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♪ [music] ♪

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- [Narrator] Welcome to 
Nobel Conversations.

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In this episode,

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Josh Angrist and Guido Imbens
sit down with Isaiah Andrews

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to discuss how the field 
of econometrics is evolving.

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

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in developing tools for
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 
the field is heading,

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sort of economics, econometrics,
the whole thing.

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To start, I'd be interested to hear

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about whether you feel like
sort of the way in which

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the local average treatment 
effects framework sort of took hold

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has any lessons for how 
new empirical methods in economics

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develop and spread
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 
the important thing

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is to come up 
with good convincing cases

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where the questions are clear

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and where kind of the methods
apply in general.

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So one thing I--

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Kind of looking back
at the subsequent literature,

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so I really like the regression
discontinuity literature

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[where there were] clearly a bunch
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
the methodological questions.

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Kind of do clear applications

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that then allow you 
to kind of think about,

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

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

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How can we improve things?"

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So having clear applications
motivating,

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these literatures, 
I think it's very helpful.

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

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

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Instrumental Variables and
Regression Discontinuity.

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And a lot of 
the econometric applications

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of regression discontinuity

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are what used to be called 
"fuzzy" RD,

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

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but just the change
in rates or intensity.

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And and the late framework helps us
understand those applications

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and gives us a clear interpretation

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for say, something like, 
in my paper with Victor Lavy,

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

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What are you getting there?

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Of course, you can
answer that question

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with a linear 
constant effects model,

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

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and an RD is still very powerful 
and illuminating,

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even when, you know,

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the correlation between the cutoff
and the variable of interest,

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in this case class size, 
is partial,

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maybe even not that strong.

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So there was definitely a kind of,
a parallel development.

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It's also interesting,

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you know, nobody talked about
regression discontinuity designs

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when we were in graduate school,

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it was something that other social
scientists were interested in,

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and that kind of grew up 
alongside the late framework

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and we've both done work on 
both applications and methods there

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and it's been very exciting 
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,
I think, towards, you know,

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credible identification strategies
causal effects...

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less, you know, making econometrics

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more about causal questions
than about models.

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In terms of the future,

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I think one thing that LATE 
has helped facilitate

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is a move towards more creative,
randomized trials, where,

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you know, there's
something of interest,

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

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but you can encourage it 
or discourage it.

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So you subsidize schooling 
with financial aid, for example.

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So now we have a whole 
framework for interpreting that,

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and it kind of opens
the doors to randomized trials

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of things that that maybe would,
you know,

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not have seem possible before.

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We've used that a lot in the work
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
and in very creative ways, I think.

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

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that make for useful research
in econometrics?

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You've alluded to it 
having a clear connection

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to problems 
that are actually coming up

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and empirical practice 
is often a good idea.

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

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I often find myself sitting in an
econometrics theory seminar,

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say the Harvard MIT seminar,

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and I'm thinking, "What problem
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
embarrassing silence if I ask

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or there might be
a fairly contrived scenario.

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I want to see 
where the tool is useful.

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There are some 
purely foundational tools,

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

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working on conceptual
foundations of, you know,

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it's more-- becomes more like
mathematical statistics.

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I mean, I remember 
an early example of that that I,

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you know, I struggled to understand

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was the idea
of stochastic equicontinuity,

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which one of my thesis advisors,
Whitney Newey,

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was using to great effect

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

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It's really foundational, it's not
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
and so there should be a problem.

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

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you know, because there's 
opportunity cost,

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the time and attention,
and effort to understand things

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to, you know, 
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
that show that it's useful,

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and here's some insight
that I get."

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

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in the direction of thinking 
more about causality

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in economics and empirical
work in economics,

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any consequences of sort of the--

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the spread of that view 
that surprised you

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or anything that you view
as downsides

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of sort of the way 
that empirical economics has gone?

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- [Josh] Sometimes I see, 
somebody does IV

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and they get a result 
which seems implausibly large.

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That's the usual case.

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So it might be, you know,
an extraordinarily large

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causal effect of some 
relatively minor intervention,

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which was randomized
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,
and, you know, I think

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it's very hard for me to believe

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that this relatively 
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 
treatment effects theorem

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

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

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And I'm reluctant to take that
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
restriction is failing. So

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Don't really have the right endogenous
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
a generic heterogeneity

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

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I think it played somewhat
of an unfortunate roll pin.

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The discussions kind of between reduced
form and structural approaches where

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I feel that wasn't quite

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right. The instrumental
variables assumptions are

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

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thinking about the economic
behavior of agents,

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and it's somehow it got
pushed in a Direction.

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That I think wasn't
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
what was happening, there was set of

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methods. People were using be
clarified what those methods were doing

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

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

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or even just the language of
reduced form versus structural.

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I find kind of funny in
the sense that the right

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the local average treatment
effect model, right?

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The potential outcomes
model is a nonparametric.

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Structural model,

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if you want to think about it, as
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
putting these two things in a position when

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yes, well, that language, of
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
when you use it, but might

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That people are hearing different. Different
people are hearing different things.

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Yeah. I think I think veggies
Farmers had become use

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a little bit of the
pejoratives. Okay? Yeah.

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

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

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

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

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ones, where any sort of formal
results has actually been

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

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

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

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the intuition does get.
You get you some distance,

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

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may be a little tricky and there may
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
that may not quite capture the intuition

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sometimes I'm somewhat uneasy with them
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
orphaned US capture part of the

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of the problem.

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

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in the way. The late paper go handle. It.
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
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
the commercialization of having the papers.

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

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

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

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

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I'm trying very hard to get
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
part of the paper that doesn't actually

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Get you much further
and understanding things

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but and it does make things much
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
be done to you should focus on the see.

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It should be reasonably
close to empirical problems.

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They should be very clear problems.

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But then often the the theory
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
people with sort of econometrics.

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Causal effect, estimation
skills in the tech sector.

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

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

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or Empirical research, and
economics. Going forward, sort of

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whether sort of a meta point, which
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
that, you know, especially at MIT.

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

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

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you can go work for these,

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you know companies that
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
econometrics are are as good or better.

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

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take some econ, take some
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
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
not make enough that I was attempted to

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

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I see a

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Cho challenge to our model
of graduate education here,

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

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it's not clear. Why? You know, we should
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
in the u.s. Is generously subsidized?

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

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

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you know, we were training, Educators and
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,
or a teaching externality.

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

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

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

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Trust me.

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It's not clear to me that the number
of jobs in academics has changed.

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It's just, I feel like this is a
growing sector whereas Consulting,

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your right to raise that, it might
be the same for for Consulting.

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But this,

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you know, I'm placing more and
more students in these businesses.

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So, it's on my mind in
a way that I've sort of,

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you know, not been attentive to consulting
jobs, you know, Consulting was always,

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It's important and I think they'll so
there's some movement from Consulting back

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into research. It's a little more fluid.

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The,

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a lot of the work in the

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in both domains. I have to say,
it's not really different but

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you know, people who are working
in the tech sector are doing things

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that are potentially of scientific
interest, but mostly it's hidden.

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Then you really I have to say, you know,
why, why is the government paying for this?

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Yeah, although yeah, I mean taquitos point,
I guess it. There's a, there's a data.

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Question here of it has the sort
of total nanak. It sort of say

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private

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for-profit sector employment of econ Ph.D.

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Program graduates increased or has
it just been a substitution from

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finance and Consulting towards tack.

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I may be a reaction to something
that's not really happening

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so bad. I've actually done some work
with some of these tech companies.

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So I don't disagree with Justice
point that we need to think

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a little bit about the funding model
whose it was in the end paying for the

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It education. But from a
scientific perspective.

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The only do these places have
have great data and nowadays.

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They tend to be very careful
with that for privacy reasons,

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but also have great questions.

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I find it very

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inspiring kind of to listen to

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the people there and kind of see
what kind of questions they have

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and often their questions.

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That also come up outside of these.

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These companies have a couple of
papers with the rights in the chat.

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And then

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as soon as an atheist kind of where we
look at ways of combining experimental data

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and observational data, and can it there.

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Rights Chetty was interested
in what is the effect

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of Early Childhood programs on outcomes
later in life? Not just kind of test scores,

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but on earnings and stuff, and
we cannot be developed methods

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that would help you shed
light on that, on the some,

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in some settings and the same problems.

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Came up kind of in this
tech company settings.

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And so for my perspective, it's

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the same kind of a stocking two
people doing a protocol work.

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I tried to kind of look at these
specific problems and then try to come up

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with more General problems that we
formulating the problems at a higher level.

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So that I can think about solutions
that work in a range of settings.

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And so from that perspective, the

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His with the the tech companies I
just very valuable and very useful.

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It's know.

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We do have students. Now spent

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doing internships there and
then coming back and writing

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more interesting thesis, as a
result of their experiences there.

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If you'd like to watch more
Nobel conversations, click here,

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00:17:48,200 --> 00:17:50,500
or if you'd like to learn
more about econometrics,

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00:17:50,600 --> 00:17:53,200
check out Josh's mastering
econometrics series.

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00:17:53,700 --> 00:17:55,500
If you'd like to learn more about he do.

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00:17:55,600 --> 00:17:58,300
Josh and Isaiah check out
the links in the description.