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- [Narrator] Welcome [br]to 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 [br]the methods 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 [br]at 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 these literatures,

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I think it's very helpful.

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- [Josh] I'm glad you mentioned

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the regression [br]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 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 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 [br]or straightforward

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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 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[br]it 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,

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you know, they're[br]special in some way."

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And, you know, [br]they just benefit extraordinarily

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from this intervention.

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And I'm reluctant [br]to take that at face value.

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I think, you know,[br]often when effects are too big,

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

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so you don't really have the right[br]endogenous variable

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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 [br]to excuse something

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that I think might be [br]a deeper problem.

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- [Guido] I think it played [br]somewhat of an unfortunate role

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when the discussions kind of [br]between reduced form

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and structural approaches,

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where I feel [br]that wasn't quite right.

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The instrumental [br]variables assumptions

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are at the core - structural [br]assumptions about behavior -

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they were coming from economic --

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thinking about the economic[br]behavior of agents,

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and somehow it got[br]pushed in a direction

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

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The way I think, initially the --

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we wrote things up, it was [br]describing what was happening,

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there were a set of methods [br]people were using,

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we clarified what [br]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 [br]it got pushed into a corner

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that I don't think [br]was necessarily very helpful.

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- [Isaiah] I mean, [br]just the language

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of reduced form versus structural

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

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

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the potential outcomes model is [br]a nonparametric structural model,

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

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So there's something a little funny

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about putting these [br]two things in oposition when --

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- [Guido] Yes.[br]- [Josh] Well, that language,

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of course, comes from [br]the [inaudible] equations framework

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that we inherited.

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It has the advantage

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that people seem to know [br]what you mean when you use it,

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but might be that people [br]are hearing different,

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different people[br]are hearing different things.

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- [Guido] Yeah. I think [inaudible][br]has sort of become --

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used in a little bit [br]of the pejorative way, yeah?

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- [Josh] Sometimes.[br]- [Guido] [The word].

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Which is not really quite what [br]it was originally intended for.

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- [Isaiah] I guess something else[br]that strikes me in thinking about

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the effects of the local average[br]treatment effect framework

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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 [br]of formal result

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has actually been established.

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

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establish late results [br]in different settings,

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I'm curious, any thoughts on that?

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- [Guido] I think there's[br]going to be a lot of cases

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where the intuition [br]does get you some distance,

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but it's going to be [br]somewhat limited

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and establishing [br]formal results there

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may be a little tricky

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and then maybe only work [br]in special circumstances,

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and you end up[br]with a lot of formality

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that may not quite [br]capture the intuition.

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Sometimes I'm somewhat [br]uneasy with them

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and they are not necessarily[br]the papers I would want to write,

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but I do think something --

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intuition often does capture [br]part 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 that the late paper [br]got handled at the journal,

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is that, actually, the editor,[br]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 -- you know, this [br]somewhat unfortunate tendency

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in the econometrics literature

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of having the papers [br]get longer and longer.

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- [Josh] Well, you should [br]be able to fix that, man.

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- [Guido] I'm trying to fix that.

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But I think this is an example [br]where it's sort of very clear

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that having it be short[br]is actually --

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- [Josh] You should impose[br]that no paper

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can be longer than the late paper.

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- [Guido] That, wow.

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That may be great.

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- [Josh] At least no theory, [br]no theory paper.

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- [Guido] Yeah, [br]and I think, I think...

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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[br]the second part of the paper

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that doesn't actually get you much[br]further in understanding things

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and it does make things [br]much harder to read

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and, you know, [br]it sort of goes back

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to how I think econometrics [br]should be done,

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you should focus on --

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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 theory[br]doesn't need to be quite so long.

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- [Josh] Yeah.

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- [Guido] I think things have gone[br]a little off track.

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- [Isaiah] A new relatively [br]recent change

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has been a seeming big [br]increase in demand

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for people with sort of [br]econometrics,

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causal effect estimation skills[br]in the tech sector.

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I'm interested, [br]do either of you have thoughts

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on sort of how [br]that's going to interact

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with the development [br]of empirical methods,

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or empirical research[br]in economics going forward?

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- [Josh] Well, there's[br]sort of a meta point,

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which is, there's this new [br]kind of employer,

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the Amazons and the Uber,[br]and, you know, TripAdvisor world,

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and I think that's great.

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And I like to tell my students[br]about that, you know, especially --

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at MIT we have a lot of [br]computer science majors.

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That's our biggest major.

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

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you know, you can go [br]work for these,

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

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because the work seems exciting,

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you know, that the skills [br]that you get in econometrics

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are as good or better

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than any competing [br]discipline has to offer.

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So you should at least

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

0:13:04.800,0:13:07.000
I did a fun project with Uber

0:13:07.600,0:13:09.770
on labor supply of Uber drivers

0:13:09.920,0:13:12.805
and was very, very exciting[br]to be part of that.

0:13:13.100,0:13:15.400
Plus I got to drive [br]for Uber for a while

0:13:15.900,0:13:17.730
and I thought that was fun too.

0:13:17.730,0:13:20.700
I did not make enough[br]that I was tempted to

0:13:21.100,0:13:25.100
give up my MIT job, [br]but I enjoyed the experience.

0:13:25.230,0:13:30.900
I see a potential challenge to our[br]model of graduate education here,

0:13:31.700,0:13:37.400
which is, if we're training people[br]to go work at Amazon, you know,

0:13:37.900,0:13:41.190
it's not clear why, you know, [br]we should be paying

0:13:41.190,0:13:42.900
graduate stipends for that.

0:13:43.200,0:13:47.280
Why should the taxpayer effectively[br]be subsidizing that.

0:13:47.280,0:13:51.400
Our graduate education[br]in the US Is generously subsidized,

0:13:51.400,0:13:53.160
even in private universities,

0:13:53.160,0:13:56.100
it's ultimately -- there's a lot of[br]public money there,

0:13:56.100,0:13:59.300
and I think the[br]traditional rationale for that is,

0:13:59.500,0:14:03.900
you know, we were training educators and[br]scholars, and there's a great externality

0:14:04.300,0:14:05.700
from the work that we do.

0:14:05.700,0:14:09.600
It's either the research externality,[br]or a teaching externality.

0:14:10.100,0:14:14.600
But, you know, if many of our students[br]are going to work in the private sector,

0:14:16.300,0:14:21.700
that's fine, but that maybe their[br]employers should pay for that.

0:14:22.300,0:14:25.100
He says, so different from[br]people working for a Consulting.

0:14:26.300,0:14:26.900
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.

0:17:44.600,0:17:47.020
- [Narrator] If you'd like to watch[br]more Nobel Conversations,

0:17:47.020,0:17:48.200
click here,

0:17:48.200,0:17:50.500
or if you'd like to learn[br]more about econometrics,

0:17:50.500,0:17:53.100
check out Josh's [br]"Mastering Econometrics" series.

0:17:53.700,0:17:56.720
If you'd like to learn more [br]about Guido, Josh and Isaiah

0:17:56.720,0:17:58.300
check out the links [br]in the description.

9:59:59.000,9:59:59.000
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