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- [Narrator] Welcome
to Nobel Conversations.
In this episode,
Josh Angrist and Guido Imbens
sit down with Isaiah Andrews
to discuss how the field
of econometrics is evolving.
- [Isaiah] So, Guido and Josh,
you're both pioneers
in developing tools for
empirical research in economics.
And so I'd like to explore
where you feel like
the field is heading --
economics, econometrics,
the whole thing.
To start, I'd be interested to hear
about whether you feel
the way in which
the local average treatment
effects framework took hold
has any lessons for how
new empirical methods in economics
develop and spread
or how they should.
- [Josh] That's a good question.
You go first.
[laughter]
- [Guido] Yeah, so I think
the important thing
is to come up
with good convincing cases
where the questions are clear
and where the methods
apply in general.
One thing I --
looking back
at the subsequent literature.
So I really like the regression
discontinuity literature
where there were clearly a bunch
of really convincing examples
and that allowed people
to think more clearly,
look harder
at the methodological questions.
Do clear applications
that then allow you
to kind of think about,
"Wow, does this type of assumption
seem reasonable here?
What kind of things do we not like
in the early papers?
How can we improve things?"
So having clear applications
motivating these literatures
I think is very helpful.
- I'm glad you mentioned
the regression
discontinuity, Guido.
I think there's a lot of
complementarity between IV and RD,
Instrumental Variables
and Regression Discontinuity.
A lot of the econometric
applications
of regression discontinuity
are what used to be called
"fuzzy" RD,
where, you know, it's not discrete
or deterministic at the cutoff
but just the change
in rates or intensity.
And the late framework
helps us understand
those applications
and gives us a clear interpretation
for something like
in my paper with Victor Lavy,
where we use Maimonides'
rule, the class size cutoffs,
and what are you getting there?
Of course, you can
answer that question
with a linear
constant effects model,
but it turns out
we're not limited to that,
and RD is still very powerful
and illuminating,
even when the correlation
between the cutoff
and the variable of interest,
in this case class size,
is partial,
maybe even not that strong.
So there was definitely kind
of a parallel development.
It's also interesting --
nobody talked about
regression discontinuity designs
when we were in graduate school.
It was something that other social
scientists were interested in,
and that grew up
alongside the LATE framework,
and we've both done work
on both applications
and methods there,
and it's been very exciting
to see that develop
and become so important.
It's part of a general evolution,
I think, towards credible
identification strategies,
causal effects...
making econometrics
more about causal questions
than about models.
In terms of the future,
I think one thing that LATE
has helped facilitate
is a move towards
more creative, randomized trials
where there's
something of interest.
It's not possible
or straightforward
to simply turn it off or on,
but you can encourage it
or discourage it.
So you subsidize schooling
with financial aid, for example.
So now we have a whole
framework for interpreting that,
and it opens the doors
to randomized trials
of things that maybe would
not have seemed possible before.
We've used that a lot in the work
we do on schools in our --
in the Blueprint Lab at MIT.
We're exploiting random assignment
in very creative ways, I think.
- [Isaiah] Related to that,
do you see particular factors
that make for useful research
in econometrics?
You've alluded to
it having a clear connection
to problems
that are actually coming up,
and empirical practice
is often a good idea.
- Isn't it always a good idea?
I often find myself sitting
in an econometrics
theory seminar,
say the Harvard MIT seminar,
and I'm thinking, "What problem
is this guy solving?
Who has this problem?"
And, you know...
sometimes there's an
embarrassing silence if I ask
or there might be
a fairly contrived scenario.
I want to see
where the tool is useful.
There are some
purely foundational tools.
I do take the point.
There are people who are working
on conceptual foundations of ...
it becomes more like
mathematical statistics.
I mean, I remember
an early example of that
that I struggled to understand
was the idea
of stochastic equicontinuity,
which one of my thesis advisors,
Whitney Newey,
was using to great effect,
and I was trying
to understand that.
It's really foundational.
it's not an application
that's driving that --
at least not immediately.
But most things are not like that,
and so there should be a problem.
And I think it's on the seller
of that sort of thing,
because there's opportunity cost,
the time and attention,
and effort to understand things.
It's on the seller to say,
"Hey, I'm solving this problem,
and here's a set of results
that show that it's useful,
and here's some insight
that I get."
- [Isaiah] As you said, Josh,
there's been a move
in the direction of thinking
more about causality
in economics and empirical
work in economics.
Any consequences of the --
the spread of that view
that surprised you
or anything that you view
as downsides
of the way that empirical
economics has gone?
- Sometimes I see
somebody does IV,
and they get a result
which seems implausibly large.
That's the usual case.
So it might be
an extraordinarily large
causal effect of some
relatively minor intervention,
which was randomized
or for which you could make a case
that there's a good design.
And then when I see that,
I think it's very hard
for me to believe
that this relatively
minor intervention
has such a large effect.
The author will sometimes resort
to the local average
treatment effects theorem
and say, "Well, these compliers,
they're special in some way."
And they just benefit
extraordinarily
from this intervention.
I'm reluctant to take that
at face value.
I think often when effects
are too big,
it's because the exclusion
restriction is failing,
so you don't really have the right
endogenous variable
to scale that result.
And so I'm not too happy to see
a generic heterogeneity argument
being used to excuse something
that I think might be
a deeper problem.
- [Guido] I think it played
somewhat of an unfortunate role
in the discussions
between reduced form
and structural approaches,
where I feel
that wasn't quite right.
The instrumental
variables assumptions
are at the core, structural
assumptions about behavior --
they were coming from economic...
thinking about the economic
behavior of agents,
and somehow it got pushed
in a direction
that I think wasn't
really very helpful.
I think, initially,
we wrote things up,
it was describing
what was happening.
There were a set of methods
people were using.
We clarified what
those methods were doing
and in a way that I think
contain a fair amount of insight.
But it somehow
got pushed into a corner
that I don't think
was necessarily very helpful.
- In just the language
of reduced form versus structural,
I find kind of funny in the sense
that the local average
treatment effect model,
the potential outcomes model
is a nonparametric
structural model,
if you want to think about it,
as you suggested, Guido.
So there's something a little funny
about putting these
two things in opposition when --
- [Guido] Yes.
- [Josh] That language, of course,
comes from the simultaneous
equations framework
that we inherited.
It has the advantage
that people seem to know
what you mean when you use it,
but that might be that people
are hearing different --
different people
are hearing different things.
- [Guido] Yeah. I think
reduced form has become
used in a little bit
of the pejorative way...
- [Josh] Sometimes.
...which is not really quite what
it was originally intended for.
- [Isaiah] I guess something else
that strikes me in thinking about
the effects of the local average
treatment effect framework
is that often folks will appeal
to a local average treatment
effects intuition
for settings well beyond ones
where any sort of formal result
has actually been established.
And I'm curious, given all the work
that you guys did
to establish late results
in different settings,
I'm curious, any thoughts on that?
- I think there's going
to be a lot of cases
where the intuition
does get you some distance,
but it's going to be
somewhat limited,
and establishing
formal results there
may be a little tricky
and then maybe only work
in special circumstances,
and you end up
with a lot of formality
that may not quite
capture the intuition.
Sometimes I'm somewhat
uneasy with them,
and they are not necessarily
the papers I would want to write,
but I do think intuition
often does capture
part of the problem.
I think, in some sense we were
kind of very fortunate there
in the way that the late paper
got handled at the journal,
is that, actually, the editor,
made it much shorter
and that then allowed us to kind of
focus on very clear, crisp results.
Where if you -- you know, this
somewhat unfortunate tendency
in the econometrics literature
of having the papers
get longer and longer.
- [Josh] Well, you should
be able to fix that, man.
- [Guido] I'm trying to fix that.
But I think this is an example
where it's sort of very clear
that having it be short
is actually --
- [Josh] You should impose
that no paper
can be longer than the late paper.
- [Guido] That, wow.
That may be great.
- [Josh] At least no theory,
no theory paper.
- [Guido] Yeah,
and I think, I think...
I'm trying very hard to get
the papers to be shorter.
And I think there's a lot of value
today because it's often
the second part of the paper
that doesn't actually get you much
further in understanding things
and it does make things
much harder to read
and, you know,
it sort of goes back
to how I think econometrics
should be done,
you should focus on --
It should be reasonably
close to empirical problems.
They should be very clear problems.
But then often the theory
doesn't need to be quite so long.
- [Josh] Yeah.
- [Guido] I think things have gone
a little off track.
- [Isaiah] A new relatively
recent change
has been a seeming big
increase in demand
for people with sort of
econometrics,
causal effect estimation skills
in the tech sector.
I'm interested,
do either of you have thoughts
on sort of how
that's going to interact
with the development
of empirical methods,
or empirical research
in economics going forward?
- [Josh] Well, there's
sort of a meta point,
which is, there's this new
kind of employer,
the Amazons and the Uber,
and, you know, TripAdvisor world,
and I think that's great.
And I like to tell my students
about that, you know, especially --
at MIT we have a lot of
computer science majors.
That's our biggest major.
And I try to seduce some of those
folks into economics by saying,
you know, you can go
work for these,
you know, companies that people
are very keen to work for
because the work seems exciting,
you know, that the skills
that you get in econometrics
are as good or better
than any competing
discipline has to offer.
So you should at least
take some econ, take some
econometrics, and some econ.
I did a fun project with Uber
on labor supply of Uber drivers
and was very, very exciting
to be part of that.
Plus I got to drive
for Uber for a while
and I thought that was fun too.
I did not make enough
that I was tempted to
give up my MIT job,
but I enjoyed the experience.
I see a potential challenge to our
model of graduate education here,
which is, if we're training people
to go work at Amazon, you know,
it's not clear why, you know,
we should be paying
graduate stipends for that.
Why should the taxpayer effectively
be subsidizing that.
Our graduate education
in the US Is generously subsidized,
even in private universities,
it's ultimately -- there's a lot of
public money there,
and I think the
traditional rationale for that is,
you know, we were training
educators and scholars,
and there's a great externality
from the work that we do,
it's either
the research externality,
or a teaching externality.
But, you know,
if many of our students
are going to work
in the private sector,
that's fine, but maybe their
employers should pay for that.
- [Guido] But maybe
is not so different
from people working
for consulting firms.
It's not clear to me
that the number of jobs
in academics has changed.
- [Josh] I feel like this
is a growing sector,
whereas consulting --
you're right to raise that,
it might be the same
for consulting,
but this, you know,
I'm placing more and more
students in these businesses.
So, it's on my mind
in a way that I've sort of,
you know, not been attentive
to consulting jobs,
you know, consulting
was always important,
and I think also
there's some movement
from consulting back into research,
it's a little more fluid.
A lot of the work in both domains
I have to say,
it's not really different
but, you know, people who
are working in the tech sector
are doing things that are
potentially of scientific interest,
but mostly it's hidden.
Then you really I have to say,
you know, why is the government
paying for this?
Yeah, although, yeah,
I mean to Guidos point,
I guess there's
a data question here
of it has the sort of
total [no-neck] sort of say
for-profit sector employment
of econ Ph.D. program graduates
increased or has it just been
a substitution from finance
and consulting towards tech.
- [Josh] I may be reacting
to something
that's not really happening.
- [Guido] I've actually
done some work
with some of these tech companies.
I don't disagree with Josh's point
that we need to think
a little bit about
the funding model,
who is it in the end paying
for the graduate education.
But from a scientific perspective,
not only do these places
have great data
and nowadays they tend to be
very careful with that
for privacy reasons,
but they also have great questions.
I find it very inspiring
kind of to listen
to the people there and kind of see
what kind of questions they have,
and often they're questions
that also come up outside
of these companies.
I have a couple of papers
with Raj Chetty and Susan Athey,
where we look at ways
of combining experimental data
and observational data,
and kind of their --
Raj Chetty was interested
in what is the effect
of early childhood programs
on outcomes later in life,
not just kind on test scores,
but on earnings and stuff,
and we kind of developed methods
that would help you shed
light on that, onto some --
in some settings
and the same problems came up
kind of in this
tech company settings.
And so from my perspective,
it's the same kind of --
I was talking to people
doing empirical work,
I tried to kind of look at these
specific problems
and then try to come up
with more general problems,
we formulated the problems
at a higher level,
so that I can think about solutions
that work in a range of settings.
And so from that perspective,
the interactions
with the tech companies
are just very valuable
and very useful.
We do have students now
doing internships there
and then coming back
and writing more interesting thesis
as a result of their
experiences there.
- [Narrator] If you'd like to watch
more Nobel Conversations,
click here,
or if you'd like to learn
more about econometrics,
check out Josh's
"Mastering Econometrics" series.
If you'd like to learn more
about Guido, Josh and Isaiah
check out the links
in the description.
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