Price settings in online markets:
Basic facts, international comparisons, and
cross-border integration
Y. Gorodnichenko (UC Berkeley) and O. Talavera (Sheffield)
NBER WP 20406
1
LAW OF ONE PRICE (LOOP)
• A very intuitive and simple concept
• Basic ingredient/assumption in many models in international
economics
• In the data:
– Large deviations from LOOP
• Explanations:
– Frictions (distance, information, tariffs)
– Sticky prices
– The data are “poor” (e.g., compare price indexes)
2
LAW OF ONE PRICE (LOOP)
• A very intuitive and simple concept
• Basic ingredient/assumption in many models in international
economics
• In the data (country- or region-level):
– Large/Heterogeneous deviations from LOOP
– Slow (if any) convergence to LOOP
• Explanations:
– Frictions (distance, information, tariffs)
– Sticky prices
– The data are “poor” (e.g., compare price indexes)
3
LAW OF ONE PRICE (LOOP)
• A very intuitive and simple concept
• Basic ingredient/assumption in many models in international
economics
• In the data (country- or region-level):
– Large/Heterogeneous deviations from LOOP
– Slow (if any) convergence to LOOP
• Explanations:
– Frictions (distance, information, tariffs)
– Sticky prices
– The data are “poor” (e.g., compare price indexes)
4
A twist to LOOP: EX Pass-Through
• Macro data
– Campa and Goldberg (2005): The United States has among the lowest
pass-through rates in the OECD, at approximately 25% in the short
run and 40% over the longer run.
• Avoiding aggregation bias:
– Imbs et al. (2005), Cruchini and Shintani (2008), Broda and Weinstein
(2008): the pass-through and the speed of price adjustment are
higher when individual, narrowly-defined goods are considered
5
Determinants of PT and Speed of Adjustment
• Factors affecting the size of the pass-through.
–
–
–
–
–
Market structure,
market power (including adjustment of mark-ups),
tariffs,
presence of multinationals
importance of non-traded inputs for price stickiness of final goods
Menon (1996), Cardasz and Stollery (2001), Gaulier, Lahreche-Revil, and
Mejean (2006), Goldberg and Hellerstein (2012), Mayoral and Gardea
(forthcoming)
6
What do we do?
We use online prices for estimating:
• Descriptive statistics
• Exchange Rate PT, speed of Adjustment
• Determinants of PT/SA
Why online prices?
7
ONLINE MARKETS
•
•
•
•
•
•
•
•
Highly integrated and fast growing markets, unlike brick and mortar retailers
Easy search for best prices
Price comparison for identical goods is easy across stores
Negligible physical cost of changing prices
Goods sold online are easy to ship and transactions costs are small
Geographical location of stores and consumers is largely irrelevant
Nearly impossible to discriminate consumers based on their location
Easy to collect data
We use these unique characteristics to re-examine one of the key puzzles using prices from
U.S. and Canadian online sellers.
8
ONLINE MARKETS
The IMRG, the UK’s industry association for
e-retail, estimated that total e-commerce sales for 2011
were £77bn (or approximately 10 percent of total retail
sales in the UK economy).
This represents an increase of 14 percent from 2010 while
total retail sales increased by 1.7 percent in 2011.
Importantly, not only have online sales risen during the past
recession, but they have historically grown much faster.
While US, UK and Japan are leaders in business to consumer ecommerce, China’s e-commerce market in particular has risen
over 130% in 2011.
There is also high growth of online retailing in Europe, with
Poland (29%), Ireland (24%), Spain (24%), France (21%) having
highest spending online.
9
10
ONLINE MARKETS
•
•
•
•
•
•
•
•
Highly integrated and fast growing markets
Easy search for best prices
Price comparison for identical goods is easy across stores
Negligible physical cost of changing prices
Goods sold online are easy to ship and transactions costs are small
Geographical location of stores and consumers is largely irrelevant
Nearly impossible to discriminate consumers based on their location
Easy to collect data
We use these unique characteristics to re-examine one of the key puzzles using prices from
U.S. and Canadian online sellers.
11
ONLINE MARKETS
•
•
•
•
•
•
•
•
Highly integrated and fast growing markets
Easy search for best prices
Price comparison for identical goods is easy across stores
Negligible physical cost of changing prices, minimal menu costs, flexible prices
Goods sold online are easy to ship and transactions costs are small
Geographical location of stores and consumers is largely irrelevant
Nearly impossible to discriminate consumers based on their location
Easy to collect data
We use these unique characteristics to re-examine one of the key puzzles using prices from
U.S. and Canadian online sellers.
12
ONLINE MARKETS
•
•
•
•
•
•
•
•
Highly integrated and fast growing markets
Easy search for best prices
Price comparison for identical goods is easy across stores
Negligible physical cost of changing prices
Goods sold online are easy to ship and transactions costs are small
Geographical location of stores and consumers is largely irrelevant
Nearly impossible to discriminate consumers based on their location
Easy to collect data
We use these unique characteristics to re-examine one of the key puzzles using prices from
U.S. and Canadian online sellers.
13
ONLINE MARKETS
•
•
•
•
•
•
•
•
Highly integrated and fast growing markets
Easy search for best prices
Price comparison for identical goods is easy across stores
Negligible physical cost of changing prices
Goods sold online are easy to ship and transactions costs are small
Geographical location of stores and consumers is largely irrelevant
Nearly impossible to discriminate consumers based on their location
Easy to collect data
We use these unique characteristics to re-examine one of the key puzzles using prices from
U.S. and Canadian online sellers.
14
ONLINE MARKETS
•
•
•
•
•
•
•
•
Highly integrated and fast growing markets
Easy search for best prices
Price comparison for identical goods is easy across stores
Negligible physical cost of changing prices
Goods sold online are easy to ship and transactions costs are small
Geographical location of stores and consumers is largely irrelevant
Nearly impossible to discriminate consumers based on their location
Easy to collect data
We use these unique characteristics to re-examine one of the key puzzles using prices from
U.S. and Canadian online sellers.
15
ONLINE MARKETS
•
•
•
•
•
•
•
•
Highly integrated and fast growing markets
Easy search for best prices
Price comparison for identical goods is easy across stores
Negligible physical cost of changing prices
Goods sold online are easy to ship and transactions costs are small
Geographical location of stores and consumers is largely irrelevant
Nearly impossible to discriminate consumers based on their location
Easy to collect data
16
Research on Online Prices
• Brynjolfsson and Smith (2000) compare online and
conventional stores prices on books and CDs.
– online prices are 9-16% lower than prices in regular stores
– the changes in online prices are much smaller for online prices,
– quotes of internet prices are quite dispersed even for precisely
defined goods.
17
Research on Online Prices
• Lunnemann and Wintr (2011) document stickiness of online
prices in the U.S. and large European markets (Germany,
France, Italy, and the U.K.).
– internet prices change less often in the U.S. than in Europe (the
opposite is true for conventional stores).
– Online prices are more flexible than their offline counterparts with
half of the spells ending within a month.
18
Dispersion in Online Markets
• Dramatic dispersion of prices in online markets because of
– information frictions
– sellers’ ability to discriminate consumers (e.g., based on what sellers
know about customers),
– differences in advertisement (e.g., investment in building brand,
reputation, etc.).
Baye and Morgan 2001, Baye and Morgan 2004, Morgan, Orzen,
and Sefton 2006, Baye and Morgan 2009
19
Boivin et al. (2012)
• Dynamics of online price differences across three book sellers:
Amazon.com (and Amazon.ca), BN.com (Barnes & Noble
website), and Chapters.ca.
– price differentials (or relative quantities) for books react to
fluctuations in the relative price of foreign competition following
exchange rate movement which is consistent with extensive market
segmentation and pervasive violations of the law of one price.
20
Cavallo et al (2014)
• Prices for all products sold by Apple, IKEA, H&M, and Zara
through their online retail stores in 85 countries.
– the law of one price holds very well within currency unions, but does
not hold outside currency unions
– good-level real exchange rates reflect differences in prices at the time
products are first introduced
21
Contribution to literature
• We use unique characteristics of online markets to re-examine
LOOP using prices from U.S. and Canadian online sellers.
22
DATA COLLECTION
• We use a popular price comparison website.
• Every Saturday at midnight, a Tcl/python script has been triggered to collect webpages with price
information.
• The script extracts
–
–
–
–
good description,
unique manufacturing product number (MPN),
prices for each seller,
sellers' unique ids and reviews.
• Our price quotes are price before taxes and shipping/handling costs.
• What we have:
– >140,000 goods
– >14 million good-seller-week-country quotes.
– 55 types of goods in four main categories: computers (20 types, e.g., laptops), electronics (14 types, e.g.,
GPS), software (11 type, e.g., computer games), and cameras (10 types, e.g., digital cameras).
23
24
25
Sample composition
USA
Canada
1 SeaBoom.com
AgileElectronics
2 NextWarehouse.com
MostlyDigital
3 Amazon.com
4 TheNerds.net
Amazon.com
5
Marketplace
B&H PhotoVideo
TigerDirect.ca
6 PCConnectionExpress
Newegg.ca
7 CompSource Inc.
Amazon.ca
8 MacConnection
PC-Canada
9 Memory4Less.com
DirectDialCanada
10 PROVANTAGE
OnHop
Cendirect.com
26
Apr
Jul
2009Oct
Apr
Jul
2010Oct
1.3
1.1
1
200
250
Jan
1.2
Nominal Exchange Rate, USD/CAD
300
Price
1
Jan
350
1.2
1.1
300
150
200
250
Price
2008Oct
WD VelociRaptor 300GB Hard Drive, by seller, CA
400
Nominal Exchange Rate, USD/CAD
1.3
450
WD VelociRaptor 300GB Hard Drive, by seller, US
350
400
US vs CA
2008Oct
Jan
Apr
Jul
2009Oct
Jan
Apr
Jul
2010Oct
27
DATA COLLECTION
• We use a popular price comparison website.
• Every Saturday at midnight, a Tcl/python script has been triggered to collect webpages with price
information.
• The script extracts
–
–
–
–
good description,
unique manufacturing product number (MPN),
prices for each seller,
sellers' unique ids and reviews.
• Our price quotes are price before taxes and shipping/handling costs.
• What we have:
– >140,000 goods
– >14 million good-seller-week-country quotes.
– 55 types of goods in four main categories: computers (20 types, e.g., laptops), electronics (14 types, e.g.,
GPS), software (11 type, e.g., computer games), and cameras (10 types, e.g., digital cameras).
28
1.3
1.2
1
1.1
.9
Start of data collection
01jan2008
01jan2009
01jan2010
01jan2011
01jan2012
29
DESCRIPTIVE STATISTICS
Cross-sectional distribution of prices
St.dev. log(Price)
IQR log(Price)
Median log(Price)
Median duration of price spell (weeks)
Size of price changes
Median dlog(Price)
Median abs(dlog(Price))
Synchronization of price changes
Properties of sellers
Number of sellers
Stability
Canada
Mean
St.Dev
(1)
(2)
USA
Mean
St.Dev
(3)
(4)
0.128
0.095
5.077
0.105
0.097
1.488
0.151
0.160
4.979
0.123
0.151
1.471
2.974
9.615
4.112
9.335
-0.007
0.041
0.030
0.058
-0.005
0.042
0.031
0.048
0.299
0.213
0.190
0.121
2.399
0.423
1.318
0.213
3.627
0.405
1.983
0.188
30
DESCRIPTIVE STATISTICS
Cross-sectional distribution of prices
St.dev. log(Price)
IQR log(Price)
Median log(Price)
Median duration of price spell (weeks)
Size of price changes
Median dlog(Price)
Median abs(dlog(Price))
Synchronization of price changes
Properties of sellers
Number of sellers
Stability
Canada
Mean
St.Dev
(1)
(2)
USA
Mean
St.Dev
(3)
(4)
0.128
0.095
5.077
0.105
0.097
1.488
0.151
0.160
4.979
0.123
0.151
1.471
2.974
9.615
4.112
9.335
-0.007
0.041
0.030
0.058
-0.005
0.042
0.031
0.048
0.299
0.213
0.190
0.121
2.399
0.423
1.318
0.213
3.627
0.405
1.983
0.188
𝑠∈𝒮𝑖𝑡𝑐
𝑆𝑦𝑛𝑐ℎ𝑟𝑜𝑛𝑖𝑧𝑎𝑡𝑖𝑜𝑛𝑖𝑡𝑐 =
𝑠∈𝒮𝑖𝑡𝑐
𝟏 𝑃𝑖𝑡𝑠𝑐 ≠ 𝑃𝑖,𝑡−1,𝑠𝑐 − 1
𝟏 𝑃𝑖𝑡𝑠𝑐 ≠ missing ∩ 𝑃𝑖,𝑡−1,𝑠𝑐 ≠ missing − 1
31
DESCRIPTIVE STATISTICS
Cross-sectional distribution of prices
St.dev. log(Price)
IQR log(Price)
Median log(Price)
Median duration of price spell (weeks)
Size of price changes
Median dlog(Price)
Median abs(dlog(Price))
Synchronization of price changes
Properties of sellers
Number of sellers
Stability
Canada
Mean
St.Dev
(1)
(2)
USA
Mean
St.Dev
(3)
(4)
0.128
0.095
5.077
0.105
0.097
1.488
0.151
0.160
4.979
0.123
0.151
1.471
2.974
9.615
4.112
9.335
-0.007
0.041
0.030
0.058
-0.005
0.042
0.031
0.048
0.299
0.213
0.190
0.121
2.399
0.423
1.318
0.213
3.627
0.405
1.983
0.188
32
INTERNATIONAL PRICE COMPARISONS
Mean
St.Dev
(1)
(2)
AR1
OLS
(4)
N
(5)
Panel A: Mean prices (net)
Relative exchange rate
0.083 0.236 0.923
Real exchange rate
0.052 0.231 0.917
1,709,011
1,709,011
Panel B: Mean prices (gross)
Relative exchange rate
0.107 0.205 0.929
Real exchange rate
0.064 0.205 0.928
830,169
830,169
Relative exchange rate ≡ log 𝑃𝑖𝑡𝐶𝐴 /𝑃𝑖𝑡𝑈𝑆
Real exchange rate ≡ log 𝐸𝑋 −1 𝑡 × 𝑃𝑖𝑡𝐶𝐴 /𝑃𝑖𝑡𝑈𝑆
33
PASS-THROUGH
Pass-through (PT): log
𝑃𝑖𝑡𝐶𝐴
𝑃𝑖𝑡𝑈𝑆
= 𝛼𝐸𝑋𝑡 + 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠 + 𝑒𝑟𝑟𝑜𝑟𝑖𝑡 ,
(1)
 The long-run pass-through and is similar to specifications estimated in Goldberg and
Knetter (1997), Campa and Goldberg (2005), Goldberg and Hellerstein (2012).
 The law of one price predicts that 𝛼 should be equal to one and larger values of 𝛼
correspond to smaller departures from the law of one prices.
34
SPEED OF PRICE ADJUSTMENT
Speed of price adjustment:
𝑑 log
𝑃𝑖𝑡𝐶𝐴
𝑃𝑖𝑡𝑈𝑆
= 𝛽 log
𝐶𝐴
𝑃𝑖,𝑡−1
𝑈𝑆
𝑃𝑖,𝑡−1
+𝜙1 𝑑 log
− 𝛼𝐸𝑋𝑡−1
𝐶𝐴
𝑃𝑖,𝑡−1
𝑈𝑆
𝑃𝑖,𝑡−1
(2)
+ 𝜆1 𝑑𝐸𝑋𝑡−1 + 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠 + 𝑒𝑟𝑟𝑜𝑟𝑖𝑡
 It is set in the error-correction/cointegration form where 𝛽 quantifies how quickly the
deviation from equilibrium is eliminated.
 In Specification (2), equilibrium relationship between relative and the exchange rate are
determined according to Specification (1).
 While the equilibrium relationship nests the law of one price, it also allows deviations
from the law of one price (i.e., α can be less than one).
35
Basic Results: Net Prices
Mean Price
Median Price
Minimum Price
Within-seller
PT
(1)
0.698***
(0.080)
0.698***
(0.082)
0.677***
(0.042)
Speed
(2)
-0.158***
(0.005)
-0.170***
(0.005)
-0.169***
(0.005)
0.284***
(0.050)
-0.116***
(0.013)
36
DECOMPOSITION OF PRICE ADJUSTMENT
𝑀𝑖𝑐𝑡 = 𝛾𝑐 + 𝜓𝑐 log
𝐶𝐴
𝑃𝑖,𝑡−1
𝑈𝑆
𝑃𝑖,𝑡−1
𝐸𝑞𝑢𝑖𝑙𝑖𝑏𝑖𝑟𝑢𝑚 𝑒𝑟𝑟𝑜𝑟 ≡ log
− 𝛼𝐸𝑋𝑡−1 + 𝜅𝑐1 𝐸𝑋𝑡−1 + 𝜅𝑐2 𝑀𝑖𝑐 ,𝑡−1 + 𝜆𝑖𝑐 + 𝑒𝑟𝑟𝑜𝑟𝑖𝑐𝑡
𝐶𝐴
𝑃𝑖,𝑡−1
𝑈𝑆
𝑃𝑖,𝑡−1
− 𝛼𝐸𝑋𝑡−1 > 0 ⇒ Canadian goods are expensive
37
DECOMPOSITION OF PRICE ADJUSTMENT
𝑀𝑖𝑐𝑡 = 𝛾𝑐 + 𝜓𝑐 log
𝐶𝐴
𝑃𝑖,𝑡−1
𝑈𝑆
𝑃𝑖,𝑡−1
𝐸𝑞𝑢𝑖𝑙𝑖𝑏𝑖𝑟𝑢𝑚 𝑒𝑟𝑟𝑜𝑟 ≡ log
− 𝛼𝐸𝑋𝑡−1 + 𝜅𝑐1 𝐸𝑋𝑡−1 + 𝜅𝑐2 𝑀𝑖𝑐 ,𝑡−1 + 𝜆𝑖𝑐 + 𝑒𝑟𝑟𝑜𝑟𝑖𝑐𝑡
𝐶𝐴
𝑃𝑖,𝑡−1
𝑈𝑆
𝑃𝑖,𝑡−1
− 𝛼𝐸𝑋𝑡−1 > 0 ⇒ Canadian goods are expensive
Moment, 𝑀 (mean prices)
CA
(1)
US
(2)
Probability of price adjustment
Any, Pr(𝑑𝑃 ≠ 0)
Increase, Pr(𝑑𝑃 > 0)
Decrease, Pr(𝑑𝑃 < 0)
-0.002
-0.080***
0.076***
0.008
0.025***
-0.016***
Probability of exit
-0.010
-0.006
38
GOOD-SPECIFIC PASS-THROUGH
Pass-through (PT): log
𝑃𝑖𝑡𝐶𝐴
𝑃𝑖𝑡𝑈𝑆
= 𝛼𝐸𝑋𝑡 + 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠 + 𝑒𝑟𝑟𝑜𝑟𝑖𝑡 ,
Speed of price adjustment: 𝑑 log
𝜙1 𝑑 log
𝐶𝐴
𝑃𝑖,𝑡−1
𝑈𝑆
𝑃𝑖,𝑡−1
𝑃𝑖𝑡𝐶𝐴
𝑃𝑖𝑡𝑈𝑆
= 𝛽 log
𝐶𝐴
𝑃𝑖,𝑡−1
𝑈𝑆
𝑃𝑖,𝑡−1
− 𝛼𝐸𝑋𝑡−1 +
+ 𝜆1 𝑑𝐸𝑋𝑡−1 + 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠 + 𝑒𝑟𝑟𝑜𝑟𝑖𝑡
Estimate 𝛼 and 𝛽 for each good separately and related variation of 𝛼 and 𝛽 across
goods to “fundamentals” such returns to search, price stickiness, etc.
39
DETERMINANTS OF PASS-THROUGH AND SPEED OF PRICE ADJ.
Regressors
Log(Median Price)
Log(Median
Price)2
Freq. of price change
Log(Sellers)
Log(Sellers)2
Stability of Sellers
Synchronization
Average Reputation
Observations
Price measure: Min
Speed of
Path-through
Adjustment
(1)
(2)
0.546***
0.007
(0.124)
(0.010)
-0.054***
-0.001
(0.011)
(0.001)
1.882***
-0.073***
(0.236)
(0.026)
2.152***
-0.147**
(0.400)
(0.060)
-0.666***
0.044**
(0.123)
(0.018)
0.903**
0.337***
(0.398)
(0.052)
-0.474*
0.039
(0.265)
(0.028)
0.139**
-0.005
(0.053)
(0.005)
11,713
11,713
A higher search intensity should
put a larger pressure on price
convergence across sellers and
countries.
40
DETERMINANTS OF PASS-THROUGH AND SPEED OF PRICE ADJ.
Regressors
Log(Median Price)
Log(Median Price)2
Freq. of price change
Log(Sellers)
Log(Sellers)2
Stability of Sellers
Synchronization
Average Reputation
Observations
Price measure: Min
Speed of
Path-through
Adjustment
(1)
(2)
0.546***
0.007
(0.124)
(0.010)
-0.054***
-0.001
(0.011)
(0.001)
1.882***
-0.073***
(0.236)
(0.026)
2.152***
-0.147**
(0.400)
(0.060)
-0.666***
0.044**
(0.123)
(0.018)
0.903**
0.337***
(0.398)
(0.052)
-0.474*
0.039
(0.265)
(0.028)
0.139**
-0.005
(0.053)
(0.005)
11,713
11,713
Sticky prices could delay price
adjustment and make it
incomplete.
Imbs et al. 2005, Mayoral and
Gardea forthcoming, Rogoff 1996,
Takhtamanova 2008
41
DETERMINANTS OF PASS-THROUGH AND SPEED OF PRICE ADJ.
Regressors
Log(Median Price)
Log(Median Price)2
Freq. of price change
Log(Sellers)
Log(Sellers)2
Stability of Sellers
Synchronization
Average Reputation
Observations
Price measure: Min
Speed of
Path-through
Adjustment
(1)
(2)
0.546***
0.007
(0.124)
(0.010)
-0.054***
-0.001
(0.011)
(0.001)
1.882***
-0.073***
(0.236)
(0.026)
2.152***
-0.147**
(0.400)
(0.060)
-0.666***
0.044**
(0.123)
(0.018)
0.903**
0.337***
(0.398)
(0.052)
-0.474*
0.039
(0.265)
(0.028)
0.139**
-0.005
(0.053)
(0.005)
11,713
11,713
Pass-through and speed of price
adjustment could be affected not
only by the degree of price
stickiness at the level of individual
seller but also to what extent price
setting is staggered.
42
DETERMINANTS OF PASS-THROUGH AND SPEED OF PRICE ADJ.
Regressors
Log(Median Price)
Log(Median Price)2
Freq. of price change
Log(Sellers)
Log(Sellers)2
Stability of Sellers
Synchronization
Average Reputation
Observations
R2
Price measure: Min
Speed of
Path-through
Adjustment
(1)
(2)
0.546***
0.007
(0.124)
(0.010)
-0.054***
-0.001
(0.011)
(0.001)
1.882***
-0.073***
(0.236)
(0.026)
2.152***
-0.147**
(0.400)
(0.060)
-0.666***
0.044**
(0.123)
(0.018)
0.903**
0.337***
(0.398)
(0.052)
-0.474*
0.039
(0.265)
(0.028)
0.139**
-0.005
(0.053)
(0.005)
11,713
11,713
0.11
0.13
The number of sellers should
be indicative of the degree
of competition and thus can
help discriminating price
stickiness vs. market power
43
DETERMINANTS OF PASS-THROUGH AND SPEED OF PRICE ADJ.
Regressors
Log(Median Price)
Log(Median Price)2
Freq. of price change
Log(Sellers)
Log(Sellers)2
Stability of Sellers
Synchronization
Average Reputation
Observations
R2
Price measure: Min
Speed of
Path-through
Adjustment
(1)
(2)
0.546***
0.007
(0.124)
(0.010)
-0.054***
-0.001
(0.011)
(0.001)
1.882***
-0.073***
(0.236)
(0.026)
2.152***
-0.147**
(0.400)
(0.060)
-0.666***
0.044**
(0.123)
(0.018)
0.903**
0.337***
(0.398)
(0.052)
-0.474*
0.039
(0.265)
(0.028)
0.139**
-0.005
(0.053)
(0.005)
11,713
11,713
0.11
0.13
Easy entry into a market and
limited time-horizons for
sellers (this limits the scope for
collusion) are likely to eliminate
arbitrage opportunities and
mis-pricing of goods faster
44
Concluding remarks
• Online retail is closer to a frictionless ideal market
– More flexible prices
• Law of one price is a reasonable approximation
–
–
–
–
Mildly persistent price differentials
Pass-through is relatively high
Speed of price adjustment is high
All margins are working to eliminate price differentials
• Large variation across goods
– This variation can be systematically related to “fundamentals”
• Soaring internet retail can bring the law of one price closer to reality
45
Concluding remarks
• Online retail is closer to a frictionless ideal market
– More flexible prices
• Law of one price is a reasonable approximation
–
–
–
–
Mildly persistent price differentials
Pass-through is relatively high
Speed of price adjustment is high
All margins are working to eliminate price differentials
• Large variation across goods
– This variation can be systematically related to “fundamentals”
• Soaring internet retail can bring the law of one price closer to reality
46
Concluding remarks
• Online retail is closer to a frictionless ideal market
– More flexible prices
• Law of one price is a reasonable approximation
–
–
–
–
Mildly persistent price differentials
Pass-through is relatively high
Speed of price adjustment is high
All margins are working to eliminate price differentials
• Large variation across goods
– This variation can be systematically related to “fundamentals”
• Soaring internet retail can bring the law of one price closer to reality
47
Concluding remarks
• Online retail is closer to a frictionless ideal market
– More flexible prices
• Law of one price is a reasonable approximation
–
–
–
–
Mildly persistent price differentials
Pass-through is relatively high
Speed of price adjustment is high
All margins are working to eliminate price differentials
• Large variation across goods
– This variation can be systematically related to “fundamentals”
• Soaring internet retail can bring the law of one price closer to reality
48