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AUDIENCE: Was that going to be-- were
we going to do that session after you do this?
ROBERT TOWNSEND: No.
AUDIENCE: [? Or ?] [? will ?] [? we ?] do it before?
ROBERT TOWNSEND: We'll get your questions first.
That way, I can't time how quickly
or how slowly to go through this lecture.
So I'd just as soon start off.
AUDIENCE: I have a question.
ROBERT TOWNSEND: Yes.
AUDIENCE: I wondered if you could
explain in more depth how--
so, when you were--
when you're simulating structural models,
and there's an obvious distinction
between structural models in which people
are forward-looking and which people are not.
And it makes it easier if people aren't, but it's not
as realistic.
So I wondered if you can kind of explain like what are
the trade-offs in making people forward-looking and then how
you--
I think there was like a [? Vieira ?] paper
about how he was able, for the first time,
to like get at the transition and not just steady state.
So I didn't understand [INAUDIBLE]..
ROBERT TOWNSEND: Yeah, OK, so there's
like three or four different things in there.
It's a good question, questions.
First of all, this forward-looking
or not forward-looking, part of it is just tractability.
The first generation of these models, half of them
basically assumed what's called warm-glow bequest motive.
So you just sort of feel warmly about passing your money
on to your kids, but, actually, it's you,
and you are reborn the next period.
We went through LEB, which is this Lloyd-Ellis
and Bernhardt, which is a simple sort of bequest motive.
So you don't take into account the utility that's
generated by the bequest.
You just model it as a reduced form.
It's a bit like overlapping generations,
and that's why overlapping generations models are still
widely used.
We had another.
There was a QJE paper on financial volatility, which
was overlapping generations, another one on China
and savings rates, which was overlapping generations.
It's just far easier to solve a two- or a three-period problem
and splice together these overlapping generations
than it is to have a forward-looking behavior.
So it's a judgement call.
Now Paco, way back to his dissertation, but also then
subsequent papers with Kaboski and so on,
have this forward-looking aspect to it.
So not only do you have to kind of work
backwards on your own decision problem, but, to the extent
that prices and wages are endogenous,
you kind of have to forecast those too.
And, in rational expectations, you
would require that the forecast be rational, that households
are kind of getting it right on average.
Actually, that reminds me to say that one
of those flow of funds papers we reviewed briefly
was Monika Piazzesi and Martin Schneider.
And, although they were using US data,
they were looking at the effect of various things,
including a demographic shift, sort of baby boom generation
having more young people in the population, relatively
speaking, than before.
And they did not have the forward-looking behavior
because they basically assumed that the expectations you
see in the Michigan surveys are accurate.
So--
AUDIENCE: What were the expectations of?
ROBERT TOWNSEND: Prices.
AUDIENCE: Would they go up?
ROBERT TOWNSEND: Inflation rates.
So it wasn't like solving for an endogenous price in the model
and then having people forecast that.
They just short circuited the whole algorithm
by assuming that people's expectations in the models
were as measured in the data, even if those decisions that
got generated might be inconsistent
with those inflation forecasts.
And it was a reminder to many of us
how straight-jacketed we've become,
in a good and a bad way, with the rational expectations
because, once you have a way of pinning down
the future via these measured expectations,
you can expand the heterogeneity in the model and deal
with a lot more realism.
AUDIENCE: So, in that inflation, how far
did they forecast inflation in the survey?
ROBERT TOWNSEND: I think it was just the following year.
So the last thing you asked about were the transitions.
AUDIENCE: [INAUDIBLE]
ROBERT TOWNSEND: Sorry, I didn't hear you.
AUDIENCE: Oh, I was just telling him they
measured for different ranges of times.
I was like one year ahead and five years ahead.
ROBERT TOWNSEND: They were concerned
with consumer durables and housing purchases and so on.
So you'd want to have expectations
of some of those asset prices.
The transitions are the hardest thing to do.
You can have a model, which is forward-looking,
and solve for the steady state.
It's like taking a little tease off of all the variables.
So, you know, things will settle down
to one interest rate and one wage rate.
That doesn't mean the decision problems are static.
People are still forward-looking,
but they're looking forward at those prices.
And, in a steady state, there's always these offsetting
fractions of the population.
So some fraction become urgent.
The rest become patient.
There's always a fixed fraction.
And there's always a fixed demand and supply of funds.
And so you get these.
And you can almost analytically, sometimes,
get the steady state of some of these models.
Or, even if not, you can compute them
much more readily, whereas the transitions, by definition,
you have a whole path, and you have
a path of guessed interest rates and wages and so on.
And people take that path as given and optimized.
And then you have to look at the excess demand
and supply for labor and for capital
not only in the current period, but all these future periods
for which they've already made a guess,
and then have this algorithm to somehow adjust the guessed path
and hope, somehow, it will converge.
So Buera and Shin did that originally
and have modified the code.
Yan, you've played around with that code.
Other people have as well.
It seems to work, but it--
if you take it to a new application,
you've got to kick it and push it around a bit
and get scared that it's not going to work at all.
And so it's certainly not a silver bullet,
but it does seem to work for particular applications.
Am I answering all--
AUDIENCE: Yeah.
ROBERT TOWNSEND: OK.
AUDIENCE: So I actually have a related question.
So I remember when we were talking
about the computation issues in one of the extra sessions.
We were talking about having myopic agents
and saying, well, in the profession,
we're not that interested in myopic agents.
But I feel like there is potentially
some middle ground between saying,
well, I have to have an agent that
looks to the infinite future, or I
have to have an agent that is totally myopic.
And I feel like it's actually not
more sensible to say this agent looks to period infinity
than to say this agent doesn't look forward at all.
So I'm wondering like what way you see like--
is there a reason I'm not understanding why one is
more ridiculous than the other?
Or like I mean--
ROBERT TOWNSEND: People take positions.
There's no question you're going to get opinions.
Some people would dismiss the myopia out of hand as outdated.
Other people are convinced there's
a lot of irrationality and behavioral things.
And I do think that taking the expectations as measured
in the data is quite reasonable thing to do.
And some overlapping generations models
are, with realistic lifetimes and mortality and all of that,
are really, in some sense, the most realistic of all.
Granted, you still have to deal with this bequest motive,
but, you know, I'm practical.
I think it depends on the question at hand.
And it's nice to be fully rational and forward-looking
and all of that, but computational,
dimensional issues will limit the model you want to analyze.
And, sometimes, it seems unreasonable.
So I mean, for example, in IO--
I'm not sure if we'll get to this today--
the state spaces just expand dramatically when
you start thinking about strategic behavior.
And then you can actually show, if you
wanted to keep track of where all financial providers are
going to locate with a finite number of nodes,
kind of working backwards and having--
that becomes a very computationally intensive
problem.
And it seems reasonable to say, well, let's just have them
forward-looking for two, three periods or something,
a finite number of periods, as an approximation.
AUDIENCE: I think it's so interesting
because, from outside the profession,
a lot of the criticism we get is, oh, you
assume that these agents are totally forward-looking
and perfectly rational.
You guys should see what happens in your models
if you don't assume that.
But then, within the profession, if you
were trying to assume that, people
would be like this is a waste of my time.
Leave me alone.
ROBERT TOWNSEND: Well, the profession comes and goes
in terms of its rational expectations,
which was kind of invented by Muth in 1960-something I think,
late '60s.
And then it took over macro with a vengeance,
well, in the sense that there were a lot of unhappy campers.
So people had stakes in doing certain--
one way or the other.
And then you need to start putting back
ingredients in the models to explain rigidities
and slow-moving behavior.
I mean, personally, I'm not so sure sticky prices is
any more compelling to me than sort of some fixed expectation.
So those are-- you'll just have to develop your own judgment
somehow, but there isn't a single right way to look at it.
I think it really does depend on the applications.
And, especially, as I said, if you want to break new ground
and explore a different kind of model,
you may have to make some approximations.
Yes?
AUDIENCE: So I have a more general question,
sort of how to interpret one of the classic models that we saw.
So it made a lot of sense to me like the last part
of the other course where we looked at insurance
and how to fit data with different models.
But there was that one lecture on TFP that I am kind of not
clear exactly how to think about it
in the context of this class.
So, the paper by Zilibotti and other people on China,
how do we--
how should we think about it in the context of this class?
ROBERT TOWNSEND: Well, the way I think about it is we're
interested in development and development issues.
We're interested in the impact of financial programs.
And, for both those reasons, it seems
like we have to take into account how
the entire economy is evolving.
People don't narrowly stay in their village.
They can migrate.
They're forward-looking.
Potentially, there's urbanization
going on and so on.
The starting point-- and I put it
at the beginning, rather than the end of the course,
was what models do we have.
Let's take an inventory of the different kinds of models
we have to try to deal with some of the puzzles
like why is money leaving China while the rates of return
are so high in that paper that you mentioned,
"Growing like China."
It is true, for example, that a lot of macro guys
are happy sort of modeling TFP as a process.
The business cycle literature, following
the lead of Prescott and Kydland,
is very much like that.
The development literature still has those ingredients
in the sense of allowing individual talent
and productivity to vary, but part
of what we see in the aggregate data
is an equilibrium phenomenon.
TFP has to do with potentially improving allocation
of resources across individuals and households.
So that brings us back to the micro data.
And the way I see the inter-relationship is I
could have equally well started with the micro underpinnings
and built up to the macro, rather
than starting with the macro and then getting into the micro.
It is true, I think, that you need to iterate back and forth.
You tend to lose the perspective when you're just reading
macro development papers.
So what does this have to do with the poor people
in an Indian village?
Or, vise versa, when you're trapped in a village,
you can't see what's going on in the rest of the economy.
I mean, it's just my firm belief that we absolutely
have to do both.
Although, how much of one versus the other
depends on the application.
But this is a macro development class.
So it really has to get us all up
to speed and comfortable with sort of macro.
And the cool thing about macro development
is this whole generation of models
that's coming along that are explicit about micro
underpinnings and potentially testable, whereas so much
of the macro literature, real business cycle
literature, new Keynesian, and so on,
just make assumptions about what's
going on at the individual micro level and never test.
Markets, institutions, the nature of financial contracts
are all primarily just assumed and taken
as given in the more standard macro
literature, whereas development is kind
of a wonderful playground, if you want to put it that way,
for seeing just, whether you're going from urban
to rural areas or one country to the next,
you can see these constraints being estimated and varying.
And the work we did in the class shows that that
matters for the macro.
AUDIENCE: Yeah, so I guess that my point of view, maybe
also because of my background, but it seemed to me
that, the last part, we really were in villages like seeing
how people do risk sharing and stuff like that and your paper
with Kaboski about the Million Baht program.
But them it seemed maybe we didn't
do that much on urban, or most of the emphasis
was on the rural side.
I mean, we said some of the implications
for the urban model and how the data do in the urban setting.
So it's kind of a little hard to me to go back to that macro
where we kind of don't have firms.
Like it's unclear to me exactly what
we're going when we're talking about individuals
having projects.
And, oh, they might employ some other people in the village.
But that still sounds to me as like little things.
And then how do we translate that
into if we think more about it in a macro perspective
where there's really no firms and [INAUDIBLE]??
ROBERT TOWNSEND: Well, the Kaboski paper it's true
used the annual rural resurvey.
So it's about village and village size.
But, before we did that, we did the paper with Alex.
And that's about obstacles.
And there we used the annual data
for both the rural and the urban or the annualized
version of the rural.
But, anyway, half of the estimation
was about urban households.
AUDIENCE: Yeah, I remember that.
ROBERT TOWNSEND: And so--
AUDIENCE: And we said that the rural [? other ?]
was very important.
But it still kind of don't resonate with me like how--
like what are we thinking about when we think--
when we think about rural?
Are we thinking about firms?
Are we thinking about little resalers?
[INAUDIBLE]
ROBERT TOWNSEND: There's a whole spectrum of size and scale
of firms in these countries.
And it's true that, in the rural areas,
they tend to be small, small or medium enterprise, household
enterprise.
That's actually true as well in the urban areas.
Most of the firms in these countries are small.
Most of them don't even hire employees.
Now, when we're doing macro proper,
we want to make sure that we also
include the medium-large firms if we
have the relevant data on them, but I'm not
sure I see so much of a difference
between urban and rural.
For example, there are very active money markets
in the urban areas.
Credit constraints, in some respects,
seem as severe or at least constrained,
very high interest rates, money lending,
despite the proximity of financial infrastructure.
So, yeah, you can study urban neighborhoods.
I once did this in Chicago.
I started looking at urban neighborhoods,
a Mexican neighborhood, a Korean neighborhood,
an African-American neighborhood,
and just seeing the differences and whether the institutions
and the data seemed consistent with certain outcomes.
So, as far as the method goes, I don't see a big difference.
It's true that you're often persuaded by the data you have,
and this monthly data is rather unique to the rural areas.
So we've done more with that, but that's just a constraint.
It's not some-- we're thinking about, god willing,
we will gather these monthly financial accounts
data in urban areas soon.
AUDIENCE: So, from my perspective,
I think that, when we talk about rural and urban,
if it's not Bangkok, urban isn't really like urban here.
It's just like small towns.
So I wouldn't think that like--
I would think that the results from the rural data [INAUDIBLE]
generalize to urban areas.
But when you think about--
I think your question is more like big, big firms
or big firms in developing countries.
AUDIENCE: Yeah, so my point is that, sometimes,
most of the time, like, when I think about TFP and studies
about TFP, they have this concept about TFP growth
like in a country, which is possibly unclear
exactly what it needs.
But it's like how much of what we're
doing at the micro level then translates into this
like super aggregate measure?
AUDIENCE: And you think that most of this
may be coming from the large firm?
AUDIENCE: I don't know.
I mean, I just-- again, like China is different,
but like that one paper was about state-owned enterprises,
which I'm assuming are big.
Like that one made sense and was sort of--
which is why I kind of can't reconcile the two.
ROBERT TOWNSEND: The Jeong, Townsend paper is about TFP
at the macro level in Thailand.
And there we use socioeconomic survey data,
which includes some data on larger firms.
I suspect it's dramatically under sampling larger firms.
And, nevertheless, we explained something
like 72% of TFP movements at the aggregate level.
So I mean that was a real eye-opener that development
in micro is all about--
largely about macro TFP.
It's just efficiency.
You know, TFP literally means nothing more than how much--
or, changing TFP, how much more output
can you get period after period, even
holding the level of capital and labor and so on fixed?
So, to the extent that intermediation allows a better
allocation of resources based on individual talent,
you should expect it to show up in TFP.
AUDIENCE: You do have a new survey
that focuses on big firms.
Do you have any work in progress that maybe the result is
different from what--
ROBERT TOWNSEND: Well, it turns out
that big firms are harder to find in some sense.
They're harder to get into.
This TEPS survey in Thailand ends up
looking a lot like the urban survey,
even though it was designed to try to pick up the larger ones.
I mean, there is something called the missing middle,
and I don't know whether it's missing because of the survey
design techniques that are used or just missing
because it doesn't exist.
But the missing middle refers to the sort
of life cycle of firms and the fact that we tend to see either
the very big ones, as in surveys, census
of manufacturers, or the very small ones, as in household
survey data.
And the interesting thing is, where do the big ones
come from.
What is their history?
Go back in time.
Did people move out of villages into the town
and set up their businesses there?
And that's something we don't know as much about
as we would like to know.
It's even hard, actually, to imagine
how to sample to figure it out because everything
is endogenous.
You can comfort yourself when you
have a fixed enumeration of households, as in a population
census.
Then you just sample at random.
Or you have an official registry of large businesses,
and then you sample them at random.
So we can take comfort that we have
an unbiased, stratified, random sample,
but that doesn't get at this missing middle if--
yeah.
Yes?
AUDIENCE: So I have another very general question
about structural model.
So we've seen a lot of models with this heterogeneous agent.
So, obviously, there's all kinds of sources of heterogeneity.
And, in your model, you only choose to model--
you cannot model all of them.
And what you're going to model is
what you think is playing a key ingredient in driving
the mechanism of the model.
So, for the other heterogeneity, it's what?
Like, in the paper with Kaboski, you just filter--
adjust your data by filtering the other heterogeneities.
I'm just wondering whether like, for example, people
think that there are three or four key heterogeneities that
are driving the decision-making at the household level,
but you're only modeling one of them,
and you're filtering out the others will be
getting a lot of [INAUDIBLE].
ROBERT TOWNSEND: Integrating up I would say,
rather than filtering out.
There is a question of the dimension
of the unobserved heterogeneity that's in that model
and in what we're going to talk about momentarily.
You have-- like an econometrician,
it's very similar in spirit.
You have the observed covariates x on the right-hand side,
and then you have some unobserved household
or other fixed effects on the right-hand side.
And you can assume all kinds of things about those unobserved
heterogeneities, but you have to assume something basically.
It's hard to do something completely non-parametric.
So you're making assumptions.
I'm not denying that at all.
And it matters what you assume, but it's not
as though the heterogeneity isn't there.
AUDIENCE: So it's not about unobserved.
So, for example, household [? that respond ?]
in that paper, the heterogeneity is
in permanent income and wealth, liquidity, wealth,
and permanent income.
ROBERT TOWNSEND: Yeah, wealth might be observed.
Permanent income is not observed.
AUDIENCE: And household composition is observed,
but it's not modeled.
ROBERT TOWNSEND: Oh, now that's something we did for the data.
And that's more a trade-off in methods.
Let me draw an analogy.
If you did real business cycle modeling,
you would basically take a trend out of the data.
I mean, in the US, there's just this huge trend.
I mean, if you looked at the business cycles
around the trend, you'd like what is this subject called
business cycle.
Of course, when you're in a recession,
it seems real enough.
So people have a filter.
You could take a linear trend out.
You could use this Hodrick-Prescott filter
and take out some kind of smooth moving average.
And then, having taken it out, you'd
just focus on the deviations around the trend.
So what we did was take out all the demographics, which
is not pleasant either, but, I mean, it at least--
the other thing to do is to leave it in the data
and use a model that doesn't have it.
And that seems weird too.
That's like doing business cycle modeling
and leaving the trend in.
AUDIENCE: So, id you don't-- for example,
but, if the question that you're--
like the model, like, if people think that household
composition or demographics is playing some sort of key role
in the household decision-making,
but you're filtering it out--
ROBERT TOWNSEND: That's a fair criticism.
AUDIENCE: So there are criticisms like that?
ROBERT TOWNSEND: Yeah, yeah, absolutely.
Yeah, well, it sounded like all the research got done.
Oh wow, we'll see you on Thursday.
No, no, those are good, productive thoughts,
which is it's not like--
you can be sort of uncomfortable with research in general
and the apparently arbitrarily way
in which different researchers make assumptions.
But where research kicks in in a very positive way is to say,
no, I don't believe that is a reasonable assumption.
I think it would matter a lot.
Let me try to incorporate it and maybe leave out
some other things and see what difference it makes.
That's where good research is born, absolutely.
Yes?
AUDIENCE: So a lot of the models that we've covered
has a wage as a key parameter.
But, in like some research in, if I remember correctly,
your paper in Econometrica doesn't find a effect
on wage, at least short term.
And, also, like Esther and Abhijit's paper
didn't also find wage, like effect on wage,
of microfinance [INAUDIBLE].
ROBERT TOWNSEND: Joe and I did find an effect
on the wage in the astructural model.
AUDIENCE: Oh, OK so that's totally [INAUDIBLE]..
ROBERT TOWNSEND: Yeah, yeah.
AUDIENCE: I missed that.
ROBERT TOWNSEND: But, again, that's
a good example again of these trade-offs, which
is you have a sort of a search algorithm
through the lens of a somewhat reduced form,
maybe instrumented variables to see what's big
and what's small.
And we found an effect on the wage.
Now it is true that in that model,
the structural Econometrica paper with Joe,
we did not try to make wages endogenous.
In fact, we allowed investment, but we moved away
from occupation choice and these key ingredients
of a lot of these macro models.
It's not like it couldn't be done.
It was just something we chose not to focus on.
AUDIENCE: I guess my point is--
so I don't think there are a lot of evidence, especially
[? treating ?] endogeneity of financial development.
But it seems to me that, at least
like RCT of like MFI and [INAUDIBLE],,
they usually didn't find, but I only know two papers,
but they didn't find like effect on wage.
So I'm kind of wondering like what's your [INAUDIBLE]..
ROBERT TOWNSEND: I think, if you think about--
you have to think about the scale of the intervention,
all right?
So, if you have an RCT, even with a fairly big sample,
in a small, open economy, it's arguably true
that you wouldn't necessarily expect
to see anything with the wage.
But, if you were scaling that same experiment up
to the national level, then you probably would see it.
Actually, that's a very good argument
for doing both basically and, in particular,
not getting misled by program impact.
Ester's thesis in Indonesia was about education reform.
And she was worried about whether the wages would
go down, as you had more educated cohorts.
So people have worried about this kind
of thing for quite a long time.
There is some work in Canada.
RCTs are a bit sort of like they look new, like people maybe
haven't been doing them much, but it's not true actually.
If you can go back into the '60s and '70s,
there was work in Canada on I think
it was either a pension or social security
reform where they did exactly what I was saying.
They kind of backed that impact locally,
and then they extrapolated to what
would happen to the rest of the country
if it were implemented nationally
with the structural model.
Yes?
AUDIENCE: So this is a very broad question,
but [INAUDIBLE] asked something about the financial
[INAUDIBLE],, especially corporate financial [INAUDIBLE]
equity markets and money markets.
So these markets of course are [INAUDIBLE]
to the way economic surplus are divided into different people
or different organizations.
So, as a result of that [INAUDIBLE] impasse,
how, for example, new projects or new policies
have benefited different types of groups or organizations--
so there are some political economy issues related
to the imperfection of the corporate financial
[INAUDIBLE].
ROBERT TOWNSEND: Impact of the corporate finance on--
AUDIENCE: Structure on-- so political economy concerns
about who benefits from new policies and new projects.
ROBERT TOWNSEND: Well, there's two things.
On almost anything we've done, there
is sort of the winners and losers.
And that, in principle, that has nothing
to do with political economy.
That's just mapping out who's going to get the benefits.
Now the next step is, if you think there are barriers,
it may be that the losers are blocking reforms.
And that's definitely a political economy.
AUDIENCE: Oh yes, then my question
is does the literature seriously investigate
the second step you just described, [? fair ?] wages.
ROBERT TOWNSEND: I mean, Daron has occupation choice models
where, basically, the oligopolists are the ones
sort of dictating the outcome.
And they are a force for financial repression
because they want to keep the wages low.
I mean, that's at least a step.
We, in this class--
let me try to think if we actually covered it.
A bit, yeah, you saw one version of that.
We can model how financial service providers
ought to be behaving if we believe
the structure of the model.
Then we look at the data, and we discover
that there are gaps in some places
and too many banks in other places.
And so, through the lens of the model,
you sort of get suspicious that--
develop a null hypothesis that there
may be political considerations at work.
Now how explicit or evil or well intended it is, that's--
you have to go in there with your eyes
open, but not necessarily assume.
AUDIENCE: I have another question if no one else does.
ROBERT TOWNSEND: OK.
AUDIENCE: So--
ROBERT TOWNSEND: Anything but the lecture, huh?
AUDIENCE: No, I just-- this is--
so I'm kind of embarrassed to ask this question because it
seems like it's something that I should have been
able to figure out on my own.
But you see a lot of people throwing
around the word structural in ways that are confusing
and in ways that sometimes--
sort of I guess the natural and then
from what we've looked at and all your papers,
I guess I'd sort of be inclined to say your models
are only structural if you are identifying
like underlying parameters that are in a model of interest
using econometrics or using calibrated simulation.
But I see a lot of people calling stuff structural that
doesn't necessarily actually identify those parameters.
Have I got the wrong end of the stick?
Or is there like a nuance that I don't see?
ROBERT TOWNSEND: I don't--
I don't actually know for sure, depending
on who's using the words, what they have in mind.
AUDIENCE: Yeah, me either.
ROBERT TOWNSEND: I think, legitimately,
many people are worried that assumptions are being made
in structural models, which are never tested,
and that you're seeing the observables
through these auxiliary assumptions.
And, if they were sort of totally off and totally wrong,
then you're not learning anything.
So that tends to push you toward data summaries.
It tends to push you toward not assuming things.
But then there's a flip side, which is actually
the lecture, which is this notion that there's
theory-free tests.
It's just not true.
Even though RCTs and--
you know, they're all assuming some things
that you might think are rather innocuous,
but, when you start to think about the structure
of decision-making, there's content there.
And those assumptions could be wrong.
So I view the tension that way, which
is trying to assume as little as possible,
if you can get away with it, in terms of the underlying
distributions of unobservables and so on,
but, at the same time, mindful that data work without theory
has big limitations.
And that's a debate, philosophical and otherwise,
that goes way back, I mean, back to business cycles.
You know, Burns and Mitchells--
Burns and Mitchell have this business cycle measurement,
the NBER dates actually.
And Koopmans came along, and he said it's just crap.
And there was a lot of fighting.
So how can you measure something if you don't have
a model in mind, et cetera?
So I'm not saying there aren't good answers to that,
but this is not just a debate in development economics.
This is, again, kind of a judgment.
AUDIENCE: So it's a kind of vague question,
but we've seen in the lecture a lot of models and evidence that
financial [INAUDIBLE],, [? let's say, ?] sort of like
what financial development does to the real economy.
And how would you compare that to the situation
in developed countries?
What is like-- what is the difference?
Because like a lot of models--
at least model-wise, you can apply it
to developed countries.
And, at first glance, like, for example,
like for consumption, people aren't risk sharing quite well.
So like it's quite close to this situation
where the market is complete.
On the other hand, if you look at macro level,
as we saw in the first lecture, like there's--
or like people tend to think that financial development
[INAUDIBLE] varies over countries
and affect [INAUDIBLE].
ROBERT TOWNSEND: Well, we've used the US as--
some of those papers used the US as like the benchmark standard,
as if it had perfect and complete financial markets.
But I think we all know now just how
badly the US and other advanced countries
have done over the last--
since '08, '09, and so on.
So, actually, what's going on in macro
is this huge rebirth of interest in modeling
the financial sector.
There's no consensus on how to do it.
And I think that literature would
benefit from the advances that have
been made in macro development.
But there's no reason why--
I mean, the same issues will come up
when thinking about the US or Spain.
I mean, Spain is an example, for a good example,
depending on whether you think it's an advanced industrialized
country or not.
Spain liberalized its financial system in the late '80s
with a series of reforms on interest rates
and the nature of business, of savings and loans,
and all these providers.
So these little regional banks, like Santander up there
in the north and, basically, along with many of the others,
expanded their financial infrastructure,
establishing branches, spreading throughout the rest of Spain
and, eventually, of course, on to Latin America.
And then now we have this mess, right?
So what went wrong?
Or what went wrong in the US mortgage crisis and so on?
So I think those are--
there's hardly a consensus, as I said,
but those are really important topics.
This is haunting countries, as well as the IMF and the World
Bank and so on, now because countries want to grow.
There's a lot of poverty, a lot of inequality.
And there is this notion that expanding
financial systems, finance, can cause growth,
and growth can trickle down and help reduce poverty and so on.
So there is this sort of development view
that finance ought to expand in some way and cause growth.
And, at the same time, there's this worry that expanded,
deeper financial systems are going
to be prone to instability.
And that's a force for repression.
And that was the first lecture of the class,
actually, was how do we deal with both these,
get both these things on the same page.
But, for sure, today in the world,
throughout Africa and Asia and so on,
people are sort of wanting to expand
and, at the same time, quite scared, as regulators,
that they're going to take the blame in the future
when something goes wrong.
All right, so I'm going to give you a 30-minute summary.
Fortunately, some of these themes have come up.
And, as always, the lecture notes
are on the Stellar website.
So it's OK if we don't cover everything in enormous detail.
Oh, by the way, one last thing, which is I
did write out, for those of you who
are worried about the generals, sort of a reader's guide
to the--
not a reader's guide, a summary of the questions
that we've been addressing in the class.
It's just kind of a way to organize the material,
and I'll post that on Stellar and--
who's also going to have some sessions.
I think there's only four or five of you that are actually
going to take the generals, but, if you are,
it's easy to get lost in all this material.
So I wrote up this sort of summary.
AUDIENCE: [INAUDIBLE] [? going to ?]
need it for next year.
ROBERT TOWNSEND: Yeah, well, by then,
research will have advanced, and we'll--
OK, so this is about this structural
versus reduced form stuff.
And the topic is the impact of expanding financial systems.
So it's both a methods and a topic that basically
is the hallmark of the class.
There's three things.
I'll probably only barely get through the first.
One is sort of IV versus structural analysis,
all in the same model, basically.
The second is this expansion of banks in Spain,
which I was already mentioning, using what the IO guys do,
the way they avoid having to spell out
the entire structure of the equilibrium
by using reduced form Markov processes.
And then, finally, if I were to have time,
I could tell you about the limitations of that.
The point is there's no one thing,
but you need to be aware of the strengths and limitations.
So this is a paper I wrote with Urzua,
and it's about occupation choice and about
unobserved heterogeneity.
So a lot of this is already familiar.
You've got sort of your wealth if you're
a wage earner, your wealth if you're an entrepreneur.
Instead of having just a flat wage for everyone,
there's sort of this productivity shifter,
which varies with households.
Or you can run a firm.
There's some setup costs unobserved, again,
varying with households, [? i. ?]
The profits come from maximizing revenue
less the cost of inputs.
In this case, there's no financial sector.
Ironically, I'll get there.
And you just have to finance the setup costs and your capital
with your initial wealth.
So you saw this.
This was day two.
This was the Lloyd-Ellis and Bernhardt model,
versions of which we took into or modified
in some of the other papers in the literature.
So you get to choose one or the other.
So we can create this decision rule, a dummy D
for do profits dominate wages.
If so, do it, and, otherwise, don't.
OK, so it's an indicator.
Note that it depends on observables,
the economy-wide wage and the wealth,
which varies across households, and these unobservables, which
are these setup costs or talent for both being a wage earner
and being an entrepreneur.
We're not going to see those.
So, as you know, it's sort of naive--
you know, incredibly simple, but, in some respects,
powerful way to write down the observable
depends on which thing you do.
But, if D equals 1, then 1 minus D is 0,
and you have the profits and earnings of being a firm
versus, if D is 0, you're going to have the wage earnings
from being a wage labor.
So you can write down, if you were running
a regression of earnings, you could let that regression
have wealth in there and wages.
And then you'd have this sort of dummy,
which is a 0, 1 binary variable, plus some epsilon term,
as regressions tend to have.
However, following the structure of this model,
we know exactly what the epsilon is.
It's this D-weighted average of the talent.
Now you don't change your talent when you make an--
you have your talent in both of those occupations.
And that's what's driving the choice, OK?
So, basically, the worry, correctly,
is that D on the right-hand side is
correlated with epsilon because that's self-selection, OK?
You choose the occupation with which you're most productive,
and part of that choice is based on unobservables.
So we see it both in the choice and in the error.
OK, so we need an instrument.
Let's imagine in this economy there's some subsidy or tax.
And it's lump sum and administered, say, randomly.
Well, then, clearly, the decision variable,
which has a phi in it now is influenced by that.
And, the higher is the subsidy, the more likely
you are to be running a firm, other things equal, right?
But, because it's lump sum, this instrument, this subsidy,
affects the choice of what occupation you're in,
but it does not influence the outcome conditioned
on choosing.
If you choose to be a firm, you're
going get that subsidy or not.
And that leaves all the other decisions about labor to hire,
et cetera, et cetera.
And none of those decisions, none of those outcomes,
depend on the subsidy because that's
sort of bygones are bygones.
You can't do much about it.
All right, so we could imagine running that regression,
but we regress the decision to be a firm in the data
onto observables, like the wage and wealth,
but also onto this subsidy as an instrument.
That's called the IV, the instrumented version
of the decision.
And then we run the outcome function
with the instrumented version, OK?
So, hopefully, this is a review.
And what is the policy outcome?
The coefficient on d after instrumenting it
is supposed to be the gain to being a firm,
as opposed to being "treated," quote, and being a firm rather
than being a wage earner.
But it isn't exactly that.
Or, at least, put on your thinking cap.
It's the local average treatment effect.
It is the average gain in income for those people induced
by the subsidy to switch from being wage earners to being
entrepreneurs.
So you're not getting--
you're not getting other things, which
are easily, similarly sounding, like treatment on the treated.
Treatment on the treated sounds like, oh, well, people
became firms.
What was their profits relative to the earnings they would have
had if they were wage earners?
That's what this says, right?
What is the average in the population of profits
with the thetas in there less wages,
given that they made the decision to be a firm,
and their wealth is basically b?
So I'm doing this wealth category by wealth category.
And this is the average treatment effect.
This is what the average would be
if the whole population became firms, taking wages
as given and so on.
So, basically, be careful because LATE is not
necessarily equal to-- what you get with the IV is not
necessarily equal to treatment on the treated
and not necessarily equal to the average treatment effect.
Is this a review for you guys?
OK, good.
So you can put more structure as a way
to back out the average treatment effect and so on.
Now this is like assuming that those error
terms are multivariate normal.
And then you run this logistics regression, basically,
binary yes, no, be firm or not.
And you can back out these variance terms,
assuming that there's no--
that the talent things have say zero
mean, or you could adjust for that.
And then you can get expressions for--
so then you want to basically run your regression of profits
onto those people who have chosen
to be firms, controlling for their wealth and their subsidy.
And, if you didn't, if you weren't careful and did OLS,
you'd have a bias, but you basically
use that first stage probit to get a propensity.
The structural model tells you what
the exact formula is for the selection based
on the unobservables.
And you put that into the regression.
You literally put this into the regression and run it.
And it, quote, "solves for the selection bias."
Yes?
AUDIENCE: How much more computationally difficult
is it to use some kind of data distribution
or like some other kind of-- because normal
is a bit of a stretch as an assumption for a talent
distribution.
ROBERT TOWNSEND: Oh, I think that's quite doable.
Look, this is just meant to be an example, a classic example,
but--
AUDIENCE: Cool, so it's not too--
OK, sounds good.
ROBERT TOWNSEND: But, I mean, this
question that you guys were asking, so here you've made--
you're able to get average treatment effect
and treatment on the treated, but only
if you make some assumptions.
And some people don't like that.
They'll say, oh, you know, it's too structural.
But, in order to get answers, sometimes, you need--
AUDIENCE: You have to make assumptions.
ROBERT TOWNSEND: OK, now, I mean, the most powerful way
to say this is we actually see the counterfactual
through the lens of the model.
We can see what the wages would have been for people
who chose to be firms.
By definition, they're not in the data.
And this is a generalized version.
And I'll just say that this is like local average treatment
effect, but really local, like just taking derivatives
of how that difference between profits and wages
is going to move, holding wealth constant,
as you vary something like a propensity score.
And the propensity score has, not too surprisingly,
everything to do with those unobservable talents.
So this is hard to implement.
This one is really not easy.
Josh doesn't like it, among other things,
but he's not disagreeing that it's wrong.
He's just thinking it's just not as operational.
Now the nice thing, if you could back this out,
then you can ignore this page, but, basically,
treatment on the treatment and the average treatment effect
are just sort of integrals of these marginal treatment
effects.
Yes?
AUDIENCE: So Josh sometimes recommends
to apply LATE to non-compliance based
on observable characteristics.
So, in the typical data set in the context
of occupational choice, what can we
observe among characteristics of [INAUDIBLE]??
ROBERT TOWNSEND: So here the model
is taking a stand that we're going
to see their initial wealth.
We're going to see the factor prices that they faced.
We're going to see the profits that they make.
And we're not going to see these unobserved things that
are driving fixed costs around.
This is not the only model around.
We're going to use the model to make these examples.
I haven't gotten sort of to the punchline.
I'm happy if it's a review, but--
OK, so we have the model, right?
So, if you don't believe this miracle,
let's simulate the data from the model.
Instead of looking at some of the cross-section that
got treated with the subsidy and others that didn't, let's
take a particular individual in the model at given parameters
and see what the behavior is, in terms of switching occupation
and so on, as you turn the subsidy on and then average up
in that wealth category.
So believing in the miracle is basically the math that you're
going to see the average of this model-generated simulation
of impact will be the IV.
It's going to be LATE under these assumptions.
So here is the IV estimate.
And, roughly averaging over wealth, et cetera, it's 0.45.
OLS is-- by the way, OLS is terrible,
but I guess you don't need to know why.
There's nothing productive about the subsidy.
The subsidy is just inducing people
who should be wage earners to be firms.
Of course, the impact is negative.
And OLS estimates it to be positive.
Why?
Because, in the observables, it's
the talented people who are firms,
but they're not being induced by the subsidy.
They're already firms, right?
I couldn't help myself.
OK, and this is the model-generated treatment where
we actually have the panel.
And you can see, numerically, it's quite close.
All right, so that's reassuring.
Now let's go back to the topic, which
is what is the impact of expanding
financial infrastructure.
Let's invent a village fund.
Let's let banks establish branches--
forgive me for the moment-- at random.
Like maybe the government told them something,
or I'll get to that, but, for now, random.
So, if we observe these travel times or the effectiveness
of local financial infrastructure,
we can call this cost here to be an instrument, right?
So, yeah, so the idea is just like the subsidy.
The lower is this number, the more inclined
you are to incur the cost to be able to do the banking.
OK, we rewrite the problem.
This is the part of the model with financial infrastructure.
They get to borrow money to cover the capitalization
and the fixed cost.
Wage earners, by the way, also get
to put their wealth in the bank and earn money, interest
rate on their savings.
You've seen this model.
This was, again, the second lecture.
And now we have the observed outcome.
If you are in--
if you chose to be in the intermediated sector,
you have another occupation choice problem
within that sector.
You can be a firm or a wage earner.
These D's denote that choice.
Notationally, it's got a w and r in there.
That r is a clue that we're in the intermediated sector
because there's an interest rate.
Because, if you were in the autarky sector,
there's no borrowing and lending of funds.
And, of course, this is the occupation choice
we had before without intermediation.
So we're going to get sort of the observed
earnings in the autarky sector and observed earnings
in the intermediated sector.
And, within both sector, there's this other margin going on,
which is the occupation choice.
All right, now we're reaching the point here.
We have a double margin.
There are two things people are choosing from,
whether or not to go to the bank and what occupation to have.
Sounds like it shouldn't cause a problem.
This is basically--
I'm sorry this is so hard to see,
and it must be even harder for you than me.
I'm right next to it.
This sort of symmetric Y is like an indicator variable
for choosing to be intermediated.
These Y's with a little bit of tail off to the right
are incomes under intermediation and autarky.
It's this way in the paper, and somehow the slides
got created this way.
But, anyway, so the gain is--
let's skip that.
So what can I say?
It's a double margin.
So you get to choose whether or not to go to the bank.
And, within that, each category, you
get to choose your occupation.
We can linearize the profits functions to be functions
of the observables and the unobservables,
substitute in those decisions, and write out the expression
for what you see in the data, namely, this income,
but it's just a weighted average of all the choices,
depending on whether these D's are 0 and 1's or these
symmetric Y's are 0 or 1's.
That was a bit of a rush, but, I mean, I think,
conceptually, you understand where this is coming from.
But, not only that, look at this error term.
Oh my god, the error term is complicated.
And it also has all these decisions,
but note it's got the occupation decisions in both sectors.
Depending on what you do, certain terms
are going to kick in and kick out.
There's massive selection going on about whether or not
to go to the bank, based on what occupation you're
going to choose, conditioned on going to the bank
or not going to the bank.
And we don't see any of this.
We just see the choices.
We don't see these error terms.
So what can an instrumented version of intermediation
give you?
It can give you the local average treatment, the income
gains that are a consequence of a lowered cost that
allows you-- makes you want to go to the bank,
as opposed to what you would do if those costs had been higher,
and you're not going to the bank.
So the local average treatment is the impact
on earnings that have to do with those people who are induced
to join the banking system as a consequence
of the placement of branches or varied costs of access.
Now you have to be careful in terms of what this isn't.
For example, do we want to look--
what do we mean when we say we want
to measure the impact of improved
financial access on the profits of entrepreneurs?
You want to do an IV for that?
Good luck.
Now what can go wrong?
Remember monotonicity and independence, OK?
So monotonicity means that more people
are inclined to do something as a consequence of the policy
variation.
Are more people inclined to be firms
as a function of having reduced cost of financial access?
Maybe if they were credit constrained and then
make a lot of money in business, but there's
another subset of the population,
those not so talented people who are in financial autarky, who
would be actually induced to leave business and put
their money in a bank.
So it's not monotonic.
The effect of improved financial access
is not monotonic on things like the income of entrepreneurs.
You can, if you're careful, think
about what is the impact on profits of entrepreneurs,
of those people whose decision to be
an entrepreneur is not affected by financial intermediation.
Now that's an interesting question,
but it is not necessarily the one
that we had in mind to begin with.
And who are those people?
Well, it kind of depends on their talent, right?
So you're making statements about where these talent
cutoffs are when you're conditioning
on, quote, "things like people who were not making their"--
if you just run an IV in this cross-section,
just be mindful of what you want to do with the occupation
categories.
Now this is where the rubber hits the road.
Am I just talking fancy theoretical models here?
I don't think so.
This is just the most innocuous kind
of unobserved heterogeneity.
And yet it tells you that IV is not a panacea.
You have to be very careful when you
use it to think about the questions
you're able to answer.
So let me just jump.
So, again, we have the model fortunately.
So we can generate any data that we want from the model
and talk about occupation choice or talk
about financial intermediation.
So, at the parameters we put in, which
have to do with that LEB thing you saw in the second lecture,
roughly, we can talk about moving people
in the model from autarky to financial intermediation,
in other words, changing their choice problem,
damning them to autarky, or allowing them to be
in the intermediated sector--
that's the conceptual experiment--
and looking at what happens to people by occupation.
So we've got guys who are wage earners before and after.
They're not moving.
And we kind of get their gains.
We've got people who move from the wage-earning category
under autarky to entrepreneurs under financial intermediation,
and you'd like to think about those
as the talented poor people who now are able to borrow.
And then there's this negative number.
And I looked at this this morning,
and I'm like, [? oh, ?] what's wrong, negative.
And then I remembered, oh yeah, the subsidy.
So the subsidy is in place here.
So you can actually get adverse selection,
if you want to call it that, of people who really shouldn't
be moving from wage earner to the entrepreneurial category.
And it's precisely those people that
get impacted on the margin.
These are local average treatment effects.
These are people who are induced to change
their behavior as a consequence of the program.
Then we've got people who are firms no matter what,
and they gain.
And, people who are from entrepreneur under autarky
to wage earner under intermediation,
so these are the not so talented guys who basically do
switch and do gain as a result of the program.
So, I mean, so you might think that, at plausible parameter
values, you wouldn't see any of these occupation switchers,
but you do.
And, again, the models that we've
been talking about sort of assume something like that's
going on.
That's where the TFP is coming from, except we
didn't have a subsidy.
Well, maybe China did.
Maybe we were removing the subsidy.
OK, this will be even shorter, but it's
a nice review in a way.
You remember that Greenwood and Jovanovic,
the forward-looking model?
People solve stochastic, dynamic decision problems
to maximize expected utility, choosing how much to save
and where to put their money in terms
of safe or risky activities or assets.
We're going to put a little heterogeneity in this discount
rate.
It could be other places.
Now we're just going to experiment and see.
So here let beta i be equal to some common average, beta bar,
and then some heterogeneity, unobserved heterogeneity,
that makes beta i different from beta bar.
And then we saw this optimization problem.
We have savings, fractions of wealth
to put in sort of safe or risky.
Or, in this case, I generalized it
to entrepreneurial and wage-earning activities.
You have aggregate shocks in both.
You have idiosyncratic shocks in both.
You have a law of motion for wealth.
Wealth today, fraction saved, fraction
put into risky and safe gets you wealth tomorrow.
We have two sectors here, intermediated
and non-intermediated.
The non-intermediated sector is like this financial autarky
sector.
So we're going to look for this value function, W0.
If you're in the intermediated sector--
oh, sorry, well, I'll do the next one.
If you're in the intermediated sector,
you're looking for another value function that
solves dynamic optimization.
Now, for each one of these, if you were there and destined
to be there forever with these constant relative risk-averse
utility functions, you have some simple sort
of facts like the savings rates are constant.
Savings rates depend on beta, but give me the beta,
and I'll give you the savings rate.
And that's true in autarky, and it's
true as well in the financial intermediated sector, OK?
But, because it affects the savings rate,
it's going to affect income.
And so part of the unobserved error
is going to be the product of income with this theta i,
but theta i was that preference parameter we don't see.
You with me?
Hopefully, you can at least conceptually see
where we're going.
OK, and so there's--
similarly, there's a savings rate and consumption
for the intermediated guys.
And then you have the big choice here.
This is Greenwood and Jovanovic.
So there's this cost Z of joining the financial system.
And, if this cost Z is sort of a random variable
in the population, it's going to affect the choice.
The issue is whether V is greater than W.
So, if all of this choosing were happening only
in the initial period at t equals 0,
then I've got a valid instrument.
I have something that affects the choice of intermediation,
even in this dynamic model.
And then you're in one sector or the other.
The savings rates depend on the error
term, which depends on theta, but this occupation choice
here--
sorry, this this participation choice is instrumented with Z.
And Z has nothing to do with the unobserved preference
heterogeneity.
So I have a valid instrument.
I have monotonicity, OK?
However, that was not the way we dealt with that model
in the lecture.
We talked about endogenous financial deepening.
That makes households forward-looking,
planning, potentially, to join the financial system
at some future date.
Well, now we're in trouble, even with the same instrument,
because what we see prior to the selection
is the endogenous evolution of wealth.
And these guys will be saving up to cover those fixed costs.
It's no longer true that the savings rates are just
constant fractions of income.
What does it mean for RCTs?
It means you should try to keep the control
population in the dark about the program
because, if they know it's coming,
and they'll eventually get treated,
they're going to change their behavior if they behave
the way they do in the model.
So Ben surprises people with the road construction
when he dug up the dirt to see what the pavement was made of.
That happened after the fact.
He didn't tell them he was going to do beforehand
for obvious reasons.
But it's very hard in the field to maintain this
because people want to know why they're
answering all the questions in the questionnaire
and not getting the program.
They're not even supposed to know about the program.
You know, there are ways to try to fix it.
Now it's true that, if you had like a surprise,
even though they're following this dynamic model,
you say, oh, today is we got a special deal.
It's not going to be repeated tomorrow.
Then you have this one-time-off experiment.
You have this Z that's lowered that they got surprised,
that prior behavior isn't influenced by it, that will
induce selection and so on.
Well, what am I doing?
I'm just reviewing, in a way, what we've already done
with these kinds of models.
This one is dynamic, but putting it
in the language of when RCTs and instruments will work
and what to be kind of leery about.
I honestly don't see how you can talk about IV or RCTs,
random as they may be, without having
some framework in your head to think about the model.
And the rest I'll skip.
It's about that financial expansion in Spain.
I'll tell you the key insight the IO guys had,
which is, basically, you worry about an equilibrium
where everyone is anticipating everybody else.
You say, well, hey, if you believe it's an equilibrium,
then it ought to be in the data.
So you start running reduced form regressions
of how your competitors behave as a function of what you did.
And that's kind of the forecasting
aspect, which you can bring into the individual problems.
I know it sounds a bit counterintuitive,
but, basically, the reason it works
is because everyone is getting these shocks.
So no node-- you know, you can be at any node
and go to any other node because there's always
a shock that's going to rationalize that choice.
It's like Hutz and Sedlacek way back when.
And it's the foundation of these discrete choice models.
Now that doesn't always work.
And the last thing I won't even tell you
is a situation where it might fail.
So the point was not to drive nails
in the coffin of one way of doing business.
The point was to have an open conversation
and have everything on the table and so
to increase our sensitivity.
If you don't think heterogeneity is--
unobserved heterogeneity is a big deal,
that would help you a lot, but, when
we looked at the impact of microcredit,
we saw a lot of heterogeneity in outcomes,
whether it was Hyderabad or Thailand or Morocco.
That seems to suggest this selection on unobservables
is quite important.
OK, good.