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- [Narrator] Welcome to [br]Nobel Conversations.

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In this episode,

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Josh Angrist and Guido Imbens[br]sit down with Isaiah Andrews

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to discuss how the field [br]of econometrics is evolving.

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- [Isaiah] So Guido and Josh, [br]you're both pioneers

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in developing tools for[br]empirical research in economics.

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And so I'd like to explore

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sort of where you feel like [br]the field is heading,

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sort of economics, econometrics,[br]the whole thing.

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To start, I'd be interested to hear

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about whether you feel like[br]sort of the way in which

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the local average treatment [br]effects framework sort of took hold

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has any lessons for how [br]new empirical methods in economics

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develop and spread[br]or how they should.

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- [Josh] That's a good question.

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You go first.

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(laughter)

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Yeah, so I think [br]the important thing

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is to come up [br]with good convincing cases

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where the questions are clear

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and where kind of the methods[br]apply in general.

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So one thing I--

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Kind of looking back[br]at the subsequent literature,

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so I really like the regression[br]discontinuity literature

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[where there were] clearly a bunch[br]of really convincing examples

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and that allowed people to kind of

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think more clearly, look harder at[br]the methodological questions.

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Kind of do clear applications

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that then allow you [br]to kind of think about,

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"Wow, do this type of assumption[br]seem reasonable here?

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What kind of things do we not like[br]in the early papers?

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How can we improve things?"

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So having clear applications[br]motivating,

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these literatures, [br]I think it's very helpful.

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I'm glad you mentioned[br]the regression discontinuity, Guido.

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I think there's a lot of [br]complementarity between IV and RD,

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Instrumental Variables and[br]Regression Discontinuity.

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And a lot of [br]the econometric applications

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of regression discontinuity

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are what used to be called [br]"fuzzy" RD,

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where, you know, it's not discrete [br]or deterministic at the cutoff,

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but just the change[br]in rates or intensity.

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And and the late framework helps us[br]understand those applications

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and gives us a clear interpretation

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for say, something like, [br]in my paper with Victor Lavy,

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where we use Maimonides'[br]rule, the class size cutoffs.

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What are you getting there?

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Of course, you can[br]answer that question

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with a linear [br]constant effects model,

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but it turns out [br]we're not limited to that,

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and an RD is still very powerful [br]and illuminating,

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even when, you know,

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the correlation between the cutoff[br]and the variable of interest,

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in this case class size, [br]is partial,

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maybe even not that strong.

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So there was definitely a kind of,[br]a parallel development.

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It's also interesting,

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you know, nobody talked about[br]regression discontinuity designs

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when we were in graduate school,

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it was something that other social[br]scientists were interested in,

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and that kind of grew up [br]alongside the late framework

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and we've both done work on [br]both applications and methods there

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and it's been very exciting [br]to see that kind of develop

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and become so important.

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It's part of a general evolution,[br]I think, towards, you know,

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credible identification strategies[br]causal effects...

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less, you know, making econometrics

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more about causal questions[br]than about models.

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In terms of the future,

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I think one thing that LATE [br]has helped facilitate

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is a move towards more creative,[br]randomized trials, where,

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you know, there's[br]something of interest,

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it's not possible or straightforward[br]to simply turn it off or on,

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but you can encourage it [br]or discourage it.

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So you subsidize schooling [br]with financial aid, for example.

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So now we have a whole [br]framework for interpreting that,

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and it kind of opens[br]the doors to randomized trials

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of things that that maybe would,[br]you know,

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not have seem possible before.

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We've used that a lot in the work[br]we do on schools in our--

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in the Blueprint Lab at MIT,

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we're exploiting random assignment[br]and in very creative ways, I think.

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- [Isaiah] Related to that, do you [br]see sort of particular factors

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that make for useful research[br]in econometrics?

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You've alluded to it [br]having a clear connection

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to problems [br]that are actually coming up

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and empirical practice [br]is often a good idea.

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- [Josh] Isn't it always [br]a good idea?

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I often find myself sitting in an[br]econometrics theory seminar,

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say the Harvard MIT seminar,

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and I'm thinking, "What problem[br]is this guy solving?

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Who has this problem?"

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And, you know,

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sometimes there's an[br]embarrassing silence if I ask

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or there might be[br]a fairly contrived scenario.

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I want to see [br]where the tool is useful.

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There are some [br]purely foundational tools,

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I do take the point, you know, [br]there are people who are

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working on conceptual[br]foundations of, you know,

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it's more-- becomes more like[br]mathematical statistics.

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I mean, I remember [br]an early example of that that I,

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you know, I struggled to understand

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was the idea[br]of stochastic equicontinuity,

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which one of my thesis advisors,[br]Whitney Newey,

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was using to great effect

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and I was trying to understand that[br]and there isn't really--

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It's really foundational, it's not[br]an application that's driving that,

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at least not immediately

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but most things are not like that[br]and so there should be a problem.

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And I think it's on the seller [br]of that sort of thing,

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you know, because there's [br]opportunity cost,

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the time and attention,[br]and effort to understand things

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to, you know, [br]it's on the seller to say,

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"Hey, I'm solving this problem

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and here's a set of results[br]that show that it's useful,

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and here's some insight[br]that I get."

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- [Isaiah] As you said, Josh, great,[br]sort of there's been a move

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in the direction of thinking [br]more about causality

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in economics and empirical[br]work in economics,

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any consequences of sort of the--

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the spread of that view [br]that surprised you

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or anything that you view[br]as downsides

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of sort of the way [br]that empirical economics has gone?

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- [Josh] Sometimes I see, [br]somebody does IV

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and they get a result [br]which seems implausibly large.

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That's the usual case.

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So it might be, you know,[br]an extraordinarily large

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causal effect of some [br]relatively minor intervention,

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which was randomized[br]or for which you could make a case

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that there's a good design.

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And then when I see that,[br]and, you know, I think

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it's very hard for me to believe

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that this relatively [br]minor intervention

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has such a large effect.

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The author will sometimes resort

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to the local average [br]treatment effects theorem

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and say, "Well, these compliers, you know,[br]they're special in some way."

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And, you know, they just benefit[br]extraordinarily from this intervention.

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And I'm reluctant to take that[br]at face value. I think, you know,

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often when effects are too big,

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it's because the exclusion[br]restriction is failing. So

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Don't really have the right endogenous[br]variable to scale that result.

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And so I'm not too happy to see

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you know, just sort of[br]a generic heterogeneity

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argument being used to excuse something[br]that I think might be a deeper problem.

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I think it played somewhat[br]of an unfortunate roll pin.

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The discussions kind of between reduced[br]form and structural approaches where

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I feel that wasn't quite

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right. The instrumental[br]variables assumptions are

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at the core structural assumptions about[br]Behavior. They were coming from economic

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thinking about the economic[br]behavior of agents,

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and it's somehow it got[br]pushed in a Direction.

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That I think wasn't[br]really very helpful. If

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the way I think, initially the

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we wrote things up. It was it was describing[br]what was happening, there was set of

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methods. People were using be[br]clarified what those methods were doing

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and in a way that I think[br]contain a fair amount of insight,

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but it somehow it got pushed into a corner[br]that I think was not necessarily very

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or even just the language of[br]reduced form versus structural.

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I find kind of funny in[br]the sense that the right

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the local average treatment[br]effect model, right?

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The potential outcomes[br]model is a nonparametric.

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Structural model,

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if you want to think about it, as[br]you sort of suggested, he does.

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So, there's something,

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there's something a little funny about[br]putting these two things in a position when

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yes, well, that language, of[br]course, comes from the area, the

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70s equations framework that we inherited.

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It has the advantage that people seem

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to know what you mean[br]when you use it, but might

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That people are hearing different. Different[br]people are hearing different things.

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Yeah. I think I think veggies[br]Farmers had become use

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a little bit of the[br]pejoratives. Okay? Yeah.

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The word, which is not really quite[br]what it was originally intended for.

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I guess something else that strikes[br]me in thinking about the effects of

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the local average treatment effect[br]framework is that often folks will appeal to

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a local average, treatment effects[br]intuition for settings. Well, beyond

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ones, where any sort of formal[br]results has actually been

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Shhhhht. And I'm curious given[br]all the work that you guys did to,

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you know, establish late results in[br]different in different settings. I'm curious

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any thoughts on that. I think there's[br]going to be a lot of cases where

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the intuition does get.[br]You get you some distance,

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but it's going to be somewhat limited[br]and establishing formal results. There

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may be a little tricky and there may[br]be only work in special circumstances,

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you need.

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And you end up with a lot of formality[br]that may not quite capture the intuition

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sometimes I'm somewhat uneasy with them[br]and they are not necessarily the papers.

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I would want to ride that the

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but I do think something do intuition[br]orphaned US capture part of the

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of the problem.

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I think, in some sense we were[br]kind of very fortunate there

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in the way. The late paper go handle. It.[br]Don't know if that, actually the editor,

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made it much shorter

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and that then allowed us to kind of[br]focus on very clear, crisp results

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where if, you know, this,

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this is somewhat unfortunate tendency in[br]the commercialization of having the papers.

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Well, you should be able to fix that, man.[br]I'm trying to take some time to fix that.

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I think this is an example where it's sort[br]of very clear that having it. Be sure.

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It's actually impose that no paper can[br]be longer than the late paper that wow.

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Great. At least no Theory. No Theory Pig.[br]Yeah, and I think, I think they're well,

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I'm trying very hard to get[br]the papers to be shorter.

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And I think there's a lot of value

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today because it's often the second[br]part of the paper that doesn't actually

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Get you much further[br]and understanding things

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but and it does make things much[br]harder to read and, you know,

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it sort of goes back to

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how I think he kind of a trick should[br]be done to you should focus on the see.

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It should be reasonably[br]close to empirical problems.

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They should be very clear problems.

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But then often the the theory[br]doesn't need to be quite so long.

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Yeah,

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I think they had things have

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On a little off track.

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The relatively recent change has been a

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seeming big increase in demand for[br]people with sort of econometrics.

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Causal effect, estimation[br]skills in the tech sector.

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I'm interested either of you have[br]thoughts on sort of how that's gonna

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how that's going to interact with[br]the development of empirical methods,

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or Empirical research, and[br]economics. Going forward, sort of

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whether sort of a meta point, which[br]is there's this new kind of employer

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the Amazons and the Uber and, you know,

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Riser world

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and I think that's great.

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And I'd like to tell my students about[br]that, you know, especially at MIT.

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We have a lot of computer science[br]Majors. That's our biggest major

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and I try to seduce some of those folks[br]into economics by saying, you know,

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you can go work for these,

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you know companies that[br]people are very keen to

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work for because the work seems exciting,

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you know that the skills that you get in[br]econometrics are are as good or better.

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Better than than any competing discipline[br]has to offer. So you should at least

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take some econ, take some[br]econometrics. And some econ.

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I did a fun project with a uber

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on labor supply of Uber drivers and was[br]very, very exciting to be part of that.

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Plus. I got to drive for Uber for a while

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and I thought that was fun tonight. I did[br]not make enough that I was attempted to

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give up by a mighty job, but[br]I enjoyed the experience.

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I see a

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Cho challenge to our model[br]of graduate education here,

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which is if we're trading people[br]to go work at Amazon, you know,

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it's not clear. Why? You know, we should[br]be paying graduate stipends for that.

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Why should the taxpayer effectively

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be subsidizing? That our graduate education[br]in the u.s. Is generously subsidized?

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Even in private universities. It's[br]ultimately there's a lot of public money.

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Me there. And I think the[br]traditional rationale for that is,

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you know, we were training, Educators and[br]Scholars, and there's a great externality

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from the work that we do.

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It's either the research externality,[br]or a teaching externality.

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But, you know, if many of our students[br]are going to work in the private sector,

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that's fine, but that maybe their[br]employers should pay for that.

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He says, so different from[br]people working for a Consulting.

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Trust me.

0:14:27.200,0:14:33.000
It's not clear to me that the number[br]of jobs in academics has changed.

0:14:33.100,0:14:37.600
It's just, I feel like this is a[br]growing sector whereas Consulting,

0:14:37.700,0:14:42.100
your right to raise that, it might[br]be the same for for Consulting.

0:14:43.300,0:14:44.400
But this,

0:14:44.500,0:14:47.500
you know, I'm placing more and[br]more students in these businesses.

0:14:47.500,0:14:50.400
So, it's on my mind in[br]a way that I've sort of,

0:14:50.800,0:14:55.500
you know, not been attentive to consulting[br]jobs, you know, Consulting was always,

0:14:55.600,0:15:00.400
It's important and I think they'll so[br]there's some movement from Consulting back

0:15:00.400,0:15:02.600
into research. It's a little more fluid.

0:15:02.900,0:15:03.500
The,

0:15:03.900,0:15:05.400
a lot of the work in the

0:15:06.400,0:15:09.800
in both domains. I have to say,[br]it's not really different but

0:15:10.100,0:15:13.700
you know, people who are working[br]in the tech sector are doing things

0:15:13.700,0:15:16.800
that are potentially of scientific[br]interest, but mostly it's hidden.

0:15:17.100,0:15:20.900
Then you really I have to say, you know,[br]why, why is the government paying for this?

0:15:21.800,0:15:25.500
Yeah, although yeah, I mean taquitos point,[br]I guess it. There's a, there's a data.

0:15:25.600,0:15:30.000
Question here of it has the sort[br]of total nanak. It sort of say

0:15:30.500,0:15:30.900
private

0:15:31.300,0:15:34.100
for-profit sector employment of econ Ph.D.

0:15:34.100,0:15:37.700
Program graduates increased or has[br]it just been a substitution from

0:15:37.900,0:15:40.200
finance and Consulting towards tack.

0:15:40.300,0:15:44.300
I may be a reaction to something[br]that's not really happening

0:15:44.400,0:15:48.200
so bad. I've actually done some work[br]with some of these tech companies.

0:15:49.100,0:15:52.300
So I don't disagree with Justice[br]point that we need to think

0:15:52.300,0:15:55.100
a little bit about the funding model[br]whose it was in the end paying for the

0:15:55.600,0:15:59.400
It education. But from a[br]scientific perspective.

0:16:00.100,0:16:03.500
The only do these places have[br]have great data and nowadays.

0:16:03.500,0:16:07.100
They tend to be very careful[br]with that for privacy reasons,

0:16:07.500,0:16:08.800
but also have great questions.

0:16:10.200,0:16:11.200
I find it very

0:16:11.600,0:16:13.300
inspiring kind of to listen to

0:16:13.300,0:16:15.800
the people there and kind of see[br]what kind of questions they have

0:16:15.900,0:16:17.300
and often their questions.

0:16:18.200,0:16:20.600
That also come up outside of these.

0:16:20.700,0:16:25.400
These companies have a couple of[br]papers with the rights in the chat.

0:16:25.800,0:16:26.300
And then

0:16:26.500,0:16:31.600
as soon as an atheist kind of where we[br]look at ways of combining experimental data

0:16:31.700,0:16:34.100
and observational data, and can it there.

0:16:35.500,0:16:38.600
Rights Chetty was interested[br]in what is the effect

0:16:38.700,0:16:44.600
of Early Childhood programs on outcomes[br]later in life? Not just kind of test scores,

0:16:44.600,0:16:48.300
but on earnings and stuff, and[br]we cannot be developed methods

0:16:48.600,0:16:51.500
that would help you shed[br]light on that, on the some,

0:16:52.700,0:16:55.000
in some settings and the same problems.

0:16:56.300,0:17:00.500
Came up kind of in this[br]tech company settings.

0:17:00.800,0:17:03.700
And so for my perspective, it's

0:17:04.400,0:17:07.500
the same kind of a stocking two[br]people doing a protocol work.

0:17:07.600,0:17:11.800
I tried to kind of look at these[br]specific problems and then try to come up

0:17:11.900,0:17:18.300
with more General problems that we[br]formulating the problems at a higher level.

0:17:18.500,0:17:22.900
So that I can think about solutions[br]that work in a range of settings.

0:17:23.400,0:17:25.500
And so from that perspective, the

0:17:25.700,0:17:30.300
His with the the tech companies I[br]just very valuable and very useful.

0:17:30.900,0:17:31.400
It's know.

0:17:31.700,0:17:33.700
We do have students. Now spent

0:17:33.800,0:17:37.200
doing internships there and[br]then coming back and writing

0:17:37.400,0:17:43.400
more interesting thesis, as a[br]result of their experiences there.

0:17:44.600,0:17:47.800
If you'd like to watch more[br]Nobel conversations, click here,

0:17:48.200,0:17:50.500
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0:17:50.600,0:17:53.200
check out Josh's mastering[br]econometrics series.

0:17:53.700,0:17:55.500
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0:17:55.600,0:17:58.300
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