Thank you.
[INAUDIBLE] They're going to present
an application then two papers.
The first one is,
this paper about currency unions and
what happens with the law of one price.
And then a more recent paper studies,
Tariff Passthrough,
what's been happening with consumer and
border level and prices in after
the escalation of the trade war that we
have seen in the last couple of years.
So let me start with the first one,
this is a paper where we tried to test,
if we could measure the LOP,
the Law of One Price,in a large set of
goods that were identical and
sold by these large global retailers and
as a motivation, clearly there are these
classic theories that says that
the below one price holds but the big
literature had showed that it failed.
In understanding what happened to these
rare prices mattered as we have said for
the behavior of your exchange rate shock.
So what we did is in a way, we expanded
the available data at a very micro
level finding identically tradable goes,
we were certainly not the first
to use identical tradable goods.
But it's just the number.
In fact number of countries that we could
show this statistics was expanded a lot.
And we were scraping data much in
the same way that you saw me scrape just
a few seconds ago.
And then we found basically the results
we showed that the LOP holds
within currency unions and
otherwise fails.
So the currency in which
spreadsheets are displays,
swamps and it's far more
important than other things we
would traditionally think
are important for determining the LOP.
So whether you're in
a similar trade union or
you have a nominal exchange
rate that is very volatile.
And then the other thing that was
interesting I believe from this paper was
the fact that we created this new
the composition since we could see
when these goods entered and
were sold for the first time.
We could try to see if we could capture
the adjustment that takes place at
the time of a product introduction
which is something pricing.
This is.
Then the nice because,
when you're creating a price index and
for inflation measurement, what tends
to happen is that you just focus on
changes of products over time and if
there's a new good that appears probably
won't get included into sample
until an older product disappears.
And the statistical agencies usually not
going to link the price of the old product
and the new product.
So there were other papers that
had speculated about this and
we sort of were able test
it nicely with this one.
So let me describe a little bit
the data we use Apple IKEA Zara and
H&M scraped everything they sold for
about 4 years,
which gave us a lot of
information on a daily basis.
Things that are important for you to
remember in this slides the prices and
want to show you include VAT taxes.
So if we make them identical, the VAT
taxes were actually different between many
of the countries within the European zone.
So it's amazing that they were making them
identical because they have very valid
reasons to argue we have to make
them a little bit higher or
lower and in some locations, we cannot
measure that resonates here because we
didn't know where the products
were actually produced.
So we restricted ourselves to a low P and
sort of the real exchange rate.
Calculation.
As I said, now, we've This is
actually where we started testing.
If the online offline prices
were generally identical,
I would go with my own phone and
take pictures of prices inside it.
to them, I felt very uncomfortable
doing that, particularly,
walking into some stores and
taking these pictures, but
we clearly started realizing there were
quite identical And these companies,
we sort of sell exactly the same
everywhere, which was a good thing.
So what is it,
the basis of the paper just let's
calculate a good level exchange rate.
You have basically the price in country.
I, you have the exchange rate
between that country and
country J so we have this very simple
expression that Brent already showed you
in love terms that
measures the good level.
And real exchange rate and
we can just build the simpler plot of the
good level exchange rate across countries.
Now I am picking here as the way
the United States and then plotting it for
example, if you focus on this histogram
You have all these different products
having a different real exchange
rate relative to the US and
you can see this wide dispersion
as a little has already shown.
Some goods are far more expensive.
Others are far cheaper and
it sort of happens across the board now.
When we refocused this in terms of
currency unions and we looked at European
countries and used as a vase, the case of
Spain, what we found was very different.
So, in the US and Spain, there was
this big differences, but you take
nearly every other European country and
all of the mass was very close to zero.
So, the real exchange rate,
so the prices were identical.
The real exchange rate was one.
>> Alberto sorry to jump in,
this is Brent.
But the x axes on these
histograms somehow look,
the axes values don't look to me properly.
I don't know if others
are having the same.
>> Yes.
>> The key thing is to maybe you can
explain where the mass and
those spiky histograms corresponds
to because it doesn't look
like zero on my side.
>> No, you're right.
I think this was PowerPoints
shifting the scale but
this mass here should be right at zero,
which means that the price is
expressed in the same currency
we're actually identical.
That's where the mass lies.
So we have a collapse in the distribution.
And the law firm price holds
when we see that mass at zero.
Maybe the next graphs.
If you look at the paper it
will become more obvious.
Now it's interesting here we have three
countries that are actually in Europe.
And where we see the histograms
are actually not so concentrated.
And we included these three in
particular because for example,
you take Norway is not part of the EU.
You can say, well, this is about
tariffs and things like that.
But then you look at Sweden and
Denmark and they're both part of the EU.
So they have these lower times
that doesn't seem to matter.
It just keeps on creating this difference.
And in fact, you have Sweden that has its
own currency that is quite bulletyme.
But in Denmark has a very
hard peg to the Euro.
So there's no volatility in the nominal
exchange rate between the Danish Kroner
and the Euro.
And you could think well, maybe it's
a matter of people being able to quickly
make a calculation or
what is the different price and
in Denmark that should have been the case,
but it isn't.
It actually has these differences in
[INAUDIBLE] So we tested this for
various subsets.
It's something that happens
in all the retailers.
We can restrict the data to just
look at a bilateral between
countries that have currencies
afloat with each other.
We did find that having a peg
when reducing the exchange
rate volatility had some of an impact.
But the vast majority of the impact as
you can see in this shrinking coefficient
comes when you are actually looking inside
the currency unit and we found this for
European countries but we also found
it for the US and Salvador, or
Ecuador that are dollar eyes countries you
can see in the paper, those distributions
suddenly shrinking and other mass
being at zero with the LOP holding.
Also for dollarized countries
which was quite surprising I think
in a way it sort of showed that
the the currency in which these
retailers had to show their prices
mattered a lot more Than other
things we would traditionally
consider more important for the LOP.
And it was not just about these nominal
exchange rate volatility like I mentioned.
Now, by the way,
we also show this in the time series.
So while we were collecting data it
happened that Latvia joined the euro.
So we can see how the pricing
actually changes and
immediately within a matter of days
the distribution shifts completely and
the LOP starts to hold
within these countries.
Now, the other thing that as
I mentioned that we could do,
is we knew when the spreads were coming
in, and so we wanted to test is maybe some
of these deviations that we were seeing
in the real exchange rate were happening
because they set the optimal price and
the LOP may be held at the beginning.
But then the fact that
there's price stickiness and
differential shocks in each
country started to create
these differences in real
exchange rates at a good level.
And we find that to be the opposite,
we did a decomposition that I'm
describing here where you can take,
I is the date at the break at
which the product is introduced.
L is the date at which the product
is last seen on the sample.
So you can take the price of each good and
split it up between the initial
price that was set for that good.
All the subsequent changes you can
observe of that price over time and
you do this for both countries,
you calculate the sort of good level real
exchange rate and you end up with these
three terms that capture intuitively,
the three ways that real exchange
rates could have changed.
One is they said at the time that they
introduced the goods the first term that
I'm going to call the good level, a real
exchange rate at the time of introduction.
Then there are changes that are coming
from these price movements that we may see
in each one of these countries.
And then there's a third term.
There's going to be what we
call the stickiness term,
which is coming from the fact that the
real exchange rate's moving around, but
maybe prices are not moving around so
much.
So we could just graph
the same histograms and
split it up between these three
components, and this is what we found.
We found that this is the original
ones that I was showing you.
I think here the scales are okay.
So, this takes care of
the problem we had before.
But then we had the real
exchange rate introduction,
changes in demands that are coming
from price changes and the stickiness.
So, the stickiness part did matter.
But clearly what seemed to be driving
all these differences were we were
seeing was the real exchange rates
that were set at the time that were
actually introducing the good.
So, it started making us think you
know what, why is this happening?
We looked at the time series as well.
We measured this real exchange rate time
of introduction, and you're looking at
loads here of how this was moving
around with the nominal exchange rate.
So if there's really no adjustment to
the changes in the nominal exchange rate,
these two things should come off together.
And that's what we found.
We found that firms were not sort of
using the advantage of introducing
a new product to pass on,
let's say some of these changes that
the nominal exchange rate was producing.
So, this had several implications if
you put these two results together.
One is that I think we added value to the
literature about market segmentations and
the LOP in particular, this idea
of the currency union was swamping
things like geography, taxes,
culture, nominal exchange
rate rigidity that we will traditionally
think is a big part of this.
To some extent for policymakers
when they introduced the euro,
many of them were actually expecting
some sort of price convergence.
But at the time since they were in the
middle of a crisis and countries that had
joined the euro, had to adjust to the
financial shocks there was a lot of talk
about Greece having to do an internal
devaluation, it had to reduce its prices
to sort of adjust to show because he
couldn't use the exchange rate to do that.
And in a way our results were
speaking precisely to that.
If it is the case that in
particular these global retailers
are pricing identically
within currency unions,
the ability of doing these internal
devaluations was becoming a quite hard.
You can think of this even in
the in the context of pricing
within countries by the way,
part of my work has also shown there's
uniformity in pricing of large
retailers within countries.
So that will make some of these
adjustments across regions quite harder.
And then I think also,
as I mentioned in the first talk,
is that the standard measures a real
exchange rate may actually omit some
critical information by not including
these prices and introductions.
We did find that these
type of retailers do not
seem to take advantage of the product
introductions to make these changes.
But I think there's a lot to be done
about other retailers that maybe have
differently, so worth keeping an eye.
And it also finally just suggests
there's an important role for
these real divergences
in financial markets.
It's interesting, I get the sense
that many of these global retailers
they're thinking less about the optimality
of pricing on a global scale and
more about the prices at which they
launch products relative to the previous
versions of the same products
being launched in those countries.
So that creates some persistent,
real exchange rate
differences across countries.
Okay, so that's one.
How am I doing on time?
What time do you want me to end, Brian?
Sorry, can you say that again?
>> I mean, do what you can, but,
you know, if you're able to end in 10,
15 minutes latest that would be good.
>> Okay it's going to be hard,
but I'm going to try.
>> Do what you can,
present your material well.
>> Okay, very good.
So tariffs passthrough, the question
you all know about the trade war.
The one thing I want to say here is that
there's been this progressive increase in
tax rates.
They fight with China in particular
in both several stages of escalation.
So there was an increase of 25%
in some tariffs that affected
mostly intermediate goods in July 2019.
And then as the escalation
kept on happening,
more and
more consumer goods were affected.
So we wanted to study how quickly
there was a pass through into consumer
prices and also see and compare these
with the exchange rate pass through and
see if the fact that the trade war was
causing a depreciation in the renminbi,
or sort of softening the blow for US
importers that may be facing higher costs.
But ultimately, you know what we wanted
to do say who was paying for this?
You probably heard,
President Trump has said the Chinese
are going to pay for this.
That could happen by the way.
You think about the problem here.
You impose a tariff at the border.
If three people could bear the burden of
tax it could be the Chinese exporters
that decide to lower
the price in US dollars.
And that's what the President was
probably hoping would happen.
Then it could be the US importer
that faces this higher cost or
ultimately could be the consumers
that are paying higher prices.
Now to know who really is a bearing
the incidence of a tax you really need to
observe prices in two locations,
at the border and at the consumer level.
So that's what we did in the paper
we looked at at the border prices,
at store prices using the BPP data, and
as I said that it's crucial
to determine the incidence.
We remain largely silent,
I should say on quantities and welfare.
But there are other papers that have
analyzed that and what did we find?
We basically find that The US within
a year and a half of the trade war,
the US is mostly still
bearing the majority or
the burden of these tariffs,
at least in terms of prices.
Let me be clear, why were the Chinese
have not reduced their export
prices by March in US dollars?
The claims that the RMB depreciation
was offsetting the impact
of the tariff is not valid in
the data because what happens is
that the passthrough into post-tariff
import prices had been much faster.
And magnitude has much larger than
the traditional passthrough from
the exchange rate shop that we
know from the literature and
we document in the papers,
as well, is relatively quite low.
So this has implications for
several leaders, but
if you look at the border prices,
that's what happens with imports.
When we look at export prices,
we do find a difference,
it turns out US exporters have
lowered their prices significantly.
And part of the reason for that,
the difference seems to have to do with
the type of goods that are being sold and
how differentiated they really are.
Turns out that both in imports and
exports,
we find that differentiated producers of
differentiated goods feel the pressure to
adjust the price at which they
sell to the other country,
because the other country, obviously,
has alternatives for these.
But then you look at the composition
of US imports and exports, and
most of the exports that have been
receiving these retaliation tariffs
are actually undifferentiated, so US
exporters had to lower their prices a lot.
While, when you look at imports,
that does not seem to be the case,
particularly in the case
of the US-China trade.
We bring into the US a lot of
differentiated goods here, so
the Chinese have not reduced
their export prices by much.
And then we look at what happened with
consumers, we found basically very
uneven passthrough here, some goods
categories adjust this, others do not.
We look at various margins of adjustments
and, essentially, we find that
the retailers have ways of absorbing
some of the costs through their markups.
But also,
by doing things like front-running or
front-loading their inventories,
they basically started importing a lot
more before the tariffs were put in place,
and we can see that in the customs data,
in the forms that they fill in.
And then, they also started gradually
diverting some of the trade and
bringing it from other countries
to avoid having to pay the taxes.
So I don't have much time to walk
you through the results, but
I'll show you a few interesting
graphs that make these very clear.
Imports from from China and
other countries here,
you're just looking at
prices post the tariff and
you see these big jumps, these
are the prices that the importers pay.
They line up very clearly with the
magnitude also of each of these increases,
everything you've seen in red
here is coming from China.
So all these waves were very clear
when we look at the imported prices.
When we look at export prices,
we find that those exports from the US
that were affected were
actually falling much quicker.
And as I said, this has to do with
the fact that the exporters were selling
things like soy beans to the Chinese,
and the Chinese have alternatives from
countries like Argentina or
Brazil to bring some of the things.
We have this passthrough equations that
try to quantify this into greater detail.
This is essentially a very standard
equations in the literature that have,
in addition to having
the exchange rate changes,
we added the changes in the tax rate
at the level of individual goods.
And let me move quickly
to to the final table,
you can see that if we look at imports,
prices seem to fall a little bit,
but that basically disappears once we
control for the exchange rate passthrough.
Much of the decline we see on
the import post-tariff was actually
because there was some
depreciation in the RMB.
But, by the way, this regression
is before the actual tax, so
we're looking at the price, let's say,
that the Chinese exporters is selling
before the tax actually happens.
Now, when we looked at exports,
we saw those it declines quite clearly.
And then, if you look at differentiated,
undifferentiated goods, like I said,
undifferentiated ones was where
the the passthrough was greater.
But it just happens to be that
numerically all the exports,
most of the exports in the US
are actually undifferentiated.
Okay, so there are some
back-of-the-envelope calculations,
I'm going to skip all this.
I'm going to show you a little bit what we
did at the retail level with the BPP data.
We did two things, first,
look at case studies of specific goods
we know are coming mostly from China.
They're easy to identify and know that
they've been affected by tariffs.
And then, for some goods, we actually
could see the country of origin and
the HS code classification.
So these graphs here show you, just focus
on a second on this graph on the right,
it's showing you the annual inflation
rate of goods like washing machines,
handbags, tires.
And these are things that you may have
heard in the media where we're going to be
impacted a lot with the tariffs,
mostly because they come from China.
And there's some interesting heterogeneity
here, washing machines, which is the blue
line, we see this big jump just a few
months after the tariffs are put in place.
And there are other papers
that have looked at these, and
there's a CPI index that matches
up quite nicely with this,
so there's been some
adjustment in that sector.
But that's a type of tariff
that apply to many countries,
it wasn't specific to the case of China.
Now, you look at the others, and
there's been a gradual adjustment in
the price level, but it's even muted for
refrigerators and things like that.
So there's clearly some heterogeneity,
some passthrough in some categories,
it's not as high as we might
have thought to consumers.
Now, the truth is these
indices that I showed you,
we weren't sure how many of the goods
were actually coming from China.
And there are also different
types of refrigerator, so
we focused on two large retailers, for
which we knew the country of origin and
we knew the exact HS
classification of each good.
Now, how do we do this?
This refers to the data,
the country of origin,
these were retailers that either showed
the information on the website so
we could scrape it, or the retailers
volunteer to give us the information.
So we had the prices from the scraping and
they gave us,
one of them gave us the country of
origin information for each group.
And then the HS codes, what we did
is we have the product description.
So we basically did what every
importer does, you hire a company that
classifies the goods for
you according to the product description.
And, in fact, this is publicly available,
the Census or Eurostat,
they provide these tools,
which are basically using machine learning
algorithms on the back, and the more
people use them, the better they get.
But you can basically plug in,
let me show you this live,
it's going to make it more interesting.
You can go here and say look,
I have a, let's say a banana And
it will automatically try to classify it,
many times,
you actually have to tell it exactly what
it is before it gives you the HS code.
Sometimes, it's much quicker,
if I put like LED TV, Samsung,
it will automatically give me the code,
okay?
>> I don't think we see the screen
that you're working on.
>> Okay, okay, okay, what are you seeing?
Let's see, okay, stop share.
I'm sorry.
All right, thank you, Chelsea, so
I'm showing you this website
from the Census Bureau.
It allows importers to realize what
is the HS code that you declare,
it's using the technology of a company
called If we see online as I was saying.
So if I put Samsung TV and
I classify it, I get the code.
By the way, you can scrape this by
supplying the product description and
you can scrape the code.
Since we have so much data, we actually
hired the company to do this for us.
But I also relied a lot on arrays for
when we had a good that we
couldn't classify automatically.
Someone had to come in and say, well,
this is actually a fresh, a banana,
and a type of plantation and
that's how you get the HS code.
So basically that's what we did,
let me go back to my slides.
We got all these individual products,
we classify them.
So when we knew the country that were
coming from and whether they were actually
impacted according to the prescription
to buy the tariff or not.
And this is the result you
get with these two countries.
You see the goods are coming from China
in red in both that are affected or
not affected by the tariffs,
nd then in blue on the other countries.
There's clearly something going on here
where the prices of every single good
starts to increase gradually.
But there's basically no difference
between goods are affected and
not affected by the tariffs over time.
So we did the passthrough regressions
in just the back of the envelope
calculation here.
For a 20% import tariff,
we basically estimated that there was
a retail price increase of
0.7% in the full database.
We did sub samples that
we thought had better or
less possibility of having
a measurement error.
And the best we could get was 3.2%.
So you sort of make a quick calculation.
Let's assume there's a distribution or
local component cost of 50% for
the 20% tariff, if that we see
is mostly borne by the importer.
Then around 18% of that is what
the importer face in addition.
Then if it's 50%,
we should have seen about 9% passthrough,
they're really pass it on to consumers.
But there was very little evidence
coming from this data that the retailers
have decided to pass this on and
could be the retailer so the wholesalers.
One thing that could happen is they
were spreading the cross across goods.
And then we did a comparison between
Canada and the US because this company
is also selling Canada where they
do not face this additional cost.
And we saw prices creeping
in generally in the US, but
the same thing happened in Canada.
So it suggests that they were in fact
absorbing some of these costs through
their markups.
And this is finally an element with this.
We looked at other ways they
could be adjusting and for these,
we went to cost customs data.
There are several companies providing this
information that you can sort of purchase.
You can see what these large retailers
were actually importing because
every time they import directly,
they have to declare in a bill of
lading where the goods are coming from.
And another things, so
we basically just track for
the storytellers the metric tons of goods
that they were importing over time.
And this first dashed line that you
see here towards the middle of 2017
was the time when it started to become
obvious that a trade war was coming.
And we see these imports from China
actually starting to pick up and
then things quite down a little
bit until the tariffs are imposed.
And then we see again
some of these spikes.
So these are the front
loading that was happening.
Now once the tariffs are in place,
this goes away and
that's why you see these
numbers coming back now.
The other thing interesting
here was these red line.
This is imports from other countries.
So this trade diversion trying
to avoid the tariffs and
we see sort of going up
significantly during 2019 and
spiking actually at the time where there
was an escalation towards the end of 2019.
We've spoken to many of these retailers,
they tell us they're doing the trade
diversion that is easier for
them in the short run.
But making bigger investments of trade
diversion would require many of them to
be convinced that the trade war is
something that is here to stay.
So we'll see what happens, but
let me just finish with that.
We think our results are basically
showing all these frictions that
have impacted clearly the importers.
They ended up paying most of it and
much of that has not been
passed on to consumers.
So it's a short term kind of story.
Over time, I think as time goes by and
becomes more obvious that the tariff
are going to be permanent.
These retailers are facing and importers
are facing this pressure to pass on some
of these higher costs to
the Chinese exporters.
They're going to be more likely to lose so
I believe because there's more
opportunities to trade diversion.
And then they're also going to try to push
that gradually towards consumers as well.
There's less of an opportunity
to do front loading and
their profits could be affected for
a while.
So we may see quite
a different type of evolution.
But in the short run,
it's mostly the importers in the US
firms that have bear the cost of these.
So we gave you two
exercises here that you can
use our data to test some of these things.
The first one is in this paper
about tariff passthrough.
We asked you to modify the replication
code to show that Chinese and
non-Chinese goods have similar
price trends after tariffs.
We gave you some solution
graphs that you can look at.
And all the data that I've been
describing, even if you wanted to take,
for example, passthrough from a particular
country of origin and things like that.
You can do it because
the data is available online.
The second one goes back
to the law of one price.
Example, we ask you to
replicate a figure there.
It's tricky here because you have to use
an exchange rate with respect to Spain.
So that's probably the hardest
thing to get right.
And I hoping many of you will sort of
scrape some data from one of these global
retailers and see the results that we
presented still hold are many reasons to
think things could change.
There's been many years
of a European crisis.
So maybe, eventually they start
to differentiate their pricing
strategies in Greece versus France and
other places.
All right, so
I'm going to finish with that.
I'm going to thank you and
then happy to take any questions.
>> Let's end there.
