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I'm going to do back-to-back lectures.
The first one is going to be on the topics that will highlight
the importance of using micro big granular data for macro questions.
And then the second lecture is going to give you more
information about data sources.
All right, so let me start with this question.
Why do we need firm level data to answer macro questions?
There are going to be three answers to this question and
all of them are important.
The first one is going to be about the importance of heterogeneous agents in
macro and international macro-finance.
So key micro foundations of macro models depend on heterogeneity.
And we obviously need data on the heterogeneity.
And this heterogeneity can come in the form of firms, banks and households.
We generally refer to that from level data, but basically without that type of
data you wouldn't be able to test the micro foundations of these models.
And the macro implications are going to be strongly tied to micro foundations.
The second method is about specific and very critical role of finance in macro and
international macro-finance again.
So here financial frictions are going to play a key role not only in post
transmission but also as response to shocks.
And these frictions now we know that they are heterogeneous by firms, by banks,
by households.
Again, this is going to require using big data on these types of
agents to understand the importance of these financial frictions.
And the final answer is a pure empirical one,
about identification and robust empirical evidence.
It is actually extremely hard to identify causal effects in macro using VAR or
limited macro observations at the country level in reduced form repressions.
So you will have a lot of powerful identification if you use big
micro data sets that will help you to establish stylized facts.
To set the stage, I'm going to do a couple topics that will serve as
examples to these three answers to the question I asked at the beginning.
And I'm going to start with corporate debt.
So the corporate debt to GDP, as you see in the slide, has been on the rise
in advanced economies and emerging market countries since 2000s.
And although the speed and the timing differs basically this is
something that has been going on for some time.
Now the questions surrounding this issue can be several.
I'm going to just put four out there and you are going to see that micro level
big data sets are going to be essential in answering these questions.
First one is why firms increase their leverage during booms.
If there's a lot of leverage during booms, then what is the role during the bust
is that from leverage and important propagator of aggregate boom-bust cycles.
The third one is does firm leverage affect monetary policy transmission and
risk taking.
And the final one is more long term question is from leverage
relate to productivity and growth, which are,
of course going to be tied to economic growth that you heard a lot about today.
So the key theme as I said is going to be the importance of granular big data and
try to answer these questions empirically identify the effects and
take it to the macro implications.
So I'm going to draw it from several papers.
I'm going to try to go through Europe-US emerging markets.
I don't think I will have time for emerging markets, but it is on slides.
So for Europe, I'm going to draw from two papers,
my paper on debt overhang and my paper on capital allocation.
For US I'm also going to draw on two papers.
The first one is my new work on multiple servers in the US that we presented this
year's NBR fg meeting in the summer and other ones endogenous leverage.
And emerging market papers are going to be about the papers on exchange rate
appreciations and corporate rate taking.
My Jackson whole paper from two years ago on US monetary policy and spillovers, and
the forthcoming paper on international spillovers and local credit cycles.
So all of them on my web page and I can answer specific questions about them.
But let me go through the general themes and let me focus on Europe first.
So I'm going to show you the same figure I showed you at the start now focusing on
Europe compared to the United States.
And also normalizing everything to arm at the start of the European
integration process in 1999.
And separating the periphery countries also in Europe, these are Portugal,
Italy, Spain, Greece in black from your area countries,
our role, which is in red and United States in blue.
So the left figure is the same corporate debt to GDP figure, but
these are trends, okay?
Now on all levels and you can see that obviously, the corporate debt
to GDP completely exploded in the periphery European countries,
although the increase in other areas also size.
The right hand side graph is the investment to GDP.
Now investment to GDP was declining rising before in different
regions different timings, before the Great Recession in 2007, 2008.
Everybody falls off the cliff at the same time in terms of investment.
What is interesting after the Great Recession is and this figure ends in 2018,
if you extend it to 2020 before coming you will get the same picture.
The recovery started in United States and Euro area but
the periphery countries that increased corporate that a lot were basically still
on the floor as you can see very sluggish recovery, very sluggish investment, okay.
So you want to understand this,
you want to understand the bust side and the boom side and
you will see that micro data big data is going to be very helpful in this end date.
So what we do is we are going to create a big data set that is going to match firms
to their banks, not European firms, so their European banks, and
to the sovereigns in the Euro area countries because that crisis had a role
for all these agents.
Here an important point that I'm going to come back in the data lecture two is
to use national representative from the data set.
What I mean by that you really have to cover the SMEs, small medium enterprises,
that will help you to make the official size distribution.
What do I mean with that?
That means less than 250 employee firms, this is an SME, is going to account for
most of the real economic activity in terms of employment act and output and
invest as in as shown in the official Census Data.
You also have to account for
other explanation of low investment if you want to understand low investment and
link it to a corporate debt, which is not like the demand and supply.
Of course, these are the things that is going to affect all the firms and
the economy.
And then if you want to separate out firm leverage and
rollover risk using firm level data here is going to help you.
Because you can differentiate firms by their leverage and by the maturity of that
debt, which is rollover risk, to identify the effect you're interested in.
And this is just basically going to tell me that from that overhang is going to
explain a large fraction of that sluggish investment, that black line that
is still at zero I showed you in the peripheral countries of Europe.
And here is an impulse responses on that.
So these are impulse responses of investment.
So just looking at the top left figure it shows you the high leverage firms
in the periphery countries has the largest drop in investment.
And five years out of the crisis after 2008, they still didn't recover.
And if you look at the bottom right,
these are the low leverage firms in the Central European countries.
They had a small drop in investment in 2008, and then five years out,
they have some company wants to become.
So the idea is leverage firms in peripheral countries decrease investment
more and don't recover.
Now, given this, why on earth,
these firms accumulate a lot of debt during the boom, right?
So to understand the path you have to go back to the boom.
So our story, this comes from another paper is about declining interest rate,
which is obviously a natural part of the EU integration process,
incentivize firms to finance investment with short term debt.
In this figure you see the real,
the clock decline in the data in real interest rates.
Which of course let's start with the Maastricht Treaty in Europe.
This is the blue line.
And the black lines are model generated decline.
This is from the paper, Matteo mentioned at the introduction on Catlins locations.
[FOREIGN] Now, what is the story here?
Why from level big big is going to be very important
because heterogeneity is very important so I understand the story.
So firm level heterogeneity in accessing finance is going to have aggregate
implications, okay?
Why is that?
So you have an aggregate shock, right?
Real interest rates declining.
This is all everyone, everybody's going to explain this real interest rate decline
starting with the Mastery treaty.
That means everybody wants to increase investment, right?
Your desired capital goes up.
Unfortunately, not every firm can do that,
large firms with high net worth they are going to be able to increase their
investment which means they are going to face lower returns to capital.
Small firms with low net worth are not going to be able to do that.
They cannot expand capital, they're going to face higher returns to capital.
That dispersion in returns to capital within a narrow well defined four
digit sector across firms is going to lead to declining productivity.
I'm sure you heard this today, this is from a book by a Chap and Pete and
so we will end up with this application and decline and productive.
A critical assumption here is obviously size dependent borrowing constraint,
large firm can borrow small firm cannot.
Do you have an evidence on that?
And the answer to that is oops, sorry.
I don't know why I cannot go to this figure, and that's about his estimate.
Yes, so this is the figure of leverage per size from European firm.
You see that it's not only positive with smoke evidence the firm here,
it is combat.
So larger you are, more and more leverage you are.
Next question is, is this something unique to Europe?
No, this is not unique to Europe.
And I'm going to argue that this is actually going to be the case for the US,
a positive relationship between firm size and firm leverage.
So aside the time constraint but
unfortunately you need data on the financial position of the small firms.
And this is the holy grail in US you don't have that faith, right?
And it says an extensive literature on employment growth items of US firms
based on the census data, I'm sure you heard some of this work today.
We don't know how these firms finance they goals.
What do we know about firm financing is based on the company's stock data in
the US, and that's about large publicly listed firms.
Unfortunately these firms cannot speak of [INAUDIBLE].
They cover only 26% of domestic employment and 44% of domestic output.
To clearly private firms in the US, their financial positions is going
to be very important to be able to understand financial constraint and
the effect of those on the transmission of macroeconomic shocks.
So, we attempt to establish a data set on this and
now there is another data set of federal reserve, I'll talk about that too.
And that will show you this is extremely important.
So what you see here is from size distribution on the left in terms of
employment, on the right in terms of revenue, green is the census data, okay?
So that's the represented the universe of firms in the US.
You see there a lot of small firms red is the Compustat data.
It's a completely different world.
So, means 6000 employees and companies that 20 in the census.
And the blue is the data set we are establishing that is rural between so
using the financial position of private firms.
And once you use that data you will get the same convex relationship between
leverage on the y-axis and
size on the x-axis in US private firms before the financial crisis.
After the financial crisis this dampens down because of tightening of financial
constraint.
You see on the right for Computastat firms there is no such relationship because
those firms are financial constrained.
So there is no point of using Computastat public firm data to understand financial
constraints, okay.
Why does heterogeneity in firm financing matter?
I told you that it matters for TFP, working to misallocation of capital.
It is also going to matter to firm integration.
And also through transmission of monetary force.
So here I would like to mention that microdata matching firms
real decision to financial decisions must progress in the future.
If you look at the endogenous growth literature paper's like Arclight and Kerr,
they will tell you that small firms are more innovative.
I just told you that small firms are more financially constrained and
in my other work we show that young firms are more financially constrained.
And in subsequent work,
we also showed that the relaxation of financial constraint depends a lot between
the interaction of collateral and monetary policy.
So this means that if you want to understand the world of creative
destruction, and how finance works in that world is finance good for
creative destruction or bad for creative destruction.
We need data on these firms, right?
If you're an economist with a lot of large incumbent firms borrowing,
suffering, periods of long debt overhang.
That means you're going to hinder entry of young small innovative firms,
you're going to end up with global.
So this is actually something that is going to be extremely important, okay.
For the final thing I want to tell you is, I'm going to tell you more about data
set but the new data set or federal reserve which is known as Y-14 is going to
tell us a lot about the private firms financing in the US.
This is a figure from that data set that tells you that share of bank debt for
small firms in the US is like everything.
So the top solid red line tells you the entire balance sheet that of a small
private firm in the US is bank that.
And the dotted line is also says the medium firms is linked the bank that.
Or all in very large private firms are going to look like Compustat firms which
means 30% of their debt is going to be bank debt that isn't going to be in
the corporate bond market because these firms borrow in the corporate market.
So this is this false that we have about yes, everybody borrows in the bond market.
No, that comes from using companies that paid over and over and
looking only at large firms.
Another important thing here is the collateral.
So the top boxes here are private firms.
It goes from left to right as small private firms, medium private firms and
large private firms.
And the bottom are the public listed companies start firms against small and
medium and large.
This is color coded but you see that after there's no collateral for
the Amazons of the world, this is bottom right box.
The dark blue is unsecured lending.
So the largest public listed firms don't even use collateral in the US.
So clearly there's no point of using that data to understand collateral constraint.
And this goes linearly with size.
If you look at the smallest private firm, they are constraints,
they have to use collateral.
What type of collateral?
The green is the traditional collateral, lamp, house, that type of fixed asset and
it's not used that much as opposed to purple and blue.
Which is account receivable and blanketly, which is the cash flow based collateral.
So just to finalize, basically how do we think about financial constraints in
macro finance and utility is extremely important here.
We think about one or two.
One says you're borrowing from firm i is going to be a tape that constant fraction
of your capital less than or equal to, that data can be size dependent.
And I told you that the data from US and Europe supports number 2, not number 1.
Now in the light of COVID, and in the light of this new data that covers small
firms, we know that earnings and cash is very important.
Which means interest rate and collateral are going to have separate roles on firms
borrowing and they can relax and tighten separately depending on the shock and
the pledge, okay.
Let me skip the emerging markets and let me conclude with the main take-aways
in this lecture go to my next data lecture.
What we learn here is corporate leverage is an important part of aggregate
boom-bust cycles both in advanced economies and emerging markets.
Corporate de-leveraging can lead to sluggish investment after large
financial crises.
Corporates increased leverage during booms given low borrowing costs and
more collateral.
In emerging markets this is going through a different phase that ties to exchange
rates and capital flows and you heard some of it during yesterday's lectures.
And finally, larger firms can borrow more, and
such heterogeneity in access to finance has implications for
aggregate productivity and growth.
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