happen.

869
00:50:39,000 --> 00:50:41,840
So maybe it's a glowing recommendation from some re entry

870
00:50:41,840 --> 00:50:45,760
program they went through something like that. Another example that

871
00:50:45,800 --> 00:50:50,960
I'm super excited about is insurance. So employers will tell

872
00:50:51,000 --> 00:50:53,599
you that what they worry about when they hire someon

873
00:50:53,599 --> 00:50:56,559
when they're considering hiring someone with a record, is the

874
00:50:56,639 --> 00:50:59,800
risk associated with that. They're worried that someone will continue

875
00:50:59,800 --> 00:51:03,239
to come crime on the job, that they might you know,

876
00:51:03,320 --> 00:51:06,639
assault another employee or a customer, they might steal from them,

877
00:51:07,960 --> 00:51:16,599
and so and employers typically all employers typically buy commercial

878
00:51:16,639 --> 00:51:21,079
insurance to cover their employees if they're theft on the

879
00:51:21,159 --> 00:51:24,639
job or something like that. But commercial insurance standard policies

880
00:51:24,679 --> 00:51:27,719
exclude coverage for anyone with a criminal record.

881
00:51:28,639 --> 00:51:30,400
Speaker 1: And so, oh, there you go.

882
00:51:31,639 --> 00:51:34,119
Speaker 2: Right, And so that kind of leads you down this

883
00:51:34,239 --> 00:51:37,280
rabbit hole of like, Okay, well why do they do that,

884
00:51:37,599 --> 00:51:40,159
and and why if the market so that we now

885
00:51:40,159 --> 00:51:43,639
have some evidence that actually providing insurance that does cover

886
00:51:43,719 --> 00:51:46,119
people with records really does move the needle. It does

887
00:51:46,199 --> 00:51:48,960
get people, does get employers to hire people with records,

888
00:51:49,360 --> 00:51:51,480
and so then the question becomes why doesn't the market

889
00:51:51,519 --> 00:51:55,000
provide this, And it might just be that they genuinely

890
00:51:55,000 --> 00:51:57,119
don't know how to price the risk. They've never covered

891
00:51:57,119 --> 00:52:00,159
this population before. And so that's a place where you know,

892
00:52:00,199 --> 00:52:02,480
I'm now out of the philanthropy. We could think about

893
00:52:02,719 --> 00:52:04,880
what could we come up with some pilot product where

894
00:52:04,880 --> 00:52:06,599
we can, like, we can cover the losses for a

895
00:52:06,599 --> 00:52:08,920
little while, just to get the data to kind of

896
00:52:09,000 --> 00:52:12,320
help make the market here. But that's the sort of

897
00:52:12,320 --> 00:52:16,320
intervention where I think I'm much more optimistic that something

898
00:52:16,400 --> 00:52:19,800
like that will be effective because it just it directly

899
00:52:19,840 --> 00:52:23,719
addresses a concern and changes the incentives that employers are

900
00:52:23,760 --> 00:52:27,880
responding to. And ultimately, employers are just they're just trying

901
00:52:27,880 --> 00:52:30,039
to make a profit, right, These are the most rational

902
00:52:30,079 --> 00:52:34,800
actors we have, and so tweaking the costs and benefits

903
00:52:35,199 --> 00:52:38,639
that they're responding to should have a big benefit, should have.

904
00:52:38,639 --> 00:52:42,079
Speaker 1: A big Yeah, Yeah, I agree, but it would I

905
00:52:42,119 --> 00:52:44,559
would just think that it would need to be subsidized,

906
00:52:44,559 --> 00:52:47,480
as you mentioned, like even if that type of insurance

907
00:52:47,559 --> 00:52:50,639
is available, I don't see employers choosing to buy a

908
00:52:50,679 --> 00:52:55,159
more expensive premium because it allows them to hire people

909
00:52:55,159 --> 00:52:58,400
that they already wanted to discriminate against in the first place, right,

910
00:52:58,559 --> 00:53:01,480
Like it would have to be subsidiz hopefully by the

911
00:53:01,519 --> 00:53:05,719
funding that we're already putting towards our criminal justice system.

912
00:53:06,119 --> 00:53:09,840
Like that seems like a fair way to monetize a

913
00:53:09,880 --> 00:53:14,079
program like that, because it's actually a program that is

914
00:53:14,480 --> 00:53:18,119
attempting to reduce recidivism.

915
00:53:18,800 --> 00:53:22,000
Speaker 2: Yep, yep, yeah, absolutely so. So you know, there's the

916
00:53:22,480 --> 00:53:26,000
make more matches component here, and you know there might

917
00:53:26,000 --> 00:53:28,719
be employers might be willing to pay something to have

918
00:53:28,760 --> 00:53:31,880
access to employees to kind of shift the risk or

919
00:53:31,880 --> 00:53:35,599
smooth the risk if they're just hiring one person. And

920
00:53:36,519 --> 00:53:39,800
but I completely agree part of the other big market

921
00:53:39,800 --> 00:53:43,519
failure here is that we have what economists would call

922
00:53:43,519 --> 00:53:47,480
externalities to an individual decision. So the business owner is

923
00:53:47,480 --> 00:53:49,960
making a decision about whom to hire based on who's

924
00:53:50,000 --> 00:53:52,679
most profitable for them, right, who's going to be most reliable,

925
00:53:52,719 --> 00:53:55,159
who's going to you know, help their business succeed as

926
00:53:55,159 --> 00:53:59,960
best as possible. But we all benefit if they hire

927
00:54:00,079 --> 00:54:03,480
someone with the criminal record because that person is less

928
00:54:03,519 --> 00:54:05,639
likely to commit crime going forward. Right, there's a benefit

929
00:54:05,639 --> 00:54:09,239
to the whole community, and it's completely reasonable that the

930
00:54:09,280 --> 00:54:12,280
employer would not take into consideration that benefit to the

931
00:54:12,280 --> 00:54:16,199
community when they're considering whom to hire. But so the

932
00:54:16,239 --> 00:54:18,960
way to address that that is still that's a market failure.

933
00:54:18,960 --> 00:54:21,960
They are social benefits that aren't being internalized. So the

934
00:54:21,960 --> 00:54:23,719
way we can do that, the way we do that

935
00:54:23,760 --> 00:54:28,000
in general with taxation and subsidies, is we can subsidize

936
00:54:28,000 --> 00:54:31,119
the product. We can have our tax dollars chip in

937
00:54:31,480 --> 00:54:34,480
to basically say, we will all benefit, so it's worth

938
00:54:34,519 --> 00:54:37,679
it to us to chip in and help subsidize your

939
00:54:37,840 --> 00:54:41,079
hiring of this person. So I completely agree that I

940
00:54:41,079 --> 00:54:44,320
think going forward there'll be some subsidy from the government,

941
00:54:44,519 --> 00:54:48,719
but just having the product exist in the first place

942
00:54:48,760 --> 00:54:50,840
well will also be important.

943
00:54:51,599 --> 00:54:54,039
Speaker 1: Yeah, And I mean, I hate to sound cynical about this,

944
00:54:54,159 --> 00:54:57,519
but like, wow, what a liberal policy Like. That's the

945
00:54:57,559 --> 00:54:59,920
thing that I think I struggle with here, is that

946
00:55:00,280 --> 00:55:03,400
like the opposite side of externalizing cost or you were

947
00:55:03,400 --> 00:55:06,880
talking about externalizing benefit, but is you know, we know

948
00:55:07,079 --> 00:55:10,360
we live in a capitalist marketplace now where so many

949
00:55:10,400 --> 00:55:14,679
externalized costs are not carried by the very actors that

950
00:55:14,760 --> 00:55:19,320
are causing them, right, like a oil a gas and

951
00:55:19,320 --> 00:55:23,639
an oil come a fossil fuel company, or a manufacturer

952
00:55:23,719 --> 00:55:26,800
like a textile or dye manufacturer, anybody who's making you know,

953
00:55:26,840 --> 00:55:31,119
synthetic chemicals. When we see these externalized costs on the environment,

954
00:55:31,199 --> 00:55:34,360
on health and human safety, that cost gets passed on

955
00:55:34,400 --> 00:55:38,159
to the people. It doesn't get until there's like a

956
00:55:38,239 --> 00:55:41,519
lawsuit usually, but it's not built into the system for

957
00:55:41,639 --> 00:55:46,880
the actual corporation or the organization to I think about plastic.

958
00:55:46,880 --> 00:55:49,920
Plastic is a great example, like Coca Cola and Aquafina

959
00:55:50,280 --> 00:55:52,800
are not paying for the cost of plastic in the environment.

960
00:55:53,119 --> 00:55:55,920
They're making us feel like it's our fault, right, Like

961
00:55:55,960 --> 00:55:59,440
they're just fully shirking the externalized costs of that. And

962
00:55:59,480 --> 00:56:01,880
I just work that our system as it stands right

963
00:56:01,920 --> 00:56:10,119
now is such an anti regulatory system that we aren't

964
00:56:10,119 --> 00:56:12,719
interested in making policy changes like that. I mean, there

965
00:56:12,719 --> 00:56:14,760
are people that are interested, you are interested in it.

966
00:56:14,800 --> 00:56:17,559
There's a whole you know, political party that's you know,

967
00:56:17,639 --> 00:56:20,199
always pushing for this. But I do worry about how

968
00:56:20,320 --> 00:56:25,719
popular these types of evidence based changes can be when

969
00:56:25,920 --> 00:56:28,280
we have politicians in front of us. When we have

970
00:56:28,599 --> 00:56:30,599
what we think of as rational actors in front of

971
00:56:30,679 --> 00:56:33,639
us and we go look at this evidence, Ultimately your

972
00:56:33,719 --> 00:56:36,840
constituents will be happier because there will be less crime,

973
00:56:37,079 --> 00:56:38,920
and they will have more money, and they will you know,

974
00:56:38,960 --> 00:56:40,760
all these great outcomes. And they go, yeah, don't like

975
00:56:40,840 --> 00:56:46,119
it doesn't make me sound tough. You know, there's still

976
00:56:46,119 --> 00:56:49,360
a human psychology component here, right, that's like hard to

977
00:56:49,400 --> 00:56:50,039
get passed.

978
00:56:50,760 --> 00:56:52,920
Speaker 2: Yeah, I think you know, when I first took this job,

979
00:56:52,960 --> 00:56:56,880
I think that was my That was my fear about

980
00:56:57,119 --> 00:56:59,599
most conversations with lawmakers. That was just going to be

981
00:56:59,679 --> 00:57:03,639
like me saying the evidence says X, you should do it,

982
00:57:03,679 --> 00:57:08,119
and they're going to say, I don't care. And that's

983
00:57:08,239 --> 00:57:11,719
just I mean, just my I've been very pleasantly surprised.

984
00:57:11,800 --> 00:57:13,840
So over the last few weeks, I was out in

985
00:57:13,880 --> 00:57:16,079
Salt Lake City in Utah. I was just came back

986
00:57:16,079 --> 00:57:19,480
from Phoenix in Arizona talking with lawmakers in both both

987
00:57:19,639 --> 00:57:25,480
very red states, and this insurance idea people loved in

988
00:57:25,519 --> 00:57:27,400
both places, right, I mean, I think part of it

989
00:57:27,480 --> 00:57:30,920
is just sort of like recognizing employers as part of

990
00:57:30,920 --> 00:57:34,239
the solution, right, and seeing this is just like, oh, yeah,

991
00:57:34,360 --> 00:57:37,119
employers are trying to like hire people, how do we

992
00:57:37,159 --> 00:57:39,719
make it easier for them to hire people that you know,

993
00:57:39,800 --> 00:57:42,280
we all see as it's you know, it's better for

994
00:57:42,400 --> 00:57:44,599
us if they can get a job. So this is

995
00:57:46,519 --> 00:57:50,159
I I think ultimately it's just a you know, it

996
00:57:50,199 --> 00:57:52,119
could have gone either way. I think I would have.

997
00:57:52,239 --> 00:57:55,159
I was sort of expecting the kind of pushback you're

998
00:57:55,280 --> 00:57:58,920
you're talking about. But my experience actually talking to lawmakers

999
00:57:59,000 --> 00:58:01,559
at the state level, and that's where most criminal justice

1000
00:58:01,559 --> 00:58:05,719
policy happens, is they're just really pragmatic, like they have

1001
00:58:05,800 --> 00:58:09,519
to balance their budget. Prison's really expensive, you know, prison's

1002
00:58:09,559 --> 00:58:12,199
really expensive. And if that is the only way we have,

1003
00:58:12,519 --> 00:58:16,480
if that's the only tool we have to reduce crime,

1004
00:58:16,960 --> 00:58:19,880
then their budgets are busted every year. And so what

1005
00:58:20,039 --> 00:58:22,159
else can we be doing. What can we do to

1006
00:58:22,199 --> 00:58:26,320
increase efficiency, What can we do to help reduce crime

1007
00:58:26,360 --> 00:58:28,559
and get more public safety in a way that isn't

1008
00:58:29,039 --> 00:58:35,400
exorbitantly expensive, and having you know, even just having a

1009
00:58:35,480 --> 00:58:38,360
kind of approach like the insurance one that is sort

1010
00:58:38,400 --> 00:58:43,519
of based on incentives and markets does appeal to a

1011
00:58:43,519 --> 00:58:46,840
lot of conservatives and people from the business community, and

1012
00:58:46,920 --> 00:58:50,360
so I think in practice, like I just I have been.

1013
00:58:52,119 --> 00:58:55,559
I'm just so I all of my interactions with the

1014
00:58:55,679 --> 00:58:57,960
with the political system at this point leave me feeling

1015
00:58:58,079 --> 00:59:01,599
so optimistic about how much there is and how much

1016
00:59:01,920 --> 00:59:05,760
like the only constraint really is just like the bandwidth

1017
00:59:05,800 --> 00:59:08,639
of me and my colleagues and researchers. We know there

1018
00:59:08,639 --> 00:59:11,480
are so many lawmakers out there. They're just they work

1019
00:59:11,519 --> 00:59:14,199
part time, they have a date, a full time job

1020
00:59:14,239 --> 00:59:16,400
on the side, right because that's what that's what state

1021
00:59:16,480 --> 00:59:19,920
lawmaker roles are, and they don't have a staff, and

1022
00:59:20,000 --> 00:59:22,880
so they're just trying to figure out, like you know,

1023
00:59:23,000 --> 00:59:25,480
there are five hundred bills that are introduced this session,

1024
00:59:25,559 --> 00:59:27,039
how are they going to read them all and have

1025
00:59:27,119 --> 00:59:29,480
an informed opinion about them and which ones should they

1026
00:59:29,519 --> 00:59:32,480
be introducing next session? And they just need some help.

1027
00:59:32,760 --> 00:59:36,559
And so I find that that's much more of the constraint.

1028
00:59:36,599 --> 00:59:40,159
They just they're just looking for new ideas that could

1029
00:59:40,159 --> 00:59:45,400
actually work, and the cable news politics of it doesn't

1030
00:59:45,519 --> 00:59:47,519
enter into the equation as much as I had feared.

1031
00:59:48,320 --> 00:59:52,079
Speaker 1: That's so good to hear, because I would agree that ultimately,

1032
00:59:52,119 --> 00:59:53,440
I mean, I think it's part of why I'm so

1033
00:59:53,760 --> 01:00:01,000
drawn to the concept and the community around science base thinking,

1034
01:00:01,039 --> 01:00:05,320
whether it's science based medicine, science based social policy, you know,

1035
01:00:05,760 --> 01:00:11,039
fill in the blank. Ultimately, the more evidence we can

1036
01:00:11,039 --> 01:00:14,440
collect and the more we can show, you know, the

1037
01:00:14,559 --> 01:00:17,840
numbers can lie, the numbers can be made to lie.

1038
01:00:17,960 --> 01:00:23,079
But ultimately, over that's the scientific community is self correcting. Right.

1039
01:00:23,119 --> 01:00:25,679
The more we amass and the more people kind of

1040
01:00:25,719 --> 01:00:29,519
contribute to the story, the more a narrative comes out

1041
01:00:29,599 --> 01:00:32,599
that has higher and higher fidelity, right, that is more

1042
01:00:32,639 --> 01:00:36,679
and more able to approximate reality. And ultimately, yes, I

1043
01:00:36,679 --> 01:00:40,800
think there will always be bad actors who say, ideologically,

1044
01:00:41,039 --> 01:00:43,960
I have an agenda and I will only choose evidence

1045
01:00:44,039 --> 01:00:46,960
that supports my agenda. I will only operate from a

1046
01:00:47,000 --> 01:00:51,719
confirmation bias perspective. And ultimately what matters most to me

1047
01:00:51,800 --> 01:00:56,440
is that my policies, you know, my ideologies are backed.

1048
01:00:56,719 --> 01:00:59,719
But I think that a lot of people's ideologies and

1049
01:00:59,760 --> 01:01:05,840
poes can actually shift and change and adjust when they

1050
01:01:05,880 --> 01:01:07,960
feel like they have a good grasp of the nature

1051
01:01:07,960 --> 01:01:11,039
of reality. And a lot of the times the problem

1052
01:01:11,440 --> 01:01:13,840
is that we don't give people alternative ways of thinking.

1053
01:01:14,159 --> 01:01:16,079
We say, well, you're wrong, and they go okay, But

1054
01:01:16,159 --> 01:01:19,320
then what's right, and we go I don't know. And

1055
01:01:19,400 --> 01:01:22,760
so one of the best approaches is not to say

1056
01:01:22,800 --> 01:01:25,239
you're wrong, mean or meaner. It's to say, have you

1057
01:01:25,280 --> 01:01:27,719
thought about it this way? Have you looked at this

1058
01:01:27,880 --> 01:01:31,039
alternative way of thinking about it? Because if there's nothing

1059
01:01:31,079 --> 01:01:35,000
to plug in, we're always going to just see pushback. Right.

1060
01:01:35,159 --> 01:01:38,000
But but totally, you know, it's it's hard when you've

1061
01:01:38,000 --> 01:01:42,119
got to bipartisan like think tank, or when you've got

1062
01:01:42,559 --> 01:01:46,960
you know, individuals on multiple sides of an aisle saying well,

1063
01:01:47,000 --> 01:01:48,599
the evidence shows us this, so we all kind of

1064
01:01:48,639 --> 01:01:52,199
agree here, it's hard to say no, I don't want

1065
01:01:52,199 --> 01:01:54,320
to yeah, And.

1066
01:01:54,280 --> 01:01:56,159
Speaker 2: I also, I mean, in the criminal justice space, you know,

1067
01:01:56,320 --> 01:02:00,599
this desire for retribution is such a driving in so

1068
01:02:00,599 --> 01:02:04,199
many conversations why incurs ary people? And so I mean,

1069
01:02:04,199 --> 01:02:07,159
this is an example of a place where you know, data,

1070
01:02:07,480 --> 01:02:10,840
empirical evidence, the kinds of studies I can do, will

1071
01:02:10,840 --> 01:02:15,199
have no bearing on how much retribution or mercy someone deserves,

1072
01:02:15,440 --> 01:02:19,159
right and how you know what they deserve based on

1073
01:02:19,760 --> 01:02:25,960
moral or ethical grounds. But regardless of what your views

1074
01:02:25,960 --> 01:02:29,760
are about retribution, presumably we would all like less crime

1075
01:02:29,800 --> 01:02:33,400
for less money. And so you know, we can like

1076
01:02:33,760 --> 01:02:37,840
take your your desire for retribution is as given and

1077
01:02:37,880 --> 01:02:40,000
say like, okay, we can work with that. Like you

1078
01:02:40,280 --> 01:02:42,400
you wait that a lot and you get a lot

1079
01:02:42,400 --> 01:02:45,280
of value out of that. That's fine. Let's just be

1080
01:02:45,360 --> 01:02:49,760
clear about how much you're paying for that, right the

1081
01:02:49,840 --> 01:02:52,800
things that you could get instead, Like they're trade offs here,

1082
01:02:52,840 --> 01:02:56,599
their opportunity costs. If we're spending a zillion dollars to

1083
01:02:56,679 --> 01:02:59,159
keep this person in prison for an extra forty years,

1084
01:02:59,719 --> 01:03:02,239
that's a lot of money that we don't get to

1085
01:03:02,280 --> 01:03:06,880
invest in solving more crimes and DNA analysis or schools

1086
01:03:07,199 --> 01:03:10,159
or healthcare or whatever else we could use with those

1087
01:03:10,199 --> 01:03:13,440
tax dollars. And so it's just sort of you know,

1088
01:03:13,599 --> 01:03:19,239
getting I think my approach and perspective, the base on

1089
01:03:19,239 --> 01:03:21,960
all my training is to really you know, take take

1090
01:03:22,000 --> 01:03:25,119
people's preferences as given, like that's it's not my job

1091
01:03:25,119 --> 01:03:28,760
to tell people they should be more merciful or not,

1092
01:03:30,159 --> 01:03:35,440
but but to just get just to really help clarify

1093
01:03:35,800 --> 01:03:37,679
to the extent that what they do care about is

1094
01:03:37,679 --> 01:03:40,480
public safety. And I think everyone cares at least somewhat

1095
01:03:40,519 --> 01:03:43,679
about what the actual impact of these policies is on

1096
01:03:43,760 --> 01:03:47,039
the ground. We you know, that's those are empirical questions.

1097
01:03:47,079 --> 01:03:50,360
And some of our programs and policies work better than others.

1098
01:03:50,920 --> 01:03:53,920
And how do we maximize the ROI there so that

1099
01:03:54,440 --> 01:03:56,800
so that we can have more more good things, and

1100
01:03:56,920 --> 01:03:59,760
you know, people are happier and more productive and feel

1101
01:03:59,800 --> 01:04:04,000
safe for in their communities. And yeah, I feel like

1102
01:04:04,679 --> 01:04:07,719
there there's a lot of room for common ground here. Again,

1103
01:04:07,800 --> 01:04:09,800
We're just we're in such an inefficient place.

1104
01:04:11,400 --> 01:04:16,079
Speaker 1: There's so much room for improvement.

1105
01:04:14,320 --> 01:04:17,440
Speaker 2: Like we can make everyone better off and everyone happier.

1106
01:04:17,800 --> 01:04:20,920
This doesn't this is not a zero a zero sum fight.

1107
01:04:21,480 --> 01:04:23,199
Speaker 1: I feel like that's such a good summary and I

1108
01:04:23,199 --> 01:04:25,239
can't help. But like here in just the last couple

1109
01:04:25,280 --> 01:04:28,320
of minutes, kind of the thing that comes to mind

1110
01:04:28,440 --> 01:04:30,719
when when you describe this idea of like I'm not

1111
01:04:30,719 --> 01:04:33,800
going to change somebody's moral preferences or they're sort of

1112
01:04:33,960 --> 01:04:36,920
ideologic reactions. I think about the death penalty right, Like,

1113
01:04:37,000 --> 01:04:40,719
I am wildly against the death penalty from an ideologic perspective,

1114
01:04:40,760 --> 01:04:42,800
but I was raised in Texas and I didn't always

1115
01:04:42,800 --> 01:04:45,840
think that way. And I know people who I think

1116
01:04:45,880 --> 01:04:47,719
I agree with on a lot of topics. Who are

1117
01:04:47,840 --> 01:04:49,800
you know, on the fence or maybe they think other

1118
01:04:49,960 --> 01:04:52,800
you know differently, But ultimately, if a person is in

1119
01:04:52,840 --> 01:04:54,760
prison for the rest of their life without any chance

1120
01:04:54,760 --> 01:04:58,320
of parol, or they are you know, killed by the state,

1121
01:04:58,719 --> 01:05:02,920
they are removed from the public sphere, right they are,

1122
01:05:03,039 --> 01:05:06,719
they are no longer a threat to the citizenry. And

1123
01:05:06,800 --> 01:05:09,559
so at that point, I think you're right. Like whether

1124
01:05:09,599 --> 01:05:13,679
you feel like it's a retribution question or not, well,

1125
01:05:13,760 --> 01:05:17,000
is it more expensive to keep, you know, to kill. Weirdly,

1126
01:05:17,079 --> 01:05:20,519
it's very expensive to put somebody on death row. Like

1127
01:05:20,559 --> 01:05:23,559
it's surprisingly you'd think it would be cheaper, but it's not.

1128
01:05:23,760 --> 01:05:25,840
And so that's where those kinds of arguments I think

1129
01:05:25,880 --> 01:05:30,280
you're right can be so gripping for a lot of

1130
01:05:30,360 --> 01:05:33,360
people who maybe you do have a moral stance about it,

1131
01:05:33,400 --> 01:05:38,159
but ultimately, you know, let's look at the balance sheet

1132
01:05:38,199 --> 01:05:40,519
as well, and let's look at how your community could

1133
01:05:40,559 --> 01:05:42,800
be improved with that savings.

1134
01:05:43,320 --> 01:05:46,599
Speaker 2: Right, I mean, most of our preferences in our desires

1135
01:05:46,599 --> 01:05:50,880
for justice and you know, whatever other moral and ethical

1136
01:05:50,960 --> 01:05:54,079
views we might have or not absolute, right, they're they're

1137
01:05:54,119 --> 01:05:56,400
you know, in conversation with other things we believe in

1138
01:05:56,440 --> 01:06:00,760
other trade offs, and so just helping people work through

1139
01:06:01,719 --> 01:06:05,360
the trade offs here I think can be really constructive.

1140
01:06:06,239 --> 01:06:13,119
Speaker 1: I love this. I love any conversation argument book that

1141
01:06:13,920 --> 01:06:17,519
evokes not just a sense of curiosity about a really

1142
01:06:17,599 --> 01:06:25,320
complex topic, but the discomfort that comes from being challenged

1143
01:06:25,440 --> 01:06:29,159
in your in your you know, kind of fundamental views

1144
01:06:29,239 --> 01:06:32,039
that the the discomfort that comes with sitting in the

1145
01:06:32,960 --> 01:06:35,119
in the unknown a little bit and saying, oh, the

1146
01:06:35,159 --> 01:06:38,400
evidence might not fully comport with what I what I

1147
01:06:38,559 --> 01:06:44,239
thought was reality. Like I'm I always so appreciate anytime

1148
01:06:45,039 --> 01:06:48,639
I feel challenged and I can grow and I can evolve,

1149
01:06:49,119 --> 01:06:51,960
especially in a field where I don't have any expertise,

1150
01:06:52,000 --> 01:06:56,239
and and you know, my perspectives are uh A an

1151
01:06:56,280 --> 01:06:59,719
amalgamation basically of just what I'm exposed to in life.

1152
01:06:59,719 --> 01:07:02,519
And so I feel like a thank you for the conversation,

1153
01:07:02,639 --> 01:07:04,960
but be for the amount of work that you've done

1154
01:07:05,320 --> 01:07:10,159
throughout your career and culminating in this wonderful book that

1155
01:07:10,880 --> 01:07:14,719
brings people to that place where they can go hmm.

1156
01:07:15,280 --> 01:07:18,599
You know, I think there's other layers of depth that

1157
01:07:18,639 --> 01:07:21,239
I can think about these these topics, or maybe some

1158
01:07:21,280 --> 01:07:23,000
of the people who pick this book up have never

1159
01:07:23,000 --> 01:07:25,679
thought about any of this at all, and that's that's

1160
01:07:25,719 --> 01:07:28,400
always a gift, right, is to just have a spark

1161
01:07:28,760 --> 01:07:31,280
and to start thinking deeply about things that we kind

1162
01:07:31,280 --> 01:07:33,960
of took for granted. So I just I so appreciate

1163
01:07:33,960 --> 01:07:35,599
the time that you spent with us today. I so

1164
01:07:35,679 --> 01:07:40,039
appreciate the book everybody. It's called The Science of Second Chances,

1165
01:07:40,239 --> 01:07:45,360
A Revolution in Criminal Justice by doctor Jennifer Doliac. Did

1166
01:07:45,400 --> 01:07:46,800
I pronounce that right, Jen?

1167
01:07:47,280 --> 01:07:48,159
Speaker 2: You did? Yes? O?

1168
01:07:48,239 --> 01:07:52,760
Speaker 1: Good? Okay. Thank you so so much for being here.

1169
01:07:52,800 --> 01:07:53,559
It was wonderful.

1170
01:07:54,320 --> 01:07:56,159
Speaker 2: Thank you so much for having me. I really enjoy

1171
01:07:56,239 --> 01:07:58,320
this conversation and everyone listening.

1172
01:07:58,400 --> 01:08:00,800
Speaker 1: Thank you for coming back week after week. I'm really

1173
01:08:00,840 --> 01:08:02,960
looking forward to the next time we all get together

1174
01:08:03,360 --> 01:08:06,920
to talk. Nerdy m