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I'm going to be talking about the data and I have basically ten minutes to do this,
so it's obviously not going to be literature review.
But I want to give you a sense on how the data has changed over time and
particularly allowed us to find some key results in this literature.
So the place to start is going to be the same.
That Brent started with we teach this decomposition of the real exchange rate
behavior, this famous paper by Engel.
What he did was very straightforward.
It's essentially the equation that Brent showed you at the beginning where you
take the real exchange or
then you decompose it into basically two parameters to two sides, two parts.
One was, you know what he calls the X is basically the traded goods,
a real exchange rate, and
then you have these white term that tells you it's sort of like a weighted
average of the relative differences between non traded and traded goods.
But Engel's point was very straightforward.
He wanted to show that contrary to the assumption that many people made,
most of the variability in the real exchange rate was not really coming from
a non traded goods sector.
But rather from the trading good sectors that people assume you know,
a LOP hold and PPP sort of holds.
So he had this, every good data paper usually has a single
graph that you Illustrates the whole point and you know, he had this
set of graphs where he showed essentially the share of
the total variability in the real exchange rate that was coming from eggs.
Those are these lines that you see here.
And he basically found that all of it was coming from the traded goods sector.
So that motivated a lot of anxieties a lot of interest in trying to understand why,
why is it that they traded real exchange rate was was failing,
now in terms of data he has something quite simple.
He just had the CPIs and PPI is from for basically six countries and
yet the sub sectors, so he could split the sectors between what we would normally
consider traded and non traded.
So it was quite a straightforward paper with publicly available data.
But we learned that you know, PPP failed for trade goods.
Now the next stage was trying to understand better
what was driving this and you know,
I keep paper here was Gopinath and Rigobon, sticky borders paper in 2008.
They came in at a time when a researchers were gradually starting
to have access to micro CPI data and you know, there's the famous Bill Keno paper.
Gopinath and Rigobon what they do is they managed to get access to micro data from
the import / export price indices that the BLS collects and
any of you can get access to these these days.
You just have to submit a form online, tell what your project is.
But these are prices that are obtained from surveys run on importers and
exporters and you have information at the product level and the prices that
the characteristics of the good and the prices that people are paying.
And they basically found the reason this was a great papers.
They had many results as I said but
they basically showed that if you condition on our pricing.
So even at the time when the pricing takes place, they found that pass
through was very low at the dock basically 22 2% for US import.
So there was something more than just about price sticking that the literature
that points into the really was the main writer.
So we I'm going to talk later on about the paper Brent and I together with Roberto
worked on that went into this literature.
But I just want to point it out here because essentially what we found was that
even at the time when you introduce a new product and the firm has the ability.
To change the price and sell it wants, even at that point we found this big
core movement between the real exchange rate and the nominal exchange rate.
So basically the firms were not adapting
to the movement in the nominal exchange rate.
And they were not doing the pass through even at a time when they were introducing
new products and had the ability to, I'm going to leave the discussion for
the next section where I'll tell you in detail why this was happening.
And if you want something more recent,
great paper to read is the paper by Blanco and Cravino which essentially,
were able to collect micro CPI data from five countries.
I cannot tell you how hard this is, you know, it's actually hard to do it for
a single country.
The fact that they made it possible for five countries is quite surprising.
They can see that there is if you want the raw data that the statistical agencies
can collect, and they basically again did a simple decomposition.
Where they calculated the real exchange rate, but then they basically divided it
between the real exchange rate of goods that actually changed.
And then you have this other term that captures the difference between
the real exchange rate was that change and the ones that do not change.
So what they wanted to show you that if these were really about price,
stickiness, this difference wouldn't really matter.
But in fact, what they showed is that the real exchange rate movements were
actually coming from the real exchanges of the goods that were actually changing.
So clearly this is not and I think when you look at CPI data in particular.
This is not something that has to exclusively about sticking.
Now, they may be clear that there are other papers
that show that as you increase the flexibility of prices,
you do get more pass through and that's certainly true.
But the point I'm making this is, was not a, there's a lot of variants in
the literature that this is not the only element.
One of the elements that became quite important was
this idea that the currency of invoicing really matters.
And Gita Gopinath and Oleg and
Roberto have this paper in 2010 that I think made it also very clear.
They found that there is a very large difference between the pass through
of US imports price in dollars versus those pricing in non dollars.
They're basically using the same data set they had used before
in the sticky borders paper.
This is the microdata of the import / export pricing this is and
one of the surprising findings of the paper.
Was that these differences in pass through that you're seeing in this picture,
below here we have the pass through of the price of the goods that are imported but
are invoice in dollars and
on the top you get the pass through of non dollar imports into the US.
Was the fact that it was British operation was the fact that this lasted so
long for such a long horizon, which led to a lot of interest in understanding
what is really driving this decision of what is the currency of invoicing.
It's something that is quite sticky if you want in the sense that the firm's
stick to this paradigm quite a lot, but it changed a little bit literature.
I think the brand was discussing this a PCP versus
LCP type of theories that we were working with.
And this sort of showed us that for a country like the US, in fact,
they was PCP for exports, LCP, for imports.
And when you look that trade data,
everything most world's most things seem to be priced in dollars.
So it led to all these subsequent papers on the dominant currency pricing and
Gita has many papers on that.
This is I'm just listing here, one of the latest ones.
But the idea here was that there was a share of the variability that you observe
in the degree of pass through is closely related to the share
of inputs that are actually priced in this dominant currency which is the US.
And as Brent was saying towards the end of his talk, we started getting interest
in what other things beyond stickiness in currency of invoicing matter.
And we had set up a first by Amiti, Itskhoki and Koning,
they basically had access here, the data changed a little,
because they started looking more closely at the border data,
but a customs data they gained access to very detailed.
They tested on Belgian exporters they could see and
you know where they were selling their goods, what prices there were setting.
And they could also understand very well what
were they countries where they're importing as well, because
they're basically exploiting a fact that becomes quite obvious in trade data,
which is that large exporters are also large importers now.
So using this this data, they basically showed that large firms that have high
import chairs were actually having a very relatively lower exchange rate passed on
to the prices that they said.
There are two words that I would highlight here one is the large and
the other one is the input shares.
And so this essentially are two mechanisms.
The large firm mechanism was about having these markups that they could
decide to change as the shock happened, they have this ability, like
Brent was describing this variable markup type of story allowed them to do so.
And then you had this input shares, which essentially was the mechanism that would
allow them to have an offsetting effect on the exchange rate pass through.
So, you know in this context,
you can think of an exporter that faces a depreciation for
example of the currency they will have an effect incentive to lower the price at
which they sell in dollars abroad, if you're talking about the Belgium firm.
But then on the other hand, they had this offsetting effects because
their inputs are becoming more expensive and
that reduces the degree of exchange rate pass through.
Now the data itself when you look at it, it's essentially custom information.
We can see these in, trade flows in great detail, and
the ability to identify the source country and
the export destination made a big impact, the source of the data this
was collected by the Belgian Central Bank, which is also, is a physical agency.
So in a sense, it was a great thing that they were able to connect
through other people to this data.
They had a follow up paper recently, which is also interesting.
I'm going to skip it in the interest of time, but
just let me tell you briefly what they did is now they looked
more internally to see how firms were reacting with the prices of they said
domestically when they took into account the prices of competing firms.
And they could really link the firm's and
understand who we're competing with whom, so
they looked at strategic complementarities and noted that there is lower cost
pass through when they can observe even the the costs that these firms have.
And you know, under certain conditions,
that leads to lower exchange rate pass when that's the link to the literature.
So worth looking if you're interested in those dimensions about real rigidities and
strategic complementarities.
And finally, since I haven't we've mostly been talking about
what happens at the boarder right, I thought I should tell you what's happening
at the consumer level because I'm going to talk a lot about that in the next session.
The papers we had in the literature, I'm going to highlight two first.
The Burstein, Neves and Rebelo sent in 2003 I think started a lot of interest
in this side of the literature.
And the idea here is even if you find incomplete pass through at the border
when you look at the consumer level, the retail level and the CPI, which matters
because of inflation dynamics, for example that Brent was referring to.
They realized there was this extra component the distribution
costs that will allow a lot of shocks.
That creep into the border prices to even have less of
an effect at the consumer level and they basically try to use surveys,
input output tables and compare the markups or the price difference
sorry within wholesale and retail prices to estimate what is the domestic.
If you want component of this distribution cost,
they they estimated It was 40% in the US 60% in Argentina.
So it was quite high most estimates in the literature around you around this
50% range and they studied this real exchange rate dynamics.
But more recently, I would say and more comprehensively because it took care
of a lot more countries was the paper right Goldberg and
Campa where they showed that CPI pasture is actually reduced by a third.
When you take into account these distribution market margins, okay?
This is the dampening effect on the exchange rate.
So now, they also noted that many of these distribution
services that we tend to think as non trader will actually use
important inputs as stray as part of the service now.
So to that extent, they noted that the relationship was not so
straightforward It also had this important input component that could
enhance the impact that the exchange rate shock would have on the level of
pass through that they're willing to pass on to the consumer prices.
And for these, they use this input and output tables in CPIs for
about 21 developed countries.
So what am I trying to convey here?
I'm trying, I hope you get a sense that the data has been changing and
it's not just a matter of getting bigger, having more coverage but
also in the types of details.
These data sets have that allow us to test different mechanisms.
So I've listed all the papers that I've mentioned today and the topics here.
And you can see at the beginning we mostly had CPIs and
PPIs data was publicly available.
We've moved into having access to the micro data behind the official statistics,
mostly CPI.
And the import and export pricing decision and there's also a lot of data on customs
data collected by customs which are the type of data that Oleg and
Marissa have used in the case of Belgium.
There are many other papers on these like, what is missing from this picture so
far is what happens at the retail level with micro data.
So that's what I'm going to be talking about next.
I'm going to be describing the efforts really to collect data online,
really see what was happening with prices at the retailer level.
But I'm going to leave that to the break and
I think we can answer your questions at this stage.
Thank you.
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