STEVEN DAVIS:
Thank you, Melissa.
[INAUDIBLE] it's
great to be here.
And I'm especially happy to be
here in such interesting times
that we live in, that are
very much related to what
I'm going to talk about today.
So I want to start by taking
a quick tour of some episodes
and developments
around the world
that it may help
motivate the research I'm
going to talk about today.
The episodes I'm going
to pick out to start with
are all things in which
the political process
or governments
have taken actions
that seem to feed into a more
uncertain economic environment.
I'm just going to
do this very quickly
and I'm sure you'll be familiar
with many of these episodes.
So we had a big
fight over tax policy
that went down the
last minute in 2012.
That was the fiscal
cliff episode
you heard so much about.
You think back to Obamacare,
the Affordable Care Act.
Since its passage in 2010
it's been highly contentious.
There's been Supreme
Court decisions on which
the act seemed to ride.
It looked for a
while as if it was
going to be a long term feature
of the US policy landscape
after the election on Tuesday.
That's not so clear.
So lots of turmoil and
uncertainty about health care.
I'll say a little bit more about
the election cycle in a second
and later on in the talk.
Some of you are
probably from Europe
and certainly you
probably pay attention
to what's going on in Europe.
Since 2010 there's been a series
of banking and sovereign debt
crises of the
eurozone countries.
They've had an extraordinary
immigration crisis
since 2015, that partly emanates
from the disaster in Syria,
in that part of the world
that's been unfolding.
During that time there was
the Brexit Referendum in June
which upset UK relationships
with the European Union.
That was just as big a surprise
is the election on Tuesday
and perhaps even
more consequential.
I can turn to more
autocratic countries.
There's also uncertainty
emanating from actions
that they take.
Russia's annexation of Crimea,
its ongoing military conflict
with Ukraine are good examples.
Other parts of
the world, Turkey,
there was a failed coup
attempt, and then there
has been a harsh crackdown
in the wake of that.
You think there's political
turmoil in the US,
you ought to look at Brazil.
I'll show you a little
chart on that later on.
And I already alluded to
the humanitarian disaster
and military conflict unfolding
in parts of the Middle East.
So this is all recent stuff, OK?
And OK, so a little bit more
about the U.S Presidential
Election.
Big surprise.
OK, I was surprised.
I think lots of
people were surprised,
except maybe people who were
working on the Trump campaign
team.
There's no doubt that
the election of Trump
has led to elevated policy
uncertainty in several areas.
Trade, immigration, just
the traditional US Support
for global institutions
underwriting
the post-war security
and economic order.
I want to come back to the
US Presidential Election
later, and talk a
little bit about what
we can say so far from the
data I'm going to talk about.
Now, but the common element
of all of these examples
I just went through
very quickly,
is it seems like they involve
the political process itself,
or in autocratic regimes
government decisions
and actions as
sources or impulses
of economic uncertainty.
OK?
But we should also
want to recognize
that the causality definitely
goes in the other direction
as well.
So a prominent
example of that is you
think back to 2008, 2009.
The failure of Lehman
Brothers in particular,
which very shortly thereafter
led to the TARP legislation,
Troubled Asset Relief program.
That was an example where there
was a major economic shock,
financial crisis in this case,
that confronted policymakers
with extraordinarily difficult
and unusual policy challenges.
So there was a great deal
of uncertainty about what
the policy makers should do.
And what would be the reactions
of the decisions they took.
And then there's also more
long term relationships
between economic performance
and political polarization,
policy uncertainty.
So if you think about
what's been going on
in the US and Europe,
there's definitely
been an increase in populist
political forces in many places
that have up ended
many aspects of the US
and European political system.
So there is some evidence
or some systematic evidence
as a paper that you can
find in my reference list.
And these slides by
three European economists
that takes a long
term 125 year look
at the relationships between
major financial crises
in particular.
And what happens to
the depolarization
of the political process
in the wake of that.
And yet there does seem to
be a systematic tendency
over that broad sweep
of human history,
that in the wake of
major financial crises
you get some political
polarization.
And you especially get an
increase in right wing populist
parties.
That kind of resonates with
what we see in several countries
around the world.
So the overall
picture you should
have in your mind
thus far, I hope,
is one in which there
is a complex interplay
between shocks that hit
the economy affecting
the political process, and
possibly leading to more policy
uncertainty.
And then some shocks emanating
within the political process
itself, which lead to a more
uncertain economic environment.
Now this is not going to be a
talk in which I spend much time
on the mechanisms that
link uncertainty, or policy
uncertainty economic outcomes.
But there's a large
literature, which
you may be familiar with some
parts of from your coursework,
that suggests that high elevated
levels of economic uncertainty
other things equal, are likely
to discourage investment.
Discourage some aspects
of consumption spending.
Lead to more asset
price volatility,
lead to higher
risk premia, which
itself is likely to
discourage investments.
So all of that's kind of
standard stuff in the economics
literature.
More on the theory side
than the empirical side.
But there is empirical support
for these claims as well.
Kind of a big
question mark which
I don't think we have
a great understanding
is, what's the connection
between uncertainty or policy
uncertainty, and just the
general level of confidence
about the economic outlook.
Or we might say what's the
connection between uncertainty
about the policy environment.
in animal spirits.
That's a much harder thing
to get your hands around.
But I mentioned it because
it's potentially important.
The research that I'm
going to talk about now,
and this is, by the way,
with Nick Blum at Stanford
and Scott Baker who was at
Kellogg and Northwestern.
So the research I'm
going to talk about
is very much in the spirit of
this quotation from Lord Kelvin
a long time ago.
You know he's the guy
who's, among other things,
that we named one of the
temperature scales for.
So he says the
first essential step
in the direction of
learning any subject,
is to find principles
of numerical reckoning
and practicable
methods for measuring
some quality connected with it.
That's very much the way
I see the research program
I'm about to talk about.
Take this concept of
policy uncertainty
which is a little bit nebulous,
as I've described it before,
not obviously amenable
to quantification.
But I'm going to
try to do just that.
I'm going to try to quantify it.
Why do that?
First, it hopefully
gives us some basis
for actually
characterizing factually
what's happened in different
episodes over time.
I'll show you a lot of that.
Second, it gives us a means for
testing hypotheses about what
leads to more or less
political policy uncertainty,
and hypotheses about what
the consequences of policy
uncertainty are.
And finally, you can think
about policy uncertainty
as you might feed into a
theoretical economic model,
or a model of the joint dynamics
of the political decision
making process and
economic outcomes.
But in all those exercises,
the first basic step, as I see,
is measurement.
OK so I'm going to
talk about measurement,
and I'm going to particularly
talk about measurement
using text methods.
Now, because policy
uncertainty is
kind of a squishy
nebulous concept,
it's even more important in this
measurement exercise than most
to be clear at the outset about
what we are trying to achieve.
OK, so this slide
is what we're trying
to achieve with the measures
that I'm going to show you.
The uncertainty about who
will be making economic policy
decisions, like who's going
to win the next election, what
economic policy actions
will be undertaken and when,
and uncertainty about the
consequences of policy actions
and decisions, both
past ones current ones
and possible future ones.
Now, I just want to be clear
that economic uncertainty can
also be induced by
policy inaction.
If you think back to
the debt ceiling fight
in the US between the
Republicans and Democrats
in the summer of
2011, that was very
much in an instance in which
the lays in decision making
were causing uncertainty.
And were causing uncertainty
as to whether the US Government
will curtail some of
its critical functions,
whether it would make timely
interest payments on US
Treasury securities and so on.
And I also want to
emphasize, and it's
really implicit in
what I've said before,
that we want to capture
uncertain economic
ramifications of
decisions and actions
which might be
primarily motivated
by non-economic considerations.
So some national security issues
might fit in this category.
So that's what I
want to capture.
OK and as I show
you what we actually
do, one thing you want
to be asking yourselves,
how well am I achieving what
I said I want to achieve.
So let me start mechanically
in talking about what we do.
And by the way, we have lots
of different measures of policy
uncertainty but I'm going
to focus almost entirely
on the newspaper based
ones in my talk today.
And that's principally
because I think
they're the most powerful
and flexible methods
for quantifying policy
uncertainty across time
and space.
But also because the
methods I'm going
to use here I hope will give
you a sense of just how powerful
and how much potential there
is in using newspaper text more
generally as a way to quantify
concepts and economics,
political science, sociology.
Other fields that
would otherwise
be hard to quantify
that can be put to use.
And if I have time, I'll
say a little bit about that.
So here's mechanically what we
do for our headline monthly US
Economic Policy
uncertainty index.
We get 10 major
newspapers, since you
write computer
programs that read
the articles in these newspapers
as stored in digital archives,
and we flag articles
that meet three criteria.
They've got to be talking
about the economy that's
the E criteria in there,
policy, as indicated
by the presence of one of
these terms in this P term set,
and uncertainty.
If an article meets all
three of these criteria,
we flag it as an article
about economic policy
uncertainty or CPU for short.
OK?
So now obviously we need some
way to scale those frequency
counts because newspapers
differ in size newspapers
in terms of number of
articles, number of articles
fluctuate over time, and so on.
So the scaling device
we use is the number
of all articles about any
subject, in the same newspaper,
in the same month.
So that gives us a
scaled frequency count
at the newspaper or month level.
Now I want to somehow average
across those newspapers.
And my goal is to give roughly
equal weight to each newspaper.
That's what we're
going to do here.
So how do I do that?
I'm going to standardize
the time series
variation of each
newspaper to have
the same standard deviation.
Once I've done that, I'm going
to average across newspapers
by month to get an overall US
Economic Policy uncertainty
index.
And then finally I'll normalize
it to something like 100.
Well, I will normalize it to 100
over a large chunk of the time
period.
So that's mechanically
what we do.
OK, now you might be
asking yourself where
did I get these terms from.
I'll come back and talk
about that in a minute.
And I think the most
problematic term set here
is the P term set.
How did they choose that?
But for now, these
are the mechanics.
Yeah.
AUDIENCE: A quick question
about [INAUDIBLE].
So how do you deal with
that they are-- [INAUDIBLE].
Economy uncertainty
is [INAUDIBLE]
and it's become [INAUDIBLE]
is going to be resolved.
One is a positive statement,
one is a negative statement--
how do you==
STEVEN DAVIS: Right.
So I will come to that.
So I think basically the
issue you're raising,
which is a very
good one is there's
no notion of directionality
in what I captured here.
And it turns out,
and I'll show you
some direct evidence on
this, that newspaper editors
and journalists talk about
uncertainty when it's high
and rising.
When it drops off they
talk about it less.
So there just aren't
very many articles
that say, wow, we're living
in very certain times.
OK?
And I'll show you that
directly in just a few minutes
when I look.
It will be the very first thing.
I was very worried about
this when we started.
It's one of the first kind
of evaluation exercise
that I will show you is
designed to deal with this.
Also in part of our audit is
actually, which I'll talk about
is well, where we have
human beings reading
thousands and thousands
of newspaper articles.
I see Sylvia there
in the audience.
She was part of this.
This is mostly
done by undergrads
at the University of Chicago.
So we had about 20
students over two years
reading these articles.
One of the things they would
look for that's directly
motivated by your question,
is if they identify
an article that has any
discussion of economic policy
uncertainty, is it primarily
in the context of policy
uncertainty falling.
And it turns out very, very
few articles fit that category.
Only about 5% of the articles
that meet the EPU criteria.
So I was worried about
that, and it turns out
not to be a huge issue.
So here's what comes
out of this exercise.
Remember things are normalized
here to 100 through 2009.
So just think about 100 is
kind of an average value
for this index, 200 is
twice as high as average.
So it would be a big departure
from the average for the US.
Let's take a quick look
at some of the spikes.
OK, you can see, well, here's
the fiscal cliff episode
we talked about earlier, and
the debt ceiling episode we
talked about earlier.
These are cases where it looks
pretty clear the uncertainties
being manufactured within
the policy making process.
Here's the global
financial crisis
where policymakers
are being hit by being
shocks that are confronting
them with difficult policy
challenges.
There are also
episodes like 9/11
being the clearest
example where there's
some shock that is external
to both the normal workings
of economy, and
the normal workings
of the political decision
making process, that nonetheless
creates a lot of
uncertainty about policy
and about the economy.
You can see that first
election of Bill Clinton, first
the election of George Bush.
It's also true for the first
election of Barack Obama.
But it was so much else going
on you can't really pick it out.
They were characterized by
elevated levels of uncertainty.
That turns out to be
a broader pattern when
you look across many countries.
But the thing I want
you to take away
from this picture
at the outset, is
while there are lots
of different things
that are moving policy
uncertainty around,
but it also reflects
the observations
I made earlier that
causality complicated here.
It's flowing both
directions and not
necessarily always over
the same time horizon.
Now we've made a
mini industry out
of constructing these indices.
I'll show you a few more.
So we've got national indexes
like the one I just showed you
for about 16 countries now.
More in the works.
For the US and the UK we've
gone all the way back to 1900,
OK, which I think is pretty
interesting from an economic
history perspective.
There's a lot you
can do with that.
We can construct category
specific indexes,
of which I'll show you a
couple in just a second.
If we have enough newspapers,
we can construct the indexes.
You need a lot of
newspapers for this purpose.
Otherwise, the text
density gets too thin.
But we've done that
for the US and the UK.
And I'm going to show
you later on what
happened to the US
Policy Uncertainty index
after the election.
We've got a daily equity
market uncertainty.
We've got some indexes
that are designed
to capture fear about
immigration and immigration
policy and certainly.
I won't show you those today.
So here are two
of the categories
specific indices for the US.
So how did we construct this?
Let's take the defense one.
So the basic idea
is straightforward.
We take all of the articles
that meet the EP and U criteria,
and I outlined earlier, and
then within those articles,
identify the subset that
meet additional criteria.
For the national defense or
national security subset,
they would include words
like military action,
defense spending, war, terrorist
actions, those kinds of things.
So it's just a subset of the
overall EPU articles rescale
to be 100.
OK, and you can see
that that picks out
events like the Gulf War I,
9/11 attack, Gulf War II.
Then I put one of these
other-- we have about a dozen
of these category
specific indexes here.
Also with the health one.
And one thing you
can see immediately,
there's kind of three
episodes that stand out
with the health care one.
This is the failed effort to the
Clinton health care initiative
that Hillary Clinton
was very much involved
in that failed in the '90s.
This was the-- Bush
made a speech in one
of the State of
the Union addresses
about Medicare Prescription
Drug Reform Act.
That's in this episode.
Here's the Affordable Care Act.
Notice the numbers over here.
The scales up like 300, 400.
So we've had
extraordinary uncertainty
in the health care
arena for several years
now, going all the way
back to the initial passage
of the legislation,
which was extremely
close to these Supreme Court
constitutional challenges
that I talked about earlier,
to all of the snafus
with the exchanges.
And now there's
probably going to be
quite a bit more uncertainty.
This from an economy-- I'm not
going to talk about it today,
I don't think, unless
you ask me about it.
But from an econometric
perspective,
when you're trying to understand
what the consequences of policy
uncertainty are, this kind
of picture is promising.
Because, it says that
different firms in the economy
are getting hit by policy
uncertainty at different times.
If your manufacturing tanks and
guns and that kind of stuff,
well, this is a big deal if
you're in the health care
business, well, then
all of this uncertainty
related to the Affordable
Care Act is a big deal.
And we kind of pursue that idea
in a much more vigorous way.
And try to quantify
differences across firms
in the economy in the extent of
their exposure to policy risks,
in order to develop methods
for actually estimating
the causal effects
of policy uncertainty
on things like investment, firm
level investment, stock price
volatility,
employment, and so on.
Now, here's-- I
mentioned Brazil earlier.
I just throw it up.
I'm not going to talk
much about Brazil,
other than they recently
impeached and removed
their president from office.
They have many of their
leading political figures,
maybe most of them, are
either under indictment
or have already been
removed from office.
It's really quite extraordinary.
And again, notice
the scale here.
It's up above 400.
There's nothing like
that in the US experience
I showed you at the national
level that's that high.
Here's one of our most
impressive measurement efforts
this is North Korea.
We don't have many
newspapers that
are reliable inside North
Korea so we just have this.
Now, there's a
serious point here,
which is some countries are
more challenging than others
to fit into this methodology.
So the most
challenging countries
that we actually
construct indices for,
are Russia and China.
So here's one for Russia.
We just use one newspaper.
It's a business
oriented newspaper
that is regarded by
Russians and people in the
know as relatively free of
government control compared
to others.
You see all this jaggedness
here because we only
have one paper rather than 10.
So you do get some benefits
by having more newspapers,
and kind of averaging
out some of the newspaper
specific noise.
This chart, if you
look at it carefully,
has a rather different
character than the one
I showed you for the US, or the
one for Brazil for that matter.
There's a lot of geopolitical
uncertainty here.
Conflicts with Ukraine
or concerns about what's
happening in Ukraine and so
on, and Chechnya as well.
Here's a global index.
So here, what
we've done is we've
taken-- remember we have 16
countries for which I have
these indices now, and these
countries make up about 2/3
of global GDP.
So we have essentially
all the big countries,
and we're continuing to
roll out the more countries.
And then we just take a GDP
weighted average of the country
specific indices.
And there's something pretty
striking about this picture,
at least to me, is
that since mid 2011
the average value of this index
has been much higher than it
was in the previous 15 years.
That suggests the last
several years are, even
before the election of Donald
Trump, a period of unusually
high uncertainty in the
global economy, all right?
And it's even higher
than during 2008 2009
which we widely
recognize as being
a period of extraordinary
economic uncertainty
around much of the globe.
So if you have the sense,
which you may or may not,
but I do and so do
some others, if you
have the sense that we're living
in unusually turbulent times,
this is a bit of evidence
that supports that view.
OK.
So let's get on to evaluating
the measurement approach,
because look, it's
not at all obvious
that this method is any good.
It certainly wasn't obvious
to me when I started.
So here's the very
first exercise
that my coauthors and I did.
And when I saw this,
it gave me some sense
that there was promise in
this idea of essentially using
scaled frequency counts of
certain word combinations
as a means of quantifying
policy uncertainty.
So what we did in this
exercise is we said,
OK, let's step back
from policy uncertainty
where there is no external
objective counterpart
to the newspaper based method.
And let's look at
a different context
where there is an
objective market based
measure of a particular
form of uncertainty.
And that's the VIX.
So I don't know if you're
familiar with the VIX.
I presume some of you are.
But the basic idea
is if you look
in the financial
marketplace there
are many options on equities.
And in particular
there are options
traded on the direction of the
S&P 500 stock market index.
Well, if you have
enough options data
to the upside and the downside
with different strike prices,
OK, you can
empirically trace out
or quantify the amount of
stock market volatility that's
implied by the option
prices themselves.
That's the idea
underlying the VIX.
There are different versions
of the VIX correspond
to different horizons.
I'm using the 30 day
ahead VIX or a 30 day
look ahead VIX here, which is
the most commonly reported one.
So that's what
you see and that's
something you can
calculate every day, OK,
because options on the
S&P 500 stock index
are a thinly traded market.
To think about having many
options all with a strike
date 30 days ahead.
Every day go and
use that information
to calculate an implied
stock market volatility.
And then average those daily
values to the monthly level.
That's what this orange line is.
The blue line is
same methodology
that I talked about before,
but take out the policy terms
and replace them by
just three terms.
Stock price, equity
price, and stock market.
OK, so I get a
newspaper based index
of equity market uncertainty.
And this was the very
first thing we try.
There's no fancy searching
over the data here
to get a good
fitting relationship.
This is literally the
very first thing we tried.
So what do you seeing here?
Well, there's obviously
a lot of noise
in the newspaper based
series, but there's also
a lot of signal.
In fact, every major
movement in the VIX
is picked up in the
newspaper based index.
And notice this, among other
things, addresses the issue
you raised, OK, because the
issue you raised with respect
to the policy uncertainty index
applies with just as much force
to the VIX.
And I don't mean to say
there's no measurement
problem of the
sort you describe,
this kind of up versus down.
But if that were
a serious problem,
it would undermine the
relationship between these two.
And you can see that
basically, these two series
are pretty highly correlated.
So when I saw this I said, OK.
This is a method which at
least in principle, can work.
Doesn't tell me that my
particular index is a good one.
But it tells me at
least in principle
you can quantify a concept
of economics uncertainty
by using these scaled
frequent scales.
So this is far from perfect
in terms of its ability
to replicate the VIX, OK?
So something we
are engaged in now.
I don't have any
results to show you.
We are instead taking a
more systematic approach
and saying, OK.
Can we get a better newspaper
based approach for quantifying
equity market uncertainty.
Because if we can
do that then we
could extend that to other
countries, most of which
don't have VIX analogs.
We can also use it to quantify
the equity market uncertainty,
or decompose it in the
same way as I did earlier
with the health and
national security indexes.
But I'm not trying to claim here
that the newspaper based method
is a perfect signal of
the underlying concept.
Rather, I'm saying
I look here where
I have two different approaches
to measuring the same concept,
they clearly have a lot
of common variability.
And that tells me now when
I go to policy uncertainty,
for which there is no-- there
isn't anything else out there.
So my basic argument is, I've
got an imperfect indicator
but it has a lot
of signal value.
Having an imperfect indicator
of policy uncertainty
is better than
having no indicator,
which was basically the
situation before our research.
OK, now I referred to
this audit study earlier.
So basically, how did we use the
audit and what it would entail?
So in the audit
we would randomly
pick newspaper articles,
present them to the auditor.
The auditor would
read them after
a vigorous intensive
training program
in which they had to pass
a test and everything else.
I'm being serious here.
So there's a 65 page
audit guide that
would guide the auditor
as to how they're supposed
to read in code and article.
They did trial tests were 100
--you had to do 100-- initially
you had to come and talk
to me and get graded,
but that might have
been kind of scary.
So then we got a
graduate student
who kind of did the
evaluation before you could
go on and do the actual audits.
And we also did little
things like, each auditor
knew that roughly a quarter
of all the articles they
were assigned would be
assigned to somebody else.
So we would have a way
to check on one auditor
against another auditor.
And then we'd have
weekly team meetings
and you'd have to explain.
Well, why did you
code it this way
and John coded it the other way?
And, if you were sleeping
while you were supposedly--
if you were watching
a football game
and not paying attention to the
article while you're reading it
you'd feel embarrassed,
because you didn't really
have a good explanation for why
you quoted the article the way
you did.
So we did things like that to
try to keep up the quality.
But now, think about
what we have now.
We basically after
all this effort
with these teams of auditors.
We've got these 12,000
articles randomly selected.
And for each one, the
human being has read it
and asked whether it --remember
that very first thing I said?
Not very first thing--
but the human being
is basically going through
the article and reading it.
Does it meet any
of these criteria?
And the human being
would code it yes or no.
And they then have to code
a bunch of other things too,
but anyway that's
the central thing.
So now I have these 12,000
articles read by human beings
and they're quoted as
being about EP or not.
Well, that's really
useful because I can then
take a term set, and this
is literally what we did.
I can then take
tens of thousands.
And we focused on the P
term set because that's
the trickiest one.
We take tens of thousands of
possible combinations of term
sets of words like this.
Where do we get
these words from?
Well, one of the
things the auditors
had to do when they audited an
article if they concluded CPU,
they have to write down the
language that the article
itself used to talk
about policy matters
in the context of the
economic uncertainty.
So that gave us
a bunch of terms.
And we took something like
the 20 most common such terms,
and then we considered
thousands and thousands
of possible combinations
of those terms
for each combination,
construct an index
or construct the classifications
from the computer side.
And then here's the key thing.
We're going to minimize
the sum of false positives
and false negatives, in the
computer's classification
relative to the
human classification.
We're basically here now
taking the human classification
as the truth, OK?
So we're minimizing that.
That's our criterion.
And over all the thousands of
combinations we considered.
This is the one that minimized
the sum of false positives
and negatives of the computer
automated classification as
compared to the human
classification, OK?
So the point of all that is just
to get a good policy term set.
And to make sure that we
are asking the computer
to read it in a way that
is consistent with how
an intelligent human being
would read the order.
OK, so that's what the audit did
but there's still other issues.
OK, so if that
audit process works,
then it means that the
computer program we write
is correctly
reading the article.
But that still doesn't
get in the issues about,
well what if the are in the
newspapers themselves are just
not very reliable, .
That's the thing I
want to take up now
and I'm going to take up
that in multiple ways.
A simple thing we did is
to try to get concerns
about slant We took
somebody else's work
who's put newspapers on a
left right political spectrum.
We took the five right most
newspapers and the five
left most newspapers,
and constructed
a right and a left
version of our index.
You can see they
look pretty similar.
And then here we moved away
from newspapers entirely
and this gets your
question earlier.
So very quickly here,
because you may not
be familiar with
these tech sources.
Think of newspapers as
being written by, sort of,
amateur economists
for average Joes, OK?
Let's say maybe that's
a positive spin on it.
But anyway, so here is basically
a text source, the beige books,
which you should--
this is written
by economists within the
Federal Reserve System
at the 12 regional feds based
on interviews and conversations
they have with business people
and union members and experts
in each region, OK?
So it's written by experts.
And who's it written for?
It's written for the FOMC,
the Federal Open Market
Committee, the people who make
monetary policy decisions.
So this is a tech source very
different than newspapers.
It's written by
experts for experts.
Now, it's only-- it comes
out every six weeks.
And it's been
coming out every six
weeks in essentially its current
form back since the early 80s.
So it doesn't have this I
can't do this across countries,
I can't go back to 1900.
But it's a very
different tech source.
And we just did two simple
things with this tech source.
The red bar just
shows you the number
of times the word
uncertainty appears, OK?
The black bar reflects
a human reading
of the passions surrounding
the appearance of uncertainty,
where we the human
being is designed
to classify the article is
about the passage about policy
or not policy.
And basically you can see that
the story here is similar.
Not identical, but
it's similar to what
comes out of the newspapers.
And the fact if
you do the category
specific decomposition, it's
also pretty similar, OK?
And fiscal policy
was the big source
of this run up in
policy uncertainty
according to both newspapers
and the Beige Book.
And then this last
one here which
has a shorter time period.
I'll just say it
briefly for those of you
who are familiar with this.
This comes out of an automated
reading of the risk factors
discussion in the
10-K filings that
are all publicly listed firms
must make with the Securities
and Exchange Commission.
So again, that's a very
different tech source
put together for a
very different purpose.
So this gives me some confidence
that newspapers are somehow
not just missing
the boat entirely,
because these other tech sources
which are quite different.
You have very different methods.
And then, well, there's
the market use test.
As an economist,
I'm pleased to say
there's a very high
demand for our product.
Of course, the price
is zero for our product
because if we give it away.
But still we do
pass the market test
at a zero price
which is something.
There are lots of
organizations using our data,
publishing our data,
circulating in our data.
Blackrock, which is the world's
biggest asset management
firm, has its own in-house
team, has basically
picked up our methods and
does their own version of it.
They've told me this is.
Exactly what they
do, they aren't
going to tell me because
they're trading on it.
OK, so a little bit about the
election and more generally so.
I already mentioned
that US EPU was
high in the months surrounding
the first elections
of several previous presidents.
In our larger sample where
we've got, at the time
we had 11 countries
with national elections,
62 national elections.
You can see there is
indeed a systematic pattern
that our policy uncertainty
indicators tend to become
elevated in the months around
a national election, OK?
It's not a huge effect-- 20%
increase a little bit more
for close elections.
So you might say, well,
why is it not bigger?
I think one reason
it's not bigger
is that in many elections, it's
like choosing between Tweedle
Dee and Tweedle Dum.
The policy stakes
are not that high.
Now, that wasn't true
this time, right?
So let me turn to
the US election.
Trump and Clinton
were very far away
from each other with respect
to many major policy issues,
and I've summarized
some of them there.
Many major economic
policy issues.
And Trump is definitely
a wildcard, right?
No track record
as a policy maker,
hard to discern a consistent
coherent set of policy
principles, and he's
certainly has a history
of intemperate remarks.
And he has as much as said as
he regards unpredictability
as an attractive philosophy
of leadership and governing.
All those things would
seem to be pushing up
policy uncertainty.
So let's look at the data.
So this is our US daily
index, and I just pulled this
from our website this morning.
As I said, all the
data is there for free
so you can look at
it any time you want.
And here's the Brexit episode,
which was a big deal even
in the US-- got above to 250.
Here is the-- this
is just what you
see after the Trump election.
Now, the default on our web page
is a seven day moving average.
So this is just three days of
post election outcome averaged
in with all the
pre-election outcome.
But I want to note two things.
First, before the election,
the EPU index is quite subdued.
Well, how do I explain that?
I think I have an
explanation for that.
And then you can
also --if you just
download our
spreadsheet-- here's
the data since the election.
Well, it went from 100, which
is right at the average value
the day before the
election, to 323 355
afterward, which is very
high for US standards.
The reason I put
this number in red
is because these archives
get filled in with some lag.
And you don't want to
put too much weight
on the most recent day
or two because they're
going to get revised, OK?
So now, these are
just a couple days
but if the EPU index stays up
at this level for a month, that
will be higher
than anything we've
had in the entire
history of the US.
I don't think it will.
It'll probably come down
some but it's still high.
So how do I explain why
things are so subdued
before the election other
than the Brexit episode, which
is clearly not
about a US decision?
Well, all the smart money
said Clinton would win, right,
with high probability.
She's definitely
a known quantity.
She'd been on the national
policy scene for a long time.
I think it's also fair to
say that she was a status quo
candidate who was seen
as unlikely to make
major departures from
Obama's policies.
And the Republicans
were also seen
as highly likely to retain
control of at least one
of the houses of Congress,
which meant we would have been
in basically the same pattern
as we've been for the last six
years, which is
democratic presidents
and the divided
control of Congress
or Republicans
having both houses.
So it looked like a
status quo transition.
That's not what
happened and that's
why you see-- that's why
all of a sudden right
after the election, people were
surprised this index spiked up.
So last thing going
forward after the election
this little bit of
editorializing here, I guess.
But you can push
back if you want.
So I think there's, as I
suggested earlier, higher
policy uncertainty and lots of
prospects for harmful policies
in the trade and
immigration area.
And I think it's fair to
say some that the election
outcome is also somewhat
surprisingly improved prospects
for tax reform just because
Republicans control all three
houses of Congress.
They don't have to fight with
the Democrats about it as much.
Fiscal stimulus
looks more likely
and I think there's certainly
a greater likelihood
than I would have thought
a week ago of a lessening
of regulatory burdens.
In terms of the
risks, and there are
lots of risks related to
trade, related immigration,
related international security,
and-- let me stop here
because we can talk
more about this
and we can talk about other
things I've talked about,
take questions and comments.
Well, the S&P 500 did
something very interesting.
It crashed overnight,
the S&P 500 futures.
But then it very
quickly recovered.
And I think part
of the reason it,
recovered and now has
arisen to, I guess,
historic levels, part of
the reason that it recovered
is what I wrote here.
I think there's a perception.
We'll see whether it's true.
There is a perception
in the market
that the likelihood for
a big fiscal stimulus
has gone way up,
because Trump has
been promoting tax
cuts without much
in the way of spending cuts.
And I think the
prospects for tax reform
have also increased, which there
is a great need in my view.
And Trump has made a pretty
consistently criticized
Dodd-Frank the
Affordable Care Act
and environmental
regulations in ways
that suggest he might roll
back some of these regulations.
And certainly there's
a lot of sympathy
for that kind of policy
initiative among Republicans
so the fact that
Republicans control
both houses of
Congress, and I think
Trump has a very different
stance towards those forms
of regulation than
Clinton had, does improve
the climate for less
burdensome regulations.
My interpretation is the market
began to come to that view,
especially as it became clear
that not only would Trump
be president, but
the Republicans
would maintain control of
both houses of Congress.
But we'll see.
Now, there's lots of
things yet to play out.
Something similar happen in the
wake of the Brexit referendum.
So there was initially a
very sharp fall in FTSE 100,
the stock index that captures
the more domestically oriented
--FTSE 100 I mean-- the more
domestically oriented firms
in the UK.
But the stock market came back
within a few days as I recall.
AUDIENCE: My question
was about your scaling
where 100 is supposed
to represent normal.
But a lot of people talked
about after 2008 there's
a new normal.
Just a general question
about how you historically
look at how much [INAUDIBLE] it
is being compared to old normal
than new normal?
STEVEN DAVIS: Yeah and I didn't
mean to suggest it's normal.
It's just the average
over some sample period.
We've got a US index that
goes all the way back to 1900.
So what you see, if you go
all the way back to 1900,
it's basically the 1930s
stand out, not surprisingly.
Think about all the major
changes that were actually
some of the more initiated
and latter part of the Hoover
Hoover's presidential term.
But then when FDR
became president,
there was an enormous
rash of new policies.
And so that the 1930s stands out
as a period of extraordinarily
high policy uncertainty.
It dropped off afterwards.
It remained low
until mid 1960 or so.
And then US policy
uncertainties just
generally drifting upwards,
according to our index,
since the 1960s.
Now, the concerns you raised
earlier are pertinent here.
You might think
that's just something
about the way the
news gets covered.
But we don't see that
pattern in the UK.
In the UK, there's no trend
over the last 50 years
where there is this
upward trend in the US.
And so we've got a paper
here that if you're
interested in that, go read.
This paper here, which we wrote
with two political scientists,
shows those
[INAUDIBLE], talks about
some potential
explanations for why
there's been this upward
drift in policy uncertainty.
So then the normalization to
100, that's without-- there
is no content there.
It's just a way to scale things.
OK, and then you should
think of this index as 200,
just means it's twice
as high as the average
during the normalization period.
So first thing to recognize
is that the stock market
covers publicly
listed firms which
produce maybe half of
US output, and account
for maybe one third of
private sector employment, OK?
So there's a big chunk
of the economy that
is outside the stock market,
and hence outside the scope
for what the VIX captures.
So there's a lot of room for
the rest of the economy that's
not owned by public
listed firms or operated
by public listed firms
that behave differently.
So that's the first
thing to note.
Second thing is the VIX
is reflecting uncertainty
about stock prices,
and that's something
about the returns to capital.
Well, things can
evolve differently
for capital and labor, right?
So even within the set
of publicly listed firms,
economic developments that might
create more or less uncertainty
for capital owners or
stock stockholders,
don't necessarily translate
one for one or in principle
even in the same direction
as changes in uncertainty
for people in the
labor market, OK?
And, of course for most
people, labor income
is their main source of income.
So, conceptually, the VIX
is picking up something
rather different from the policy
uncertainty index and those two
big respects that
I just captured,
that I just mentioned.
It's not on my slide deck.
It's in this paper.
You can see the exact
comparison you're asking for.
So if you look at
this paper, there's
a picture that overlays the VIX
against our policy uncertainty
index.
And you will see there are
some episodes in which they
behaved quite similarly, like
the global financial crisis.
And there are other
periods, and I
think you were alluding to
this in your question, in which
the VIX is coming down but
the policy uncertainty index
stays high.
So that's been true
in the 2012 period
you mentioned has
that character.
So yes, they behave
somewhat differently,
but then conceptually
they're also picking up
somewhat different concepts.
And so we shouldn't be
surprised if they're not
perfectly aligned.
So the US index I showed you,
the first one I showed you,
has 10 newspapers fixed
over the entire time period.
The historical index that
we use fixes six newspapers.
Goes back all the way to 1900.
When we turn to the daily index
we need way more newspapers.
So the daily index reflects
in recent years thousands
of newspapers.
And even in the early part,
hundreds of newspapers.
So why do we do that?
Well, because if you're
trying to construct this index
at a daily frequency, and you
just use a few newspapers,
it's going to be very jumpy, OK?
So for the daily index
and for the category
specific indexes
as well, we turn
to another text, another
source, and other archive.
And that archive has
hundreds or thousands
of newspapers, which is great.
The downside of using
this other source
is we don't control
exactly what newspapers
are in the source when.
The newspapers come in ad out.
So it's an unbalanced panel,
so to speak, as opposed
to a balanced panel.
So I think the
balance panel is nice
because we know exactly
what we're getting.
We can control exactly
what's happening.
But this unbalance panel
with hundreds or thousands
of newspapers is
really useful when
we try to slice the
data really finally.
So everything I've
showed you today
is equally weighting
all newspapers.
And you might say, well,
should I weight newspapers
by circulation,
because you might
think that larger circulation
newspapers have more influence?
That's a reasonable argument.
It turns out, at least
among the 10 newspapers,
that they have very
similar patterns.
So if you were to weight
them by circulation
rather than weight
them equally it
wouldn't make much difference.
I want to make sure
you get a sense,
since I've talked entirely
about policy uncertainty,
as to the potential scope and
power of using newspapers.
So I just put a few other slides
here-- a few other articles
here.
That top one by
Azzimonti, what she does,
is she takes the same
methodology as we do.
But instead of constructing
an index of policy uncertainty
she constructs an index
of political polarization.
So [INAUDIBLE] for the
US, very similar approach.
And you see some interesting
similarities and differences
there.
The main difference is that
national security crises
caused the two indices to
move in different directions.
So 9/11, remember there was a
big spike in policy uncertainty
after 9/11?
But political
polarization collapses.
Essentially there was a
rally around the flag effect
during national
security crises, that
caused the to move in
different directions.
But in other episodes --like the
fiscal cliff episode, the debt
ceiling crisis, and so on--
the polarization index,
the political polarization
index and the policy uncertainty
index move in a
very similar way.
This paper also constructs
measures of policy uncertainty,
but doesn't with
a different tech
source and different methods.
Comes, again, very similar
patterns that we found.
What's the tech source?
It's earnings calls.
Earnings conference calls.
So this be a little esoteric.
So public listed
firms will typically
have quarterly
earnings calls in which
the CEO or the CFO of the
company will get on the phone
and will make a statement
about the outlook for the firm,
and will take a few questions
and respond to them from-- it
could be journalists,
it could be investors,
could be market
analysts and so on.
So take transcripts of
those and analyze them.
That's the tech source.
Now, they don't do an audit.
Instead, they do something
else, which is also interesting,
They say, we're going
to take two textbooks.
Were to take an
accounting textbook,
and we're going to find frequent
terms that frequently appear
in the accounting textbook.
And we're going to think
of those as A political.
And then I want to take a
political science textbook,
and take terms that frequently
appear in the political science
textbook, OK?
I want to treat those
terms as political.
So they're basically using
the political science textbook
as a way to identify
policy related terms.
They're using the
accounting textbook
as a way to identify
apolitical terms, OK?
So there have a
different method,
and they have a
different tech source
but they're also constructing
policy uncertainty indexes.
This article takes the same kind
of methods or similar methods.
They're not exactly the same.
But instead of quantifying
policy uncertainty,
they're trying to predict
violence using newspaper text.
I just got this
article last week.
I haven't read it yet, but I
spoke to one of the authors
and they claim that
they outperform
traditional approaches
to predicting violence,
civil conflict wars, and so on.
And this is for dozens of
countries around the world
in recent decades.
So but again, I'm
trying to give you
a sense of the power
of using newspapers.
This is the article
I referred to earlier
that has the evidence that
there is a systematic tendency
for, after major
financial crises,
that the political
process tends to polarize
democratic societies.
AUDIENCE: Using
the newspapers, do
you find that to be a leading
indicator or a trailing one--
of markets?
STEVEN DAVIS: Yeah, so
it's a leading indicator
in the following sense.
We haven't looked so much at
markets as for the economy.
So here I'm going to use
a little bit of jargon.
If you're not
familiar with it, I'll
try to state it in as
simple a way as well.
So we take standard off the
shelf, time series models
that macroeconomists
use to characterize
dynamic relationships
in the economy.
We take vector
autoregressive models.
We take standard variables
like GDP, employment growth,
investment, stock price
index, policy variables
--like the interest rate--
Fed policy rate, and so on.
So think of fitting a time
series model of a vector
autoregressive model to
that set of variables,
and then adding to that standard
set, our policy uncertainty
indicator.
And then you can
ask, well, if there's
a surprise movement in our
policy uncertainty indicator,
does that contain
additional information
about the future
performance of the economy.
And the answer is yes it does.
And what it says is
that upward innovations
in policy uncertainty
foreshadow a deterioration
in future macroeconomic
performance in terms
of investment rates, employment
growth rates, GDP growth
rates, conditional on
the standard variables
that macroeconomist typically
put in these models.
So that says it has
predictive value.
Now, I'm trying to
be very careful not
to say what the causal
relationship is because that's
a much trickier thing to infer.
But basically,
these measures have
they're capturing some
information that's
informative about future
economic performance that
is not captured by more
traditional macroeconomic time
series.
And I think that's one reason
why they've been picked up
in the financial
community in particular,
because these measures
are picking up something
that they're not getting from
more traditional economic times
series.
AUDIENCE: What extent
does globalization
affect both your international
and US policy uncertainty
index?
[INAUDIBLE] the rising in the
past two years, to what extent
does it have to do with
the fact that just what
happens outside the US
now matters more than
in previous years?
STEVEN DAVIS: There's
two or three studies that
look at that question directly.
And what they do is
they adapt standard kind
of macro economic
time series methods,
and they try to estimate in the
international spillover effects
of policy uncertainty indexes.
So the IMF has done some
work along these lines.
And IMF finds that increases in
US economic policy uncertainty,
they're bad for the US in the
sense that I described earlier.
They portend a deterioration
in economic performance.
But there's also a spillover
effect to other countries
so in policy uncertainty
arises in the US
it has some spillover effects
on the global economy.
And that's not too
surprising because the US
is in many respects still
the central economic player
in the global economy, not
just in terms of its GDP.
But in terms of its influence
on how international
policy organizations operate.
The Fed is in some sense
the world's central bank.
There's less work on
the other direction,
whether policy
uncertainty develops
and developments in the rest of
the world spill back to the US,
OK?
I think it's a little
harder to tease that out
because Suppose
I look at Brazil.
Brazil's had tremendous amount
of policy uncertainty recently.
But if I were to try to estimate
just the impact of policy
uncertainty in Brazil
on the US, the effect
would probably be
so small that I
would be unable to discern it
in an ocean of other things
that are affecting US
economic performance.
My hope and one of the
reasons we just constructed
this global index that I
referred to earlier --I just
wrote this paper
last month-- but part
of the point of
writing this paper
is to do exactly
what you suggested.
Take a global economic
policy uncertainty index.
See whether it affects each
country's economic performance
after we condition on the
country's own national policy
uncertainty index.
And I've done that.
It's not yet in a
published paper.
It's a working paper
that we're going
to try to get in the next
month or two for Japan.
So in Japan I'm working
with some Japanese authors
and we're looking at the
role of policy uncertainty
and its effects on
the Japanese economy.
We find effects of Japan's
policy uncertainty index
on Japan, similar to what
I described for the US.
And on top of that we find
an additional effect coming
from global economic
policy uncertainty,
in the same direction
but smaller in magnitude,
which sort of makes sense, OK?
So I see this
index in particular
is very much designed to help
answer the kind of question you
raised.
And it's brand new so there's
not much work using it yet,
but I bet within
six months, there'll
be several papers that
have done exactly that.
We've understood for a
long time in macroeconomics
that people might
feel pessimistic today
about future
economic performance,
because something
external has happened
to make them pessimistic.
So you might be more pessimistic
about the prospects for exports
and imports in the US because
Trump got elected, OK?
But we also understand that
if for some external reason,
people become more
pessimistic or more
optimistic about the future
economic performance, that will
feed into recurrent decisions.
Certainly, will feed into
current investment decisions.
So to the extent newspapers are
shaping people's perceptions
of where the economy is going,
then the newspapers themselves
could have some indirect effect
on where the economy actually
goes.
I think that's what I
understand you to be asking.
And so this is another reason
why the causal relationships
between policy uncertainty
and economic performance
are likely to be quite
subtle, and complicated
and have multiple elements
because expectations are
perceptions about the future
can actually affect the present
and that in turn will affect
what happens in the future.
It's the kind of
thing that makes
macroeconomics such
a tough subject
these kind of dynamic feedbacks.
All right well thanks very much.
I hope you enjoyed that,
and thanks a lot for coming.
