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How can you connect the real decisions and financial decision at the firm now?
What do I mean with a real decision their employment decision,
their production decision, their investment decision to the financial side,
which is how much to borrow?
What is the measure to borrow from where to borrow, okay?
Loans or bonds?
What type of data do you use to understand these type of linkages, okay.
So let me start with US, US is going to be the most problematic country believe it or
not even more problematic than emerging markets,
some emerging markets have a better data than US.
European countries for sure a better data than US on this.
US is improving though and
I will tell you about this very new exciting data for the US.
Now, what we have in the US so far are basically on the real side census data.
And the entire literature on firm dynamics from road user census data,
to different data sets.
One is LBD that has no capital needs but
advantages it is kind of it is covering all the sectors of the economy.
The other one is ASM.
This is survey of manufacturers I'm sure you heard a lot about the status that it
is only manufacturing but it has capital in it.
So a lot of work looks at capital and so location and
things like that using data set, other work use LBD,
the key point for our lecture is none of them has finance in it, okay?
We don't know anything about how firms finance those employing on investment and
growth decision from these data sets, but
these data sets are the state of the art in terms of firm dynamics.
What do we do in the literature to remedy this problem?
What do we use to understand financial decisions of the firm?
Of course, first and foremost,
the data set that is used a lot in the US is Compustat.
This is listed firms and I hope in the previous lecture, I convinced you that
listed firms doesn't really matter that much for the agricultural economy.
The other data set that is widely used is Capital IQ.
There is the belief that this has private firms.
Yes, but it is only very few and very large private firms,
it is going to be mostly companies that unfortunately.
So the private firms this data set covers is going to be very large I said
it's over 10 million.
More than 500 shareholders by no definition of SME,
small medium enterprise, you stay the covers, so it doesn't cover that.
VentureX.
This is a very special data set that focuses on venture capital finance firms,
it is going 10,000 firms in it.
80% of the firms are younger than 3, with employment less than 20.
So it is very specific if you want to write the venture capital paper,
clearly this is the data set to you because you capture it from right at
the startup level.
And nice thing about it is a panel going back to 1980s,
but you don't have anybody else.
You'll only have these guys very young, very smart.
Survey Small Business Finance.
This is something that has been used a lot.
It was a survey from Fed.
It is not discontinued unfortunately, it is a very non-representative data set.
It's a select sample of 3000-4000 firms, less than 500 employees,
that's correct, which is SME but very select sample are plotted on a panel.
It's a four ways of cross section that is, it's not done in here.
Kauffmann firm survey, very specific data set again 5000 firms born in 2004.
The advantage is that these firms are follow, so
it doesn't mean employment around 5.
So you are following various small firms pretty much during these 3,5,6 years, so
a lot of people writing entrepreneurship type of papers is the status.
QFR, QFR is the other census based data set.
Widely use in the literature of response to monetary policy and
shocks because the quarterly data set.
Unfortunately is again very non-representative.
It is only manufacturing,
it is going to have a lot of risk to firms in at 5000 large corporations.
It's going to have SMEs selected again,
there's a series selection problem 5000 selected SMEs.
Only manufacturing and it's not a panel and
it cannot be matched to census data to allow you to fix that selection problem.
So you cannot fix a selection problem.
Two commercial data sets are very promising, unfortunately, they also have
selection problems but they are more promising than these other datasets.
Sageworks these are the auditor's firms.
Is going through a sample similar to Orbis data set around 400,000 firms much bigger
sample than the others.
Unfortunately firms are anonymized so you cannot match for
anything you don't know anything about the firms and Orbis.
Pretty much until this new federal reserve data was the best thing out there for US,
it's a similar sample to say drove around 200,000 firms.
But you know,
the firm ID is employer identification numbers it can be matched to census.
And you can create propensity weights to correct for the selection problem,
which is what basically, I showed you in the previous lecture.
I'm going to show in this lecture how to do that, okay.
Let me say a couple things about the new data.
The new data collected by Federal Reserve known as Y-14.
This is regulatory data.
It is started being collected after the Great Recession,
as part of the Dodd-Frank act.
It is collected as part of basically Federal Reserve's capital assessment and
stress exercise.
For all bank holding companies in the US with total consoled assets
above 50 billion, threshold is raised in 2019.
This is basically US credit,
the closest we'll come in US ever to a credit register data set.
It is firm-bank-loan quarter level with a reporting threshold of 1 million.
You see everything.
All the contractual terms and all the firm balance sheet items.
It covers all the factors true panel at the quarter level.
The caveat is of course it starts in 2012,
you cannot go back almost 4 million longer observations for
over 150,000 corporations, most of which actually are going to be SMEs.
They have asked us for some time.
So, the coverage even though it is the banks part of the stress testing,
these are all important banks in the USA, they account for
85% of all the assets in the banking sector in the US.
And even more importantly they provide 70% of all CNI when commercial
industrial lending in the US.
It is super visual data on price from financing,
it is better than everything we have on the table right now in the US.
Representative relative to everything you can imagine corporate structure for
deals can cap like whatever other private firm financing data you have from US.
And why 14 firms account for 65% of the total US corporate sector debt and 78% of
average US goes out then remember how low that number for the established firms.
The official definition of an SME is a firm with less than 500 employee,
these firms account for 52% of the private sector employment and
48% private sector offerings, yes, okay.
So let me, okay,
I have one minute, let me talk a little bit about the other countries.
So always against the world wide data set it's going to have the most comprehensive
coverage can be made representative for
many countries, especially European countries given the regulation.
Because small firms have to file to the public registries
listed firms are going to be 1% of the sample.
The other worldwide data sets are going to be world scope and compustat, okay?
All right, so state of the art in this literature right now there's the credit
register data set match the firm and bank balance sheet and
census unfortunately, these are hard to get.
These are all confidential administrative data sets.
You know a lot, you can do a lot by controlling demand for credit,
supply for credit, looking at the contractual terms.
But I'm actually using the census to understand entry and exit dynamics and
all that.
But you have to have access through administrative unit in that country and
this is true for all the countries, okay.
Let me say one more thing and then I'm going to stop and start getting questions.
For any micro data set, whatever you use, you use the state of the art data sets or
you use some other data set.
Even use road scope even you use compustat, so listed from data
set cleaning selection and coverage are going to be extremely important.
So you have to clean the data set first and the cleaning is going to differ if
you're doing a manufacturing firm sample are all from sample.
For example how you think three things like negative equity,
capital is going to be different.
Okay, so these are the details you have to pay attention to.
The association is going to be important even you
do a true cleaning because of outliers, data mistakes and all sorts of things.
These are very big data sets, you cannot see things with your eyes, so
you have to do a lot of tests.
And I have a general guideline on this,
it's also in Dropbox or my web page and here an example but
basically, this is something that you should be very careful about.
Selection is another issue, especially for US data sets.
Not only selection in the cross section but selection varying over time,
you have to correct for selection if you can.
And the way to correct for selection is match the data set you are using with
the selection problem to a data set that covers universal firms so
that will be census.
And then run regressions for
reporting status here this is reporting financial for firm i in time t.
All characteristic of that firm like employment, age, industry,
geographic, region, to obtain propensity rates and
then to regulate the data so you can make it through presentable.
Let me show you an example for that.
For example, this is age, by the way not informed
there's a typo LBD data is shown here with a dark blue.
So firms between zero and five years and more than 25 years,
and commercial data set or based we call it logos is the gray.
So you see that it is extremely weighted towards all the firm's when you do this
correctly for
selection exercise you get the light you get closer to the census data.
Same exercise for employment, LBD data is going to have,
as 1 to 19 employee firms capturing this much.
More than 500 firms capturing this much, you use a commercial data selection
problem, you weight it to the larger firms as shown in the gray bar.
You do the professor's formatted by matching the data processors and
calculating your rates you are going to get closer to the representative sizes
data as shown in lightning.
If you cannot do this, if you cannot match distances according for selection, but
argue your data is representative then you have to show.
You have to show coverage statistics.
Here's, for example for Orbis for several countries.
What we do here is, let me tell you about Spain,
you pick a variable, it can be sales, here, it is wage bill,
it can be employment, you aggregate it in your data and you divide it.
Whatever the census of Spain telling you, then you do that, you show your audience
that you cover 70% of the economy, and this is something consistent over time.
It may not be that German.
Germany data used to cover very little only 30% of economy late
2000 now it is 80% of people and why?
Because low change in Germany,
that now small firms have to file to the status that to the public registry.
So you have to be also very cognizant of that fact in the change
in the regulation and the law of the country in terms of who is filing,
what size of firm filing rank, what type of financial value, okay.
Let me skip diversification because I don't have time, just to conclude, and
then get your questions and
then maybe I can talk about identification further there.
Micro data on firms real and financial linkages can help to answer many macro
questions with robust identification.
But it takes a lot of effort, time and care to work with such data.
Thank you very much.
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