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Real Exchange Rate Behavior:
New Evidence from Matched Retail Prices
Alberto Cavallo
MIT and NBER
Preliminary
Brent Neiman
U. Of Chicago and NBER
Roberto Rigobon
MIT and NBER
Classic question: how are tradable goods� prices and exchange rates related?
A vast literature attempts to characterize the level and behavior of the the Real Exchange Rate
Common finding � little co-movement between relative prices in local currencies (RP) and nominal exchange rates (NER), particularly at the retail level
The RER co-moves with the NER
Passthrough is low (Goldberg and Knetter (97), Burstein and Gopinath (2011))
RER shocks are persistent � PPP Puzzle (Rogoff (96))
RER for tradeables is just as volatile as for non-tradeables (Engel (99))
Motivation
Standard explanation would be Balassa-Samuelson-type effects. But, hard to find evidence that traded good RER behaves so differently (Engel 1999).
Other possibilities include:
Distribution costs (Burstein et al 2003)
Selection into trade and variable markups (Atkeson and Burstein 2008)
Sectoral aggregation more problematic in CPI (Imbs et al 2005, Carvalho and Nechio 2011)
Biases from temporal aggregation more problematic in CPI (Taylor 2001)
Biases from disregard of entering and exiting goods (Nakamura and Steinsson 2012)
Motivation
Most papers use CPIs/PPIs/IPIs
Not designed for international comparisons
Different index methods and goods across countries
No levels, no entry/exit
Existing micro-data limited to few countries (e.g. US-Canada), goods (eg.
Big Mac index), and/or degree of product matching (eg. EIU).
World Bank’s International Comparisons Program comes close but among
other issues is extremely low frequency.
Summarized Nicely by Taylor (ECMA 2001):
Huge Empirical Challenge
“To meet the desired standard we would be hoping that hundreds of price inspectors
would leave a hundred or more capital cities on the final day of each month, scour
every market in all representative locations, for all goods, and come back at the end
of a very long day, with a synchronized set of observations from Seoul to Santiago,
from Vancouver to Vanuatu. We cannot pretend that this happens.”
We use web-scraping methods to substitute for Taylor’s hundreds of price
inspectors” and achieve:
Better matching of products and methods (relative to CPIs)
Higher frequency (relative to ICP)
Match more than 50,000 individual goods (“varieties”) to approx. 350 narrow
product definitions (“products”) chosen to fully cover the CPI categories of
food, fuel, and electronics at the retail level in 9 countries from (some subset
of) 2010-2016.
Evaluate traded good real exchange rate behavior (bilaterally with U.S.) in our
data and compare with CPI-based equivalents
What We Do
Regress relative prices on exchange rates and find roughly 0.75 with our
matched retail price data compared to 0.34 for equivalent measures in the
tradable CPI data (same countries, sectors, and time period)
Source of differences mostly be fact that we exactly match prices together with
a margin from price levels at entry/exit of varieties
What We Find
International Finance
PPP Puzzle Debate: Rogoff (96) Imbs et al. (QJE 2005), Chen and Engel (PER 2005), Carvalho
and Nechio (AER 2011)
Decomposing RER Adjustment, Macro and Micro: Bergin et al. (ReStat 2013), Crucini and
Shintani (JME 2008)
Extensive Margin and Price Indices: Nakamura and Steinsson (2012), Gagnon, Mandel, and
Vigfusson (2014)
RER Behavior Using Online Prices: CNR (2014, 2015), Gorodnichenko and Talavera (2014)
Passthrough Goldberg and Knetter (97), Engel (99), Gopinath, Itskhoki and Rigobon (2007),
Burstein and Gopinath (2011)
International Comparisons
Penn World Tables: Heston and Summers (88,96), Nuxoll (94), Feenstra, Inklaar and Timmer
(2013)
Measuring PPPs: Diewert (99), Hill(99), Deaton and Heston (10)
Poverty and PPPs: Deaton(06), Deaton (10)
ICP Rounds and “Surprises”: Diewert(10, 13), World Bank (14), Inklaar and Rao (16), Deaton
and Aten (16)
Overview of Related Literature
Private company linked to the Billion Prices Project
Use web-scraping methods to collect daily data for all goods sold by thousands of
large retailers in 50 countries
Unlike CNR (2014, 2015), difficult to find goods in broad categories that are truly
identical. Barcodes, package sizes, flavors, brands, etc., vary by country.
We follow the ICP methodology:
Create a list of narrow product definitions � a “product”
Each individual good/UPC we find at the store is a “variety”
Match varieties to each product
Look within largest retailers (typically 3-5, but varies) for matches, code
algorithm to generate unit values where needed
Data from PriceStats
Classification System
“Product” level
UN’s Classification of Individual
Consumption According to Purpose
Standard
COICOP
Levels 1-4
New
Levels
CPI weights only
available up to Level 3
(or 1 in some
countries)
• Products chosen to have good category coverage, also found in many countries
Product Definitions
Convert to Unit Prices, Calculate product-level RER
Ground Coffee - Regular
Adjusting for size
Step 1: Unit Prices for all varieties
in each country
Prices need to be adjusted by package size (which can be extremely large in the US)
Product Varieties (Size)
Convert to Unit Prices, Calculate product-level RER
The price for product i, country y, time t is:
Ground Coffee – Regular – 1 gram
where is the set of varieties captured by
our scraping.
Average Price US (dollars) Average Price UK (pounds)
Step 2: Average unit prices for all
products in each country
Methodology
Bilateral Product RER
Ground Coffee – Regular – 1 gram
The nominal exchange rate is expressed as units of z per unit of y
(increase is appreciation of y)
Step 3: Form relative prices,
combine with NER,
form RER
Matched Dataset
2011 Comparison with World Bank’s ICP at 3-digit level
2011 Comparison with World Bank’s ICP at 1-digit level
Decomposing the Relationship Between our RERs and CPI-
Based Ones
Step 1: Generate RER using All Item CPIs
Step 2: Only use 1-Digit CPIs corresponding to our PPP dataset
Step 3: Only use the 3-Digit CPIs within those 1-digits the correspond to our
PPP dataset
Step 4: Create the 3-digit CPI-based RERs using Fisher weights, then aggregate
Step 5: Do this for PPP Data, but only using continuing goods (i.e. like a Matched
Model)
Step 6: Do this for all goods, including entries/exits
Our RERs and CPI-Based RERs
Our RERs and CPI-Based RERs
Our RERs and CPI-Based RERs
Our RERs and CPI-Based RERs
Our RERs and CPI-Based RERs
Our RERs and CPI-Based RERs
Our RERs and CPI-Based RERs
Our RERs and CPI-Based RERs
Literature employs many econometric methods:
Correlations and Granger Causality
AR1s and Stationarity Tests
Vector Error Correction Models
Have tried some with potential for useful results, but difficult
given short time-series.
We run regressions of relative price on exchange rate (ala
Gorodnichenko and Talavera, AER 2016):
Always run bilaterally with US
β = - 1 implies fixed RER
Note that these are relative passthrough coefficients.
Beyond Visual Evidence
Relative Passthrough is 75% on average
Our RERs and CPI-Based RERs: Step by Step
CPI-based
Goods CPI or manufacturing PPI often used as rough proxies for
tradables (eg. Engel (99))
We do a bit better:
1-digit CPI: uses official CPIs for level 1 sectors in our data:
Food and Beverages
Transportation
Recreation and Culture
Sectoral differences
Our RERs and CPI-Based RERs: Step by Step
Same Sector
(food, fuel, elect)
Goods CPI or manufacturing PPI often used as rough proxies for
tradables (eg. Engel (99))
We do a bit better:
1-digit CPI: uses official CPIs for level 1 sectors in our data:
Food and Beverages
Transportation
Recreation and Culture
3-digit CPI: uses level 3 official CPIs (where available), to build an
aggregate CPI that excludes sub-sectors that are not in our data �
eg non-tradable services, eg. public transport, packaged holidays
Sectoral differences
Our RERs and CPI-Based RERs: Step by Step
Same Sector
(food, fuel, elect)
Same subsectors
Following the international comparisons (ICP) literature, we measure
RERs using Fisher indices that start with 3-digit relative prices and
aggregate up using expenditure weights from both countries.
When computing RERs with CPIs, the standard procedure is to first
get the aggregate CPI in each country separately, and then compute
the ratio.
This can cause a formula or “extrapolation bias”(see Deaton (2012)
and Inklaar and Rao (2016)):
Formula or “Extrapolation” Bias
Our RERs and CPI-Based RERs: Step by Step
Formula bias
Matched Data
Row (5) is an index that uses matched data (but does not yet
incorporate the effect of entry and exit price levels)
Passthrough rises 26 percentage points
Why?
Below discuss some possibilities and rule out others
Our RERs and CPI-Based RERs: Step by Step
PPP Data (matched
products)
Differences in Online and Offline Prices? Not likely.
Source: Cavallo (2017) “Are Online and Offline Prices Similar? Evidence from Large Multi-Channel Retailers”, American Economic Review Vol 107(1)
Difference in stickiness vs CPI data? Not really…
Our PPP data is actually stickier than comparable in US CPI data (food drives
this result)
Cavallo (2016) “Scraped Data and Sticky Prices” Review of Economics and
Statsitics � CPI imputations and scanner data averaging can increase the
frequency of price changes.
Matched products more flexible than other online data
Our hypothesis: Matching procedure “picks” particularly tradable goods
Imagine that true passthrough is one, but imperfect matching means we
use a proxy price which differs by some error term
If error term is correlated with the exchange rate, changes estimates:
Highly plausible that proxy uses different imported inputs, different
currency of invoicing, affected by global shocks
What could cause lower passthrough?
If CPIs include products with less imported inputs
If CPIs favor products with local currency of invoicing
Product Matching and Error Bias
Product Entry/Exit Bias
Ignoring a constant term, we can write t+1 prices as a function of
new and continuing prices
Alternatively, we can focus on the decomposition at time t (instead
of t+1) between exiting and continuing goods
Share of intros Price of intros Share of continuing Price of continuing
Product Entry/Exit Bias
Combining these equations we obtain
where the first term is the matched-model index
Combining this equation across countries and products gives:
Our RERs and CPI-Based RERs: Step by Step
Product Intro
and exit Particularly
important for
electronics
Product Entry/Exit Bias
Surprising that entry/exit appears to matter so much.
On one hand, plausible measurement and scraping issues might
accentuate margin (e.g. goods disappearing and re-entering
seasonally, with price changes).
On other hand, note that entry/exit huge for electronics, moderate
for food, zero for fuel. This seems economically sensible.
We build a high-frequency dataset of closely-matched products in 9
countries � construct RER, RP time series
We find strong co-movement of RP and E (not seen with CPIs)
We quantify sources of measurement bias in passthrough estimates:
Sectoral differences & formula [4 % points]
Product matching bias (matched goods, relative prices) [26 % points]
Product entry/exit bias [11 % points]
Summary