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How Is Econometrics Changing? (Josh Angrist, Guido Imbens, Isaiah Andrews)

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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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    where you feel like
    the field is heading --
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    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
    the way in which
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    the local average treatment
    effects framework 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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    - [Guido] 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 the methods
    apply in general.
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    One thing I --
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    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 think more clearly,
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    look harder
    at the methodological questions.
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    Do clear applications
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    that then allow you
    to kind of think about,
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    "Wow, does 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 these literatures
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    I think is very helpful.
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    - I'm glad you mentioned
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    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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    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 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 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
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    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 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,
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    you know, they're
    special in some way."
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    And, you know,
    they just benefit extraordinarily
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    from this intervention.
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    And I'm reluctant
    to take that at face value.
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    I think, you know,
    often when effects are too big,
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    it's because the exclusion
    restriction is failing,
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    so you don't really have the right
    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
    a generic heterogeneity
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    argument being used
    to excuse something
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    that I think might be
    a deeper problem.
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    - [Guido] I think it played
    somewhat of an unfortunate role
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    when the discussions kind of
    between reduced form
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    and structural approaches,
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    where I feel
    that wasn't quite right.
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    The instrumental
    variables assumptions
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    are at the core - structural
    assumptions about behavior -
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    they were coming from economic --
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    thinking about the economic
    behavior of agents,
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    and somehow it got
    pushed in a direction
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    that I think wasn't
    really very helpful.
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    The way I think, initially the --
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    we wrote things up, it was
    describing what was happening,
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    there were a set of methods
    people were using,
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    we 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
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    that I don't think
    was necessarily very helpful.
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    - [Isaiah] I mean,
    just the language
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    of reduced form versus structural
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    I find kind of funny
    in the sense that,
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    right, the local average treatment
    effect model, right,
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    the potential outcomes model is
    a nonparametric structural model,
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    if you want to think about it,
    as you sort of suggested, Guido.
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    So there's something a little funny
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    about putting these
    two things in oposition when --
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    - [Guido] Yes.
    - [Josh] Well, that language,
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    of course, comes from
    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
    what you mean when you use it,
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    but might be that people
    are hearing different,
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    different people
    are hearing different things.
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    - [Guido] Yeah. I think [inaudible]
    has sort of become --
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    used in a little bit
    of the pejorative way, yeah?
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    - [Josh] Sometimes.
    - [Guido] [The word].
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    Which is not really quite what
    it was originally intended for.
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    - [Isaiah] I guess something else
    that strikes me in thinking about
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    the effects of the local average
    treatment effect framework
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    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 result
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    has actually been established.
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    And I'm curious, given all the work
    that you guys did to, you know,
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    establish late results
    in different settings,
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    I'm curious, any thoughts on that?
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    - [Guido] I think there's
    going to be a lot of cases
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    where the intuition
    does get you some distance,
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    but it's going to be
    somewhat limited
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    and establishing
    formal results there
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    may be a little tricky
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    and then maybe only work
    in special circumstances,
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    and you end up
    with a lot of formality
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    that may not quite
    capture the intuition.
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    Sometimes I'm somewhat
    uneasy with them
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    and they are not necessarily
    the papers I would want to write,
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    but I do think something --
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    intuition often does capture
    part 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 that the late paper
    got handled at the journal,
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    is that, actually, the editor,
    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 -- you know, this
    somewhat unfortunate tendency
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    in the econometrics literature
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    of having the papers
    get longer and longer.
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    - [Josh] Well, you should
    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
    where it's sort of very clear
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    that having it be short
    is actually --
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    - [Josh] You should impose
    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,
    no theory paper.
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    - [Guido] Yeah,
    and I think, I think...
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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
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    that doesn't actually get you much
    further in understanding things
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    and it does make things
    much harder to read
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    and, you know,
    it sort of goes back
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    to how I think econometrics
    should be done,
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    you should focus on --
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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 theory
    doesn't need to be quite so long.
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    - [Josh] Yeah.
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    - [Guido] I think things have gone
    a little off track.
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    - [Isaiah] A new relatively
    recent change
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    has been a seeming big
    increase in demand
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    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,
    do either of you have thoughts
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    on sort of how
    that's going to interact
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    with the development
    of empirical methods,
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    or empirical research
    in economics going forward?
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    - [Josh] Well, there's
    sort of a meta point,
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    which is, there's this new
    kind of employer,
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    the Amazons and the Uber,
    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
    about that, you know, especially --
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    at MIT we have a lot of
    computer science majors.
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    That's our biggest major.
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    And I try to seduce some of those
    folks into economics by saying,
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    you know, you can go
    work for these,
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    you know, companies that people
    are very keen to work for
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    because the work seems exciting,
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    you know, that the skills
    that you get in econometrics
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    are as good or better
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    than any competing
    discipline has to offer.
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    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 Uber
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    on labor supply of Uber drivers
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    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 too.
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    I did not make enough
    that I was tempted to
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    give up my MIT job,
    but I enjoyed the experience.
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    I see a potential challenge to our
    model of graduate education here,
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    which is, if we're training people
    to go work at Amazon, you know,
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    it's not clear why, you know,
    we should be paying
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    graduate stipends for that.
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    Why should the taxpayer effectively
    be subsidizing that.
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    Our graduate education
    in the US Is generously subsidized,
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    even in private universities,
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    it's ultimately -- there's a lot of
    public money there,
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    and I think the
    traditional rationale for that is,
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    you know, we were training
    educators and scholars,
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    and there's a great externality
    from the work that we do,
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    it's either
    the research externality,
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    or a teaching externality.
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    But, you know,
    if many of our students
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    are going to work
    in the private sector,
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    that's fine, but maybe their
    employers should pay for that.
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    - [Guido] But maybe
    is not so different
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    from people working
    for consulting firms.
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    It's not clear to me
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    that the number of jobs
    in academics has changed.
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    - [Josh] I feel like this
    is a growing sector,
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    whereas consulting --
    you're right to raise that,
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    it might be the same
    for consulting,
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    but this, you know,
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    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,
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    you know, consulting
    was always important,
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    and I think also
    there's some movement
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    from consulting back into research,
    it's a little more fluid.
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    A lot of the work in both domains
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    I have to say,
    it's not really different
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    but, you know, people who
    are working in the tech sector
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    are doing things that are
    potentially of scientific interest,
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    but mostly it's hidden.
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    Then you really I have to say,
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    you know, why is the government
    paying for this?
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    Yeah, although, yeah,
    I mean to Guidos point,
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    I guess there's
    a data question here
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    of it has the sort of
    total [no-neck] sort of say
  • 15:31 - 15:35
    for-profit sector employment
    of econ Ph.D. program graduates
  • 15:35 - 15:38
    increased or has it just been
    a substitution from finance
  • 15:38 - 15:40
    and consulting towards tech.
  • 15:40 - 15:42
    - [Josh] I may be reacting
    to something
  • 15:42 - 15:44
    that's not really happening.
  • 15:44 - 15:46
    - [Guido] I've actually
    done some work
  • 15:46 - 15:48
    with some of these tech companies.
  • 15:49 - 15:52
    I don't disagree with Josh's point
    that we need to think
  • 15:52 - 15:54
    a little bit about
    the funding model,
  • 15:54 - 15:56
    who is it in the end paying
    for the graduate education.
  • 15:57 - 15:59
    But from a scientific perspective,
  • 16:00 - 16:03
    not only do these places
    have great data
  • 16:03 - 16:05
    and nowadays they tend to be
    very careful with that
  • 16:05 - 16:07
    for privacy reasons,
  • 16:07 - 16:09
    but they also have great questions.
  • 16:10 - 16:13
    I find it very inspiring
    kind of to listen
  • 16:13 - 16:16
    to the people there and kind of see
    what kind of questions they have,
  • 16:16 - 16:17
    and often they're questions
  • 16:18 - 16:22
    that also come up outside
    of these companies.
  • 16:22 - 16:27
    I have a couple of papers
    with Raj Chetty and Susan Athey,
  • 16:27 - 16:32
    where we look at ways
    of combining experimental data
  • 16:32 - 16:34
    and observational data,
    and kind of their --
  • 16:36 - 16:39
    Raj Chetty was interested
    in what is the effect
  • 16:39 - 16:43
    of early childhood programs
    on outcomes later in life,
  • 16:43 - 16:46
    not just kind on test scores,
    but on earnings and stuff,
  • 16:46 - 16:48
    and we kind of developed methods
  • 16:49 - 16:52
    that would help you shed
    light on that, onto some --
  • 16:53 - 16:57
    in some settings
    and the same problems came up
  • 16:57 - 17:01
    kind of in this
    tech company settings.
  • 17:01 - 17:03
    And so from my perspective,
  • 17:03 - 17:05
    it's the same kind of --
  • 17:05 - 17:08
    I was talking to people
    doing empirical work,
  • 17:08 - 17:10
    I tried to kind of look at these
    specific problems
  • 17:10 - 17:13
    and then try to come up
    with more general problems,
  • 17:15 - 17:18
    we formulated the problems
    at a higher level,
  • 17:18 - 17:23
    so that I can think about solutions
    that work in a range of settings.
  • 17:23 - 17:25
    And so from that perspective,
  • 17:25 - 17:28
    the interactions
    with the tech companies
  • 17:28 - 17:30
    are just very valuable
    and very useful.
  • 17:32 - 17:35
    We do have students now
    doing internships there
  • 17:35 - 17:38
    and then coming back
    and writing more interesting thesis
  • 17:38 - 17:43
    as a result of their
    experiences there.
  • 17:45 - 17:47
    - [Narrator] If you'd like to watch
    more Nobel Conversations,
  • 17:47 - 17:48
    click here,
  • 17:48 - 17:50
    or if you'd like to learn
    more about econometrics,
  • 17:50 - 17:53
    check out Josh's
    "Mastering Econometrics" series.
  • 17:54 - 17:57
    If you'd like to learn more
    about Guido, Josh and Isaiah
  • 17:57 - 17:58
    check out the links
    in the description.
  • 17:59 - 18:01
    ♪ [music] ♪
Title:
How Is Econometrics Changing? (Josh Angrist, Guido Imbens, Isaiah Andrews)
ASR Confidence:
0.80
Description:

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Video Language:
English
Team:
Marginal Revolution University
Duration:
18:03

English subtitles

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